Search Results (4,289)

Seasonal tropical wetlands such as the Brazilian Pantanal are increasingly threatened by climate variability and extreme hydrological events, creating a need for robust monitoring tools that capture flood dynamics at high spatial and temporal resolution. This study used Sentinel-1 Synthetic Aperture Radar (SAR) imagery to map and monitor flooding in the northern Pantanal, a Ramsar site renowned for its wildlife, between 2017 and 2020. Ground Range Detected (GRD) VV-polarized scenes were preprocessed using radiometric terrain normalization and speckle filtering (Lee filter, 5 × 5 window) to improve the separability of water and non-water surfaces. Flooded areas were initially extracted with Otsu’s histogram thresholding and validated using high-resolution optical imagery (PlanetScope and Landsat-8). A supervised Random Forest classifier then refined land-cover discrimination into three classes (open water/flood, open land/vegetation, and others), achieving an overall accuracy of 97.70% on the independent testing dataset (n = 6622), while temporal consistency was supported by Cuiabá River hydrological data. The results revealed strong interannual variability in flood extent, with inundation covering 34.7% of the reserve in March 2017 compared with 0.75% in March 2020 and reaching a peak of 79.9% in April 2017. Overall, Sentinel-1 SAR effectively delineated open water and flood-affected surfaces under persistent cloud cover, demonstrating its value for complementing existing products such as MapBiomas, strengthening wetland management, and supporting scalable flood monitoring in other tropical flood-prone Ramsar sites. Full article

Climate change is altering the frequency, duration, and seasonality of low flows, which are critical for water availability, ecosystem functioning, and river management. Low-flow characteristics, defining the minimum, often seasonal, flow levels in rivers or streams primarily fed by groundwater, snow or glacier melt, or lake drainage, are essential for assessing hydrological droughts and water resource vulnerability. In the Upper Vistula River Basin, variable precipitation and rising air temperatures increase the risk of droughts, impacting both natural systems and human water use. This study analyzed long-term trends in annual low flows and associated parameters, including drought frequency, duration, and deficit volume, across 41 small- and medium-sized catchments. Two datasets were considered: 25 stations with 58-year daily discharge records (1961–2019) and 41 stations with 38-year records (1981–2019). Low flows were identified using the threshold level method (TLM) at 70% and 90% exceedance (FDC70 and FDC90). Trends were assessed with the Mann–Kendall test, and spatial drought patterns were mapped to evaluate regional variability. Deep and shallow low flows occurred at all analyzed cross-sections. For the period 1961–2019, deep low flows (FDC90) occurred almost annually in 18 of the 25 cross-sections since 2012. Statistically significant increasing trends in deep low-flow parameters were detected in five cross-sections for 1961–2019 and in seven cross-sections for 1981–2019. Shallow low flows (FDC70) occurred in all sections; four rivers exhibited annual shallow droughts during 1961–2019, whereas 12 rivers showed annual events in 1981–2019. Summer droughts predominated over winter events, reflecting enhanced evapotranspiration and higher seasonal water demand. These findings highlight the relevance of analyzing low-flow parameters for understanding hydrological droughts. Such information can support water resource management, planning, and ecosystem protection under variable climatic conditions. Full article

Agriculture in dryland regions faces increasing pressure from climate variability, water scarcity, and competing urban and environmental demands. A recent basin-wide technical analysis for the Rio Grande/Rio Bravo in the United States of America (USA) and Mexico shows that consumptive water use in the river system overall is on an unsustainable path. The Middle Rio Grande Basin (MRGB) of central New Mexico (USA) exemplifies these sustainability challenges, where irrigated agriculture persists despite low precipitation, high evaporative demand, and prolonged drought. This study provides analytical spatial description of irrigated agriculture in the MRGB, examining farm size distribution, crop composition, groundwater access, and consumptive water use measured by evapotranspiration (ET) and effective ET. Using 2021 remotely sensed crops and ET data, groundwater well records, and GIS-based aggregation to the irrigator farm level, the analysis reveals a highly fragmented agricultural landscape dominated numerically by micro-scale and small farms, which together account for 55.9% of total agricultural ET. Alfalfa and other hay crops occupy nearly three-quarters of irrigated acreage and consume 74% of total ET, reflecting the prevalence of forage production. Groundwater access is highly uneven, with most wells concentrated among large farms, creating resilient disparities. The findings highlight that consumptive agricultural water use in the MRGB is diffuse rather than concentrated: non-commercial farms (<12 hectares) account for 55.9% of basin-wide ET, while commercial farms contribute only 14.4% despite occupying about one-fifth of irrigated land. This complicates water conservation efforts. Resilient management strategies must therefore engage thousands of small, largely non-commercial irrigators through mechanisms that recognize both hydrological and spatial realities. The study provides an empirical basis for designing sustainable irrigation and water-management strategies in dryland agricultural systems facing increasing climatic and institutional pressures. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

