Hydrology doi: 10.3390/hydrology13080212

Authors: Han Yang Bin Xu Zhe Yuan Xiaofeng Hong Liqiang Yao

Quantifying the soil moisture (SM) response to precipitation is pivotal for predicting hydrologic resilience and ecosystem stability in fragile cold regions. This is true in alpine permafrost environments characterized by variable precipitation phases and strong seasonal freeze–thaw dynamics. However, critical knowledge gaps persist due to the scarcity of high-resolution, multi-layer in situ observations in these remote areas. Using hourly data from three sites in the Tanggula Mountains (2020–2024), this study employs an event-based analytical framework combining logistic regression and linear regression to quantify multi-layer (10–100 cm) SM responses to rain, snow, and mixed-phase precipitation across seasons. Core findings indicate the following: (1) Precipitation thresholds with 80% probability of triggering SM responses rise sharply with depth during the cold period (10 cm: 1–11 mm; 50–100 cm: often >15 mm or unreachable) but increase gradually in the warm period (10 cm: 0.4–5 mm; 50 cm: <15 mm). Mixed-phase precipitation refers to the lowest amount of precipitation (0.4–2.5 mm at 10 cm), followed by rain (1–11 mm) and snow (2–5 mm). (2) Warm-period regression slopes are consistently steeper than cold-period slopes (at 10 cm, 0.0024 vs. 0.0010 for rainfall). Mixed-phase precipitation yields the steepest slopes, approximately 50% higher than rainfall at 10 cm in the warm period (0.0037 vs. 0.0024), due to its longer duration and dual-supply mode. For lag time, cold-period values are more widely dispersed due to multiple interacting factors, while warm-period values are concentrated; only warm-period rainfall exhibits a clear monotonic increase in lag time with depth, consistent with unsaturated flow theory. (3) The quantified regression slopes, threshold values, and phase-specific efficiencies provide transferable metrics for calibrating infiltration models and evaluating frozen-ground hydrology schemes. The finding that mixed-phase events are the primary driver of deep-layer recharge, despite accounting for a smaller fraction of the total event count, has direct implications for water resource assessment in high-altitude catchments where precipitation phase composition is often oversimplified. Overall, this study moves beyond qualitative descriptions by providing quantifiable, transferable metrics that advance the mechanistic understanding of precipitation–SM coupling in alpine permafrost regions.

Hydrology doi: 10.3390/hydrology13080211

Authors: Noah Goodwin Bain Carlos G. Ochoa Derek C. Godwin Abigail Tomasek Arshdeep Singh

Agricultural systems face heightened risks from extreme weather events and water insecurity. Producers commonly use irrigation ponds to secure or improve crop yields. The hydrology and storage efficiency of irrigation ponds in the Willamette Valley, Oregon, USA, are not well understood. This study evaluated the hydrological interactions of a tile-drain-fed irrigation pond. A water balance approach was applied over the irrigation season using weather data, evaporation estimates, metered irrigation withdrawals, and bathymetry analysis for pond stage–volume estimates to quantify water budget components. Irrigation withdrawals were the largest output, with 75% of effective pond storage utilized, followed by evaporation (24.5%). Evaporation far exceeded precipitation over the same period. The unaccounted-for proportion of the water balance was negligible, indicating that net drain tile inflows and groundwater exchange had a minimal impact on seasonal irrigation water availability and seepage losses. This study provides an example for measuring water balance components and assessing water input–output relationships of an irrigation pond within a headwaters stream and catchment (<5 ha) of an important agricultural corridor in the Pacific Northwest region in the USA. The study methodology can be replicated in other similar agricultural areas with irrigation ponds worldwide. Findings from this study can be used by farmers, irrigation districts, and other stakeholders to better inform irrigation planning and water management decisions for similar site conditions.

Hydrology doi: 10.3390/hydrology13080210

Authors: Nutchanart Sriwongsitanon Chainarong Ophaphaibun James Alexander Williams Raj Mehrotra Hubert H. G. Savenije

Accurate land use/land cover (LULC) classification in monsoon-driven and heterogeneous landscapes is challenged by strong seasonal variability and inconsistencies between dynamic satellite observations and static reference datasets. This study proposes a time-series-based framework integrating MODIS-derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) with unsupervised K-means clustering and a temporally consistent refinement strategy. Multi-temporal NDVI (23 composites year−1) and NDII (46 composites year−1) data from 2010–2021 were used to derive spectral clusters and aggregate them into five land use classes using percentile-based temporal signatures and RMSE-based similarity with Land Development Department (LDD) data. To reconcile discrepancies between dynamic satellite observations and static reference datasets, a refinement procedure combining spatial agreement and temporal similarity was applied to reassign misclassified pixels. Initial classifications achieved Overall Accuracies (OA) of 57.35% for NDII and 51.27% for NDVI, increasing to 87.28% and 86.24% after refinement, with Kappa coefficients of 0.82 and 0.81, respectively. NDII consistently outperformed NDVI, highlighting the value of moisture-sensitive indices for distinguishing vegetation classes in tropical environments. The modular Python-based version 3.11 implementation ensures reproducibility and transferability, providing a robust and scalable framework for LULC classification in dynamic landscapes.

Hydrology doi: 10.3390/hydrology13080209

Authors: Oluwaseun Temitope Faloye Oluwaseyi Matthew Abioye Abiodun Afolabi Okunola Olusegun K. Abass Peter Pelumi Ikubanni Natdanai Sinsamutpadung Laemthong Laokhongthavorn Viroon Kamchoom

Accurate prediction of wetted width and wetted depth is essential for optimizing water use efficiency in drip irrigation systems. Existing empirical models are often restricted to specific soil textures and cannot adequately capture the complex nonlinear interactions among soil hydro-physical and chemical properties, irrigation variables, and different soil textures. This study evaluated four machine learning algorithms—Linear Support Vector Machine (Linear SVM), Medium Gaussian Support Vector Machine (Medium Gaussian SVM), Matern 5/2 Gaussian Process Regression (GPR), and Boosted Tree Regression—for predicting wetted width and wetted depth in sand and sandy loam soils. Model inputs included emitter discharge, irrigation duration, and selected soil hydro-physical and chemical properties. Models were developed using a 70% training dataset and validated with the remaining 30%. The Matern 5/2 GPR achieved the highest training accuracy for wetted width (R2 = 0.99; RMSE = 0.74) and wetted depth (R2 = 0.98; RMSE = 0.90), but validation errors increased to RMSE values of 2.27 and 3.84, respectively. Medium Gaussian SVM yielded the lowest validation RMSE (2.11) for wetted width, whereas Boosted Tree Regression achieved the best wetted depth prediction (RMSE = 2.11; MAE = 1.69). These findings demonstrate the importance of model-specific selection for reliable irrigation management.

Hydrology doi: 10.3390/hydrology13080208

Authors: Adane Weldengus Meresa Muuz Gebretsadik Gebremariam Anthony Lehmann Mostafa Jafari

Climate change and irrigation expansion are expected to substantially alter hydrological processes and agricultural productivity in the semi-arid watersheds of northern Ethiopia; however, their combined impacts remain insufficiently quantified. This study evaluated the effects of future climate change and irrigation management on watershed hydrology and crop yield in the Geba watershed using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated using observed daily streamflow data for the 2006–2020 period and driven by an ensemble of five bias-corrected CORDEX Africa regional climate models (RCMs) under the RCP 4.5 and RCP 8.5 scenarios for the mid-century (2046–2060) and late-century (2086–2100) periods. Two agricultural management systems, namely rainfed and irrigation-rainfed integrated management, were evaluated. Model performance was satisfactory for streamflow simulation, with NSE values of 0.56 and 0.50 and KGE values of 0.64 and 0.54 during calibration and validation, respectively. The results indicate a progressive shift toward an evapotranspiration-dominated hydrological regime under future climate conditions. Under rainfed management, surface runoff and evapotranspiration increased by up to 60% and 30%, respectively, whereas groundwater recharge and lateral flow declined substantially. Irrigation scenarios intensified hydrological stress by reducing percolation, lateral flow, and water yield by up to 80%, 60%, and 55%, respectively. Statistical analyses revealed that climate forcing, management type, and their interactions significantly affected hydrological responses (p < 0.001), with emission pathways representing the dominant driver of variability. Crop responses varied considerably among management systems and crop types. Rainfed maize and wheat exhibited moderate yield increases under mid-century conditions, whereas teff consistently showed negative responses under most climate scenarios, indicating high vulnerability to warming and moisture stress. Under irrigation management, most crops experienced substantial yield reductions during late-century periods, although tomatoes showed localized gains under high-emission scenarios. Overall, the findings demonstrate that irrigation expansion alone may not provide sustainable adaptation under increasing climate stress because it intensifies evapotranspiration and reduces groundwater recharge. Integrated watershed management, climate-resilient crop selection, efficient irrigation practices, and soil-moisture conservation strategies are therefore essential for sustaining agricultural productivity and water availability in semi-arid Ethiopian watersheds.

Hydrology doi: 10.3390/hydrology13080207

Authors: Franklin Schwartz Ganming Liu

This paper explores the intricate relationship between novel paleo-hydrological settings and the sustainability of ancient human societies in the Sahara Desert, focusing on the sandstone massifs of Tassili n’Ajjer, Tadrart Acacus, and Messak Settafet. While this region is currently hyper-arid, archeological evidence reveals a history of significant human settlement facilitated by the African Humid Period (AHP). The core of the research is the idea that the natural geological and hydrogeological settings worked to magnify rainfall in a manner that is analogous to modern techniques in water systems engineering. Serendipitous features of geology, structural settings, and stream networks are presented, along with illustrative calculations to suggest how this system functioned as an accidental rainwater harvesting system, concentrating runoff into conveniently located lakes. On the Messak Settafet, the archaeologic evidence points to a rising water table and more robust groundwater flow as runoff infiltrated. We conceptualize this behavior as a managed aquifer recharge system. This natural system worked effectively by storing ephemeral surface water in a sandstone aquifer, shielded from the high evaporation rates of the Sahara. These “natural technologies” created perennial water sources such as lakes, ponds, and springs that supported hunter-gatherers and pastoralist societies. Long after the end of the Holocene AHP, the Garamantian Empire arose with the help of qanat technology that was able to produce the stored groundwater. This paper illustrates how an unlikely array of components worked to create natural technologies able to provide “livable niches.” These findings offer instructive lessons for modern sustainability, demonstrating how integrated landscape management can secure water resources in water-stressed environments.

Hydrology doi: 10.3390/hydrology13080206

Authors: Kassaye Hussien Yali E. Woyessa

Reliable climate data are essential for hydroclimatic assessment and water-resources management in data-scarce regions. This study evaluated the performance of the WorldClim 2.1 historical weather dataset (WC2.1– CRU-TS v4.09), downscaled and bias-corrected from CRU-TS v4.0 using WorldClim 2.1 climatology, against observed meteorological records within the semi-arid C5 Secondary Drainage Region (C5 SDR; comprising the Riet and Modder River catchments) in central South Africa for the period 1950–2023. Precipitation, maximum temperature (TMAX), and minimum temperature (TMIN) were assessed using statistical performance evaluation metrics, scatter and residual analyses, Innovative Trend Analysis (ITA), Rescaled Adjusted Partial Sums (RAPS), and extreme-event evaluation based on the 95th-percentile threshold. The results showed strong agreement between observed and gridded precipitation records, with correlation coefficients ranging (R) from 0.78 to 0.90 and Nash–Sutcliffe Efficiency (NSE) values between 0.61 and 0.90. Temperature datasets exhibited similarly good performance, with TMAX showing stronger agreement than TMIN. ITA and RAPS analyses demonstrated that the dataset successfully reproduced long-term climatic trends, hydroclimatic regime shifts, and interannual variability observed in station records. Performance varied spatially, with the strongest agreement occurring at lower-elevation stations and comparatively lower performance at stations influenced by localized convective rainfall and topographic variability. Extreme-event analysis revealed that although the dataset effectively reproduced the timing and occurrence of high-rainfall years (R2 = 0.974–0.997), it systematically underestimated the magnitude of extreme precipitation events, with percent bias values ranging from −5.5% to −21.0%. In contrast, extreme temperature events were reproduced with very high accuracy and minimal bias. Overall, the WC2.1– CRU-TS v4.09 dataset provides a reliable climatic baseline for hydroclimatic assessments in the C5 SDR. However, caution is required when applying the dataset to analyses sensitive to localized precipitation extremes. The results provide confidence in the use of this dataset for climate characterization, drought assessment, hydrological modeling, ecosystem service evaluation, and future climate-change impact investigations in data-scarce semi-arid environments.

Hydrology doi: 10.3390/hydrology13080205

Authors: Yujiao Liu Mengzhu Liu Borui Li Hongwei Pei

The water use efficiency (WUE) in North China is undergoing rapid changes due to climate warming and vegetation “greening”, significantly impacting the ecosystem’s carbon and water cycles. Existing research lacks quantitative analysis of WUE or an understanding of future trends. This study selected the rapidly greening Agro-Pastoral Ecotone of Northern China (APENC) as a case study, utilizing linear regression, Hurst index analysis, and residual analysis to analyze the past and future changes and driving mechanisms of WUE. The results indicated that: (1) The multi-year (2001–2023) annual mean WUE in the APENC spatially ranged from 0.32 to 2.50 g C kg−1 H2O. (2) Gross primary productivity (GPP), evapotranspiration (ET), and WUE showed significant increasing trends of 10.22 g C m−2 yr−2, 5.62 kg H2O m−2 yr−2, and 0.01 g C kg−1 H2O yr−1, respectively. (3) Precipitation had highly positive impacts on GPP and ET, while non-climatic factors (land use, human activities, etc.) explained 62% of WUE variations in the APENC, and energy conditions (air temperature and solar radiation) were not the decisive factor of WUE. (4) The Hurst exponent of WUE indicates that WUE in the APENC region generally exhibits anti-persistent behavior. In terms of future trends, WUE is projected to shift from rising to declining in 58.9% of the region, while 28.5% is expected to continue increasing.

Hydrology doi: 10.3390/hydrology13080204

Authors: Riccardo Aigotti Eugenio Alladio Alberto Asteggiano Claudio Medana

Groundwater systems sharing similar major-ion facies may still differ in their hydrochemical organization and mineralization pathways, particularly in structurally complex aquifer settings. This study evaluated multivariate chemometric approaches for investigating two hydrochemically similar groundwater systems (MAJA and MAJA2) examined within the regulatory framework for natural mineral water recognition. The dataset consisted of a 13-month monitoring campaign complemented by an independent multi-year validation dataset. Hydrochemical variables were organized into chemical and process-related blocks, including major ions, physicochemical parameters, D’Amore indices, and mineral saturation indices. SIMCA was applied to evaluate the intra-class hydrochemical structure, and OPLS-DA was used to investigate predictive and orthogonal sources of variability. Model robustness and parameter reproducibility were assessed using jackknife resampling, Leave-One-Month-Out cross-validation, repeated double cross-validation, and permutation testing. SIMCA identified PC1 as the only consistently reproducible latent component across resampling iterations. An exploratory Structural Response Coefficient (Rj) was introduced as a model-derived descriptor integrating explained and residual variance within the SIMCA model. OPLS-DA models showed stable class-related latent structures under nested validation conditions. Electrical conductivity, sulphate, potassium, SI_gypsum, SI_halite, and D’Amore index A were the variables most consistently associated with discriminant variability. Stable isotope data indicated a common meteoric origin and similar recharge conditions for both systems. The results illustrate how multivariate chemometric analysis, combined with stability-oriented validation procedures, may aid the interpretation of hydrochemical variability in compositionally similar groundwater systems.

Hydrology doi: 10.3390/hydrology13080203

Authors: Sufen Zhou Xinming Zhang Zhiwen Huang Limo Tang

The Lower Ganjiang River is a multi-branch delta with highly uneven spatial and temporal flow distribution, and conventional static diversion or threshold-based operation fails to stabilise the water level or optimise flow allocation under varying inflows. This study develops a hydrodynamic regulation framework that couples an improved integral time-delay model and model predictive control (MPC). A nonlinear state-space equation is constructed using a quadratic storage–water level relationship and rolling optimisation is solved with CasADi-IPOPT to minimise water-level tracking error, discharge deviation and control effort. The framework is validated offline against MIKE21 simulations for three historical flow scenarios (September 2016, February 2017 and March 2018). Under these scenarios, the Waizhou water level is maintained at 15.5 ± 0.2 m, daily water level variation is limited to ≤0.5 m/d, and the diversion ratio deviation is ≤5%. Compared with the natural state, water level fluctuation is reduced by 21.3% (September 2016 storage scenario). The proposed MPC framework effectively alleviates the spatiotemporal hydrodynamic imbalance of the Lower Ganjiang River, showing satisfactory model accuracy, constraint compliance, and engineering applicability, and offers a promising approach for advanced regulation of complex multi-branch river networks.

Hydrology doi: 10.3390/hydrology13080202

Authors: Ana Carolina Torregroza-Espinosa Juan Camilo Restrepo Rodney Correa-Solano David Alejandro Blanco-Álvarez Laura Salas Cantillo

Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector of the lower Magdalena River basin (Colombian Caribbean), over the period 1990–2025. Multi-temporal Landsat imagery was used to derive the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), enabling the evaluation of seasonal and interannual ecohydrological variability under contrasting dry and rainy conditions. In addition, land-use classification was performed using a CORINE Land Cover methodology adapted for Colombia (CLC-C) to characterize the spatial organization of the landscape and its influence on vegetation–water interactions. Results show that vegetation dynamics are strongly controlled by hydroclimatic seasonality. Dense vegetation consistently expands during rainy periods, while dry seasons promote the expansion of open and sparse vegetation, reflecting seasonal vegetation stress rather than long-term degradation. NDWI patterns indicate that surface water and soil moisture are highly seasonal and spatially constrained, with open water largely confined to the Magdalena River channel and localized floodplain depressions. Extreme hydroclimatic events associated with the El Niño–Southern Oscillation (ENSO) produce abrupt but temporary changes in vegetation structure and surface moisture distribution. A strong inverse correlation between NDVI and NDWI reflects the contrasting spectral responses of vegetation and water surfaces resulting from the shared near-infrared (NIR) band in both indices. This spectral relationship is consistent with the observed seasonal variations in vegetation greenness and surface moisture across the floodplain. Land-use analysis reveals the progressive consolidation of the landscape, where the agropastoral matrix expanded from ~18,000 ha in 1990 to over 22,000 ha by 2025, driving a systematic reduction in natural and semi-natural forest structures. Forest conservation areas serve as critical ecological buffers, exhibiting lower seasonal variability in vegetation greenness. Overall, the results indicate that the Zambrano floodplain functions as a structurally stable yet highly responsive ecohydrological system, where vegetation dynamics and surface water availability are predominantly governed by interannual hydroclimatic pulses rather than long-term directional degradation. These findings demonstrate that while the structural matrix of the floodplain exhibits strong baseline resilience, its ecological functioning remains critically coupled with, and vulnerable to, the extreme phase shifts in ENSO cycles.

