Search Results (2,570)

Climate change and population growth are intensifying water scarcity in arid regions, yet previous analyses focusing on a single driver may not fully capture the compounded effects of climatic and anthropogenic factors. This study integrates water-balance analysis, trend analysis, and correlation-based statistical analysis to examine the combined effects of hydroclimatic anomalies and socioeconomic activities on water resource dynamics in ecologically vulnerable Northwest China. Our results show that despite increasing precipitation, warming-associated increases in evapotranspiration, together with irrigation-based water use accounting for 89.8% of total consumption, have offset the potential runoff gains, suggesting that agricultural water use is a major anthropogenic contributor to regional water stress. Based on these findings and a comparative review of representative arid-region practices in Israel, Australia, and Saudi Arabia, we propose a technology-market-institution tripartite governance framework for Northwest China. This framework is intended to support more proactive adaptation in regional water management and to provide a context-specific reference for advancing SDG 6 and SDG 13 in dryland regions. Full article

Mountain flood susceptibility in complex mountainous basins is strongly influenced by terrain–climate interactions; however, the linkage between spatial susceptibility patterns and hydrological processes remains poorly understood. This study proposes a process-oriented framework that explicitly links flood susceptibility patterns with hydrological processes, moving beyond conventional approaches that rely on independent model integration. The Baishuijiang River Basin, located in Wenxian County, southern Gansu Province, China, is selected as a representative mountainous watershed for this analysis. The specific conclusions are as follows: (1) Flood susceptibility was mapped using a Particle Swarm Optimization (PSO)-enhanced Maximum Entropy (MaxEnt) model based on multi-source environmental variables, including climatic, terrain, soil, land cover, and vegetation factors. The model achieved high predictive accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.912), identifying precipitation of the driest month (bio14), elevation, and land use as dominant controlling factors. Medium-to-high-susceptibility areas account for approximately 22% of the basin and are mainly distributed along river valleys and flow convergence areas. These patterns are strongly associated with reduced infiltration capacity under dry antecedent conditions and enhanced flow concentration in steep terrain, and they exhibit clear nonlinear responses and threshold effects. (2) Hydrological simulations using Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) show good agreement with observed runoff (Nash–Sutcliffe Efficiency (NSE) = 0.74−0.85). Sensitivity analysis indicates that runoff dynamics are primarily controlled by the Curve Number (CN), recession constant, and ratio to peak, corresponding to infiltration capacity, recession processes, and peak discharge amplification. The spatial consistency between high-susceptibility areas and areas of strong runoff response demonstrates that susceptibility patterns can be physically explained through hydrological processes, providing a process-based interpretation rather than a purely statistical prediction. (3) Future projections indicate that medium–high-susceptibility areas remain generally stable but show a gradual expansion (+5.2% ± 0.8%) and increasing concentration along river corridors under climate change scenarios. This reflects intensified precipitation variability and enhanced runoff concentration processes, suggesting a climate-driven amplification of flood risk in hydrologically connected areas. Overall, this study goes beyond conventional susceptibility assessment by establishing a physically interpretable framework that provides a consistent linkage between environmental controls, susceptibility patterns, and hydrological responses. The proposed approach is transferable to similar mountainous basins with strong terrain–climate interactions, although uncertainties related to data limitations and single-basin application remain and require further investigation. 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

Climate change has intensified extreme hydrological risks, particularly in basins characterized by frequent seasonal streamflow interruptions and discontinuous hydrological records, where traditional process-based models exhibit limited capability for adaptive water resource management. This study develops a hybrid SWAT-LSTM framework that integrates SWAT-derived hydrological variables with meteorological factors and applies SHAP interpretability analysis to quantify dominant drivers and identify threshold inflection points of runoff variability. Using the upper and middle reaches of the Huolin River Basin as a case study, the coupled model outperformed the standalone SWAT model during the test period (NSE: 0.876 vs. 0.710; R²: 0.884 vs. 0.736) and more accurately reproduced extreme flood and drought events. Future projections (2026–2100), driven by the optimized FGOALS-g3 climate model under SSP2-4.5 and SSP5-8.5 scenarios, indicate increasing precipitation, accelerated minimum temperature rise, and a non-stationary runoff pattern characterized by a mid-century decline followed by a late-century increase. The SHAP results reveal strengthened meteorological dominance, particularly for precipitation and minimum temperature, while soil moisture, evapotranspiration, and percolation remain key hydrological controls. The upward shift in the minimum temperature threshold reflects strengthened temperature control on runoff dynamics under warming. The proposed framework improves extreme runoff prediction and provides a quantitative basis for climate-adaptive basin management. 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