The Tianshan–Pamir mountain region, serving as the core “water tower” for countries in Central Asia east of the Aral Sea, is a critical bulwark for sustaining downstream socioeconomic systems. However, constrained by complex topography and harsh climatic conditions, this region suffers from a severe scarcity of long-term, continuous hydrological observation data. This study focuses on a typical data-scarce mountainous area, coupling UAV and satellite imagery-based (e.g., Landsat/Sentinel) flow inversion with a hybrid spatial regionalization method—integrating spatial proximity, basin similarity, and regression-based hydrograph reconstruction—to quantitatively estimate long-term discharge time series. The results indicate that, for the validation of instantaneous discharge inversion, the Nash–Sutcliffe efficiency coefficient (NSE) at 29 river cross-sections was consistently greater than 0.80, with the coefficient of determination (R²) reached 0.94 (p < 0.01). Subsequently, for the long-term discharge series reconstructed using the regionalization method, the NSE values at three representative verification sites—each corresponding to a distinct basin type—were 0.88, 0.84, and 0.86, respectively. These findings exhibit higher precision compared to direct temporal upscaling, confirming the reliability of the regionalization method across varying temporal scales. An analysis of monthly discharge trends from 1989 to 2020 revealed a decreasing trend in the discharge of glacier-dominated rivers, with an average rate of change of −2.89 ± 2.54% (p < 0.05); the Pamir Plateau experienced the largest decline (−4.89 ± 6.58%), which is closely linked to large-scale glacial retreat within the basins. Conversely, the discharge of non-glacier-dominated rivers showed an increasing trend, with a multi-year average rate of change of +0.32 ± 8.43% (n.s.), primarily driven by shifts in precipitation and vegetation cover. This research introduces a new approach for hydrological monitoring in data-scarce regions and provides essential data and methodological support for water resource management decisions in arid zones. Full article

Surface water–groundwater interactions within oasis–desert ecotones of arid regions play a pivotal role in sustaining regional water security and ecological stability. Taking the Niya River Basin in Xinjiang, Northwest China, as a representative inland watershed, this study systematically elucidates the mechanisms and seasonal dynamics of surface water–groundwater coupling under the combined influences of natural processes and anthropogenic activities. A total of 68 surface water and groundwater samples were collected during the dry, normal, and wet hydrological periods. Integrated hydrochemical characterization, mineral saturation index analysis, and stable isotope (δ²H and δ¹⁸O) mass balance modeling were employed to quantify recharge contributions and unravel hydrogeochemical evolution pathways. Results indicate that the waters in the study area are predominantly brackish to saline, with consistent dominant ionic assemblages (SO₄²⁻ and Na⁺) across all hydrological periods, highlighting evaporite dissolution as the primary control on solute composition. Hydrochemical evolution is jointly regulated by evaporation concentration, water–rock interactions, and cation exchange processes. Surface water chemistry reflects the combined effects of silicate weathering and evaporite dissolution, whereas groundwater chemistry is mainly governed by evaporite dissolution coupled with pronounced cation exchange. Stable isotope signatures reveal substantial secondary evaporation of regional precipitation prior to recharge. Frequent bidirectional recharge between surface water and groundwater was observed, exhibiting distinct seasonal transitions. During the dry period, groundwater provides significant baseflow support to surface water (48.6% in the oasis zone and 54.3% in the desert zone). In the normal period, recharge direction reverses, with surface water becoming the dominant source of groundwater recharge (99.0% in the oasis zone and 76.6% in the desert zone). In the wet period, spatial heterogeneity becomes evident: surface water continues to dominate groundwater recharge in the oasis zone (92.7%), whereas groundwater recharge to surface water prevails in the desert zone (50.5%). This study identifies a seasonally dynamic “discharge–infiltration–zonal regulation” bidirectional recharge pattern in arid inland river systems. The findings advance the mechanistic understanding of hydrological connectivity reconstruction within oasis–desert ecotones and provide a scientific basis for optimized regional water resource allocation and groundwater salinization risk mitigation. Full article

