Search Results (575)

Harmful algal blooms (HABs) caused by Karenia brevis (K. brevis) present a persistent ecological and public health challenge across coastal Florida. Reliable bloom forecasting is critical for protecting public health, supporting coastal economies, and enabling timely management responses. This study develops a regionally integrated machine learning framework to predict weekly K. brevis bloom occurrence using environmental data from both the Peace and Caloosahatchee Rivers, combined with coastal bloom records from Southwest Florida and Tampa Bay to enhance the spatial and temporal continuity of the response record. A Random Forest classifier was trained on a multi-decadal dataset incorporating river discharge, nutrient concentrations (total nitrogen and total phosphorus), wind forcing, sea surface temperature, salinity, and sea surface height anomalies as a proxy for Loop Current variability. The model achieved strong predictive performance on a chronologically withheld test set, with an overall accuracy of ~90%, balanced accuracy of 87.6%, and ROC–AUC of 0.972, indicating strong discrimination between bloom and non-bloom conditions with high precision and recall for bloom events. Bloom timing and persistence were captured with strong agreement during ongoing bloom periods, while non-bloom conditions were identified with low false-positive rates. Feature-response analyses indicated that bloom probability increased most sharply under moderate discharge and nutrient conditions, with diminished sensitivity at higher extremes. Learning curve analysis demonstrated robust training performance and stable generalization, with validation accuracy plateauing near 84%, suggesting a data-limited ceiling on forecast skill. By aggregating nutrient inputs across multiple watersheds and integrating spatially aligned bloom observations, this study demonstrates the utility of multi-source machine learning frameworks for regional-scale HAB prediction. The results support the development of early warning tools and provide a reproducible foundation for evaluating how combined watershed loading and physical forcing are associated with K. brevis bloom occurrence in complex estuary systems with watershed and coastal coupling. 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

In the context of climate change, the hydrological processes and water resource system vulnerabilities in inland river basins of arid regions are intensifying. Understanding their evolutionary patterns and driving mechanisms is crucial for sustainable water resource management, agricultural development, and the protection of ecological security. This study focuses on the Kaidu River Basin, systematically analyzing the temporal and spatial variations in hydrological cycle elements in the basin from 1998 to 2023 based on multi-source precipitation data, the SWAT hydrological model, and the glacier degree-day model. The study also identifies the main driving factors using a geographic detector. The results show that the SWAT model performs well (calibration period R² and NSE ≥ 0.75, validation period R² and NSE of 0.75 and 0.70, respectively), indicating reliable simulation results. The surface water resources and the contribution of glacier meltwater to runoff in the basin both show a fluctuating downward trend, while potential evapotranspiration increases. The contribution of glacier meltwater during the ablation season decreased from 69.86% in 2014–2016 to 45.01% in 2017–2021. The hydrological processes exhibit a spatial pattern of “mountain areas generating runoff, non-mountain areas consuming water”. The geographic detector results indicate that precipitation is the decisive factor for the spatial differentiation of hydrological processes (influence degree q = 56.9%), with temperature, potential evapotranspiration, and altitude playing important synergistic roles. Moreover, the explanatory power of multi-factor interactions is much greater than that of individual factors. The findings of this study provide a scientific basis for the optimized allocation of watershed water resources, efficient agricultural irrigation, and the sustainable development of oasis ecosystems under changing environmental conditions, thereby supporting the goals of water security and sustainable development in inland river basins of arid regions. Full article

