Search Results (681)

Urban cities have been intensely prone to floods during extreme rainfall events and water scarcity issues during dry periods in recent years. In this context, identifying rainwater harvesting potential (RWHP) regions in urban environments provides a sustainable approach to mitigate both urban flooding and water security, thereby improving urban stormwater management. Geospatial mapping of RWHP has tried to consider various hydrometeorological, topographical and other geospatial datasets, but integrating socio-economic factors over urban environments has not been explored much. The present study integrated remote sensing and hydrological-based information, such as slope, soil type, drainage density, geomorphology, topographic wetness index (TWI), land use land cover (LULC), rainfall, runoff coefficient, proximity to roads, and proximity to settlements for geospatial mapping of RWH potential zones for Hyderabad city using multi-criteria decision analysis (MCDA) and weighted overlay analysis (WOA). The resulting RWH potential map indicates that 80.20% of the area falls within the “low” potential category, 17.53% as “moderate”, 2.0% as “very low”, and only 0.25% as “high” potential, mainly in the southeastern portion near the Hussain Sagar outlet. These categories are spatially verified using Sentinel-2 LULC and Google Earth imagery to assess the qualitative plausibility of the mapped RWH potential zones. Northwestern areas, with loamy soils and mild slopes, demonstrate suitability for rooftop collection and percolation structures, highlighting the effectiveness of the proposed modelling framework for sustainable stormwater management for urban environments. Full article

The carbonate massifs of the southern Italian Apennines host extensive karst aquifers, which represent the principal drinking water resources. This study focuses on the Dragone Plain polje, a vast closed karst depression located in the main recharge sector of the Terminio–Tuoro carbonate massif. The polje drains a ~55 km² endorheic catchment and may be flooded during the cold and wet season, forming a temporary lake. We employed continuous hydroclimatic time series (rainfall, groundwater level, spring discharge, and river level) together with sparse Sentinel-2 true color satellite images for the period 2020–2024 to analyze the flooding process in the polje and its hydraulic connection with the saturated zone of the karst aquifer. Results indicate that lake formation depends on the balance among soil moisture, rainfall intensity, and runoff development, which were modeled on a daily scale. Daily recharge was also estimated and compared with groundwater level time series from the deep karst aquifer. The modeling was integrated with cross-correlation analysis of the time series, providing insights into the propagation of precipitation pulses through the hydrogeological system. This case study represents an important example for understanding the relationship between karst polje hydrological functioning and climate in a Mediterranean area. Full article

Climate change poses an escalating threat to global water resources, with semi-arid regions such as Morocco being particularly vulnerable due to high climatic variability and limited adaptive capacity. In these regions, including the Tassaoute watershed in central Morocco, data scarcity and uncertainties related to data availability and quality frequently hinder robust assessments of climate change impacts. Recent advances in data science and remote sensing offer promising alternatives to overcome these limitations. This study investigates the potential of the PERSIANN-CDR satellite-derived precipitation product for assessing climate change impacts on water resources. The capability of PERSIANN-CDR to reproduce observed precipitation patterns and associated hydrological responses is evaluated through a comparative analysis using observed precipitation data. Results indicate that PERSIANN-CDR generally underestimates peak precipitation events and total rainfall amounts compared to in situ observations. Runoff is simulated using two hydrological models: GR2M (Génie Rural 2 parameters Mensuel) and the Thornthwaite water balance method, both driven by observed meteorological data and PERSIANN-CDR precipitation. The future water availability was assessed using 5 climate models, under two scenarios: RCP4.5 and RCP8.5 for the periods 2030–2060 and 2061–2090. Results show a marked temperature increase of 2–3 °C across all models, accompanied by a general decline in precipitation ranging from −30% to −60% under RCP4.5 and −20% to −80% under RCP8.5. These climatic changes translate into substantial reductions in runoff, with stronger decreases projected under the high-emission scenario and during the dry season. Monthly analyses reveal pronounced seasonal contrasts, highlighting the increased sensitivity of low-flow periods to climate forcing. Overall, runoff is projected to decrease by 50–90%, with model and data-source differences highlighting the importance of multi-model and satellite-derived approaches in data-sparse regions. These results emphasize the utility of satellite precipitation datasets in guiding climate-adaptive water management strategies. Full article