Flood hazard mapping remains challenging in regions with limited hydrological and topographic data, despite increasing flood risk driven by climate change and land-use dynamics. This study aims to demonstrate that preliminary flood inundation maps can be developed under data-scarce conditions by integrating limited field observations with publicly available datasets and simplified hydrodynamic modeling. The Khlong Wat watershed in southern Thailand, where flood hazard maps had not previously existed despite recurrent flood events, was used as a case study. Flood simulations were conducted using the HEC-RAS model with a simplified terrain representation to approximate river bathymetry, acknowledging uncertainties in channel geometry. Hydrodynamic results show a systematic increase in flood extent and depth with increasing flood recurrence intervals, with inundated areas expanding from 1.43 km² for a 10-year flood to 4.02 km² and 5.97 km² for 100- and 500-year events, respectively. Agricultural land is consistently the most affected category, accounting for more than two-thirds of the flooded area across all scenarios, with rubber plantations being the dominant land use. Urban exposure increases with flood magnitude, although most buildings remain affected by shallow inundation below 0.5 m. The results confirm that meaningful flood hazard assessments can be achieved in data-limited regions and provide a transferable framework to support flood risk management and spatial planning in similar environments. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Mediterranean temporary streams are characterized by high hydrological variability that climate change is expected to intensify, increasing drought frequency and severity. These conditions represent a major threat to freshwater mussels, an imperiled group with limited mobility and strict habitat and host requirements. This study explored key factors shaping freshwater mussel community structure, including the spatial distribution, species composition and abundance of coexisting species, in two temporary streams of the Guadiana river basin (southwestern Iberian Peninsula). Pool systems in both streams were characterized and compared under average dry season and extreme drought conditions using aerial imagery, whereas mussel abundance patterns and host–mussel relationships were assessed in the larger and more hydrologically stable stream. Results showed that drought severity had different effects on pool refugia persistence, longitudinal distribution and host fish availability between streams. The smaller stream experienced extensive pool desiccation during extreme drought, causing widespread mussel mortality, whereas the larger stream retained numerous pools that allowed mussel persistence. Mussel abundance showed no relationship with pool size. However, Unio tumidiformis abundance was positively associated with native fish abundance, particularly in upstream reaches. These results highlight hydrological stability and host availability as key drivers of freshwater mussel persistence in Mediterranean temporary streams. Full article

Understanding how climate and land use interact to shape freshwater quality remains challenging across heterogeneous river basins. This study analysed monthly citizen-science measurements of nitrate (NO₃), phosphate (PO₄), and turbidity, collected between 2016 and 2024, across seven Italian river basins representing contrasting climatic and land-use contexts. A non-parametric analytical framework combining Kruskal–Wallis tests, aligned rank transform analyses, principal component analysis (PCA), and basin-specific Somers’ D statistics was applied to ordinal concentration data. Significant differences among basins revealed persistent spatial structuring of water-quality regimes. PCA identified two largely independent gradients: a dominant nutrient axis defined by NO₃ and PO₄, and a secondary turbidity axis. Urban and industrial land use aligned with higher nutrient categories, while vegetated landscapes were associated with lower concentrations. Climatic effects were basin specific. Precipitation showed opposing relationships with NO₃, suggesting both mobilisation and dilution processes, whereas temperature was positively associated with PO₄ in several basins and negatively related to NO₃. Turbidity displayed variable links with precipitation and temperature, reflecting hydrological and seasonal controls. Overall, results indicate that land use represents the primary structural driver of nutrient variability, while climatic factors modulate basin-specific responses. The integration of citizen science observations with robust non-parametric approaches provides a scalable framework for detecting environmental drivers and supporting the targeted management of Mediterranean river systems. Full article

The Jordan Valley is a heavily modified, data-limited transboundary river basin where water availability is constrained by both climate conditions and intensive human intervention. This study applies an integrated hydrological and hydrogeochemical modeling framework using the Soil and Water Assessment Tool (SWAT) to quantify basin-scale water availability and quality and to assess climate change impacts for the period 2000–2021. Results indicate that the basin is strongly evapotranspiration-dominated, with mean annual precipitation of 298.9 mm and precipitation-derived evapotranspiration accounting for 66.3% of rainfall. When externally supplied irrigation water is included, total evapotranspiration increases markedly, highlighting the strong dependence of agriculture on imported surface water and groundwater abstractions. Only a small fraction of total water input contributes to river discharge toward the Dead Sea, indicating a very limited internal water surplus. Hydrological dynamics are largely controlled by upstream dams and transboundary diversions, while nitrate and sediment simulations demonstrate a close coupling between hydrology, land use, and water quality. Climate projections suggest further reductions in water availability during the 21st century, exacerbating existing water scarcity. Overall, the study illustrates how intensive regulation and irrigation dependency constrain water availability in the Jordan Valley and in similar heavily modified transboundary river basins. Full article