The Juncal Norte Glacier (33°00′ S, 70°06′ W) is in the Dry Andes of central Chile within the Juncal Basin, a headwater watershed of the Aconcagua River, a semi-arid region experiencing an ongoing megadrought since 2010 and a 37% reduction in streamflow relative to pre-1990 baselines. This study provides the first glacier-specific annual melt and runoff estimate for Juncal Norte during mature megadrought conditions. Mass balance was estimated using a temperature index (positive degree day, PDD) model calibrated with automatic weather station (AWS) air temperature data and glacier hypsometry, assuming limited snow accumulation given that 2018–2019 precipitation and snow water equivalent (SWE) were extremely low relative to the long-term mean. Basin runoff was evaluated using a closure method comparing proglacial sub-basin-integrated discharge with modeled glacier melt volumes. Modeled glacier melt for 2018–2019 was equivalent to approximately 30% of observed annual discharge at the proglacial sub-basin, a disproportionate contribution given the glacier covers only 2.7% of the total basin area. The lower ablation zone (2900–4000 m), comprising 30% of glacier area, produced 90% of total melt volume. A + 1 °C temperature perturbation increased glacier-wide melt by 21.4%, confirming high climatic sensitivity. These results underscore the glacier’s critical but increasingly vulnerable buffering role for downstream water availability in the Dry Andes. Full article

Vegetation in desert ecosystems is strongly affected by seasonal climatic fluctuations and soil physical and chemical properties. Rawdat Nourah is a natural watershed depression within the King Abdulaziz Royal Reserve in Saudi Arabia. It is colonized by grasses, herbs, and shrubs. Climatic variability and soil heterogeneity are influencing the vegetation dynamics and regeneration patterns in this ecosystem. Based on the literature review, no previous study analyzed and determined either the vegetation composition or the soil seed-bank of Rawdat Nourah. So, the general objective of this study is to examine the vegetation composition and its relationships with soil physicochemical properties and soil seed-bank composition across Rawdat Nourah across different seasons. Floristic analyses, vegetation composition, soil properties, and soil seed-bank were performed within two seasons (winter–spring and summer–fall seasons) of 2023–2024. The obtained data were analyzed using multivariate and statistical approaches. Six plant associations were identified: winter–spring (WVG I: Zilla spinosa–Malva parviflora; WVG II: Rhazya stricta–Zilla spinosa; WVG III: Cynodon dactylon–Convolvulus pilosellifolius) and summer–fall (SVG I: Calotropis procera–Pulicaria undulata; SVG II: Cynodon dactylon–Zilla spinosa; SVG III: Rhazya stricta–Schismus arabicus). Species richness was higher in winter–spring (2.4 species stand⁻¹) than in summer–fall (1.66 species stand⁻¹), while the seed-bank densities were 633.9 and 575.1 seeds m⁻², respectively. Vegetation responded strongly to marked seasonal contrasts in temperature and moisture (~15 °C, 11 mm vs. ~36 °C, 3 mm). Moderate human activity enhanced vegetation cover, whereas prolonged grazing exclusion reduced diversity through the dominance of a few species. The response of vegetation structure and species richness to climatic factors varies greatly depending on the increase in water availability, and moisture content during the mild weather Winter–Spring season (mean temperature is 15 °C and rainfall is 11 mm), compared to the Summer–Autumn season (mean temperature is 36 °C and rainfall is 3 mm). The richness and cover of the plants were generally affected by human activity, where long-term grazing will reduce species richness and increase competition between species, making one or two species dominant. Although above-ground vegetation exhibited clear seasonal and spatial shifts in species composition and abundance, these changes were not reflected in the soil seed-bank. This relation suggests that above-ground communities and seed-banks are regulated by different ecological processes under arid conditions. The data of the present study showed low correlation between the current vegetation and the soil seed bank, which reflects a degradation in this region. Therefore, these findings suggest that sustained protection of the King Abdulaziz Royal Reserve is essential for enhancing seed-bank persistence, vegetation recovery, and ecosystem resilience under arid conditions. Full article