A significant portion of Europe is prone to flooding, including severe events occurring over very small areas. Recent flood hazard mapping methods can cover large regions, but often fail to capture processes driven by small streams or direct rainfall. This study presents the authors’ experience in the application of a fully hydrodynamic model over an entire territory, with direct rainfall input (rain-on-grid approach at the basin scale). The case study is the Neto River basin in Calabria (Italy), covering approximately 1000 km², a region that represents an ideal natural laboratory for investigating flash flood processes in Europe. Simulations were carried out using the TUFLOW 2D commercial modelling tool. A key objective is to demonstrate that the Chicago hyetograph enables a constant return period across the entire domain. Additionally, specific procedures are proposed to represent numerous minor crossings (e.g., small bridges, culverts, and road and railway underpasses) and dam outlets without refining the computational grid or abandoning the Shallow Water Equations (SWE). This approach allows identification of major river floods, flash floods, runoff-related hydraulic effects, and pluvial flooding. Results show that the fully hydrodynamic rain-on-grid model is highly effective for flood hazard mapping, with strong agreement between simulations and observed events, confirming its predictive reliability and enabling high-resolution, comprehensive territorial analysis. Full article

This study of the Ethiopian Rift Valley meso-climate (5° N–9° N, 37° E–40° E) employed space–time statistical methods over the period 1981–2025. Links between weather conditions at Hawassa (7.1° N, 38.5° E, 1700 m) and the Indian Ocean Dipole (IOD) were uncovered, among 3–4 yr oscillations and a weak upward trend. Seasonal anomalies of local dewpoint temperature (Td) and IOD cross-correlated at R = 0.61 over the four-decade study. Mean annual cycling revealed a narrow range for Td from April to October, in contrast with bi-modal rainfall and asymmetric runoff. Diurnal cycle analysis indicated that evening rainfall was driven by midday heat (0.6 mm/h) and moisture fluxes (0.1 mm/h). A case study revealed how shallow cloud bands extend westward from cool, forested highlands to the warm Rift Valley. Composite differences between warm and cool IOD events exhibited contrasting effects for zonal and meridional airflows, which explains why the equatorial trough and its associated rainfall are confined to the southeastern escarpment of Ethiopia. While earlier studies had anticipated drying trends, wetter conditions during the warm IOD events of 2019 and 2023 resulted in rising lake levels (1.8 m) and crop yields (4 T/ha). These findings enhance our understanding of regional climate dynamics to support adaptive management. Full article

This study aims to investigate the relationship between basin hydrology and estuarine processes such as dynamics that influence salinity and water quality in the Shatt Al-Arab River, southern Iraq. Extensive samplings were conducted at 25 sites along the river course over one hydrological year. Runoff estimates were obtained using the soil conservation service–curve number (SCS-CN) model. During winter, peak rainfall (76.8 mm month⁻¹) and runoff (12.38 mm month⁻¹) promote the shortest salt wedge extension (8 km) and the highest water quality (median water quality index (WQI) = 22). In contrast, during fall, minimal rainfall (6.51 mm month⁻¹) and runoff (0.14 mm month⁻¹) result in a salt wedge extension of 109 km and the lowest water quality (median WQI = 250). Strong correlations between rainfall–runoff estimates, salt wedge extension, and water quality parameters demonstrate that water quality status can be predicted using hydrological inputs alone. Thus, this study introduces a novel quantification of the flushing influence required to maintain the Shatt Al-Arab River’s ecological health. A strong (r² = 0.87) significant (p < 0.05) negative correlation was detected between the runoff coefficient (a proxy indicator of catchment wetness) and the standard deviation of WQI. Such a negative correlation implies that hydrological flushing fosters water quality stability. Principal component analysis (PCA) further revealed how natural and anthropogenic sources contribute to water quality. The findings illustrate how seasonal hydrological variability control mixing processes, salt wedge propagation, and water quality in estuarine-influenced river systems, presenting a framework adaptable to similar systems worldwide. Full article

To evaluate the runoff pollution characteristics of roofs in an arid region, this study focused on Yinchuan City, China. It analyzed the runoff properties of various roof materials, including tile, asphalt, and color steel plate. Five rainfall events were monitored during 2024, with samples collected manually at roof pipe outlets and analyzed for suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH₃-N). The results indicated that the concentration of pollutants in runoff from these roofs decreased as rainfall duration increased. The event mean concentration (EMC) of TN and COD in runoff from all three roof materials exceeded the Class V surface water quality standards in China. The first flush of pollutants in roof runoff followed a descending order: SS > COD > TP > TN > NH₃-N. Cluster analysis of three rainfall parameters—dry period, precipitation, and rainfall intensity—revealed that dry period exerted the strongest influence on runoff quality, indicating that the overall quality of roof runoff was primarily influenced by the cumulative effects of atmospheric deposition, with rainwater scouring being the secondary factor. These findings provide critical insights for designing stormwater management strategies and rainwater harvesting systems in arid and semi-arid cities, emphasizing the need to prioritize first-flush control and consider local climatic conditions. Full article