Hydrology doi: 10.3390/hydrology13080201

Authors: Wenxue Wang Liuyang Li Donghui Su Xin Zhang Haibin Tong Tiantian Shao Jiaxin Fan

Accurate runoff prediction is an essential foundation for water resource management, flood prevention, and drought warning. Despite the superior performance of deep learning models in runoff prediction, the high-dimensional hyperparameter optimization limits their widespread application. To address this challenge, this study proposed a hyperparameter optimization strategy that integrated Uniform Design (UD) and Meta-Learning (ML) within the Transformer framework (UD-ML-Transformer) for daily runoff prediction. Performance of the proposed model was systematically evaluated against five benchmark models, including the UD-Transformer, Particle Swarm Optimization (PSO)-Transformer, and three Receptance Weighted Key Value (RWKV)-based models (PSO-RWKV, UD-RWKV, and UD-ML-RWKV), using hydroclimatic data spanning 1980 to 2014 from the Rio Pueblo de Taos watershed in USA. Results showed that the UD-ML-Transformer model performed the best in both prediction accuracy and peak flow, with the highest Nash-Sutcliffe Efficiency (NSE) of 0.906, and the lowest Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) of 0.004, 0.062, and 0.034, respectively. The UD-Transformer ranked second in performance, followed by the PSO-Transformer. The integrated UD-ML hyperparameter optimization strategy also improved the performance of RWKV-based models. Compared with the PSO-RWKV and UD-RWKV models, the UD-ML-RWKV model exhibited an NSE improvement of 0.45–7.65% and an RMSE reduction of 1.47–21.18%, respectively. Moreover, cross-watershed validation conducted in the Ford River watershed, USA, also demonstrated the satisfactory performance of the proposed UD-ML-Transformer model, with the highest NSE of 0.890, and the lowest MSE, RMSE, and MAE of 0.088, 0.296, and 0.141, respectively. These findings highlight the superiority of integrating UD and ML for hyperparameter optimization in runoff forecasting.

Hydrology doi: 10.3390/hydrology13080200

Authors: Jinhan Wang Xinjun Yan Shaolei Wang Kewu Han Kebin Shi Dexin Zhao

Pumped-storage hydropower (PSH) can modify reservoir evaporation in arid regions by altering water-level dynamics, surface-area exposure, and thermal exchange between reservoirs. This study quantifies operation-induced evaporation changes at the Fukang PSH station in Xinjiang, China, using a one-dimensional lumped hydrodynamic–thermal model driven by hourly station observations and ERA5 reanalysis for 2024. A four-scenario factorial design separates thermal, surface-area, and interaction effects within a unified water energy framework. Under station forcing, fully coupled operation reduces annual system-scale evaporation from 135.36 × 104 m3 to 115.45 × 104 m3, corresponding to a net reduction of 19.91 × 104 m3 (14.7%). Energy-budget analysis identifies advective heat transport as the main pathway linking dispatch, reservoir thermal evolution, and evaporation response, with annual cumulative values of +205 TJ in the upper reservoir and −333 TJ in the lower reservoir. Dispatch-regime experiments further show that stronger exchange-flow operation does not necessarily increase evaporation reduction: the low, baseline, and enhanced schedules produce system-scale net changes of 37.70 × 104 m3, 19.91 × 104 m3, and −3.41 × 104 m3, respectively. These results indicate that evaporation effects in arid-region PSH systems depend on the timing of surface-area exposure relative to local evaporative demand, rather than on exchange-flow magnitude or operating duration alone.

Hydrology doi: 10.3390/hydrology13080198

Authors: Mohamed El-Sayed El-Mahdy Sally Sayed Saad Ibraheem A. H. Yousif Mohamed Ahmed Shahba Abd-Alrahman S. Ahmed

Groundwater vulnerability refers to an aquifer’s susceptibility to contamination based on natural hydrogeological properties, including geology, soil, topography, and unsaturated zone characteristics. In low-recharge arid systems, limited recharge reduces dilution and flushing, allowing contaminants introduced through anthropogenic activities to persist over time. This study assesses groundwater vulnerability in El-Farafra, El-Kharga, and Tushka using a GIS-based DRASTIC approach, enhanced with a Land-Use DRASTIC model to incorporate human activities. Parameters, including the depth-to-water table, net recharge, aquifer media, soil media, topography, the vadose zone, and hydraulic conductivity, were spatially analyzed to generate vulnerability indices. Sentinel-2 imagery was used for land-use classification. In addition, expert elicitation from twenty hydrogeology specialists provided alternative parameter weightings, which were compared with the standard DRASTIC weights. Results show that incorporating land use and expert-based weights refines vulnerability patterns, particularly in agricultural, urban, and industrial zones. El-Farafra exhibits the highest vulnerability due to intensive land use and hydrogeological conditions, El-Kharga shows moderate vulnerability, and Tushka shows lower vulnerability, where recharge from Lake Nasser enhances dilution and reduces contaminant persistence. The study highlights the importance of integrating land-use information and expert knowledge to improve vulnerability assessment in data-scarce arid environments and supports improved groundwater management strategies.

Hydrology doi: 10.3390/hydrology13080199

Authors: Zhaokai Wang Hongwei Yuan Jiwei Yang Tao Shen Youzhen Wang

Groundwater levels in gated agricultural drainage networks respond to canal-stage changes, rainfall, antecedent storage, and changing operating conditions. We examined groundwater and surface-water records from the Chezegou Watershed, Huaibei Plain, China (2019–2024), using analytical solutions of the linearized Boussinesq equation. Groundwater was measured mostly at intervals of about five days, and analyses used the original observation dates. Using the half-power criterion |Z|2 = 1/2 and hydraulic diffusivities of 5.27 × 103–1.05 × 104 m2 d−1, cutoff periods were 56.1–111.7 d at 150 m and 399.1–794.1 d at 400 m; the half-power distance for a 30 d cycle was 77.7–109.7 m. The record also includes a 106 mm storm on 12 July 2020. Groundwater depth at J5 (490 m from the canal) decreased from 2.23 to 0.54 m in 48 h, a 1.69 m water-level rise, while J9 (1020 m) rose by 1.79 m over five days. These observations show a rapid shallow-groundwater head response, although water-level records alone do not separate vertical recharge from hydraulic-pressure transmission. Canal influence depends on forcing duration and aquifer properties, while rainfall responses also reflect lateral boundaries and the shrink-swell behavior of Shajiang black soil. The calculated time-distance relations provide site-specific reference values for canal operation.

Hydrology doi: 10.3390/hydrology13070197

Authors: Xuemin Tang Yutong Peng Jianli Zhang Dandan Li Yang Cao Weiquan Zhao Yunjie Wu

Urban rocky desertification areas are characterized by shallow soils, rock–soil mosaics, and strong human disturbance, so infiltration processes may differ from those in homogeneous soils. However, interactions among multiple controlling factors remain insufficiently quantified. This study compared soil infiltration under artificially restored vegetation (planted grassland (PG) and planted woodland (PW)), and natural secondary vegetation (secondary grassland (SG) and secondary woodland (SW)). Saturated hydraulic conductivity (Ks) and falling-head duration (T) were measured using falling-head tests on undisturbed soil columns. Soil physical properties were then integrated with partial least squares structural equation modeling (PLS-SEM) to assess the effects of rocky desertification, soil aggregates, and porosity. Soil bulk density was significantly lower under artificially restored vegetation, whereas capillary porosity, non-capillary porosity, and water-holding capacity were significantly higher (p < 0.05). Infiltration performance followed PW > PG > SW > SG. PLS-SEM indicated that rocky desertification (−0.78), porosity (0.51), and aggregates (−0.03) jointly regulated infiltration, with non-capillary porosity as the dominant positive factor. Higher infiltration in artificially restored plots was mainly associated with improved pore structure. These findings support vegetation configuration and soil–water management in urban rocky desertification areas.

Hydrology doi: 10.3390/hydrology13070196

Authors: Alexey Terekhov Ravil Mukhamediev Gulshat Sagatdinova Igor Savin

Freely meandering rivers flow through gently sloping plains composed of loess and fluvial sediments. Low-gradient alluvial plains are formed without the influence of landscape features such as rock outcrops or other features that distort the flow path. The channel configurations of such rivers, and in particular the size of meanders and oxbow lakes, depend on the average long-term water discharge. Large rivers form large meanders, while small rivers form correspondingly small ones. Morphological analysis of river channel configurations can offer a metric for estimating the average long-term water discharge of a river based solely on the sinuosity of its channel. The study examined six freely meandering rivers in Kazakhstan, with discharges ranging from 4.5 to 760 m3/s and channel slopes from 0.005 to 0.06%. The morphological analysis of river channels was based on the Relative Elevation Model, specifically its version based on the Copernicus Global Digital Elevation Model, with a spatial resolution of 30 m. River channel configurations were approximated using a set of inscribed circles, the diameters of which formed the basis for the river’s average long-term water discharge metric. The largest diameter circles, which could support the river channel with a sector of at least 135°, were expertly inscribed into river bends. The diameters of the inscribed circles within these sets varied from four times for small rivers to ten times for large rivers. These sets of circles, sorted by size, can characterize the average long-term water discharge of the analyzed rivers. For example, a sample of average median values of inscribed circle diameters has a high correlation with the average long-term water discharge, with a linear approximation reliability of R2 = 0.997. The scope of the developed method for assessing the average long-term water discharge of freely meandering rivers includes retrospective analysis of changes in average long-term average long-term water discharge. This can provide significant historical depth of analysis, spanning centuries and millennia, since the analysis is based on describing the results of very slow processes of natural deformation of river channels. Thus, the method proposed in this study for assessing the average long-term water discharge of freely meandering rivers based on morphological analysis of their channel configurations expands the arsenal of tools for reconstructing certain paleoclimate elements related to the hydrology of territories.

Hydrology doi: 10.3390/hydrology13070195

Authors: Katharina Pilar von Pilchau Christoph Mudersbach Udo Nehren Klaus Maas

In order to mitigate the negative effects of heavy rainfall events, natural water retention measures (NWRM)—such as hedges, erosion control strips, vegetated drainage channels, wooded strips, retention basins and ditch pockets—have gained renewed attention as an effective climate adaptation strategy. To identify potential areas for NWRM, this study applied and methodologically expanded an existing approach for identifying dominant runoff processes (DRPs) to an agricultural sub-catchment in the Weserbergland region of Germany. The DRP were determined using a Geographic Information System (GIS) and validated through field surveys. Potential areas for water retention within the same runoff process classes were identified for three defined objectives: improving infiltration, extending flow paths, and redirecting runoff to surrounding areas. Spatial differentiation was achieved using accumulated catchment area and overland flow distance. The watershed is predominantly characterized by surface runoff (Hortonian Overland Flow). Field validation confirmed the DRP classification for around two-thirds of the study area, with deviations occurring predominantly on arable land. Supplementing the DRP approach with a topographic analysis allowed for further differentiation, focusing on small, topographically defined sub-watersheds. The identified areas offer significant potential for interventions. Combined with supplementary data, analyses of the water network and the involvement of local stakeholders, the resulting potential map provides a solid basis for planning smaller-scale water retention measures.

Hydrology doi: 10.3390/hydrology13070194

Authors: Zhi-He Jin

PFAS-laden fluid-filled porous media may be subjected to various mechanical loads which induce solid deformation and fluid flow and hence PFAS transport. This work employs a poroelasticity theory for unsaturated porous media to address the coupled solid deformation, fluid flow and PFAS transport in the vadose zone subjected to a mechanical load. The governing equation of the aqueous PFAS concentration is derived based on the PFAS mass balance that also considers the water content variation in the pores due to the solid deformation. Vertical PFAS transport in a finite soil layer under mechanical compression is studied using a finite difference method and the solutions of the pore fluid pressures and volumetric strain. Numerical results of the aqueous concentrations of perfluorooctane sulfonic (PFOS) in loamy sand and clay loam indicate that mechanical compression has pronounced effects on the spatial distribution of PFOS. In a loamy sand with relatively higher permeability, mechanical compression at the top drained surface leads to movement of PFOS from the topsoil to the surface thereby reducing the PFOS concentration in the topsoil especially at higher water saturations. The PFOS concentration in the subsoil, however, is not significantly influenced. The effect of mechanical compression on the PFOS concentration distribution in a clay loam can also be observed but is not as significant as in the loamy sand. The mechanical loading effects may be further explored to develop new technologies for PFAS risk assessment and remediation strategies.

Hydrology doi: 10.3390/hydrology13070193

Authors: Gadadhara de Figueiredo Ferraz Tamás Krámer

River–aquifer exchange during floods controls groundwater recharge and bank storage, yet its sensitivity to floodplain geometry, flood duration, and subsurface permeability remains insufficiently quantified. A coupled model integrating one-dimensional surface-water flow, two-dimensional groundwater flow, and vertically resolved unsaturated floodplain infiltration was parameterized using observation-derived bank-storage estimates from Hungarian Danube floods. Idealized scenarios evaluated the effects of floodplain width, flood duration, and aquifer and soil permeability on river–aquifer exchange, bank storage, and flood attenuation. Simulations indicated that, under the investigated conditions, vertical floodplain infiltration dominates exchange processes, accounting for approximately 82–92% of total exchange and bank storage, whereas horizontal riverbed exchange remains secondary. Floodplain geometry and flood duration exerted influences on bank storage and flood attenuation comparable to those of subsurface permeability. Wider floodplains enhanced infiltration, storage capacity, and flood attenuation. Short floods produced high but transient infiltration and strong peak attenuation, whereas longer floods promoted sustained infiltration, larger bank storage, and delayed attenuation. Subsurface permeability regulated exchange efficiency but exhibited a nonlinear influence on bank storage. These results demonstrated that flood attenuation during overbank flooding depends not only on river–aquifer exchange magnitude, but also on its partitioning and the temporal evolution of subsurface storage.

Hydrology doi: 10.3390/hydrology13070192

Authors: Halima Hilal Nourelhouda Karmouda Tarik Bouramtane Youssef Hamou-Ali Ismail Mohsine Houssne Bouimouass Hassan Mosaid Mounia Tahiri Nadia Kassou Ilias Kacimi Marc Leblanc

The Normalized Difference Vegetation Index (NDVI) is widely used as an indicator of irrigation activity in arid and semi-arid agricultural regions. This study evaluates how NDVI extraction scale and statistical metric influence groundwater level prediction accuracy in the irrigated Tadla Plain, MoroccoA total of 96 Long Short-Term Memory (LSTM) models were developed by combining six NDVI extraction scales, from the well pixel to 20,000 m buffers, and four statistical metrics: mean, median, maximum, and minimum. Results demonstrate that extraction scale is a critical factor controlling model performance. Across the four monitored wells, larger buffers (≥2500 m) generally outperformed smaller ones (≤1000 m), with the optimal scale occurring at 15,000 m for three wells, while one well achieved its best performance at the 1000 m scale. The best models achieved RMSE values between 0.09 and 0.625 m and R2 values ranging from 0.94 to 0.997. Maximum NDVI provided the highest predictive accuracy for three wells, whereas minimum NDVI performed best for one well. Statistical analyses further confirmed that extraction scale generally exerts a stronger influence on prediction performance than the choice of NDVI metric. Spatial validation revealed that irrigated areas more than doubled between 2001 and 2017, and model performance improved as NDVI captured this broader irrigation footprint. These findings suggest that groundwater level variations in the Tadla Plain are more strongly associated with NDVI signals extracted at broader spatial scales than with strictly local vegetation conditions around individual wells, highlighting the importance of optimizing both NDVI extraction scale and metric for groundwater forecasting in irrigated semi-arid regions.

Hydrology doi: 10.3390/hydrology13070191

Authors: Máté Chappon Katalin Bene Richard Ray

This study presents a data-driven methodology to develop and close the continuous water balance of a cascaded hydrological system consisting of two upstream regulating reservoirs and a downstream lake. The analysis is based on monthly hydrometeorological and hydrographic data for the period 1998–2024. A baseline water balance model using raw measured data was first implemented, followed by two bias correction approaches based on multiple linear regression coefficient estimation. The first approach applies uniform coefficients across all months, while the second accounts for seasonal variability by estimating coefficients only for the December–May period, when systematic errors are most pronounced. Model performance was evaluated using calibration (1998–2017) and validation (2018–2024) periods, with Nash–Sutcliffe efficiency (NSE) and residual diagnostics used as evaluation metrics. The baseline model showed substantial cumulative deviations due to systematic errors (NSE: −0.28 and −5.13 for the two reservoirs and −18.70 for the lake), confirming the need for bias correction. Both correction approaches significantly improved model performance, with NSE values up to 0.89 for reservoirs and 0.83–0.86 for the lake during calibration. In validation, the seasonally adjusted model performed more stably in simulating water levels for Lake Velence (NSE = 0.72) than the uniform coefficient model (NSE = 0.60), particularly under extreme hydrological conditions. Residual analysis further indicated improved independence and homoscedasticity when seasonal structure was considered. The results demonstrate that water balance closure in the system is affected by distributed errors across multiple components rather than a single dominant source. The proposed methodology provides a practical, scalable framework for reconstructing consistent water balance time series in data-limited, regulated systems and supports the development of water management scenarios.

Hydrology doi: 10.3390/hydrology13070190

Authors: Darud E. Sheefa Stanley Vitton Zhen (leo) Liu Brian Barkdoll

Despite the availability of several scour calculation methods, bridge scour analysis for scour susceptible rocks is much less understood than its counterpart for sands, because scour calculation equations were traditionally obtained using flume tests with sands. As a result, there are three major knowledge gaps in the bridge scour analysis for susceptible rocks: (1) a rock classification system is needed for scour considerations to serve as a pre-screening process before conventional scour analysis involving sampling, erodibility testing, and scour calculation, (2) there has been a lack of information to guide the scour analysis for rocks including detailed procedures, needed and available data, and interpretation of results, especially for real bridge sites, and (3) detailed comparisons between the available rock scour calculation methods and the widely used sand-based scour depth calculation equations are rare. This paper presents a study to bridge the above knowledge gaps. First, a rock classification system was proposed for the bedrock of Michigan to exemplify the use of a pre-screening tool for rock preliminary scour susceptibility determination. Then, two methods were selected for two modes of scour that are common to bridges: the Erodibility Index Method for the quarrying and plucking mode of rock scour and the NCHRP-717 method for the abrasion mode. Case studies were conducted at two real bridge sites using these methods as well as sand-based equations. These case studies are intended to demonstrate practical implementation using available project data, compare the outcomes of different scour-analysis methods, and identify common data limitations and implementation challenges.