Mountain ecosystems are susceptible to climate change, and alpine treeline ecotones (ATEs) represent one of the significant responsive indicators of climate-driven environmental change. This study examines long-term spatiotemporal dynamics of the ATE across the Eastern Slopes of the Canadian Rocky Mountains (ESCR) from 1984 to 2023, with the objective of assessing whether regional climate warming has influenced ATE extent and elevation across different aspects and watersheds. Multi-decadal Landsat imagery, ERA5-Land temperature data, and topographic variables were integrated within a Google Earth Engine (GEE) framework to map ATEs using the Alpine Treeline Ecotone Index (ATEI), a probabilistic approach designed to capture transitional vegetation zones. Temporal trends were evaluated using non-parametric statistics, correlation analyses, and watershed- and aspect-based comparisons. Results indicate that the total alpine treeline ecotone (ATE) area in the ESCR was approximately 13.3% larger in 2023 than in 1984. However, the temporal evolution of ATE extent and elevation was non-monotonic, and linear trend analyses did not detect statistically significant increasing or decreasing trends over the full study period. ATE elevation and expansion exhibited pronounced spatial heterogeneity, with greater changes occurring on north- and northwest-facing slopes and within selected watersheds. In contrast, summer (July–September) temperatures increased significantly (+2.84 °C), exceeding global land-only warming rates, and vegetation greenness (NDVI) showed a strong, statistically significant positive relationship with temperature. These findings show that while climate warming has clearly increased vegetation productivity, elevational ATE dynamics remain spatially heterogeneous and temporally non-synchronous with summer temperature trends. Full article

The research focuses on analyzing historical climate variability over the period 1982–2022, as well as future projections of climate change over the period 2025–2099, with regard to the Souss-Massa watershed, a semi-arid region with high dependency on agricultural activities. Precipitation and temperature data were collected annually from five meteorological stations, Agadir, Amaghouz, Amsoul, Aoulouz, and Taroudant, in order to analyze long-term climatic trends and predict possible scenarios of climate change. A trend analysis was carried out using a combination of the Mann–Kendall test and Sen’s slope estimator. The findings of this study indicate that there is an increase in mean annual temperature that is statistically significant (p < 0.05) across all stations, ranging from +0.28 °C per decade at Agadir, which is located along the coastal region of Morocco, to as high as +0.45 °C per decade at Taroudant, which is located inland. Conversely, the precipitation trend is decreasing and not statistically significant (p > 0.05). For projecting future climatic conditions, we used the Statistical Down-Scaling Model (SDSM v4.2.9) with global climate models using outputs from CanESM2 under two emission scenarios, namely RCP 4.5 and RCP8.5. The calibration period (1982–2001) and the validation period (2002–2022) were satisfactory, as indicated by the high values of the coefficients of determination (R² > 0.6) for temperature and moderate values (R² = 0.5–0.6) for precipitation. Projections indicate an increase in temperature, with the mean temperature change ranging from +4.8 °C and +8.7 °C by 2099 depending on the station’s location. Projected precipitation decreases are found under both scenarios, but with stronger decreases under RCP8.5, especially along the coastal regions, with decreases as large as −53.8% at Agadir. However, the precipitation projections have to be used with caution due to the limitations associated with the downscaling methods and the use of a single global climate model. All the projections indicate a trend towards arid conditions, emphasizing the need for adaptive water resources management and improving the ensemble models for climate projections. Full article

In order to set nutrient and sediment load targets for the Chesapeake Bay, projections of changing environmental conditions through 2055 have been previously considered. This article expands the analysis through 2085. Under future ensemble scenarios of General Circulation Models (GCMs), temperature and precipitation trends for the Chesapeake Bay watershed prior to midcentury have a rate of change more than twice that of the post-midcentury trend. Prior to midcentury, runoff and nutrient loading to the Bay estuary are projected to increase. In this analysis, model simulations for post-midcentury suggest the trend of increasing runoff may be reduced. The combined effect of a reduced trend in temperature and precipitation increases post-midcentury with continued sea level rise in the ensemble scenarios leads to a decreasing trend in Chesapeake hypoxia post-midcentury, resulting in a leveling off of dissolved oxygen water quality degradation. Full article