Combined sewer overflow (CSO) pollution has consequently become a critical challenge, yet its formation depends on tightly coupled dry- and wet-weather processes. This study aims to integrate high-resolution field monitoring with statistical analysis to characterize the full “accumulation–transport–discharge” cycle of CSO pollution in a representative combined sewer catchment located in the Yangtze River basin, China. A dynamic analytical framework was established, combining multiple pollution media and linking dry-weather accumulation with rainfall-driven transport, enabling quantitative source apportionment of pollutant contributions. Results indicated that during dry periods, domestic sewage exhibited strong enrichment, with concentrations of total inorganic nitrogen (TIN), chemical oxygen demand (COD), and total phosphorus (TP) being 2.1-, 2.3-, and 1.9-fold higher, respectively, than the Chinese secondary discharge standards (GB 18918-2002). Surface sediment showed pronounced spatial heterogeneity, with greater loads in residential than transportation areas and substantial fine-particle accumulation on roofs (particle size < 150 μm, accounting for 73% by mass). Sewer sediments, dominated by coarse inorganic particles (over 77% by mass), represented the main pollutant reservoir. Rainfall produced distinct hydrodynamic and water quality responses. Light rain following long antecedent dry periods generated a high-concentration but low-load regime with a strong first flush, whereas moderate rain yielded lower concentrations but higher loads. Overflow occurred when rainfall exceeded ~14 mm, with pollutant peaks lagging rainfall by 20–45 min in the studied area. TIN and TP peaked sharply at rainfall event onset, and first-flush intensities followed TIN > TP > COD > suspended solids (SS). Source apportionment identified sewer sediments as the dominant CSO source, followed by surface runoff and domestic sewage. These findings clarify the mechanisms linking dry-weather accumulation to wet-weather transport and support targeted CSO pollution control and urban water quality management. Full article

Riverbed siltation in estuaries affects ecosystem functioning, water quality, and navigation. This study investigates the flow-regulated Osellino Canal, a freshwater tributary of the Venice Lagoon that crosses a largely urbanized area and is undergoing progressive siltation. High-resolution measurements of discharge (Q) and suspended sediment concentration (SSC) were performed using hydroacoustic instrumentation from September 2019 to December 2021. The analysis examined discharge dynamics, sediment transport, and rainfall-runoff relationships. Results indicate a mean annual discharge of 2.1 m³ s⁻¹ and an average annual suspended sediment load of ~2900 ± 330 t. Discharge patterns were strongly influenced by water management, resulting in anomalous runoff coefficients (δ > 1) during dry periods. Sediment export proved to be strongly event-driven: episodic high-flow events accounted for about 23% of the total load despite representing only a small fraction of the study period. Furthermore, a strong linear relationship between runoff and sediment load (R² = 0.94) confirms an advection-dominated regime, where net export is regulated primarily by hydrodynamic volume rather than fluctuations in sediment supply. Bathymetric comparisons (2011–2019) reveal a mean annual sediment retention of 400 ± 100 t yr⁻¹, corresponding to a trapping efficiency of approximately 12 ± 3% relative to the gross sediment input. These findings, supported by SSL–runoff regression residuals, consistently indicate net sediment accumulation associated with the long-term malfunction of a miter-gate system that impedes efficient sediment export. This study provides a critical pre-rehabilitation baseline, establishing a benchmark to evaluate the effectiveness of ongoing restoration efforts initiated in March 2022 and the future hydromorphological recovery of the canal. Full article

Rapid urbanization has led to increasing structural modification of river catchments through dam construction and concrete-lining of natural channels as flood management measures. These interventions can alter the natural hydrology. This necessitates assessment of their influence on hydrology at a catchment scale. However, such evaluations are particularly challenging in data-scarce regions such as the Chongwe River Catchment, where hydrometric records capturing conditions before and after structural modifications are limited. Therefore, we applied a 2D rain-on-grid approach in HEC-RAS to evaluate changes in high-flow responses to short-duration, high-intensity rainfall events in the Chongwe River Catchment in Zambia, where structural interventions have been implemented. The terrain was modified in HEC-RAS to represent 21 km of concrete drains and ten dams. Sensitivity analysis conducted on five key model parameters showed that parameters controlling surface runoff generation, particularly curve number, exerted the strongest influence on simulated peak flows, while routing-related parameters had a secondary effect. Model calibration and validation showed strong performance with R² = 0.99, NSE = 0.75 and PBIAS = −0.68% during calibration and R² = 0.95, NSE = 0.75, PBIAS = −2.49% during validation. Four scenarios were simulated to determine the hydrological effects of channel concrete-lining and dams. The results showed that concrete-lining of natural channels in the urban area increased high flows at the main outlet by approximately 4.6%, generated localized instantaneous maximum channel velocities of up to 20 m/s, increased flood depths by up to 11%, decreased lag times and expanded flood inundation widths by up to 15%. The existing dams reduced peak flows by about 28%, increased lag times, reduced flood depths by about 11%, and reduced flood inundation widths by up to 8% across the catchment. The findings demonstrate that enhancing stormwater conveyance through concrete-lining must be complemented by storage to manage high flows, while future work should explore nature-based solutions to reduce channel velocities and improve sustainable flood mitigation. Therefore, the study provides event-scale insights to support flood-risk management and infrastructure planning in rapidly urbanizing, data-scarce catchments. Full article