Hydrology doi: 10.3390/hydrology13070189

Authors: Mingze Xiao Fangli Su Di Wang Zining Wang Pengxing Su Hao Xu Fei Song Chao Wei Haifu Li Shuang Song

Semi-arid wetlands are highly sensitive to changes in hydrological regimes, as strong evaporation often exceeds limited natural recharge. Ecological water replenishment is widely used to restore these systems, but schemes designed only to meet water-volume targets may cause excessive hydrodynamic disturbance, promote sediment resuspension, and increase the release of internal pollutants. In this study, we developed an ecological water-replenishment assessment framework for Chahannaoer Wetland that incorporates ecological water-demand thresholds, suspended-solids disturbance, and an AHP–entropy weight–TOPSIS decision model. Using hydrological and meteorological data from 2014 to 2024, six replenishment scenarios were evaluated in terms of water-balance recovery, disturbance control, and habitat suitability. The results show that Chahannaoer Wetland experienced a persistent evaporation-dominated water deficit. The mean annual natural recharge was 0.225 × 108 m3, with a mean annual ecological water shortage of 1.03 × 108 m3 and an evapotranspiration-to-recharge ratio of 3.42–4.56. Based on the previous comprehensive water-quality assessment using DO, COD, NH3-N, TN, and TP, the minimum water volume required to maintain Class IV water quality was 0.86 × 108 m3, whereas the suitable ecological water demand ranged from 1.27 × 108 to 1.56 × 108 m3. With the total replenishment volume held constant, centralized replenishment met the required water volume but substantially increased near-bed disturbance and sediment resuspension risk. By contrast, decentralized uniform replenishment performed best, with the highest relative closeness coefficient of 0.9105, a disturbance index of approximately 0.32, and water depths maintained within the suitable habitat range of 30–50 cm. These findings suggest that ecological restoration in semi-arid wetlands should move beyond volume-based water supplementation and pay greater attention to the timing, pathway, and hydrodynamic effects of replenishment. The proposed framework provides a quantitative basis for optimizing ecological water replenishment in evaporation-dominated wetlands and other inland lakes in arid and semi-arid regions.

Hydrology doi: 10.3390/hydrology13070188

Authors: Gabriele Iemmolo Andrea Petroselli Nunzio Angiola Ciro Apollonio

Accurate estimation of depth–duration–frequency (DDF) curves is essential for hydrological analyses and flood-risk mitigation. Regionalization methods are particularly important in areas with limited observations, but their performance may deteriorate where topography and climate generate strong spatial variability. This study investigates the Gargano promontory in southern Italy, an area that is commonly treated as a single hydrologically homogeneous zone despite its marked morphological and climatic contrasts. Annual maximum rainfall data from eight rain gauges, together with topographic and climatic descriptors, were analyzed to assess whether local factors help explain the variability of DDF-curve parameters. The analysis focused on elevation, distance from the sea, and a wind-effect index derived from a digital elevation model. The results indicate that the regional behavior of extreme rainfall is not spatially uniform and that several DDF-curve parameters are influenced by local physiographic controls. Different controls emerge for different station groups, and a physiographically based subdivision of the Gargano promontory into windward and leeward sectors provides a more coherent representation of DDF-curve parameters than the currently adopted single-zone regionalization.

Hydrology doi: 10.3390/hydrology13070187

Authors: Aili Yang Jiaona Guo Yueyu Su Yurui Fan Xiuquan Wang

Understanding how different drought indicators characterise future drought conditions is essential for climate monitoring and adaptation planning. This study quantified annual severe- and extreme-drought occurrence rates at the 1- and 3-month timescales using the Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI), and their concurrent signals. Historical conditions during 1951–2010 were compared with projections for 2041–2100 under SSP245 and SSP585 using an ensemble of 12 CMIP6 General Circulation Models. Inter-model uncertainty was assessed using the 5th and 95th ensemble quantiles. The results reveal marked contrasts between precipitation-based and potential-evapotranspiration-sensitive drought indicators. Globally, SPI-based severe-drought occurrence decreases under both scenarios, whereas SPI-based extreme-drought occurrence increases, particularly at the 3-month timescale under SSP585. Stronger and more spatially extensive increases are identified using SPEI. The global mean occurrence rate of SPEI1-based extreme drought increases from 0.231 month/year historically to 0.791 month/year under SSP245 and 1.197 month/year under SSP585. For SPEI3, the corresponding rate increases from 0.207 to 1.090 and 1.716 month/year, respectively, with approximately 92.3% of land grid cells showing increases under SSP585. Concurrent SPI-SPEI severe-drought occurrence decreases, while concurrent extreme-drought occurrence increases across approximately 70–77% of land grid cells. This contrast indicates a redistribution of concurrent drought months from the severe to the extreme severity class under a mutually exclusive classification scheme, particularly at the 3-month timescale. The Mediterranean region, the Amazon and other parts of South America, southern Africa, parts of West and Central Asia, and Australia consistently emerge as major hotspots. Ensemble-quantile results support the direction of increasing SPEI-based and concurrent extreme-drought occurrence, although substantial uncertainty remains in the magnitude of change. These findings demonstrate the value of jointly considering precipitation-based and evaporative-demand-sensitive indicators in drought monitoring and regional climate adaptation planning.

Hydrology doi: 10.3390/hydrology13070186

Authors: Xinyu Wang Ningkun Kang Zihan Zhu Jingli Zhang Guanyu Chen Samuel A. Cushman Guifang Wang Yawen Wu Tian Bai

Variations in urban surface runoff are often attributed to static infrastructure, neglecting the non-stationary hydrological responses induced by rapid urbanization and intricate micro-topography. This study introduces a Pressure–Trend–Pulse (PTP) framework to examine surface runoff dynamics in Kunming, China. By integrating continuous Soil and Water Assessment Tool (SWAT) simulations (2005–2024), Sen’s slope estimation, the Mann–Kendall test, robust residual analysis, and Self-Organizing Map (SOM) clustering, we quantify these multi-dimensional changes. The findings indicate: (1) runoff displays a structural north–south gradient, with the generation centroid migrating northward at a rate of 2.3 km per decade; (2) non-stationary positive trends (0.16 mm/year) are exclusively concentrated in northern sub-basins, which constitute 26.74% of the total area, thereby exacerbating long-term cumulative pressure; and (3) detrended residual analysis reveals high-frequency pulse volatility predominantly in the southern sink areas. Overlaying 139 historical waterlogging points confirms that trend-driven and pulse-driven risks account for 30.22% and 13.67% of urban disasters, respectively. Furthermore, approximately 22% of waterlogging occurrences fall within non-significant downstream zones, implying a potential upstream-downstream source–sink decoupling. The PTP framework highlights the necessity of differentiated resilience planning: upstream source-control and downstream adaptive buffering.

Hydrology doi: 10.3390/hydrology13070185

Authors: Mehdi Rahimi Bahram Malekmohammadi Mohammad Karimi Firozjaei Reza Kerachian Farhad Bahmanpouri

Floods are among the most destructive natural hazards, posing a significant risk to lives and infrastructure worldwide. Effective flood risk management demands precise, multidimensional approaches. In this direction, the current study aims to evaluate and compare two methods for flood-risk assessment: the scenario-based Ordered Weighted Averaging (OWA) and the data-driven Random Forest (RF) method. To this end, the Great Karun watershed in Iran was chosen due to its complex hydrological and climatic conditions. Hydro-climatic, hydrological, topographic, land-cover datasets, and actual flood observations were applied and analyzed based on fifteen influencing factors recommended by expert opinion. In the OWA approach, while factor weights were determined using the Best-Worst Method, flood-risk maps were produced based on five scenarios: very optimistic, optimistic, intermediate, pessimistic, and very pessimistic. In the RF approach, factor importance index was calculated via the mean decrease impurity algorithm, and the model was trained to generate flood-risk maps. Results showed that distance from rivers and slope were the most influential factors in OWA, while precipitation and flow accumulation dominated in RF. Prediction rate for OWA scenarios ranged from 1.4 to 3.5%, while RF achieved 16.0%, and the Area Under the Curve (AUC) was 0.984. Optimistic scenarios overestimated, and pessimistic scenarios underestimated risk, with the OWA intermediate scenario most closely matching RF results. RF demonstrated superior performance for flood-risk classification, highlighting its applicability for precise flood management. Overall, this study presents a novel comparative framework by integrating scenario-based OWA-BWM and Random Forest approaches to investigate the effects of decision-maker preferences and data-driven learning on flood susceptibility mapping.

Hydrology doi: 10.3390/hydrology13070184

Authors: Ahyahudin Sodri Geovanny Branchiny Imasuly Nuraeni Nuraeni Annisa Layyina Ihsani

Flooding is one of the most frequent and damaging natural disasters, accounting for nearly half of global disasters and posing a major challenge in Indonesia, where floods represent approximately 77% of all nationally recorded disaster events. Rapid urbanisation, land-use change, and climate-induced extreme rainfall have intensified flood risks nationwide. However, existing vulnerability assessments remain fragmented and localised, limiting their relevance for national-scale adaptation planning. This study develops a measurable and explainable framework for assessing urban flood vulnerability across Indonesia using cloud-based geospatial data and interpretable machine learning. The approach integrates CEMS-GLOFAS (flood hazard), WorldPop (population exposure), SRTM (topography), and ESA WorldCover (land cover) datasets within Google Earth Engine (GEE). Flood vulnerability is quantified through a modified Flood Vulnerability Index (FVI) combining hazard, exposure, and physical vulnerability components. The Extreme Gradient Boosting (XGBoost) model predicts FVI values, while SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) enhance model transparency and identify the influence of key variables such as flood depth, population density, and elevation. The model achieved high predictive accuracy (R2 = 0.89; RMSE = 0.04728 FVI units, dimensionless) and revealed substantial spatial heterogeneity across 514 districts, with the highest FVI (0.75–0.85) in Banda Aceh, Mojokerto, Pasuruan, Samarinda, and Merauke. The integration of GEE and explainable AI offers a transparent, scalable framework to support data-driven flood risk mitigation and urban climate resilience in Indonesia.

Hydrology doi: 10.3390/hydrology13070183

Authors: Julianne Webb Maggie Duncan Glenn Tootle Wolfgang Gurgiser Abel Andrés Ramírez Molina

Understanding long-term hydroclimatic variability in the central Alps is essential when placing recent changes in precipitation and streamflow within a broader temporal context. This study reconstructs warm-season hydroclimatic variability in the central Alps using tree-ring-based hydroclimatic proxies from the Old World Drought Atlas (OWDA). Seasonal April–May–June–July–August (AMJJA) precipitation at Innsbruck, Austria, and seasonal May–June–July–August (MJJA) streamflow at the St. Jodok gauge were reconstructed using OWDA self-calibrating Palmer Drought Severity Index (scPDSI) predictors and moving-window Stepwise Linear Regression (SLR) models. Calibration windows of 30, 40, and 50 years were developed to account for temporal variability in predictor–climate relationships, and reconstruction uncertainty was quantified using multi-model ensemble bounds. An independent Deep Learning reconstruction was also developed for precipitation to provide an assessment of reconstruction skill and long-term climate trends. Specifically, the results demonstrate a robust reconstruction skill, with mean calibration R2 values of 0.65 for streamflow and 0.59 for precipitation. The streamflow reconstruction indicates that recent sustained increases represent the strongest positive anomaly in approximately 650 years, while reconstructed precipitation suggests recent decades are among the wettest sustained intervals of the last ~2000 years. Both records reveal a pronounced transition from severe late 19th-century drought conditions to persistent modern pluvial conditions. Agreement between regression and Deep Learning reconstructions supports the robustness of the identified long-term wetting trend and highlights the exceptional nature of recent hydroclimatic conditions in the central Alps.

Hydrology doi: 10.3390/hydrology13070182

Authors: Anujit Phumiphan Anongrit Kangrang

Reservoir operation under hydrological uncertainty has become increasingly challenging under changing climate conditions. This study proposes a Probabilistic Dynamic Reservoir Operation Framework (PDROF) that integrates stochastic inflow modeling, Monte Carlo simulation, and dynamic rule extraction for adaptive reservoir management. Historical inflow records were transformed into stochastic inflow ensembles and propagated through reservoir operation simulations to generate reservoir storage trajectories under varying hydrological conditions. From these trajectories, a representative operational rule, referred to as the Most Likely Line (MLL), was extracted to characterize the dominant storage behavior of the system. The results demonstrate that conventional deterministic rule curves are constrained by predefined hydrological classifications and limited flexibility under variable inflow conditions. In contrast, the proposed framework effectively captures seasonal variability and propagates hydrological uncertainty throughout the operational cycle. Long-term simulation over a 23-year period resulted in a total spill volume of 17.74 million cubic meters (MCM), with spill events occurring in only 7 months, indicating improved operational robustness and storage stability. A real flood event in 2024 further demonstrated reductions of 47.42 MCM in spill volume and 3.46 MCM in reservoir storage compared with conventional operation. These improvements are attributed to the anticipatory storage behavior of the MLL-based operational rule, which preserves flood-buffer capacity prior to peak inflow periods and reduces the likelihood of uncontrolled spill events. The proposed framework provides a practical transition from deterministic reservoir operation toward uncertainty-aware and adaptive water resources management. The methodology is scalable to data-scarce and climate-sensitive regions and can be further extended through real-time forecasting and multi-objective optimization in future studies.

Hydrology doi: 10.3390/hydrology13070181

Authors: Normakhmad Ravshanov Kamola Shadmanova Istam Shadmanov

This paper presents a comprehensive numerical modeling of nonlinear groundwater flow in a synthetic heterogeneous four-layer porous medium. Multilayered aquifer systems present significant modeling challenges due to nonlinear filtration and interlayer exchange processes. The mathematical model consists of four coupled nonlinear parabolic partial differential equations, where the nonlinearity arises from the dependence of hydraulic conductivity on hydraulic head. Vertical exchange between layers is described by Darcy’s law through separating aquicludes. The system is solved using a fully implicit finite-difference scheme by employing an alternating-direction implicit approach, resulting in a block-tridiagonal system of equations. The model is verified using analytical solutions and mass conservation tests. Application to a synthetic aquifer system demonstrates the model’s ability to reproduce complex transient behavior, including delayed response of upper layers to pumping and asymmetry of water-level drawdown cones due to nonlinear conductivity. The model’s greatest sensitivity is observed to the conductivity of the pumped layer and the vertical conductivity of the separating layers. The proposed approach represents a robust tool for groundwater management in structurally complex geological settings.

Hydrology doi: 10.3390/hydrology13070180

Authors: Jelena Kovačević-Majkić Nikola Rosić Dragoljub Štrbac Vujica Šarenac Andrijana Todorović

This study presents the Multi-dimensional Flood Vulnerability Index (M-FLOVI), calculated by using an index-based method specifically tailored to capture the impact of nature-based solutions (NbSs) on vulnerability. It aggregates five vulnerability dimensions (physical, economic, environmental, social and institutional) into a single index within a multi-level framework. Each dimension is calculated from a set of indicators that can be computed with moderate data demands. These calculations generally require information about buildings, infrastructure, land cover, population, protected areas and cultural heritage, which can partly be obtained from open-access data. M-FLOVI ranges between 0 and 1, and it can be readily mapped and combined with flood hazard to produce flood risk maps. This paper elaborates a step-by-step M-FLOVI calculation in the Tamnava River Basin, Serbia, where various NbSs were proposed. Under the baseline conditions, most of the study area exhibits either moderate (83.5%) or low vulnerability (15.8%). These NbSs decrease future vulnerability in 6.6 km2 (1.34%) of the study area. Afforestation (0.59%) and retention ponds (0.42%) decrease environmental vulnerability, while flood plain restoration (0.33%), which is expected to create a protected bird habitat, increases environmental vulnerability. These results suggest that M-FLOVI can effectively capture NbSs’ impacts on future vulnerability to floods.

Hydrology doi: 10.3390/hydrology13070179

Authors: Juan Pescador Luis Silva Boris Lora-Ariza Juan Felipe Landinez Mónica Vaca Pedro Romero Adriana Piña Leonardo David Donado

Sustainable management of hydrogeological systems that supply water and exhibit high hydrologic complexity can be studied through pragmatic numerical modeling supported by field-constrained conceptualization. This study develops a local-scale three-dimensional groundwater flow numerical model using FEFLOW for the Barranca Lebrija settlement in Aguachica town, where the Lebrija River, the Musanda floodplain lake, and groundwater system converge. The numerical model incorporates: (i) the three-dimensional distribution of geological units and lithology; (ii) water level observations from the Musanda floodplain lake; (iii) stage records from the Lebrija River; (iv) boundary conditions and flux estimates inherited from a previous regional groundwater model; and (v) hydraulic heads from two monitoring wells and five community wells. Steady-state and transient conditions were calibrated, and a sensitivity analysis was performed to identify the parameters that most strongly control surface water–groundwater exchange. The simulations reproduce seasonal groundwater level trends and demonstrate the exchange pathways among the river, floodplain lake, and groundwater system. Results indicate dual behavior: during wet periods, flooding of the Musanda floodplain lake driven by high river levels seeps into the underlying aquifer, whereas in dry periods the floodplain lake reverses its role and becomes a principal discharge boundary. This local-scale, boundary-driven approach provides a computationally tractable framework to quantify SW–GW exchange in data-scarce tropical floodplains and supports monitoring design and water-supply management.

Hydrology doi: 10.3390/hydrology13070178

Authors: Cairui Fan Teng Wang Xiu Li Bo Zhai Dandan Luo

Water yield is a key indicator for regional water resource assessment and directly concerns multidimensional socio-ecological sustainability. However, in arid and semi-arid regions, integrated long-term water yield simulation and nonlinear interpretation of driving factors remain insufficient. Therefore, Inner Mongolia was selected to analyze the spatial pattern and nonlinear driving mechanism of water yield depth for sustainable water resource management. Based on the InVEST model, water yield depth during 2001–2024 was simulated, and trend analysis was conducted. Annual XGBoost models with SHAP were used to explain nonlinear driver effects. Results showed a significant east-high and west-low pattern, with significantly increasing and decreasing areas accounting for 12.35% and 4.5%, respectively. Precipitation was the dominant driver, with higher ∣SHAP∣ values in wet years than in dry years. Zonal SHAP showed Pre led in all zones (48.8%, 63.5%, 37.7%), with secondary drivers shifting from forest/topography in the East to temperature in the West. SHAP values increased rapidly after precipitation exceeded thresholds of 200–300 mm in dry years and 400–500 mm in wet years. Under high precipitation, precipitation–non-forest interactions increased rapidly, whereas forest interactions changed little or became negative, showing a scissor-like divergence pattern. XGBoost reproduced the InVEST-simulated water yield depth well (R2 = 0.91 ± 0.03). This workflow provides a reproducible pathway for water resource assessment in arid and semi-arid regions.

Hydrology doi: 10.3390/hydrology13070177

Authors: Carlos Andrés Maldonado Chávez Benito Guillermo Mendoza Trujillo Andrés Santiago Cisneros Barahona Guido Patricio Santillán Lima Nelson Bravo Yumi Tamia Samai Nuñez Cruz María Rafaela Viteri Uzcategui

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m3 s−1 consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R2 = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15.