Long-term fire histories are well-documented across most North American temperate forest systems, yet the fire regimes of high-alpine treeline environments remain poorly understood. Here, we present a millennial-scale fire history from the Sawtooth Fen Palsa (SFP), a rare permafrost fen palsa located in the high-alpine treeline ecotone of the Beartooth Plateau, Wyoming, a permafrost system now unraveling due to recent decades of rapid warming. Analysis of paleoenvironmental proxies from peat sediments overlying the permafrost reveals a multi-century peak in fire activity at the beginning of the record, ca. 10,000 cal yr BP, coinciding with the afforestation of newly deglaciated, ice-free sites. This initial surge in high-severity fire activity was followed by a decline when solar-orbitally driven increases in growing-season temperatures likely promoted forest opening and more surface fire activity within the SFP watershed. High-severity fire activity increased again during the mid-Holocene (ca. 5800–5000 cal yr BP), when effective moisture increased, favoring subalpine forest expansion and increased connectivity of woody biomass (sagebrush and forest), enhancing the potential for canopy fire spread. Only two small fire episodes were recorded in recent millennia when a rapid change in the sedimentation rate may indicate a partial loss of the sediment record. Rapid warming in recent decades has triggered the formation of dozens of thermal collapse ponds across the fen palsa. The frequency of these features has more than doubled since 2000 CE, underscoring the degradation of underlying permafrost in response to changing climatic conditions. Continued warming is expected to cause the complete loss of the permafrost lens and alter ecosystem dynamics, disturbance regimes, and carbon and nutrient cycling in alpine systems throughout the Rocky Mountains. Full article

Climate change exerts a pronounced influence on streamflow regimes by altering precipitation characteristics and potential evapotranspiration, thereby affecting global water availability and hydrological functioning. This study investigates the hydrological behavior of the Upper Indus River Basin (UIRB), a strategically important transboundary mountainous watershed, under a range of future climate scenarios. An integrated modeling approach combining process-based simulation and data-driven techniques is employed to generate new insights relevant to the advancement of the Sustainable Development Goals (SDGs). The Soil and Water Assessment Tool (SWAT) and a Long Short-Term Memory (LSTM) neural network were calibrated and validated using daily streamflow observations spanning 1995–2014. During the calibration phase, SWAT yielded an R² of 0.71, a Nash–Sutcliffe Efficiency (NSE) of 0.59, and a PBIAS of 20.3%. In comparison, the LSTM model demonstrated improved predictive performance, achieving an R² of 0.72, an NSE of 0.71, and a PBIAS of −1.85%. Future discharge simulations were derived from bias-corrected climate projections obtained from 11 General Circulation Models under SSP245 and SSP585 scenarios for four future time slices (2015–2035, 2036–2055, 2056–2075, and 2076–2099), using 1995–2014 as the reference period. Under the high-emission SSP585 pathway, basin-wide precipitation is projected to increase by 14.7% by the late century, accompanied by substantial rises in maximum and minimum temperatures of 17.9% and 36.25%, respectively. SWAT simulations indicate streamflow increases of 7.1–9.9% under SSP245 and 10.1–11.7% under SSP585, whereas the LSTM model projects more pronounced increases of 17–25.6%. The outcomes of this research contribute significantly to multiple SDGs, with quantified impacts on SDG 6 (Clean Water and Sanitation, 35%), SDG 13 (Climate Action, 30%), SDG 2 (Zero Hunger, 15%), SDG 15 (Life on Land, 12%), and SDGs 8 and 9 (Economic Growth and Infrastructure, 8%). The proposed integrated modeling framework supports enhanced water security through optimized resource planning, reinforces climate resilience by strengthening adaptive capacity, promotes agricultural sustainability in irrigation-reliant regions, safeguards fragile mountain ecosystems under accelerating glacier retreat, informs the development of climate-resilient agricultural sustainability in irrigation-reliant regions, and informs the development of climate-resilient infrastructure. Collectively, these findings highlight the urgent necessity for adaptive water management policies to address climate-induced hydrological uncertainty in stressed transboundary river basins and offer a transferable framework for achieving water-related SDGs in climate-sensitive regions worldwide. Full article