This article presents the results of hydrological research on the Ruda River, which is the largest tributary of the Cetina River, located in the Dinaric karst of Croatia. The hydrology of this river has been altered after the construction of the Orlovac Hydropower Plant (HP) and the Buško Blato reservoir in 1973. The main aim of this study was to generate new knowledge about the hydrological functioning of the river, with a focus on the discharge and water temperature regimes that experienced the most severe alterations. The methodology is based on classical hydrological, statistical, and time-series analysis methods, adapted to the particularities of the study area and available data. Daily and hourly time series of air temperature, precipitation, water temperature, and discharge are analyzed to find trends, change points, inter-annual, seasonal, and sub-daily variations, durations, time shifts, and linear dependencies. The results obtained provide information on the effects of climate change, the duration of diffuse, conduit, and mixed flow, the importance of groundwater exchange, retention times, heat transfer times, and reference water temperatures. It determined the role of the operational mode of the Orlovac HP in discharge from the spring, in inter-annual and sub-annual water redistribution, and in hydropeaking and thermopeaking. The obtained information defines the present state of the Ruda River hydrology and illustrates alterations. Full article

The Soil and Water Assessment Tool (SWAT) model has been widely used to simulate ecohydrological processes in watersheds. However, the SWAT model uses a simplified Environmental Policy Impact Climate (EPIC) model to simulate the leaf area index (LAI), creating a critical gap in accurately simulating evapotranspiration (ET) and runoff in semi-arid regions. This work aims to fill this gap by modifying the SWAT source code to integrate high-resolution Global Land Surface Satellite (GLASS) leaf area index (LAI) data. The modified version was applied to the semi-arid Wuding River Basin and calibrated using a Fortran-based dynamic dimension search (DDS) algorithm. The results show a relatively significant improvement in the accuracy of the daily-scale runoff simulation (R² from 0.52 to 0.71 and NSE from 0.52 to 0.7 for the calibration period, and R² from 0.21 to 0.58 and NSE from 0.2 to 0.51 for the validation period). The improved version also corrects the unrealistic default LAI peak (from >5.0 to 1.5–3.0), correcting the multi-year average ET from 251.7 mm to 341.8 mm. The improved vegetation growth module of the SWAT model effectively improved the accuracy of hydrologic simulation in the semi-arid region and enhanced the structural robustness of SWAT for water management. Full article

Climate change significantly affects hydrological responses, yet studies addressing future water availability in the Paraopeba River Basin (PRB), an important tributary of the São Francisco River Basin in Brazil, remain limited, particularly under CMIP6 scenarios and using distributed hydrological modeling approaches. In this context, this study evaluated the hydrological responses of the PRB, under climate change using the MHD-INPE. Future projections were based on an ensemble of seven climate models from the NEX-GDDP-CMIP6 collection, considering a baseline period (1992–2014), three future periods 17(2040–2060, 2061–2080 and 2081–2100) and two socioeconomic scenarios (SSP245 and SSP585). The model satisfactorily reproduced observed streamflow during the baseline period. Under the SSP585 scenario, the projections indicate stronger alterations in water availability, with a potential intensification of flood and drought events, as reflected by reductions in minimum streamflows (Q₉₀) and increases in maximum streamflows (Q₁₀), particularly in sub-basins 4 and 5, where Q₉₀ reductions approach 30% and Q₁₀ increases reach 11.7%. Additionally, a decrease in Q_7,10 values was observed, which enabled the analysis of the Conflict Index (Icg), indicating that water withdrawals currently granted may exceed the limits established by existing legislation in future scenarios (Igc > 1). Full article

Active Channel Width (ACW) provides a robust indicator for tracking river corridor dynamics, yet automated extraction from multisensory imagery remains limited by spatial and temporal variability in spectral conditions. We developed and validated a workflow in Google Earth Engine (GEE) to delineate the active channel using multispectral indices derived from annual composite Landsat and Sentinel-2 imagery. The indices include the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI). The 34 km study segment of the Lircay River (Chile) served as a demonstration site undergoing substantial geomorphic change over a 20-year period (2003–2023) that spanned a decade-long mega drought (2010–2023) and two major floods (2006, 2023). Multispectral index thresholds were calibrated using manually digitized active channel polygons for a reference year and validated for five different years within the study period to assess their spatial transferability across reaches and temporal stability under varying hydrologic regimes. Sentinel-2 annual composites with the MNDWI-EVI pairing achieved the highest overall accuracy in estimating ACW (mean Kling-Gupta Efficiency = 0.72; Percent Bias = 12.69 across study reaches). Threshold values were tested at the cross-sectional and reach scales. Using cross-section-specific thresholds enhanced the accuracy of ACW estimation, indicating that threshold performance is strongly conditioned by the local characteristics present in the immediate surroundings of each cross section. These results suggest that spectral threshold selection is sensitive to small scale factors that vary across the river corridor, underscoring the need to explicitly consider local geomorphic and ecological conditions when defining thresholds. This reproducible, open-source workflow links automated channel delineation with cross-section-based morphology and explicitly quantifies uncertainty from spatiotemporal spectral variability. It enables high-resolution, repeatable measurements of river corridor change and underscores the need to consider evolving spectral and vegetation conditions when interpreting remotely sensed geomorphic indicators. Full article