Hydrology doi: 10.3390/hydrology13070176

Authors: Eduardo Cochrane Novo Monica de Aquino Galeano Massera da Hora José Paulo Soares de Azevedo

This study investigated the relationship between precipitation indicators and water availability in the Muriaé River Basin (MRB), Southeast Brazil, using rainfall and streamflow series from 1961 to 2020. Monthly mean precipitation (MMP), the total annual precipitation (PRCPTOT), the Rainfall Anomaly Index (RAI), and the Q95 low flow parameter were analyzed to evaluate hydrological variability and drought conditions. Trend analyses were performed using the Mann–Kendall test and Sen’s slope estimator, and Pearson correlation analysis was applied to assess the relationship between precipitation and low flow availability. The results showed marked temporal variability in precipitation and hydrological conditions throughout the basin. Although statistically significant increasing trends in annual precipitation were identified at Carangola and Patrocínio do Muriaé, no generalized long-term reduction in precipitation was observed in the MRB. In contrast, Q95 exhibited reductions at all monitored stations, with decadal decreases ranging from approximately 31% at Carangola to 56% at Itaperuna. The RAI analysis indicated predominance of very dry and extremely dry events during the most recent decade, coinciding with reduced low flow availability. The results indicate that changes in water availability are linked to the temporal distribution and persistence of dry anomalies. These findings can influence decisions in hydrological monitoring and water resource management strategies in the basin.

Hydrology doi: 10.3390/hydrology13070175

Authors: Zainab Salah Abd Alameer Amer A. Mohammed Ali A. Al Maliki Ahmed Gad Muhammad Aufaristama Alaa Ahmed

Groundwater quality in arid and semi-arid regions is increasingly affected by salinization, evaporation, abstraction, and agricultural return flow. This study evaluates the hydrochemical evolution, isotopic characteristics, 222Rn activity, and water-use suitability of groundwater and associated waters in Karbala Governorate, central Iraq. Seventeen groundwater, lake water, and municipal supply water samples were analyzed for physicochemical parameters, major ions, δ18O, δ2H, and 222Rn. Hydrochemical, isotopic, and water-quality assessment methods were applied to evaluate groundwater evolution, salinization, and suitability for drinking and irrigation. The waters are near-neutral, with pH values of 6.18–7.35, but are strongly mineralized. Electrical conductivity ranges from 1440 to 16,305 µS/cm, and total dissolved solids (TDS) range from 592 to 10,191 mg/L. Most samples belong to a Ca–Mg–SO4–Cl facies, indicating sulfate- and chloride-rich hard water evolution. The highest mineralization occurs near Karbala proper and lake-influenced sites. Ion ratios and chloro-alkaline indices indicate that evaporite dissolution, gypsum/anhydrite dissolution, carbonate interaction, evaporation, and local ion exchange jointly control groundwater chemistry. Stable isotopes indicate meteoric origin with variable evaporative enrichment; however, highly saline but isotopically depleted water, particularly W8, shows that evaporation alone cannot explain salinization. 222Rn activities range from below detection to 11.28 Bq/L and mainly reflect local aquifer contact and degassing. High TDS, sulfate, chloride, and very high hardness limit suitability for drinking-water use. For irrigation, the sodium hazard is low, but salinity, hardness, magnesium hazard, and permeability constraints make most samples unsuitable or restricted. Management should prioritize salinity and hardness control, treatment or blending before domestic use, restricted irrigation of the least saline wells under drainage and soil-salinity monitoring, protection of less mineralized recharge zones, and long-term monitoring of lake-adjacent and agriculturally influenced wells.

Hydrology doi: 10.3390/hydrology13070174

Authors: László Palcsu Andor Hajnal István Csige Árpád Csámer Krisztián Baranyi Danny Vargas Marianna Túri

The tritium–helium-3 (3H/3He) dating method was applied to quantify groundwater apparent ages and estimate the migration of anthropogenic tritium in the shallow alluvial aquifer surrounding the Paks Nuclear Power Plant (Hungary). Groundwater samples were collected from monitoring wells between 2013 and 2016 and analyzed for tritium and dissolved noble gases. The investigated aquifer consists mainly of highly permeable sand and gravel deposits hydraulically connected to the Danube River. Reference wells indicate apparent groundwater ages between 26 and 43 years, with an average apparent 3H/3He age of approximately 37 years. Wells located within the operational area of the power plant show apparent 3H/3He ages ranging from 1.3 to 14.1 years, reflecting the transport of tritium released during leakage events associated with damaged sewer pipelines between 2005 and 2007. The spatial distribution of apparent ages reveals heterogeneous groundwater flow paths, and highlights the influence of well-screen sampling on age interpretation. The paper demonstrates that anthropogenic tritium released from nuclear infrastructure can serve as an effective age dating method and improve conceptual models of flow dynamics in shallow alluvial aquifers.

Hydrology doi: 10.3390/hydrology13070173

Authors: Yongjiang Chen Kui Wang Mingjie Zhao Gang Liu Jianfeng Liu

Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood discharge data with strong fluctuations and high noise, this study introduces a method for filling in missing dam inflow flood discharge based on Dam Monitoring Data Reconstruction Model (DSVD). The method constructs a non-repeating sequence monitoring matrix, introduces a hard singular value threshold for adaptive denoising, and completes time series data imputation combined with a weight optimization model, which effectively improves the imputation accuracy of strongly fluctuating flood discharge data. Taking the measured inflow flood discharge data of Jinjiaba Reservoir in Chongqing as the research object, this study systematically analyzes the influence of column-to-row ratio (Ra) and data missing rate on imputation performance, and conducts a comparative verification against other models. Experimental results indicate that the optimal Ra value is 6. The coefficient of determination (R2) stays above 0.830 within a missing rate range of 5–40%, showing strong robustness against data loss. Compared with other benchmark models, the method has the highest R2 (0.875) and the lowest Root Mean Square Error (RMSE, 7.771), exhibiting stronger adaptability to mountainous flood discharge data with steep rise and fall characteristics. The research findings provide a new method for the high-precision recovery of missing dam inflow flood discharge data and reliable data support for reservoir flood risk analysis and safe operation.

Hydrology doi: 10.3390/hydrology13070171

Authors: Kenny Pabón Cevallos Luis Angel Espinosa Miguel Costa João Pedro Pêgo

Recurrent meteorological droughts, projected to intensify under climate change, affect the cross-border Lima River Basin shared between Portugal and Spain, highlighting the need for robust early warning systems to support proactive water management. Within the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic risks in the cross-border Minho–Lima River Basins—this study develops a regionalised forecasting framework to evaluate meteorological drought forecast skill using precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System 5 (SEAS5) for the Portuguese section of the Lima River Basin. A precipitation-only 12-month Standardized Precipitation Index (SPI12) is employed to isolate the contribution of seasonal precipitation forecasts. SPI12 is computed from hybrid 12-month accumulations combining observed monthly precipitation (October 1979 to February 2025) and SEAS5 forecasts (October 2018 to February 2025). Four hybrid configurations (1 to 6 months lead time) are evaluated: 11 obs + 1 fcst, 10 obs + 2 fcsts, 9 obs + 3 fcsts, and 6 obs + 6 fcsts. Forecast performance is assessed from October 2018 to February 2025. Deterministic SPI12 forecasts and categorical drought classifications are evaluated using regression-based metrics (e.g., Pearson correlation and RMSE) and contingency-table metrics (e.g., FAR and F1-score), across SEAS5 ensemble members, percentiles, and spread-based indicators. The 11 obs + 1 fcst configuration, particularly when using the Dry Spread (SpD; Q10 + Q25 percentiles) and the Q75 percentile, exhibits the highest skill, achieving a Pearson correlation coefficient of r=0.97 and an RMSE of approximately 0.17, alongside near-perfect categorical performance (POD = 1.00; FAR = 0.00), although these scores are partly conditioned by the shared observed accumulation window. Conversely, longer lead-time configurations exhibit degraded performance, with the 6 obs + 6 fcsts configuration showing weak or negative skill relative to climatology, indicating that 6-month lead forecasts should be interpreted with caution. These results demonstrate that SEAS5 precipitation forecasts can provide skilful drought predictions at lead times of several months in the Lima River Basin within the SPI12 framework. The proposed blending methodology provides a transparent benchmark and a technical basis for the early-warning system being developed under the RISC_PLUS project to support drought risk management in the Minho–Lima region and complement data-driven drought forecasting approaches.

Hydrology doi: 10.3390/hydrology13070172

Authors: Minghao Chen Cong Li Taihua Wang

Streamflow variations in the Middle Minjiang River Basin (MMR) are vital for the flood mitigation and water resources management of the Chengdu metropolitan area which is important for the development of Southwest China. However, how climate change, Chengdu metropolitan area and Zipingpu Reservoir influence streamflow in the MMR remains unclear. Hence, we coupled the Geomorphology-Based Ecohydrological Model (GBEHM), the Physic-aware Hybrid Learning (PaHL) model and the Extreme Gradient Boosting (XGBoost) model to reproduce streamflow variations at Pengshan station—the outlet cross section of MMR—from 1980 to 2019, subsequently performing attribution analysis. Annual streamflow at Pengshan station exhibits a decreasing trend from 1980 to 2019. Coupled simulations effectively reproduce daily streamflow at Pengshan station during 35 years, with values of NSE, R2 and KGE exceeding 0.96. The dominant influence of anthropogenic disturbance on daily streamflow decrease is generally steady at Pengshan station, explaining 62.3% and 430.8% of it before and after the impoundment of Zipingpu Reservoir (in 2006), respectively. Majority of the climate change’s influence is notably concentrated from June to September, suggesting a potential temporal imbalance in water resources and a threat of extreme hydrological events. Our study contributes to flood mitigation and water resources management in the MMR.

Hydrology doi: 10.3390/hydrology13070170

Authors: Mehjabeen Khan Ruishan Chen Sheheryar Khan

Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of Pakistan’s first national efforts to address the gap between flood risk assessment and prioritization through a unified geospatial assessment. This study assesses flood susceptibility across Pakistan for 2002, 2012, and 2022 using a GIS-based AHP approach by integrating climatic, environmental, topographic, hydrological, soil, LULC, and anthropogenic indicators. The study results were further analyzed through district-level assessments, risk change analysis, persistence mapping, LULC exposure assessments, and the Comprehensive Flood Risk Priority Index (FRPI). The results show that high and very high flood susceptibility zones are primarily concentrated along the Indus River corridor, lower floodplains, and coastal Sindh, accounting for more than 7% of the total land area of Pakistan. Persistent flood hotspots are identified in Rann of Kutch (66.6%), Jacobabad (65.0%), and Jafarabad (61.1%), indicating strong temporal stability of flood-prone conditions. LULC exposure analysis reveals that cropland is the dominant exposed class, with the highest district-level exposure observed in Badin (17.1%) and Larkana (10.1%). The FRPI further identifies priority flood-risk zones where susceptibility, persistence, risk change, and exposure converge, with the highest FRPI values observed in Jacobabad (0.742), Rann of Kutch (0.738), and Badin (0.711). Model validation demonstrates strong predictive performance, with susceptibility ROC-AUC values ranging from 0.85 to 0.87 and FRPI AUC reaching 0.85. The proposed framework provides a robust decision-support tool for targeted flood-risk management and climate-resilient land-use planning in Pakistan.

Hydrology doi: 10.3390/hydrology13070169

Authors: Yunyi Wang Wei Wu Chengjun Yang Xiaoyu Liu Linxuan Li Yuyue Chen Yang Liu

To further improve prediction accuracy, a VMD-VPPSO-LSTM model is proposed in this study, which combines Variational Mode Decomposition (VMD) for signal decomposition, Velocity-Pause Particle Swarm Optimization (VPPSO) for parameter optimization, and Long Short-Term Memory (LSTM) for runoff prediction. The model was evaluated at Huangtaiqiao station in the Xiaoqing River Basin, Dawenkou station in the Dawen River Basin, and Tangnaihai station in the source region of the Yellow River Basin. The proposed model achieved the best overall performance among all comparison models, with Nash–Sutcliffe Efficiency (NSE) values of 0.970, 0.962, and 0.994 and Root Mean Square Error (RMSE) values of 1.357, 0.989, and 46.804 at the three stations, respectively. Compared with VMD-LSTM, VPPSO further reduced the RMSE at all stations and maintained training-test NSE gaps below 0.006, indicating strong generalization performance. The model also achieved the lowest Peak Percent Standard Deviation (PPSD) values for high-flow events, reaching 9.03%, 14.42%, and 3.88% at the three stations, respectively. These results demonstrate that VMD-VPPSO-LSTM is a reliable and effective model for daily runoff prediction.

Hydrology doi: 10.3390/hydrology13070168

Authors: Andra-Teodora Nedelcu Tiberiu Pazara Manuela Rossemary Apetroaei

High-resolution CTD profiles, with SVP cross-validation of the sound speed field, were recorded at a single station in the outer roadstead of the Port of Constanța (northwest Black Sea; 44°07′41″ N, 28°53′15″ E; depth ≈ 25 m; June 2024), revealing a strongly stratified, five-layer water column driven by three combined forcing mechanisms: seasonal thermal stratification with an abnormally shallow Cold Intermediate Water layer (7.3–15.6 m), Danube-sourced freshwater input, and anthropogenic disturbances consistent with port and anchorage activity. A contextual hypothesis is proposed that conflict-related marine traffic intensification may contribute to observed signals, but physical measurements cannot establish causation. At the main pycnocline (7.31–15.62 m), a density difference of Δρ = 4.02 kg m−3 yields a maximum Brunt–Väisälä frequency of N2 = 2.37 × 10−3 s−2, reducing vertical eddy diffusivity by two orders of magnitude (Kz ≈ 10−6 m2 s−1). Physical conditions—a shallow mixed layer (~0.7–1.2 m) and strong pycnocline—support the theoretical expectation of surface-layer contaminant accumulation; however, no chemical measurements were carried out to confirm contaminant presence. All contamination inferences rely exclusively on physical proxies (turbidity, dissolved oxygen, and density gradients), and contaminant retention remains untested for lack of direct chemical evidence. A dimensional Stratification-Controlled Retention Index (SCRI = N2/Kz; units: m−2 s−1) is introduced, and its consistency with the observed hydrographic structure is demonstrated.

Hydrology doi: 10.3390/hydrology13070167

Authors: Ruinan Zhao Changlei Dai Xinyu Wang Xiao Yang Wenzhao Xu

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a−1). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin.

Hydrology doi: 10.3390/hydrology13070166

Authors: Adil Zerouali Mohamed Jalal El Hamidi Abdelkader Larabi Mohamed Faouzi Omar Chafik

The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any water deficit. In this context, the Berrechid plain represents a relevant case study illustrating both the practical and theoretical challenges of groundwater governance. The aquifer is heavily exploited to satisfy agricultural, industrial, and domestic needs. This study develops a hybrid “grey-box” modeling approach for predicting groundwater depth (GWD) fluctuations under climate change (CC). Unlike conventional black-box machine learning models, our framework combines a deterministic physical engine with a stochastic machine learning corrector. The physical component simulates aquifer mass balance using the Hargreaves method for evapotranspiration, linear drainage, climate memory via exponential decay, and an anthropogenic trend parameter (xi). The machine learning component—XGBoost with quantile regression—is trained exclusively on physical model residuals and predicts the 5th, 50th, and 95th percentiles, providing explicit 90% confidence intervals. Hydrological states (dry, normal, wet) are identified via K-means clustering for context-aware correction. The model is calibrated using historical data (1972–2019) and validated using blocked time-series cross-validation. Climate projections under the RCP 4.5 and RCP 8.5 scenarios were used to forecast GWD up to 2100. At piezometer 3933/20, the best performance was achieved, with an RMSE of 0.347 m and a KGE of 0.742 during the validation period. The proposed approach is suitable for seasonal GWD forecasting and offers practical value for water managers and decision-makers in the Berrechid region.

Hydrology doi: 10.3390/hydrology13070165

Authors: Delaiah Antwi Nyarko Larry Pax Chegbeleh

Seawater intrusion presents a significant risk to coastal aquifers, particularly in low-lying locations where groundwater resources are intensively exploited. This study assesses the vulnerability of the Keta Strip aquifer in Southeastern Ghana to seawater intrusion using the GALDIT model; a widely applied index-based approach that evaluates seawater intrusion risk based on six key hydrogeological indicators: groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater level above sea level (L), distance from the shoreline (D), impact of existing intrusion (I), and aquifer thickness (T). These parameters were analyzed using data from 105 monitoring wells within a Geographic Information System (GIS) environment. The resulting vulnerability index was spatially grouped into four categories: low, moderate, high, and very high vulnerability. Results indicate that very high and high vulnerability regions are predominantly clustered along the coastal margins and central portions of the study area, driven mainly by low hydraulic gradients, proximity to the shoreline, and high hydraulic conductivity. Moderate vulnerability zones dominate inland areas, while low vulnerability zones are limited and confined to northern sections. Sensitivity analysis reveals that hydraulic head (L) and distance from shoreline (D) are the most influential parameters, whereas TDS exhibits relatively low contribution to overall vulnerability. The findings highlight the critical role of hydrogeological controls and anthropogenic pressures in shaping seawater intrusion risk and provide a scientific basis for sustainable groundwater management in the Keta Strip and similar coastal environments.

Hydrology doi: 10.3390/hydrology13070164

Authors: Jehangir Ijaz Marko Šrajbek Muhammad Azaan Irshad Takai Eddine Yahi

Nutrient stoichiometry, particularly the balance of carbon (C), nitrogen (N), and phosphorus (P), plays a fundamental role in regulating freshwater ecosystem dynamics, primary production, and biogeochemical cycling. This study presents one of the first dedicated reviews to combine bibliometric mapping with ecological synthesis of C:N:P ratios in inland waters, drawing on 1004 publications indexed in the Web of Science Core Collection (2000–2025), comprising peer-reviewed articles and review articles refined by document type, language, and research area. Bibliometric mapping using VOSviewer (version 1.6.20) identified exponential growth in publications after 2010, with phosphorus dynamics and eutrophication emerging as the most-cited themes, while recent years have shown increasing attention to C:P ratios as reliable ecological indicators. Four dominant thematic clusters were identified: Nutrient Cycling and Biogeochemistry; Phytoplankton and Food Web Dynamics; Eutrophication and Water Quality; and Climate Change and Ecosystem Responses. Ecological synthesis demonstrated substantial deviations from the canonical Redfield ratio (106C:16N:1P), with pronounced stoichiometric variability across trophic states, latitudes, and ecosystem types. Case comparisons revealed high C:P ratios in Arctic and alpine lakes linked to dissolved organic carbon inputs, low N:P ratios in tropical waters that promote cyanobacterial dominance, and stable, low phosphorus concentrations in deep African lakes. These findings emphasize the significance of flexible stoichiometry in predicting ecosystem tipping points, managing harmful algal blooms (HABs), and guiding nutrient restoration strategies. By integrating bibliometric and ecological evidence, this study identifies C:P ratios as a promising candidate indicator that merits further field validation for freshwater management, while underscoring persistent research gaps in microbial stoichiometry, cross-scalar modeling, and policy uptake in the Global South.