While harmful cyanobacterial blooms (HCBs) are extensively characterized in eutrophic lakes, the ecological dynamics of connected river networks remain oversimplified, obscuring the mechanisms of community assembly and toxin distribution across the lake–river interface. This study investigated the spatial heterogeneity of HCBs and microcystins (MCs) in the Lake Taihu watershed, revealing a complex functional divergence between lotic and lentic ecosystems. The rivers functioned as primary nutrient sources, with Total Nitrogen (3.35 ± 1.52 mg·L⁻¹) and Total Phosphorus (0.21 ± 0.22 mg·L⁻¹) concentrations being 1.7-fold and 1.8-fold higher, respectively, than those in the lake during peak periods. Conversely, the lake acted as a biological sink, supporting a peak cyanobacterial density (3.32 × 10⁷ cells·L⁻¹) nearly 1.5 times that of the river network. Phytoplankton community analysis revealed distinct ecological niches: while the lentic lake environment was almost exclusively dominated by colonial Microcystis (>90% relative abundance), the lotic river networks harbored a diverse assemblage with significant contributions from filamentous Oscillatoria and Dolichospermum. Correspondingly, intracellular MC (IMC) in the lake (up to 14.5 μg·L⁻¹) significantly exceeded riverine levels (generally <1.0 μg·L⁻¹). Despite these compositional differences, toxin dynamics exhibited strong bidirectional coupling (r > 0.75, p < 0.01), suggesting a spillover effect where the lake determines the watershed’s toxin burden during rivers outflow period. Redundancy Analysis (RDA) further elucidated that limnetic blooms were primarily regulated by water temperature and pH, whereas riverine communities were strictly constrained by hydrodynamic flow. Consequently, the health risk assessment revealed a highly heterogeneous landscape where, beyond the northern lake bays, specific semi-lentic river segments emerged as cryptic hotspots. These findings demonstrate that while nutrients fuel the system, hydrodynamic conditions act as the ultimate ecological filter determining the spatiotemporal distribution of cyanobacterial risks, necessitating an integrated approach to monitoring the entire lake–river continuum. Full article

Climate change can alter both the amount and timing of inflows to water supply reservoirs while also increasing heat-driven demand and the likelihood of stressful warm-season conditions. Climate-driven changes in inflow to Eagle Mountain Lake Reservoir (Texas, USA) were quantified by integrating (i) a calibrated SWAT model evaluated at four USGS stream gauges, (ii) statistically downscaled CMIP6 daily precipitation and minimum/maximum temperature at seven stations/grid points for a historical baseline (2003–2022) and two future windows (2031–2050 and 2081–2100) under SSP1-2.6, SSP2-4.5, and SSP5-8.5, and (iii) observed reservoir operations (lake level, water supply releases, and flood discharge; 1990–2022). A standard watershed climate workflow is reframed through an operations-focused lens, wherein projected inflow changes are translated into decision-relevant indicators via the utilization of observed thresholds and operating mode signals. Included within this framework are spring refill-season inflow shifts, a hot–dry month metric, and storage threshold performance measures, which are coupled with screening-level probabilities linked to multi-year inflow deficits. Across models and stations, mean annual temperature increases by 0.7–1.9 °C in the 2030s and by 0.7–6.1 °C in the 2080s, while annual precipitation changes remain uncertain (−24% to +55%). Daily projections show a strong increase in extreme heat days (daily Tmax above the historical 95th percentile), from about 18 days yr⁻¹ historically to about 30–33 days yr⁻¹ in the 2030s and about 34–82 days yr⁻¹ by the 2080s. Hot–dry months (monthly mean Tmax above the historical 90th percentile and monthly precipitation below the historical median) increase modestly by mid-century and rise to about 1.5 months yr⁻¹ on average by the 2080s under SSP5-8.5. SWAT simulations indicate that the mean annual inflow declines by 17–20% across scenarios, with the largest reductions during the spring refill period (March–June). Historical operations show that hot–dry months are associated with approximately double the mean water supply release (7.2 vs. 3.5 m³/s) and a lower monthly minimum lake level (about 0.30 m; about 1.0 ft lower on average). Flood discharges occur almost exclusively when lake elevation is at or above about 197.8 m and follow multi-day rainfall clusters (cross-validated AUC = 0.99). Together, these results indicate that earlier-season inflow reductions and more frequent hot–dry stress will tighten the operational margin between refill, summer demand, and flood management, underscoring the need for adaptive drought response triggers and integrated drought–flood planning for the Dallas–Fort Worth region. Full article