Flood events driven by climate variability and change pose significant risks for socio-economic activities in the Awash Basin, necessitating advanced forecasting tools. This study benchmarks five deep learning (DL) architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and a Hybrid CNN–LSTM, for daily discharge forecasting for the Hombole catchment in the Upper Awash Basin (UAB) using 40 years of hydrometeorological observations (1981–2020). Rainfall, lagged discharge, and seasonal indicators were used as predictors. Model performance was evaluated against two baseline approaches, a conceptual HBV rainfall–runoff model as well as a climatology, using standard and hydrological metrics. Of the two baselines (climatology and HBV), the climatology showed limited skill with large bias and negative NSE, whereas the HBV model achieved moderate skill (NSE = 0.64 and KGE = 0.82). In contrast, all DL models substantially improved predictive performance, achieving test NSE values above 0.83 and low overall bias. Among them, the Hybrid CNN–LSTM provided the most balanced performance, combining local temporal feature extraction with long-term memory and yielding stable efficiency (NSE ≈ 0.84, KGE ≈ 0.90, and PBIAS ≈ −2%) across flow regimes. The LSTM and GRU models performed comparably, offering strong temporal learning and robust daily predictions, while BiLSTM improved flood timing through bidirectional sequence modeling. The CNN captured short-term variability effectively but showed weaker representation of extreme peaks. Analysis of peak-flow metrics revealed systematic underestimation of extreme discharge magnitudes across all models. However, a post-processing flow-regime classification based on discharge quantiles demonstrated high extreme-event detection skill, with deep learning models exceeding 89% accuracy in identifying extreme-flow occurrences on the test set. These findings indicate that, while magnitude errors remain for rare floods, DL models reliably discriminate flood regimes relevant for early warning. Overall, the results show that deep learning models provide clear improvements over climatology and conceptual baselines for daily streamflow forecasting in the UAB, while highlighting remaining challenges in peak-flow magnitude prediction. The study indicates promising results for the integration of deep learning methods into flood early-warning workflows; however, these results could be further improved by adopting a probabilistic forecasting framework that accounts for model uncertainty. Full article

Estimating runoff in ungauged catchments remains a major challenge in hydrology, particularly in remote Andean headwaters where limited accessibility and budgetary constraints hinder the long-term operation of monitoring networks. This study integrates satellite-derived rainfall data, hydrological modeling, and benthic macroinvertebrate diversity analysis to explore how short-term antecedent flow conditions relate to temporal variation in community structure. The research was conducted in a pristine 0.26 km² micro-catchment of the upper Collay basin (southern Ecuador). Daily simulated discharge was used to compute antecedent flow descriptors representing short-term variability and cumulative changes in stream conditions, which were related to taxonomic (i.e., H = Shannon diversity, E = Pielou evenness, and D = Simpson dominance) and functional indices (i.e., Rao = Rao’s quadratic entropy, FAD1 = Functional Attribute Diversity, and wFDc = weighted functional dendrogram-based diversity) using Generalized Additive Models. Results showed progressively higher hydrology–biology associations with increasing antecedent flow integration length, suggesting that biological variability responds more strongly to cumulative than to instantaneous flow conditions. Among hydrological descriptors, the cumulative magnitude of negative flow changes was consistently associated with taxonomic diversity. H and E showed more coherent and robust patterns than functional metrics, indicating a faster response of community composition to short-term hydrological variability, whereas functional diversity integrates slower ecological processes. While based on modeled discharge under severe hydrometeorological data limitations, this study provides a practical ecohydrological starting point for identifying short-term hydrological memory signals potentially relevant to aquatic biodiversity in ungauged headwater systems. Full article