Hydrology doi: 10.3390/hydrology13060163

Authors: Despina Giannadaki Antonis Bezes Vassiliki Kotroni Kostas Lagouvardos Katerina Papagiannaki Christina Oikonomou Haris Haralambous

Flooding triggered by intense precipitation is a significant natural hazard affecting Mediterranean regions, where complex terrain, rapid hydrological response and increasing urbanization can amplify flood impacts. This study assesses flood susceptibility in two representative Mediterranean River catchments: the Koiliaris in Crete, Greece, and the Pediaios in Cyprus. A compact Flood Hazard Index (FHI) was developed by integrating the Topographic Wetness Index (TWI), Curve Number (CN), and R20 heavy rain frequency index, representing the principal geomorphological, hydrological and climatological controls of flood generation. Spatial datasets including EU-DEM elevation data, CORINE land cover, European soil databases, and Copernicus CERRA precipitation reanalysis were combined within a GIS-based multi-criteria framework using Analytic Hierarchy Process weighting. The resulting FHI maps identify high flood susceptibility along river corridors, low-lying accumulation zones, and urbanized areas. In the Koiliaris basin, 34% of the area fell within the high and very high susceptibility classes, mainly in downstream alluvial zones, whereas in the Pediaios basin, 29% of the area fell within the high and very high susceptibility classes, concentrated around the urbanized Nicosia corridor. The analysis of historical flood events provided a qualitative consistency assessment of the FHI patterns, acknowledging that the absence of spatially explicit flood-inundation footprints limits quantitative validation.

Hydrology doi: 10.3390/hydrology13060162

Authors: Yulia Kharanzhevskaya

Surface oscillation is an important mechanism for the hydrological self-regulation of mires: it prevents the attenuation of flooding by storing water during high precipitation events and snowmelt. To investigate the spatial and temporal variability in surface oscillation, we conducted monthly measurements of the surface elevation and water level at three monitoring sites in the Great Vasyugan Mire (GVM), Western Siberia, over a nine-year period (2017–2025). Surface oscillation in the GVM varied from 14 to 25 cm in winter and early spring as a result of frost heaving, and from 2 to 16 cm in the frost-free period. Surface oscillation depends on the water table level variation, which is disturbed when the water level rises above the surface during freezing–thawing periods and due to released biogenic gases. Our data showed that within large mire systems, such as the Great Vasyugan Mire, the spatial variability in surface oscillation is influenced by several key factors: the type of plant community, peat properties, and the location relative to water flow pathways. Surface oscillation increased along a transect extending from the sedge–Sphagnum community to the pine–dwarf shrub–Sphagnum community, which runs parallel to the slope toward the marginal area. Long-term records demonstrate an increasing trend in surface elevation in the central part of the GVM, while showing a decrease at the mire boundary.

Hydrology doi: 10.3390/hydrology13060161

Authors: Daniel Camilo Ortiz-Hernández Carlos Alfonso Zafra-Mejía Amed Bonilla Pérez

Landslides constitute one of the most lethal and costly hydrometeorological hazards at the global scale. There is a growing trend associated with the increase in extreme precipitation and the expansion of urban development on unstable slopes. In the tropical Andes, this problem is intensified under climate change scenarios. The objective of this study is to develop a logistic regression model to analyze socioeconomic risk due to landslides in the Bogotá Savannah (Colombia). An integrated risk model was developed using binary logistic regression and a socioeconomic vulnerability index. A total of 12 physical–biotic variables and SSP climate projections (2021–2040) were used. A GIS-based environment was implemented to generate prospective spatial risk scenarios. The model demonstrated high robustness and predictive capability, with an improvement in statistical goodness-of-fit of 8.2% (AIC: 2574–2367), adequate probabilistic calibration (Pseudo-R2: 0.675; Brier Score: 0.084), and excellent predictive performance (AUC: 0.935; sensitivity: 84.7%; specificity: 90.0%). Simulations estimated maximum risk probabilities close to 0.600 (scale between 0 and 1), concentrated in geomorphologically critical sectors. Simulations under SSP scenarios showed a progressive increase in risk toward 2040 (up to 0.673), associated with precipitation increases between 10 and 30%. Integrated modeling constitutes a reliable technical tool for land-use planning, climate adaptation, and prospective landslide risk management in urbanized Andean regions.

Hydrology doi: 10.3390/hydrology13060160

Authors: Aida Alejandra Guerrero de León Valerie Natalia Salazar-Zepeda Virgilio Zúñiga-Grajeda Hasbleidy Palacios-Hinestroza Walter Ramírez Meda Jesús Barrera-Rojas

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year public hydrochemical database from 10 monitoring nodes with SAR-derived discharge estimates and thermodynamic metal modeling (PHREEQC). To validate the structural integrity of the mass load estimates against hydrometric uncertainties, a deterministic boundary-sensitivity analysis was conducted. Results empirically refute the classical dilution paradigm, introducing the “Anthropogenic Pulse” to describe the non-linear acceleration of pollutant export during high-flow events (discharge Q surging from 36.62 to 286.13 m3/s). While climate-driven parameters follow seasonal cycles, industrial stressors (COD, Pb, Cd) remain in a chronic steady state, decoupling from volumetric dilution. Based on coupled × CQ × C (discharge × concentration) estimates, this dynamic induces a synchronized flushing of toxic burdens, exporting monthly peak loads exceeding 51,000 kg of Zinc, 6500 kg of Lead, and 3100 kg of Cadmium. Thermodynamic simulations reveal that this hydrological flushing functions as a chemical activator; the seasonal dilution of natural Alkalinity and Hardness suppresses the river’s theoretical buffered pH (from 8.5 to 7.0), maintaining metals in their uncomplexed free-ion states (Me2+). Modeling indicates that nearly 90% of the exported Cadmium remains in this highly labile, toxic form due to a dual coupling with both river Discharge (rs = 0.87) and pH (rs = 0.79). The identification of stochastic arsenic peaks 100 times above regulatory limits at Paso de Guadalupe (RS-08) underscores the failure of concentration-based monitoring. Our findings suggest that restoration strategies should shift toward mass-loading-based regulatory frameworks and targeted sediment management at critical nodes to mitigate the chronic export of bioavailable industrial waste.

Hydrology doi: 10.3390/hydrology13060159

Authors: Rogerio Gonçalves Lacerda de Gouveia

Land use and land cover change (LULCC) is increasingly recognized as a dominant driver of hydrological alteration in tropical watersheds, often exceeding the influence of climatic variability. This study evaluates the spatiotemporal dynamics of LULCC and their implications for hydrological sustainability in the Uberabinha River Basin, southeastern Brazil, between 1990 and 2020. Utilizing MapBiomas data and statistical analysis, the results reveal a marked expansion of mechanized agriculture, particularly soybean cultivation, which grew from 3426 ha to 54,162 ha, and urban areas, which expanded by approximately 89.4%. Conversely, natural vegetation and pasturelands decreased continuously, with pastures showing the sharpest absolute reduction, from 72,248 ha to 34,535 ha. Despite a 10.76% increase in annual precipitation between 1990 and 2020, the hydrological response exhibited a severe decline in streamflow, characterized by a 76.35% drop in minimum flow. Furthermore, the runoff index decreased from 0.0574 in 1990 to 0.0211 in 2020, indicating a critical loss in the basin’s capacity to convert rainfall into streamflow. These findings demonstrate a clear decoupling between precipitation and streamflow driven by LULCC, posing a severe threat to regional water security and highlighting the urgent need for integrated land–water management.

Hydrology doi: 10.3390/hydrology13060158

Authors: Jhonatan Hinojosa Mamani Benito Pepe Calsina Calsina Yalmar Temistocles Ponce Atencio Juan Manuel Tito Humpiri Henry Pizarro Viveros Maribel Erika Cahuana Huichi

Climate change is expected to significantly alter hydrological regimes in high-altitude tropical basins, where water availability strongly depends on precipitation variability and groundwater processes. The Ramis River basin, a major tributary of Lake Titicaca in the Peruvian Altiplano, is particularly vulnerable to hydroclimatic variability due to its dependence on seasonal water resources. This study evaluates the impacts of climate change on runoff, groundwater recharge, percolation, and renewable water resources using the SWAT hydrological model calibrated and validated for the period 1981–2024. Future projections were developed using the MPI-ESM-MR and ACCESS1-0 global climate models under RCP 4.5 and RCP 8.5 scenarios for the period 2025–2100, applying bias correction through CMhyd. The results indicate a strong sensitivity of basin hydrology to climate forcing. Under the MPI-ESM-MR model, runoff decreases by up to 68% under RCP 4.5, while extreme increases exceeding 130% are projected under RCP 8.5. In contrast, ACCESS1-0 shows moderate reductions in most scenarios. Renewable water resources exhibit a general declining trend (−23% to −41%), suggesting increasing water scarcity conditions. Additionally, the Standardized Precipitation Index (SPI) reveals a higher frequency and persistence of drought events toward the end of the century, particularly under high-emission scenarios. Overall, the findings indicate that the Ramis River basin may face a dual hydroclimatic risk characterized by reduced water availability and increased hydrological extremes. These results highlight the need to integrate climate projections into water resource management and to implement adaptive strategies to reduce future water vulnerability in high-Andean basins.

Hydrology doi: 10.3390/hydrology13060157

Authors: Guanghong Dai Liping Guo Qing Ye Yongfen Zhang Yan Wang Zhiming Xia Huimin Zhu Yue Zhong Yuxiang Liao Xiulong Chen

With global warming continuously worsening drought hazards, the Fuhe River Basin urgently requires insight into drought evolution laws to support resilient water resources management. However, traditional univariate indices such as the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) are limited by their inability to capture the coupled meteorological-agricultural drought process and the time-lag effects between precipitation and soil moisture response. Therefore, a multivariate drought index—which integrates both precipitation and soil moisture information—is needed as a core tool for drought early warning and precise regulation. In this study, the calibrated SWAT model was used to simulate monthly soil moisture content in the Fuhe River Basin over the past 60 years. On a 3-month time scale, a Multivariate Standardized Drought Index (MSDI) was established by coupling the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) using the Copula function. The main findings are as follows: (1) The Nash–Sutcliffe efficiency coefficient (NS) of the SWAT (Soil and Water Assessment Tool) model during the validation period reached above 0.70, indicating favorable performance in monthly runoff simulation. (2) The MSDI revealed frequent drought events in two periods, namely 1960–1979 and 2000–2019, demonstrating the periodic fluctuation pattern of droughts in the basin. (3) Wavelet analysis showed that compared with the previous two periods, the frequency of droughts in the basin increased significantly after 2000, with weakened periodic characteristics, intensified extreme drought events, and a further rise in drought risks. This study deepens the understanding of drought dynamics in the Fuhe River Basin and provides a scientific basis for regional sustainable water resource management and the formulation of climate adaptation strategies.

Hydrology doi: 10.3390/hydrology13060156

Authors: Jemal Tefera Esubalew Adem Mohammed Abegaz Aliy Yimer Mohamed Elhag

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments.

Hydrology doi: 10.3390/hydrology13060155

Authors: Manatchanok Pannak Ketvara Sittichok Chaiyapong Thepprasit Chuphan Chompuchan

In many orographically complex monsoon basins, rain gauge networks are sparse and lack the long-term continuous records required for reliable precipitation probability analysis. Traditional regional frequency analysis assumes spatially uniform precipitation across the analysis zone, which is inadequate for basins with steep rainfall gradients and strong seasonal variability. Gridded precipitation products (GPPs) provide spatially continuous, long-term records that enable grid-cell-level probability distribution fitting. However, GPPs may exhibit local biases and errors, and statistical evaluation against gauge observations is necessary before application. This study was conducted in the Phetchaburi–Prachuap Khiri Khan River Basin, western Thailand, a region with steep orographic and coastal rainfall gradients. Four GPPs, namely CHIRPS, CHELSA, WorldClim, and PERSIANN-CCS-CDR, were evaluated against gauge observations. The best-performing product, after monthly bias correction, was then used to generate spatially continuous monthly exceedance probability maps using grid-cell gamma distribution fitting. CHELSA showed the best overall performance across all evaluation metrics (correlation coefficient (r) = 0.908, percent bias (PBIAS) = 7.0%, root mean square error (RMSE) = 48.3 mm), passing the Kolmogorov–Smirnov (KS) goodness-of-fit test at all 96 station-months. CHIRPS and WorldClim showed satisfactory overall performance but exhibited localized biases in complex terrain, whereas PERSIANN-CCS-CDR substantially overestimated wet-season rainfall, limiting its suitability for this basin. Spatial precipitation patterns varied markedly between monsoon regimes, shifting from a dominant west-to-east orographic gradient during the southwest monsoon to a less differentiated advective pattern during the northeast monsoon. Furthermore, analysis at the 75% exceedance probability level showed that mean-based effective rainfall overestimated reliable water supply in high-variance months, leading to underestimation of supplemental irrigation demand. The generated maps provide spatially explicit dependable rainfall estimates across the basin, supporting probabilistic agricultural water management at multiple planning scales in orographically complex monsoon basins.

Hydrology doi: 10.3390/hydrology13060153

Authors: Racky Diallo Serigne Faye Djim M. L. Diongue Abib Ndiaye Maimouna Sane Salifu Dumbuya Mohamed Saber

This study assesses the future availability of water resources in Lake Guiers by 2050, considering the combined impacts of climate change and human activities, using the Water Evaluation and Planning System. As Senegal’s main freshwater source, the lake faces growing pressure from agricultural expansion, aquatic plant overgrowth, competing stakeholder demands, and increasing water use. The study combines field data on hydrological flows and agricultural water use with climate projections under the Shared Socioeconomic Pathways 4.5 and 8.5 scenarios. Climate data were downscaled and bias-corrected using CMhyd, multiple linear regression, and the Mann–Kendall test. Model calibration showed strong performance (NSE = 0.95; R2 = 0.96). Results reveal decreasing precipitation and rising temperatures under both scenarios. Agricultural withdrawals (79,331,457.14 m3/year) already exceed crop water needs (69,115,088.03 m3/year), resulting in significant water losses estimated at over 10 million m3 per year. Scenario analysis indicates that high water demand under Shared Socioeconomic Pathways SSP8.5 could lead to critical declines in lake volume as early as 2026 (550 million m3), while moderate demand growth under SSP4.5 could maintain water availability until 2050. The proposed PREFERLO-Grand Transfer project would add further stress to the lake’s capacity. These findings emphasize the urgent need for sustainable water management and policy actions.

Hydrology doi: 10.3390/hydrology13060154

Authors: Rudolf Dugovič Peter Malík Martin Zatlakovič Natália Bahnová

Baseflow separation was performed for 42 small catchments completely built up of either crystalline rocks or folded/unfolded Paleogene flysch rocks. Three different methods were applied—Local minimum (BFI), Kille’s and Rambert’s. Mean total annual runoff in individual catchments varied from 179 to 1132 mm, with an average of 498 mm. Taking into account results for the whole dataset, baseflow participated in 45% ± 15% ratio of the total runoff. Local minimum and Kille’s method results were quite similar: both showed average baseflow participating on 39%/40% of total runoff in unfolded Paleogene catchments, on 29%/29% in folded flysch and 44%/45% in catchments with crystalline basement. Rambert’s method results were 10% to 12% higher from the previous two, reaching 50% in unfolded flysch Paleogene catchments, 41% in folded flysch and 56% in crystalline catchments. Differences might be caused by the nature of Rambert’s method, which is based on recession curves analyses, while the previous two result from discharge statistics. Still, usually only less than 50% of unevaporated precipitation is able to infiltrate and recharge groundwater resources in crystalline rocks and flysch sediments, and folded flysch rocks are sometimes able to absorb only 10–20% of unevaporated precipitation.

Hydrology doi: 10.3390/hydrology13060152

Authors: Ana Hilza Barros Queiroz Marco Antônio Vieira Callado Iago Vasconcelos Gadelha Barbosa Thaís Angélica da Costa Borba Marcelo Rollnic

Tropical estuaries are socioeconomically important yet highly vulnerable environments. In the eastern Amazon, the Pará River Estuary (PRE) and adjacent water bodies support the city of Belém and are increasingly affected by environmental pressures but remain underrepresented in numerical modeling efforts. The influence of key input parameters on hydrodynamic model performance in these systems remains poorly characterized, hindering the development of reliable simulation tools for this region. We present the calibration and validation of a two-dimensional hydrodynamic model for the PRE, Guajará Bay, and the Guamá River, examining how parameters such as bathymetry, roughness, and tidal and discharge forcings influence model performance. Delft3D-FM was applied using tidal harmonics and seasonal river discharge as primary forcings, with model skill evaluated against observed water levels and discharge across ten seasonally distinct scenarios over seven calibration iterations. Tidal forcing and bathymetric representation emerged as the dominant performance drivers: replacing global tidal datasets with locally derived harmonics substantially reduced simulation errors, and bathymetric refinements also improved discharge representation. Final performance met established satisfactory thresholds at the majority of observation points and cross-sections. The calibrated model provides a basis for investigating processes governed by local hydrodynamics, such as water quality assessments, contaminant dispersion, and infrastructure planning.

Hydrology doi: 10.3390/hydrology13060151

Authors: Artur Gafurov

Spaceborne precipitation products such as NASA IMERG V07 provide sub-hourly data required for hydrological modelling, but systematic biases in wet-event frequency and extreme-intensity representation limit their reliability for applications sensitive to precipitation extremes. This study develops a three-stage calibration pipeline combining probability-of-precipitation frequency adaptation, empirical quantile mapping of the distribution body, and Generalised Pareto Distribution tail modelling with constrained blending. The approach is calibrated against 202 Roshydromet stations using 3-hourly observations and evaluated on 15 spatially independent stations over a 9-year validation period. At the station-optimal blending weight, the proposed pipeline reduces median absolute percentage bias at the P99 quantile from 43.9% to 10.2%, while maintaining comparable volume balance (|PBIAS| 6.5%). To suppress a disaggregation artefact arising from amplification of multi-hour accumulations, the operational gridded R-factor product instead adopts a more conservative blend (|PBIAS@P99| = 24.9%) together with an empirically constrained accumulation cap, although the absence of sub-hourly calibration data remains the principal limitation. The calibrated dataset is applied to derive a 24-year (2001–2024) rainfall erosivity climatology for the Volga region, yielding a domain-mean R-factor of 254 ± 55 MJ mm ha−1 h−1 yr−1 with no detectable monotonic trend. The proposed framework improves the representation of precipitation extremes and provides a transferable preprocessing approach for hydrological modelling applications.