Climate warming impacts arctic and subarctic lands, subjecting it to a generalized rise in soil temperature and causing changes in the surface cover. Land cover is a key control parameter for soil hydrothermal states, and its study by satellite imagery is necessary for monitoring boreal surface changes over time at large scales. Understanding the links between land cover and environmental conditions is also crucial to anticipate the impacts of atmospheric changes on continental surfaces. Sentinel-1 and Sentinel-2 data combined with a field campaign in July 2024 were used to produce a 10 m spatial resolution land cover map in the Abisko region, northern Sweden, covering 2180 km² and including three watersheds with an overall accuracy exceeding 94%. In parallel, temperature and precipitation fields were statistically downscaled at 100 m spatial resolution using topography, ordinary kriging based on weather stations and reanalysis. The relationships between surface areas and average summer temperature–precipitation clusters reveal that the vegetation distribution closely reflects the recent atmospheric conditions with the treeline following the 10.2 °C July–August isotherm in the considered area. This study provides a spatial basis for investigating the complex atmosphere–surface interactions and for assessing the sensitivity of boreal landscapes to ongoing climate warming. Full article

This study examines the hydrological response of the Klambu Dam Catchment in Central Java, Indonesia, to climatic and land cover changes from 2000–2023, with simulations extending to 2040. Utilizing CHIRPS satellite data calibrated with six ground stations, monthly precipitation and temperature datasets were analyzed and projected via linear regression aligned with IPCC scenarios, revealing a marginal temperature decline of 0.21 °C (from 28.25 °C in 2005 to 28.04 °C in 2023) and a 17% increase in rainfall variability. Land cover assessments from Landsat imagery highlighted drastic changes: a 73.8% reduction in forest area and a 467.8% increase in mixed farming areas, alongside moderate fluctuations in paddy fields and settlements. The Thornthwaite-Mather water balance method simulated monthly discharge, validated against observed data with Pearson correlations ranging from 0.5729 (2020) to 0.9439 (2015). Future projections using Cellular Automata-Markov modeling indicated stable volumetric flow but a temporal shift, including a 28.1% decrease in April rainfall from 2000 to 2040, contracting the wet season and extending dry spells. These shifts pose significant threats to agricultural and aquaculture activities, potentially exacerbating water scarcity and economic losses. The findings emphasize integrating dynamic land cover data, climate projections, and empirical runoff corrections for climate-resilient watershed management. Full article

The water yield (WY) service is a critical ecosystem service in arid regions, and understanding its spatiotemporal heterogeneity and controls is important for sustainable watershed management. Annual water yield (WY) in the Aksu River Basin (ARB), China, from 2000 to 2020 was simulated using the InVEST model, with validation against observed runoff (NSE = 0.840, R² = 0.846, RMSE = 1.787). The results revealed a decline in WY from 66.49 mm in 2000 to 43.15 mm in 2015, while retaining a clear north–south gradient, with higher values in the north. Areas showing decreasing and increasing trends accounted for 45.34% and 3.14% of the basin, respectively. WY exhibited strong spatial autocorrelation (global Moran’s I = 0.912–0.941), with high-value clusters in the north and low-value clusters in the south. GeoDetector identified precipitation, temperature, and potential evapotranspiration as key drivers (q = 0.889, 0.880, and 0.832, respectively), with precipitation-related interactions generally exceeding 0.9, indicating enhanced explanatory power through multi-factor coupling. After variable screening and collinearity control, MGWR revealed spatially varying effects of drivers and significant spatial non-stationarity. Overall, despite the declining trend, WY in the ARB maintained a relatively stable spatial structure, with its heterogeneity primarily driven by the coupling of climatic forcing and topographic constraints, providing a scientific basis for zonal water resource management in arid river basins. Full article