Achieving sustainable agricultural intensification in inland valleys while limiting the adverse environmental impacts and uncertainties related to water availability requires an analysis of the long-term hydrological behavior of the catchment. Such a task is particularly challenging in West Africa and Benin due to the limited availability of climate and hydrological data. This study evaluates the applicability of the lumped GR4J model for simulating streamflow in three inland valleys of the Sudanian zone of Benin (Lower-Sowé, Bahounkpo and Nalohou). Additionally, we test the reliability of satellite-based rainfall data (GPM-IMERG, CHIRPS or GSMAP) in modeling hydrological dynamics in these small catchments. The results demonstrate that the GR4J model is effective in simulating daily discharge in the three inland valleys (KGE > 0.5 during both calibration and validation periods), with particularly interesting performance in mean-flow conditions. The modeling using GPM-IMERG and GSMAP rainfall data shows mitigated results with acceptable performance at Nalohou and less accurate results at Bahounkpo and Lower-Sowé. CHIRPS emerged as the most consistent among the evaluated products, providing a sound basis for reconstructing general trends and seasonal variations in historical streamflow time series. The approach of combining historical CHIRPS data and the GR4J model provides insights and can support decision-making related to water resource management in terms of resource capacity and volume in the study area. Except for Nalohou (KGE = 0.19 with GPM-IMERG data), we observe limitations in predicting high flows with satellite-based climatic data at Bahounkpo (KGE = 0.02 with GPM-IR) and Lower-Sowé (KGE = −0.01 with CHIRPS), where the near-zero KGE scores indicate marginal improvement over a mean-flow benchmark. Future work should explore how hybrid or flexible modeling approaches can improve the accuracy of runoff simulations in inland valleys, particularly for extreme (low- and high-) flow conditions. Additionally, the analysis of the trends of indicators of hydrological alteration (IHA) must be deepened in these important ecosystems, especially under climate and land-use change scenarios. Full article

Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin (242.7 km², 1823–3140 m elevation), Eastern Anatolia, Turkey. Three automatic weather stations spanning 872 m elevation gradient provided meteorological forcing, while MODIS MOD10A2 8-day composite products supplied operational snow cover observations validated against Landsat-5/7 (30 m resolution, 87.3% agreement, Kappa = 0.73) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band). Uncalibrated model performance was modest (R² = 0.384, volumetric difference = 29.78%), demonstrating necessity of site-specific calibration. Systematic adjustment of snowmelt and rainfall runoff coefficients yielded excellent calibrated performance for 2009 melt season: R² = 0.8606, correlation coefficient R = 0.927, Nash–Sutcliffe efficiency = 0.854, and volumetric difference = 3.35%. Enhanced temperature lapse rate (0.75 °C/100 m vs. standard 0.65 °C/100 m) reflected severe continental climate. Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors (R² = 0.881). Results confirm SRM’s operational feasibility for seasonal forecasting and flood warning in data-scarce snow-dominated basins, with modest requirements (daily temperature, precipitation, and satellite snow cover) aligning with operational monitoring capabilities. The methodology provides a transferable framework for regional water resource management in climatically vulnerable mountain environments where snowmelt supports agriculture, hydropower, and municipal supply. Full article

The application of two-dimensional (2D) hydrologic and hydraulic modeling tools is increasing for overland flow simulation, as they represent spatial changes in depth, velocity, and flow conditions more accurately. Recently, the US Army Corps HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) added the capability to import an unstructured 2D mesh, which enables the routing of excess precipitation across the mesh, as a fully distributed hydrological model. In HEC-HMS, the 2D diffusion-wave component functions as a hydrologic transform representing overland flow routing. In contrast, HEC-RAS 2D (Hydrologic Engineering Center-River Analysis System), initially applied to river flow simulation, can apply either the 2D shallow-water equations or the 2D diffusion-wave option. Similarly to HEC-HMS, HEC-RAS also includes rain-on-grid (RoG) capability and infiltration algorithms, and in this fashion has some hydrological modeling capabilities. Still, while HEC-HMS is capable of representing extended-period hydrological simulations, HEC-RAS hydrological capabilities are limited to event-based simulations, as there are no provisions to represent abstractions such as evapotranspiration or groundwater/baseflow contributions together. Studies performing a direct comparison between the HEC-HMS RoG and HEC-RAS RoG approaches for representing urban hydrology remain scarce. This study aims to fill that gap by assessing their performance in Moore’s Mill Creek Watershed, in Lee County, Alabama, with a focus on continuous rainfall-runoff modeling. Both models run on the same unstructured mesh and use identical rainfall, terrain, land-use, and soil data. Model simulations are compared over an extended period to evaluate simulated depth, velocity, and flow hydrographs against field observations. The comparison shows HEC-HMS’s superior performance for extended simulation and provides practical guidance on parameter alignment, data needs, and tool selection. Full article