Hydrology doi: 10.3390/hydrology13060150

Authors: Jehangir Ijaz Bojan Đurin Yuping Su Muhammad Zahir Mobeen Jamshed Khattak Sheraz Akhtar Gil

Freshwater reservoirs face increasing threats from eutrophication and anthropogenic nutrient enrichment, yet practical multicriteria tools for ranking site-specific vulnerability remain underutilized. This study applies the PROMETHEE/GAIA multicriteria decision analysis framework to rank water quality vulnerability at five sampling sites (L1–L5) in Shanzai Reservoir, Fujian Province, China, using ten water quality parameters (TN, TP, COD, DO, Chl-a, pH, temperature, N:P ratio, transparency, and carbon ratio) measured monthly from April 2023 to April 2024. The PROMETHEE II complete ranking and the GAIA biplot together provide both a spatial vulnerability ranking and parameter-level diagnostic visualization. The Reservoir Centre (L5) ranked first (Φ = +0.32), exhibiting the most favorable water quality, while the River Channel (L3) ranked last (Φ = −0.44), with mean TN (1.15 mg/L) and TP (0.088 mg/L) exceeding Chinese Class III standards and Chl-a (35.89 µg/L) surpassing eutrophication thresholds. Intermediate rankings: L4 (Φ = +0.20), L1 (Φ = 0.00), L2 (Φ = −0.04). Spatial vulnerability followed a clear zone-level gradient: the riverine zone (L1, L3) was most vulnerable, the transitional zone (L4) showed intermediate performance, and the lacustrine zone (L2, L5) was most favorable, consistent with reservoir hydrodynamic theory. The GAIA biplot revealed that nutrient criteria (TN, TP, Chl-a) were the primary drivers separating site vulnerability classes. A sensitivity analysis across eight weighting scenarios confirmed that L3 ranked last in all scenarios (Φ = −0.450 to −0.694), demonstrating the robustness of the recommendation to prioritize intervention at the river channel inflow zone. These findings offer a practical, reproducible decision-support framework for water quality management prioritization in subtropical freshwater reservoirs, subject to confirmation through multi-year monitoring programs.

Hydrology doi: 10.3390/hydrology13060149

Authors: Yi Zhang Gan Luo Yanxia Liu

Groundwater resources are essential to global freshwater supply, and accurate groundwater level prediction is critical for sustainable water resource management. To overcome the limitations of traditional deep learning models in long-sequence groundwater forecasting, including weak generalization, reduced long-term prediction accuracy, and limited interpretability, this study proposes a dual-path Informer-p model integrated with residual theory. The main path captures nonlinear temporal dependencies and long-term hydrological patterns, while the residual path provides a stable linear prediction baseline to enhance local fluctuation representation and robustness to extreme events. The model was validated using long-term groundwater observations from 34 monitoring stations across five major ecosystems in China. Results from representative stations, including Ailao Mountain, showed that Informer-p achieved excellent predictive performance with RMSE = 0.05 m, MAPE = 1.2%, R2 = 0.95, and KGE = 0.95, reducing RMSE and MAPE by 37.5% and 52%, respectively, compared with the original Informer. Across all stations, Informer-p outperformed the original Informer at 22 stations, with the greatest improvement observed in forest ecosystems. SHAP analysis identified window maximum, original groundwater level, and window minimum as the dominant predictive features. The proposed model provides an effective tool for national-scale groundwater level prediction and sustainable groundwater management.

Hydrology doi: 10.3390/hydrology13060148

Authors: Saravanan Subbarayan Ramanarayan Sankriti

In many developing regions, the lack of on-site weather data impedes the estimation of rainfall-driven processes, such as soil erosion. Satellite-based precipitation products (SPPs) can support hydrological modelling in gauge-sparse regions by providing continuous rainfall estimates. Accurate rainfall estimation is crucial to soil erosion modelling, particularly in data-scarce regions such as the TRB. In this study, seven satellite-based precipitation products—CHIRPS, IMERG, TRMM, ERA5, GLDAS, and PERSIANN-CDR, along with the IMD gridded dataset—were evaluated for their ability to represent rainfall patterns and support R-factor estimation in the RUSLE framework. This is the first comprehensive evaluation of multiple SPPs for RUSLE-based soil erosion modelling in the Tungabhadra river basin (TRB), providing insights for ungauged watersheds in India. CHIRPS and IMERG displayed relatively smooth and continuous patterns, while PERSIANN-CDR and TRMM exhibited fragmented rainfall zones. ERA5 and GLDAS demonstrated consistent but moderate values across the basin. IMD data served as the reference product for comparison. The findings reveal that the choice of precipitation dataset directly affects the accuracy of erosion estimation. Therefore, multi-dataset evaluation is recommended for reliable assessment of soil loss and watershed planning in ungauged or partially gauged catchments.

Hydrology doi: 10.3390/hydrology13060147

Authors: Alessandro Comegna Stella Lovelli Shawkat Basel Mostafa Hassan Antonio Coppola Antonio Satriani

Nitrogen applied in excess of plant demand in intensive agricultural systems can be lost through runoff and leaching into surface and groundwater, with potentially negative effects on water quality. Zeolites, due to their high cation exchange capacity and internal porosity, can adsorb ammonium (NH4+) and help mitigate excessive nitrate (NO3−) leaching. Owing to such properties, zeolites can play an important role in reducing the potential negative impact associated with the extensive use of nitrogen-based fertilizers. In this study, we investigated the effects of two commercial natural zeolites on selected hydraulic properties, water storage, and solute transport parameters of three sandy-loam soils with different pedological characteristics. Laboratory experiments were conducted on disturbed soil columns. The leaching of NO3− and NH4+ ions was monitored using ion-selective electrode analysis. The results indicate that zeolite application reduces the mobility of nitrate and ammonium. This effect can be attributed to changes in the original pore size distribution of the investigated soils, characterized by a reduction in macropore regions and a corresponding increase in meso- and micropore regions. In the case of ammonium, adsorption mechanisms are also involved, which further contribute to retarding its mobility. These effects were consistently observed across the investigated soils. For a given soil, the magnitude of the observed effects depended on both the type of zeolite used and the amount of zeolite mixed with the soil. Finally, ANOVA tests and multivariate analyses were applied to the full dataset to provide statistical support for the observed changes in the selected parameters.

Hydrology doi: 10.3390/hydrology13060146

Authors: María Florencia Degano Sabrina Beninato José Pasapera Mauro Ezequiel Holzman Raúl Eduardo Rivas

Soil moisture (SM) is a key variable for assessing plant water availability, especially in rain-fed systems where imbalances strongly affect crop development. Satellite missions such as SMAP provide global SM estimates, though representing vertical SM variability remains challenging. This study evaluates the performance of SMAP Level 4 Global 3-hourly 9 km grid EASE-Grid Surface and Root-Zone Soil Moisture Geophysical Data (SPL4SMGP, version 7 and the new and scarcely evaluated version 8) using field observations from the Argentine Pampas, a region dominated by Typic Argiudolls soils (~16 million ha). The analysis covered normal-wet and dry conditions across several crop seasons. Surface (SSM, ~5 cm) and root zone (RZSM, 0–100 cm) soil moisture were compared against field data using Pearson’s correlation (r), bias, and unbiased root mean square deviation (ubRMSD). Both SSM and RZSM achieved ubRMSD values close to the SMAP accuracy target (≈0.04 m3/m3). SSM correlated moderately with observations (r = 0.57–0.72) and showed a consistent negative bias (−0.08 ± 0.05 m3/m3). In contrast, RZSM exhibited low sensitivity to soil profile variability and a narrow dynamic range. Version 8 showed similar performance to version 7, with a tendency toward overestimation, mainly during dry periods. Overall, SPL4SMGP products effectively capture SSM dynamics but show limited skill in representing root zone variability in Typic Argiudolls.

Hydrology doi: 10.3390/hydrology13060145

Authors: Dian Feng Shaoni Huang Yibo Du Lihao Zhou Jun Zhang

Flash-flood and floodplain inundation simulations are highly sensitive to the spatiotemporal variability of convective rainfall, particularly during the initial runoff generation stage. However, coarse-resolution numerical weather prediction (NWP) forcing tends to smooth localized rainfall extremes, limiting its ability to accurately represent hydrological responses in low-relief floodplains. In this study, we couple a diffusion-enhanced radar nowcasting model, Diff_ConvLSTM, with a spatial resolution of 1 km and a temporal resolution of 6 min, to assess the hydrological value of high-resolution rainfall forcing over the middle Yangtze River floodplain. We introduce a monotone piecewise cubic Hermite interpolation scheme to ensure a stable transition from discrete high-frequency rainfall inputs to continuous hydrodynamic integration. Evaluation using a radar dataset from 2023 to 2024 shows that Diff_ConvLSTM better preserves intense convective echoes and rainband structures compared to the baseline ConvLSTM, increasing the Probability of Detection at the 40 dBZ threshold by 65.8%. A forcing-replacement experiment for the flood event on 30 June 2023 demonstrates that AI-based nowcasting rainfall forcing reduces peak-discharge underestimation, improves volumetric consistency, and produces inundation patterns that are closer to the observation-driven reference than those generated by low-resolution forecast forcing, although positive biases in inundation area and water depth persist. An additional event in 2024 confirms that the improvements are primarily reflected in discharge magnitude and flood volume representation, while enhancements in peak timing remain limited. Overall, the results illustrate both the added value and the remaining limitations of AI-enhanced nowcasting for hydrologically informed flood forecasting.

Hydrology doi: 10.3390/hydrology13060144

Authors: Nighat Gulzar Xin Liao Zhongyuan Xu Amir Rehman

Fluoride contamination in groundwater poses a significant public-health concern in most semi-arid areas such as the Punjab alluvial aquifers of Pakistan, with local concentrations exceeding the WHO guideline. Reliable fluoride dynamics prediction and mechanistic interpretation of fluoride is key for targeted monitoring and risk mitigation. This paper built an integrated hydrogeochemical machine learning model to predict the fluoride concentration and classify exceedance risk in the Rechna Doab aquifer Tehsil Jaranwala, Punjab, Pakistan. Nested cross-validation and independent test evaluation were performed on conventional models (linear regression, random forest, XGBoost) and a deep tabular model (FT-Transformer). Model reliability was evaluated using discrimination and probability-calibration metrics, while Shapley Additive Explanations (SHAP) and permutation importance were applied to identify the main hydrogeochemical controls on fluoride prediction. Moreover, the robustness was tested by noise sensitivity experiments. Fluoride concentrations showed a positive skewed distribution with some local exceedances related to the geogenic and hydrochemical influences. Nonlinear models greatly outperformed the linear baseline; XGBoost showed robust regression performance (test R2 = 0.878; RMSE ≈ 0.190 mg/L). The FT-Transformer showed strong exceedance-classification performance, with high sensitivity (recall = 0.875) and good probability calibration (Brier ≈ 0.021). Interpretability analyses identified EC/TDS, Mg2+, and Ca2+ as important predictors, linking fluoride enrichment to chemically evolved groundwater with reduced calcium activity, sodium enrichment, and alkalinity buffering. The proposed framework provides accurate, interpretable, and risk-oriented support for groundwater fluoride monitoring in alluvial aquifer systems.

Hydrology doi: 10.3390/hydrology13060143

Authors: Marta Chiesi Sofia Ortenzi Paulina Bartkowiak Matteo Camporese Mariapina Castelli Jacopo Dari Luca Fibbi Beatrice Gatto Christian Massari Maurizio Pieri Silvana Vanucci Fabio Maselli

Accurate estimation of actual evapotranspiration (ETa) is essential for understanding hydrological processes and managing water resources, especially in regions characterized by intensive agriculture and complex groundwater–surface interactions. This study intercompares three independent satellite-based ETa estimation approaches applied over Northeast Italy. The first two methods correspond to the classical MODIS algorithm (MOD16), which is based on a simplified Penman–Monteith approach, and to the more recent Sen-ET modelling framework, which relies on a surface energy balance principle. The outputs of these methods are compared to those produced by a water balance algorithm, NDVI-Cws, which predicts ETa through the combination of conventional ancillary data and MODIS NDVI imagery. The results obtained show that, while the MODIS algorithm yields ETa estimates which are generally lower than those of Sen-ET and NDVI-Cws, the latter methods produce similar predictions for most cover types examined. The same two methods are potentially capable of providing higher spatial resolution daily ETa estimates depending on the satellite inputs used; out of them, however, only NDVI-Cws can yield spatially complete and temporally continuous datasets. The analysis therefore provides insights into the reliability and usability of different remote sensing approaches for regional-scale water resource monitoring.

Hydrology doi: 10.3390/hydrology13060142

Authors: Roger Francisco Ferreira de Campos Indianara Fernanda Barcaroli Carolina Fruet de Lima Cláudia Maté Rosana Claudio Silva Ogoshi Cristiane Maria Tonetto Godoy Cristine Vanz Borges Levi Hülse Lincon Bordignon Somensi Eliana Rezende Adami

Surface water degradation has intensified due to anthropogenic pressures, especially in urban areas, where unplanned land use compromises the integrity of aquatic ecosystems. This study investigated the relationship between water quality and land use in a Permanent Preservation Area (PPA) within an urban perimeter in Caçador, Santa Catarina, Brazil. Monthly sampling was conducted throughout 2024 at 11 points distributed along urban and rural sections of the river and its tributaries. Physicochemical and microbiological parameters were evaluated, and the Water Quality Index (WQI) established by the National Sanitation Foundation (NSF) was calculated in order to associate the results with the sampling points, complemented by Principal Component Analysis (PCA) to identify multivariate patterns of spatial variability in water quality across the study area. In parallel, the PPA within the urban perimeter was delimited according to current environmental legislation, and land use was classified using ArcGIS and Google Earth Pro. The results revealed greater water quality degradation in urban stretches of the river, particularly at sampling point SP7, which recorded the lowest dissolved oxygen concentration (3.10 mg L−1), alongside elevated values of biochemical oxygen demand (5.23 mg L−1), total phosphorus (2.94 mg L−1), nitrate (18.75 mg L−1), and thermotolerant coliforms (2759.20 MPN 100 mL−1). The WQI ranged from 40.18 (SP7: bad category) to 73.57 (SP1: good category), reflecting a pronounced spatial gradient of water quality degradation associated with increasing urbanization along the river course. Mapping of the PPAs revealed that only 43.72% of the total area was covered by native vegetation, while the remaining 56.28% was occupied by anthropogenic land uses, including miscellaneous use (30.32%), agriculture (9.09%), buildings (6.09%), roads (4.64%), and railway infrastructure (5.81%). PCA accounted for 89.06% of the total data variance and indicated that greater interaction of sampling points with urbanized areas was consistently associated with reduced water quality, thereby demonstrating the direct influence of anthropogenic activities on the environmental parameters assessed throughout the study area. These findings demonstrate that land use patterns directly affect water quality and reinforce the need for riparian forest restoration, expanded sanitation infrastructure, and more sustainable urban planning.

Hydrology doi: 10.3390/hydrology13060141

Authors: Ali Barzkar Mohammad Reza Goodarzi Majid Niazkar

Coastal aquifers are more exposed to pollution and salinity than other hydrogeological systems due to their proximity to the sea, increasing groundwater withdrawals, and climate change. The aim of this study is not only to evaluate and compare the vulnerability of coastal aquifers using the DRASTIC and GALDIT models but also to investigate effects of different weighting methods on the results of vulnerability zoning. The spatio-temporal vulnerability assessment was conducted for coastal aquifers in Hormozgan Province in Iran over a 15-year period (2010–2024). After collecting information layers required for both models, vulnerability maps were calculated for three consecutive five-year periods using three weighting methods: (a) normal weighting, (b) Shannon entropy, and (c) particle swarm optimization (PSO) algorithm. The results indicate that the coastal areas of the western part of the province have the highest vulnerability in both models, and the intensity and extent of high-risk zones have increased in recent periods. Comparison of weighting methods revealed that normal weighting provided a conservative and uniform distribution, while the entropy method, due to its reliance on statistical dispersion of data in some areas, led to a hyperbole of the risk. In contrast, the PSO algorithm provided the most accurate and realistic results compared to classical fixed-weight and entropy-based vulnerability maps, as it was able to identify critical areas with higher spatial concentration and hydrogeological coherence. The combined results of DRASTIC and GALDIT demonstrated that parts of the coastal aquifers of Hormozgan are simultaneously in a critical state in terms of inherent vulnerability and salinity potential. The findings of this study can be used as a scientific basis for sustainable management of groundwater resources, withdrawal control, and protection climate adaptation planning in coastal areas.

Hydrology doi: 10.3390/hydrology13060140

Authors: Josean da Silva Vanessa B. Paula Cleonilson Protásio de Souza Ana M. Antão-Geraldes

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation.

Hydrology doi: 10.3390/hydrology13060139

Authors: Muhammad Usman Lloyd H. C. Chua Kim N. Irvine Lihoun Teang

Natural ecosystems are facing threats from natural and anthropogenic stressors. Wetlands are among the most delicate natural ecosystems and are particularly vulnerable to the impacts of urbanization. One of the intended purposes of the wetlands is to mitigate the impact of urbanization (e.g., stormwater), but we often lack a comprehensive understanding of their capacity in doing so. Determination of water balance is essential in understanding the efficacy of a wetland when it comes to treating excess stormwater. This study therefore considers the Sparrovale Wetland in Victoria, Australia, to assess its performance in mitigating the impacts of urbanization in the surrounding catchment areas. A 1D model (HYDRUS-1D) was previously developed by the authors based on extensive field and laboratory measurements on one side (north) of the wetland. It was crucial to understand the two-dimensional water balance dynamics in the Sparrovale Wetland to utilize its full potential for managing excessive stormwater. This study therefore employed the HYDRUS-2D model (based on HYDRUS-1D) supported by extended, spatially explicit in situ measurements. The model was run (with additional input of inflow added to the rainfall) on the average Van Genuchten parameters obtained from the previously developed HYDRUS-1D model and the extended determination of the parameters. The model performance in simulating measured water content was good for both the south (average RMSE = 0.013 m3/m3) and the north side (average RMSE = 0.028 m3/m3). The model was also used to simulate surface water levels in the wetland and showed a good agreement (RMSE = 0.1 m AHD and R2 = 0.72) with in situ surface water level measurements. This developed model was used to determine the water balance dynamics (infiltration, evapotranspiration, soil water storage, surface and bottom boundary flux) in the Sparrovale Wetland. Our results indicate that evapotranspiration is the major factor controlling the water flux losses in the Sparrovale Wetland, while the role of infiltration was minimal, which might be attributed to the dominant soil type (clay) and shallow groundwater levels in the Sparrovale Wetland. Insights provided by this study might be helpful in optimizing the performance of the Sparrovale Wetland in managing the excess stormwater arising from the surrounding catchments.

Hydrology doi: 10.3390/hydrology13050138

Authors: Mohamed Elhag Abdulaziz Alqarawy Aris Psilovikos Wei Tian Imene Benmakhlouf

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological data to quantify spatial and temporal ET variations across a 25 km buffer. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) to derive crop coefficients (Kc) within a Kc–ET0 framework, where reference ET (ET0) was obtained from ERA5-Land potential evaporation. All processing utilized Python (Version 3.14) on Google Colab and Google Earth Engine for scalable computation. Eighty-eight cloud-free Landsat 9 scenes were processed following cloud and shadow masking. Mean NDVI, Kc, and daily ET values were compiled into a comprehensive time-series dataset. Model performance was evaluated through cross-validation with MODIS MOD16A2 and internal consistency checks, demonstrating strong statistical agreement (R2 = 0.82, NSE = 0.71, PBIAS = +8.3%). Results revealed pronounced seasonal variability closely linked to vegetation activity and atmospheric demand, with peak ET occurring during summer months (June–July: 7.2–7.5 mm day−1) and minima in winter (January–February: 2.0–2.6 mm day−1). Findings demonstrate that cloud-based techniques provide reliable, cost-effective ET monitoring in data-scarce, groundwater-dependent regions. Validation confirms Kc-ET0 estimates reliably capture spatial and temporal patterns, supporting practical irrigation management applications. This approach aids precision irrigation and long-term water sustainability planning in Al-Hofuf, contributing significantly to national water conservation objectives under Saudi Arabia’s Vision 2030 and National Water Strategy.

Hydrology doi: 10.3390/hydrology13050137

Authors: Farah Ben Souissi Pierre Masselot Taha B. M. J. Ouarda Emna Gargouri-Ellouze

Tunisia is characterized by high precipitation variability, which results in frequent extreme floods and droughts. This study aims to develop long-term forecasting models for total and daily maximum annual precipitation by incorporating information related to climate variability. These models use low-frequency climate oscillation indices as predictors. A linear functional model for scalar response is developed for this purpose. The model based on functional data analysis is also compared to a linear regression model. The station under study is located in north-eastern Tunisia. The association between precipitation and four climate indices is evaluated: the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Mediterranean Oscillation (MO) and the Western Mediterranean Oscillation (WeMO) climate indices. The results show that both linear and functional regression provide good and comparable results, likely due to the limited length of the data series. NAO, PDO and MO are the best indices to forecast total annual precipitation with an RMSE between 3.564% and 4.151% of the average precipitation, while MO seems to be the best index to forecast daily maximum annual precipitation achieving slightly higher RMSE between 11.174% and 11.916% of the average maximum precipitation. These results suggest that total precipitation at the study station is controlled by large-scale climatic processes operating over the Atlantic, Pacific, and Mediterranean regions, whereas the few most extreme precipitation events are primarily driven by regional climatic phenomena occurring at the Mediterranean scale. The results may have practical applications to improve disaster response preparedness and water resource management.

Hydrology doi: 10.3390/hydrology13050136

Authors: Weijiang Yu Yipeng Zhang

Subsurface freshwater in oceanic islands is typically shaped like a thin lens due to limited land area and recharge, often the primary freshwater source for local communities and highly vulnerable to seawater intrusion (SWI). Freshwater injection (FI) is considered as a feasible strategy for mitigating SWI in coastal aquifers. However, its transient effectiveness for freshwater lens (FWL) development and SWI mitigation in island aquifers and how the design parameters like FI depth, intensity, duration and injectant concentration affect its performance remain poorly understood. To address this, this study employs a two-dimensional, variable-density island groundwater model to simulate the transient responses of FWL development and SWI mitigation to various FI patterns. Five indicators are developed for comprehensive evaluation, including (1) freshwater recovery efficiency (FRE), and the relative changes in (2) average water table elevation (WTE), (3) FWL depth, (4) FWL volume, and (5) total aquifer salt mass. Results reveal FI universally raises average WTE, expands FWL dimensions, and promotes aquifer desalinization. Injection intensity is the primary driver of WTE rises and salt mass reduction, with higher intensities consistently yielding greater WTE rises and salt mass reductions. Deeper injection within the mixing zone increases FWL depth, but reduces the net gain in FWL volume. Moreover, early-stage FI is highly efficient for expanding FWL volume, often yielding FRE values above 100%, but FRE converges toward zero over time as the system moves toward a new hydrodynamic equilibrium, returning diminishing marginal benefits for long-term FI.

Hydrology doi: 10.3390/hydrology13050135

Authors: Mohammed S. Al Nadabi Mohammed El-Diasty Talal Etri Mohammad Reza Nikoo

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model.

Hydrology doi: 10.3390/hydrology13050134

Authors: Abhinna Manandhar Christopher S. Lowry

Emerging Large Language Model capabilities create opportunities for applying AI reasoning across various domains with minimal technical complexity. Motivated by the development of citizen scientists submitting photos of water levels on staff gauges and the increasing need for hydrologic data in ungauged watersheds, this research develops an artificial intelligence approach to measuring stream stage across an existing citizen science monitoring network. To lower the barrier to entry for professional scientists, this research develops a methodology leveraging a Large Language Model (LLM) to extract water levels from images submitted by citizen scientists, and then follows a human-in-the-loop workflow for validating the final results, leaving space for correcting reasoning errors and hallucinations. Various techniques, such as labeling the input image, are also explored in this research to extract maximum accuracy from the LLM.

Hydrology doi: 10.3390/hydrology13050133

Authors: Weijiang Yu Yipeng Zhang Wenqi Liu

Subsurface freshwater in oceanic islands is typically shaped like a thin lens due to limited land area and recharge, often the primary freshwater source for local communities and highly vulnerable to seawater intrusion (SWI). Saltwater abstraction (SA) is considered as a feasible strategy for mitigating SWI. However, its transient effectiveness for freshwater lens (FWL) development and SWI mitigation in island aquifers, and how the design parameters like SA depth, intensity, and duration affect its performance, remain poorly understood. Therefore, this study employs a two-dimensional, variable-density island groundwater model to simulate the transient responses of FWL development and SWI mitigation to various SA patterns. Six indicators are developed for comprehensive evaluation, including: (1) freshwater recovery efficiency, and the relative changes in (2) average water table elevation (WTE), (3) WTE at the SA well, (4) FWL depth, (5) fresh groundwater volume, and (6) total aquifer salt mass. Simulation results highlight SA depth as the primary determinant of its effectiveness, characterized by critical thresholds that dictate whether SA imposes net positive or negative effects on FWL depth, volume, and aquifer desalinization, with SA intensity and duration serving as scaling factors that amplify the magnitude of these responses. Moreover, while SA can effectively expand FWL volume and shift it toward a more favorable hydrodynamic equilibrium, the diminishing marginal benefits over time cause the FRE to approach zero, indicating SA is a potent short-term restoration strategy rather than a long-term solution from a cost–benefit perspective.

Hydrology doi: 10.3390/hydrology13050132

Authors: Maryame El-Yazidi Mohammed Benabdelhadi Brahim Benzougagh Yasmine Boukhlouf Malika El-Hamdouny Manal El Garouani Mohammed Mouad Mliyeh Hassan Tabyaoui Zineb El Attar Soufi Soukaina El Aissaoui Khaled Mohamed Khedher Abderrahim Lahrach

The Souss-Massa basin, one of Morocco’s major agricultural regions, is increasingly affected by water scarcity and climatic stress. However, the long-term interactions between hydro-climatic change and farmers’ cropping system adjustments remain insufficiently documented. This study analyzes hydro-climatic trends and agricultural transformations over the period 1995–2021. The methodology combines statistical trend analysis of meteorological data (Mann–Kendall test and Sen’s slope estimator), diachronic land use/land cover mapping using Google Earth Engine, Crop Water Stress Index (CWSI) assessment, and groundwater piezometric analysis. Results reveal declining and highly variable precipitation, together with a significant warming trend reaching +0.116 °C/year. In parallel, cultivated cereal areas (rainfed and irrigated) declined, while irrigated forage crops expanded, particularly Berseem/Maize. Despite increasing aridity, CWSI results indicate maintained crop vigor in irrigated areas, suggesting growing dependence on groundwater extraction. These findings highlight an ongoing agricultural transition that increases pressure on already vulnerable water resources and underscores the need for integrated climate adaptation and groundwater management strategies in the basin.

Hydrology doi: 10.3390/hydrology13050131

Authors: Yanbing Huang Xiaoli Yang Xungui Li Jian Sun Qiyong Yang Xu Dong Yongjun Huang

Drought exhibits a complex coupling response to regional meteorological factors, hydrological characteristics, land cover, and large-scale teleconnection climate indices, while their direct and indirect influences on multi-scale meteorological and hydrological droughts remain insufficiently understood, particularly in karst basins. This study investigated drought dynamics in China’s Yujiang River Basin using an integrated framework combining run theory, drought propagation analysis, and the partial least squares–structural equation model (PLS-SEM). We analyzed the 1-, 3-, 6-, and 12-month standardized precipitation index (SPI) and standardized streamflow index (SSI) at four hydrological stations during 1984–2014, together with meteorological factors, land cover indices, large-scale climate indices, areal precipitation, and naturalized streamflow. The results show that precipitation and streamflow exhibited slight declining tendencies with marked seasonal variability, and that drought durations of all severity levels generally decreased with increasing time scales. At the same time scale, SSI was more stable than SPI, and both indices tended to become more stable as the time scale increased. SPI-3 and SSI-1 were identified as the optimal time scales for monitoring meteorological and hydrological drought, respectively, providing a practical basis for drought identification and early warning in karst basins. Hydrological drought lagged meteorological drought by 1–3 months, indicating a measurable propagation time that is valuable for improving drought preparedness and water resources regulation. PLS-SEM further revealed that precipitation and streamflow were the dominant direct drivers of drought development, while land cover exerted a persistent negative effect, and climate-related factors mainly influenced drought indirectly. These findings enhance the understanding of drought propagation and multi-factor coupling mechanisms in karst basins and provide scientific support for regional drought monitoring and water resources management.

Hydrology doi: 10.3390/hydrology13050130

Authors: Andrea Petroselli

Assessing hydropower potential in ungauged basins requires consistent derivation of key hydrological variables from heterogeneous geospatial and climatic data. Conventional GIS-based approaches often rely on fragmented, user-dependent workflows, limiting reproducibility and comparability. This study presents HYDROPOT, a web-based geospatial framework for the automated and reproducible extraction of hydrologically relevant basin descriptors for regional-scale hydropower screening. The platform integrates centralized datasets with server-side geoprocessing to delineate upstream catchments and compute quantitative basin descriptors, including drainage area (2–400 km2), Curve Number (CN), concentration time, and spatially aggregated monthly thermo-pluviometric variables derived from 95 stations over the 2004–2022 period. These descriptors provide essential inputs for rainfall–runoff modeling and preliminary discharge estimation, thereby supporting (although not directly performing) the assessment of water availability in ungauged basins. By eliminating manual preprocessing, HYDROPOT ensures consistent and reproducible analyses, reducing user-induced variability and improving comparability across applications, without implying increased predictive accuracy. The framework, applied to the Lazio Region (Central Italy) over the 2004–2022 period, enables rapid and transparent screening of river reaches, offering a scalable decision-support tool for preliminary, input-based screening in early-stage small hydropower planning.

Hydrology doi: 10.3390/hydrology13050129

Authors: Mohammed Khelifi Abdessamed Derdour Tayeb Nouri Tayyib Moussaoui Said Bouarfa Sanliana Wan Abd Al Qadr Imad Wan-Mohtar Bilel Zerouali Yong Jie Wong

Small hill reservoirs in arid North Africa face accelerating threats from soil erosion and siltation, yet integrated assessments linking erosion dynamics, water quality, and soil fertility remain scarce. This study presents a multi-component geospatial assessment of the 345 km2 Es-Sabba watershed in the Saharan Atlas of southwestern Algeria. Soil loss was quantified using the revised universal soil loss equation (RUSLE) integrated with Sentinel-2 imagery, a 30 m digital elevation model (DEM), and GIS analysis for 2016–2025. The mean annual soil loss reached 26.3 t/ha/yr, with 68.4% of the watershed under high-to-severe erosion; topography and vegetation cover were the dominant controls. Estimated sediment delivery to the reservoir is 135,300 t/yr, projecting a functional lifespan of 11–15 years without intervention. Hydrochemical analysis classified reservoir water as alkaline- and sulfate-rich, yet suitable for irrigation with very low sodicity risk (sodium adsorption ratio, SAR = 0.08) and an excellent Irrigation Water Quality Index (IWQI = 91.75). Soils exhibited low-to-moderate fertility (mean soil fertility index, SFI = 0.416), with widespread nitrogen deficiency constraining vegetation-based erosion control. The integrated framework identifies circular-economy opportunities through nutrient-rich sediment reuse and provides actionable guidance for climate-resilient reservoir management in arid catchments.

Hydrology doi: 10.3390/hydrology13050128

Authors: Juseth E. Chancay Jorge Luis Sánchez-Lozano Bryan G. Valencia Mario Germán Trujillo-Vela E. James Nelson Riley C. Hales Angélica L. Gutiérrez

Accurate streamflow information is critical for early flood and drought warning. However, global hydrological forecasting systems are affected by residual errors in meteorological forcing, model structure, and routing, which propagate into simulated streamflow. Within the GEOGLOWS River Forecast System (RFS), ERA5 runoff biases are routed into streamflow simulations. The most effective operational bias-correction method, MFDC-QM, requires local discharge observations and cannot be applied consistently in ungauged basins. This study evaluates a pre-routing, grid-scale runoff bias-correction framework that adjusts ERA5 runoff before routing by combining Flow Duration Curve (FDC) mapping and Sparse Cumulative Distribution Function (CDF) matching, using GSCD as a spatially distributed reference runoff data. Baseline GEOGLOWS RFS, pre-routing correction, and MFDC-QM were compared for 1980–2025 using 16,517 gauging stations, Kling–Gupta Efficiency (KGE), and paired significance tests. Globally, the median KGE increased modestly from 0.16 to 0.22, compared with 0.48 for MFDC-QM. Results demonstrate a clear regional dependence: pre-routing correction produced statistically significant gains in South America and Africa (p < 0.05), where ERA5 runoff exhibits stronger residual biases, but had limited effects in Europe and North America, where dense hydrometeorological networks likely impose stronger observational constraints on the underlying reanalysis. These patterns show that pre-routing correction is most valuable where residual forcing bias is large and observational constraints are limited, complementing observation-based post-processing in ungauged, data-limited regions.

Hydrology doi: 10.3390/hydrology13050127

Authors: Valeria Hernández Zambrano Luis Simancas Martínez Andrés Hatum Pontón John J. Ramirez-Avila

Tropical coastal regions exhibit pronounced spatial and temporal variability in rainfall driven by seasonal atmospheric circulation and coastal–orographic interactions. Accurate representation of the temporal distribution of rainfall is essential for hydrologic modeling and infrastructure design. This study develops regionalized Huff curves for the Department of Magdalena, Colombia, addressing a critical gap in the characterization of rainfall temporal patterns in tropical coastal regions. A total of 270 short-duration (5–6 h) rainfall events from automatic stations were converted into normalized cumulative mass curves. The resulting curves were grouped into homogeneous temporal patterns using clustering algorithms. Three dominant storm types were identified: early-peak (Curve 1), intermediate (Curve 2), and uniform (Curve 3), reflecting the region’s coastal, lowland, and orographic influences. Probability envelopes and representative design hyetographs were derived to quantify intra-event variability. Rainfall–runoff simulations for a 100-km2 watershed showed peak-flow differences of up to 132% between storm types, highlighting the sensitivity of hydrologic response to rainfall temporal distributions. The resulting regionalized Huff curves provide a practical and transferable framework for hydrologic modeling, flood-risk assessment, and infrastructure planning in tropical regions with limited high-resolution rainfall data.

Hydrology doi: 10.3390/hydrology13050126

Authors: Xiaoya Wang Shenglian Guo Hua Chen Bokai Sun Xin Xiang

Under the influence of climate change and human activities, hydrologic regime and runoff in the upper Yangtze River basin (UYRB) have exhibited significant alterations. This study aims to address the primary drivers of runoff change and the destination of runoff reduction. Based on hydro-meteorological data from 1980 to 2022 and other related datasets, the temporal trend in hydro-meteorological variables was analyzed, and the impacts of climate change and human activities on runoff were quantified using the SWAT model. The destination of runoff reduction was also addressed based on the water balance equation. The SWAT model was calibrated using a top-down sequential strategy at five hydrological stations. The results show that despite a slight increase in precipitation and a pronounced rise in potential evapotranspiration, the annual average runoff at Yichang station is decreased by 22.3 billion m3. The SWAT model can simulate the monthly runoff hydrograph well with the NSE exceeding 0.85 during calibration and validation periods in the UYRB. Attribution analysis reveals that the contribution rate of climate change and human activities on runoff are 36.21% and 63.79% at the Yichang station, respectively. The annual average runoff change can be attributed to four pathways: (1) actual evapotranspiration increases due to land use and land cover (LULC) change and basin greening (−12.85 billion m3); (2) water intake and consumption increase (−2.94 billion m3); (3) reservoir dead storage impoundment (−3.34 billion m3); and (4) ground water storage variations (−3.21 billion m3). These findings highlight the impact of human water abstraction and land use change on runoff, providing a scientific basis for water resource management in the UYRB.

Hydrology doi: 10.3390/hydrology13050125

Authors: Nabil Allataifeh Ramesh Rudra Prasad Daggupati Pradeep Goel Shiv Prasher Rituraj Shukla

NDVI-based approaches offer an efficient method for estimating the C-factor, providing continuous spatial coverage and enabling monitoring of short-term changes in vegetation and management practices. This study aims to evaluate the performance of nine well-established NDVI-based C-factor models compared to RUSLE2 model estimates across a specific crop type, different tillage methods, and multiple time scales (monthly, seasonal, and yearly). While some NDVI models showed promising agreement with RUSLE2 estimates, this alignment was not sufficient to ensure accurate C-factor representation in the Gully Creek watershed. The results show that NDVI-based model performance varies systematically with crop type, tillage practice, and temporal scale. Monthly estimates generally reflect broader seasonal patterns, indicating that finer temporal resolution captures intra-seasonal variability without altering overall trends. These findings highlight the importance of accounting for spatial and temporal heterogeneity in C-factor estimation, as model effectiveness depends on local crop composition, management intensity, and temporal resolution rather than a single universally applicable approach.

Hydrology doi: 10.3390/hydrology13050124

Authors: Simon Chidodo Oforo Didas Kimaro Lulu Zhang Karl-Heinz Feger

Integrated watershed management relies on distributed hydrological models to simulate water transport processes and support decision-making. However, model reliability is often constrained by the resolution and quality of input data, particularly soil information. High-resolution soil datasets remain scarce in many regions of Sub-Saharan Africa, limiting the representation of spatial soil heterogeneity in hydrological simulations. This study evaluates the effect of detailed soil information derived using the Soil–Land Inference Model (SoLIM) on the performance of the Soil and Water Assessment Tool (SWAT) in the Sigi River watershed, a topographically complex watershed in northeastern Tanzania. Two model setups were compared: (i) a high-resolution SoLIM-based soil dataset and (ii) the coarser global ISRIC SoilGrids database. The SoLIM-informed model better reproduced hydrographs and flow duration curves and showed stronger parameter sensitivities, achieving superior calibration performance (NSE = 0.87, PBIAS = 8.7%) compared to SoilGrids (NSE = 0.86, PBIAS = 11.1%). Hydrological component analysis further revealed that SoLIM enhanced baseflow (181 vs. 60 mm/year) and percolation (349 vs. 135 mm/year) while reducing surface runoff (263 vs. 474 mm/year). These findings demonstrate that high-resolution soil data measurably improve the representation of subsurface processes and moderately improve streamflow performance, especially for baseflow and low-flow regimes; reduce model uncertainty; and improve the robustness of SWAT simulations, thereby supporting more effective watershed management in data-scarce and heterogeneous landscapes.

Hydrology doi: 10.3390/hydrology13050123

Authors: Wati Asriningsih Pranoto Rijal Muhammad Fikri Doddi Yudianto Steven Reinaldo Rusli Obaja Triputera Wijaya

Urbanization, together with shifts in rainfall patterns, has become an increasingly important driver of hydrological extremes in many rapidly developing tropical regions. In the Cimanceuri River Basin, Tangerang Regency, Indonesia, these processes have intensified over the last decade, raising concerns regarding flood risk. This study examines the combined influence of urban expansion and rainfall variability on flood dynamics over 2013–2025. Multi temporal land use classification based on Landsat imagery indicates a pronounced growth of impervious surfaces, primarily driven by rapid urban development and the conversion of agricultural land. To assess the hydrological consequences of these changes, rainfall–runoff processes and flood inundation were simulated using the Soil Conservation Service Curve Number (SCS–CN) method within a coupled HEC-HMS and HEC-RAS 2D modelling framework. Simulations were performed for multiple temporal conditions and design rainfall scenarios. Model calibration relied on observed flood events recorded in March 2025 in the Mustika Residential Area, Tangerang. The results suggest that urbanization has contributed to measurable increases in both peak discharge and inundation extent. Between 2013 and 2025, impervious surface coverage expanded by approximately 67%, accompanied by a rise in the composite Curve Number from 85.86 to 86.63 and an estimated 5.2% increase in flood extent. Also, the design rainfall increased from 85.01 to 90.95 with an average increase of 7.34%. Comparison between simulated inundation patterns and aerial imagery shows satisfactory agreement, with an average deviation of less than 10%, indicating acceptable model performance. Hydrologic analyses generated two discharge scenarios, consisting of event-based flow from the 5 March 2025 rainfall data and return-period flows derived from design rainfall under different rainfall-shift periods. The rainfall-shift analysis quantified changes in design rainfall and corresponding discharge using progressively updated rainfall records. Together, the results emphasize the combined effects of urban expansion and shifting rainfall patterns on flood dynamics, underscoring the need for adaptive land-use planning and climate-responsive water management in rapidly urbanizing catchments.

Hydrology doi: 10.3390/hydrology13050122

Authors: Zhengyang Tang Yichen Zhao Zhangkang Shu Ziwei Li Yuchen Li Junliang Jin

Accurate flood simulation in regulated, low-lying river basins is crucial for forecasting and risk mitigation, but performance depends strongly on whether models represent floodplain hydrodynamics and human regulation. This study evaluates three coupled hydrological–hydrodynamic schemes in the Huai River Basin upstream of Bengbu Station using identical meteorological forcing and VIC-generated runoff: (I) a linear routing scheme (VIC–Routing), (II) a natural hydrodynamic scheme (VIC–CaMa-Flood), and (III) an extended hydrodynamic scheme that incorporates reservoir regulation and levee effects (VIC–CaMa-Flood with Dam). Results reveal clear spatial differences in scheme suitability. The linear routing scheme performs best in upstream reaches, with NSE and KGE generally exceeding 0.81, but tends to overestimate peak discharge in downstream lowland sections. Incorporating hydrodynamic processes and regulation representation further reduces peak flow bias. Scheme III achieves the most consistent downstream improvement, particularly for high flows (>2000 m3/s), with NSE exceeding 0.80 in long-term simulations and improved agreement with satellite-driven inundation patterns. However, simplified reservoir operating rules can increase uncertainty in water level dynamics. During the 2020 plum rain flood, Scheme II yielded more accurate water levels in some reaches, suggesting that generalized operation rules may introduce compensating errors even when discharge accuracy improves. Overall, reliable flood simulation in well-managed basins requires an explicit representation of both floodplain hydrodynamics and regulation, and scheme selection should be guided by the dominant controls along the river network.

Hydrology doi: 10.3390/hydrology13050121

Authors: Michael G. Mengistu Andries C. Kruger Sifiso M. S. Mbatha Sandile B. Ngwenya

Climate-related extremes such as floods and droughts have been the main causes of natural disasters in southern Africa in recent years, with noticeable trends in climate extremes being observed. The Limpopo Province in South Africa has been especially prone to these extremes. The extreme precipitation in Limpopo is mainly caused by a mix of intense tropical weather systems and La Niña conditions, both exacerbated by climate change. Climate change exacerbates current water challenges across the province by affecting precipitation patterns, distribution, timing and intensity, leading to extreme climate events such as floods and drought. The historical and future trends of precipitation and relevant extreme indices using observed data from the South African Weather Service and CORDEX ensemble model simulations under the RCP4.5 and RCP8.5 scenarios were examined. An analysis of all precipitation data suitable for the study of long-term variability and trends indicates that most areas underwent drying to various degrees over the last century, especially the central and western parts. Drier conditions over the eastern parts have become more prevalent over the last 50 years. Also, more extremes on a sub-seasonal basis were experienced. Regarding future scenarios, three projected time periods compared to the baseline period (1976–2005) were examined: Current climatology (2006–2035), near-future (2036–2065), and far-future (2066–2095). Most areas will experience a further decrease in precipitation under both emission scenarios, especially in the south-east, central and extreme northern parts. In addition, these areas are expected to experience a decrease in the frequency of heavy precipitation days for all periods under both RCP scenarios, mainly due to drying. Consecutive dry days are expected to increase significantly. Transitioning to renewable energy and enhancing natural carbon sinks can reduce emissions, while prioritizing resilience through renewable energy, water management, and climate-smart agriculture will help address climate change challenges in the province.

Hydrology doi: 10.3390/hydrology13050120

Authors: Ashen Randika Samadhi Athauda Ruizhe Wang Zhineng Hao Yuansong Wei Yawei Wang Hui Zhong Madhubhashini Makehelwala Sujithra K. Weragoda Rohan Weerasooriya

Coastal groundwater in monsoon-dominated regions faces compounding threats from seasonal hydrological extremes and seawater intrusion (SWI), yet the molecular-scale response of dissolved organic matter (DOM) remains poorly understood. We conducted a two-season investigation in Mannar District, Sri Lanka, integrating hydrochemistry, fluorescence spectroscopy, and Fourier-transform ion cyclotron resonance mass spectrometry to characterize DOM dynamics across shallow and deep groundwater. Dry-season chloride averaged 302 mg/L (shallow—5 to 12 m) and 505 mg/L (tube wells—20 to 30 m), then declined by 60–80% during monsoon recharge. Despite this freshening, DOM dynamics were decoupled from salinity: shallow wells showed dry-season DOC peaks (6.64 mg/L) driven by soil concentration, while tube wells exhibited wet-season enrichment (5.02 mg/L). Shallow aquifers maintained consistently high humification indices (around 0.70) and aromatic-rich DOM, indicating sustained buffering by soil-derived inputs. In contrast, wet-season recharge in tube wells appeared to stimulate microbial processing, as indicated by elevated protein-like fluorescence (C2: 26% to 36%) and a higher contribution of nitrogen-bearing formulas (CHONs: 31.4% to 37.1%). Tube wells also accumulated reduced, energy-rich DOM with correspondingly high molecular lability indices. Paradoxically, correlation networks suggested that these saturated aliphatic and halogenated structures persist due to kinetic protection under low oxygen, high-salinity conditions. These findings indicate that aquifer structure and redox conditions control DOM biogeochemistry in coastal groundwater systems. At the molecular level, DOM dynamics are influenced by aquifer depth and seasonal recharge, leading to a decoupling between salinity and organic matter transformation.

Hydrology doi: 10.3390/hydrology13050119

Authors: Enrico Chinchella Arianna Cauteruccio Filippo Calamelli Daniele Rocchi Luca G. Lanza

The impact of wind on disdrometer measurements has not yet been demonstrated through controlled reproducible physical experiments. This study aims to provide quantitative evidence of the deviation in raindrop trajectories approaching the sensing area of an optical disdrometer (the Thies Clima LPM) when immersed in a wind flow with a known velocity and direction relative to the sensor orientation. To this end, water drops with diameters between 0.9 mm and 1 mm were released in a wind tunnel and directed towards the instrument’s sensing area. Their trajectories were measured using a high-speed camera and compared with those expected in undisturbed conditions, as well as with the airflow field around the instrument body as measured in previous studies. This experiment provided the first direct measurement of the deviation in individual drop trajectories induced by wind near the Thies Clima LPM, a disdrometer commonly used in hydrological studies and applications. The effect of the non-radially symmetric geometry of the instrument on wind direction was observed, identifying the configuration most affected (parallel to the laser beam). The repeatability of the drop releasing system was checked by releasing multiple drops from the same position. This allowed attributing differences in the observed trajectories to a variation in the drop diameter. The collected dataset can be used to validate numerical models of the wind-induced bias of disdrometers and to develop adjustment functions for field measurements.

Hydrology doi: 10.3390/hydrology13040118

Authors: Leticia Gaspar Ana Navas

In subhumid Mediterranean agroecosystems, runoff drives soil erosion by controlling particle detachment and transport, with its generation and connectivity strongly influenced by land use. In areas affected by land abandonment and reforestation, terracing modifies hillslope morphology and flow pathways, thereby altering soil redistribution patterns. Fallout 137Cs has been widely used to assess medium term soil redistribution, and in situ gamma ray spectrometry using scintillation detectors provides an alternative for improving spatial coverage, yet the influence of factors specific to the site on measurements remains insufficiently explored. This study investigates how 137Cs counts obtained in situ with a LaBr3 detector can be used to interpret soil redistribution patterns in two paired catchments that experienced land abandonment since the mid-1960s. Following abandonment, catchment A underwent natural revegetation, whereas catchment B was terraced for reforestation, allowing the effects of water erosion and terracing on soil mobilisation to be analyzed through the spatial distribution of 137Cs. By linking 137Cs counts with catchment physiography, land use, flow pathways, and NDVI, the study aims to identify the main controls on soil redistribution in both catchments. 137Cs counts were significantly higher in catchment A (156.8 ± 108.2 counts) than in catchment B (53.2 ± 68.1), with coefficients of variation of 69% and 128%, respectively. The in situ 137Cs measurements provide reliable indicators of soil redistribution patterns controlled not only by runoff but also by anthropogenic modifications of hillslope morphology that alter flow pathways and hydrological connectivity following terracing. The paired catchment approach, combined with in situ 137Cs measurements, provides valuable insights into the key controls on soil redistribution, which is essential for effective land management.

Hydrology doi: 10.3390/hydrology13040117

Authors: Evangelos Leivadiotis Aris Psilovikos Silvia Kohnová

Intensity–Duration–Frequency (IDF) curves are the cornerstone of hydraulic infrastructure design, yet standard methodologies often fail to account for the complex dependence structure of rainfall characteristics and the non-stationary effects of climate change. This study develops a robust Regional Copula Framework for the Kastoria Lake basin, Greece, utilizing sub-hourly rainfall records from four meteorological stations (2007–2024). We employ a forensic data quality control process to pool 277 independent storm events. Unlike traditional approaches, our analysis demonstrates that the Generalized Extreme Value (GEV) distribution (ξ = 0.348) significantly outperforms the standard Lognormal distribution in modeling heavy-tailed rainfall intensities. The dependence between storm duration and intensity was found to be consistently negative (τ = −0.35), a structure best captured by the Rotated Gumbel (90°) copula, which physically reflects the region’s convective storm dynamics. Trend analysis revealed a statistically significant decrease in peak intensity (τ = −0.14) coupled with an increase in storm duration (τ = 0.22), a hydro-climatic shift that contrasts with increasing intensity trends reported in the wider Balkan region. These findings suggest a regime transition from flash-flood dominance to volume-critical events, necessitating updated design criteria that integrate both multivariate dependence and local climatic non-stationarity.

Hydrology doi: 10.3390/hydrology13040116

Authors: Marin Paladin Josip Paladin Dijana Oskoruš

This research develops a new low-cost method for continuous flow monitoring in open channels. Flow is calculated using a standard 1D hydraulic model that integrates surveyed cross-sections and water level measurements at the boundaries of a studied reach, from which the name Boundary Water Level Method (BWLM) is derived. By implementing low-cost ultrasonic sensors for water level measurement, the method gains advantage for application on smaller channels, which are often not included in national hydrological monitoring networks due to limited budgets. New and innovative monitoring methods in hydrology are a necessary alternative to increasing the monitoring budgets, especially for continuous, real-time flow monitoring. Like any novel method, it requires validation under the intended environmental conditions, especially when designed primarily for ungauged channels. Validation was conducted on two test-sites by comparing the BWLM discharge and the discharge from official hydrological stations, with an error of up to 15%. BWLM provides reliable discharges using estimated hydraulic roughness values based on the literature and experience. Sensitivity analysis of the estimated hydraulic roughness coefficient demonstrated a substantial influence on the resulting discharge values. This has to be considered when implementing the method in unstudied basins.

Hydrology doi: 10.3390/hydrology13040115

Authors: Asyma Koshim Zhassulan Takibayev Abror Gafurov Aida Munaitpassova Damir Kanatkaliyev Aktoty Bekzhanova Aidar Zhumalipov Zhanerke Sharapkhanova

Seasonal snow cover in mountainous regions represents a critical natural freshwater reserve for arid and semi-arid areas of Central Asia. This study evaluates the long-term (2000–2024) spatiotemporal dynamics of snow cover in the Arys River basin, located within the Western Tien Shan. The research utilizes daily satellite data from MODIS Terra and Aqua, along with data from the MODSNOW automated processing system. Terra-Aqua composite imagery was employed to minimize cloud cover effects. Satellite-derived estimates were validated against observational data from five meteorological stations of the Republican State Enterprise (RSE) “Kazhydromet”. The results indicate significant interannual variability in snow cover extent: the snow-covered area during the cold season ranged from 16.2% to 54.1%, with a mean value of 34.4%. Trend analysis revealed a weak negative trend, while Sen’s slope estimator showed an average annual reduction in snow cover area of 0.37% per year. The most pronounced decline in snow accumulation was observed in mid-elevation mountain zones. These findings suggest potential increased risks to seasonal water availability in the Arys River basin and, more broadly, across the Syr Darya basin under ongoing climate change conditions. The results provide a scientific basis for quantifying climate impacts and developing adaptation strategies for integrated water resources management in Central Asia.

Hydrology doi: 10.3390/hydrology13040114

Authors: Irenee Felix Munyejuru James H. Stagge

Hydrologic models are instrumental in understanding the behavior of the Nile River Basin (NRB), yet their effectiveness is often limited by the basin’s complex hydrology and sparse observational records. This study applies a basin-scale hydrological modeling approach to simulate near-natural, pre-reservoir flow conditions in the NRB, while incorporating lake and wetland submodels. The basin was discretized into 34 sub-watersheds with an outlet at Aswan. The conceptual GR4J rainfall–runoff model was implemented within the Raven modeling framework, chosen for its parsimony and suitability for data-limited conditions. Multi-objective calibration used discharge data from the Global Runoff Data Centre (GRDC), supplemented by digitized historical records to improve spatial and temporal coverage. A stepwise calibration strategy was applied at 18 sites, focusing on pre-reservoir periods to capture natural flow dynamics. The calibrated model reproduces observed discharges with high skill. At the Aswan outlet, Nash–Sutcliffe Efficiency (NSE) values were 0.87 (calibration) and 0.80 (validation), with percent bias (PBIAS) values of 6.1% and 5.0%, respectively. Model performance was strongest in the Blue Nile, White Nile headwaters, and the Nile main stem. The model also successfully simulated the hydrological step-change observed in Lake Victoria during the 1960s, underscoring its robustness in simulating regional hydroclimate disruptions. This calibrated model enables reconstruction of historical Nile discharge and simulation of past hydrologic disturbances, including those driven by major volcanic eruptions over the past millennia.

Hydrology doi: 10.3390/hydrology13040113

Authors: Jinjin Shi Xin Tong Meng He Panrui Xia Xuemian Wei Xin Sun Xiaomin Liu Ping Miao Haixia Wu Jiwen Wang

Accurate spatial identification of check dams is a key prerequisite for evaluating soil and water conservation benefits and optimizing dam system planning on the Loess Plateau. Current deep learning models face severe misclassification and omission issues under complex terrain due to the scarcity of check dam samples and the lack of prior geographic knowledge. This study proposes a recognition method based on Faster R-CNN, constrained by suitable areas and microtopography. The Xiliugou watershed in Inner Mongolia was selected as the study area. Based on Google Earth imagery and field survey data, a check dam sample dataset was constructed, integrating the morphological features of “linear dam body with a trapezoidal slope.” Using the construction suitable area constraints defined by the Technical Specifications for Check Dams and microtopography standard deviation (δ) derived from DEM as dual spatial filtering mechanisms, these were deeply embedded into the Faster R-CNN model to limit the search space and enhance geographic plausibility. Experimental results show that the constrained Faster R-CNN model achieved a precision and recall of 92.86% and 96.89%, compared with the accuracy rate of only deep learning model recognition (60.61%), which significantly increased by 32.25%, indicating that geographical constraints have an enhancing effect. Using this method, a total of 191 embankment dams were identified in the Xiliugou Basin. New 30 unrecorded embankment dams (21 small dams and 9 micro-dams) were discovered. The model’s good generalization ability was verified in the Han Tiechuan geographical isolation area, which contained 153 embankment dam samples, with an accuracy rate of 72.94%. Spatial analysis further revealed the “successive interception along tributaries” distribution pattern and strong spatial aggregation characteristics (box dimension D ≈ 0.36) of check dams in the Xiliugou watershed. This study confirms the critical role of suitable area and microtopography constraints in improving the accuracy and reliability of deep learning models and provides a transferable technical paradigm for automated, high-precision surveys of regional soil and water conservation projects.