Nature-based solutions (NbSs) are increasingly recognized as sustainable and cost-effective strategies for mitigating drought impacts. However, robust quantitative evidence on the effectiveness of NbSs for drought mitigation, especially under future climate change scenarios, remains limited. In particular, the extent to which grazing management can reduce agricultural and hydrological droughts over long time horizons is still poorly understood. This study examines the long-term effectiveness of grazing management as a NbS for mitigating drought under historical and future climate conditions in the Ganale Dawa River Basin, Ethiopia. We combined remote sensing, machine learning, and climate projections to simulate soil moisture and runoff using a long short-term memory (LSTM) model. Protected areas were used as proxies for light grazing, while adjacent non-protected areas represented heavy grazing. Agricultural and hydrological droughts were quantified using the standardized soil moisture index (SSMI) and standardized runoff index (SRI), respectively. The results show that light grazing consistently reduced drought severity compared to heavy grazing across all periods. Agricultural drought severity was reduced by up to ~15% under SSP2-4.5 and SSP5-8.5, while hydrological drought severity showed substantially larger reductions, exceeding ~40% in mid- and late-future periods. Differences between grazing regimes widened under stronger climate forcing, indicating that grazing management benefits become more pronounced under future climate stress. These findings demonstrate that grazing management is an effective NbS for enhancing long-term drought resilience. Scaling up sustainable grazing practices could, therefore, serve as a practical climate adaptation strategy for drought-prone basins in Ethiopia and similar regions. Full article

Runoff time series often exhibit nonlinear and fluctuating characteristics, and their complexity has further increased with the intensification of global climate change; high-precision daily-scale forecasting remains a core challenge in the field of hydrological forecasting. Addressing the shortcomings of existing methods in terms of runoff feature extraction capabilities and limited forecasting accuracy, this paper aims to improve the accuracy of daily runoff forecasting in small watersheds by constructing a hybrid forecasting model that integrates optimization algorithms, signal decomposition, and deep learning models. Specifically, the original runoff data is first preliminarily decomposed using a variational mode decomposition (VMD) method optimized by the grey wolf optimization (GWO) algorithm. The mode components obtained from the decomposition are evaluated using Fuzzy Entropy (FE), and the selected high-entropy components (IMFs) are then input into a second-order decomposition using an optimized Wavelet Transform (WT) to further extract latent features. After decomposition, the mode components are reassembled; second, a bidirectional long short-term memory (BiLSTM) model for daily runoff prediction is constructed for each subcomponent, and the model’s hyperparameters are optimized using an optimization algorithm; finally, the prediction results are reconstructed to obtain the final output. Case studies were conducted using three hydrological stations—Nanfeng, Baiquan, and Shaziling—in the Xujiang River basin of the Fuhe River. The experimental results indicate that by incorporating an optimization mechanism and a two-stage decomposition strategy, the proposed model achieved an NSE of over 0.95 at all three stations. Compared to the baseline BiLSTM model, the proposed model reduced the RMSE by 76.69%, 75.82%, and 65.92% at the three stations, respectively, and reduced the MAE by 64.77%, 73.54%, and 50.46%, and NSE increased by 27.82%, 40.06%, and 38.02%, respectively. This demonstrates that the model exhibits excellent reliability and superiority in daily-scale runoff forecasting for small watersheds. Full article

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. Full article

This study investigates the transformation of river runoff and its sensitivity to changes in large-scale atmospheric circulation in the Zhaiyk–Caspian water management basin during the period of 1951–2023. The analysis is based on hydrometeorological observations data, the Vangengeim–Girs classification of macro-circulation patterns, and the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices. Correlation analysis, the Mann–Kendall test, Sen’s slope estimator, and the Pettitt test were applied to identify trends, structural shifts, and the spatial coherence of hydroclimatic changes. The results show that interannual variability in river runoff is characterized by a degree of spatial coherence, with correlation coefficients between annual streamflow records at most gauging stations reaching up to 0.95. It is demonstrated that the most pronounced changes in the hydrological regime occur during the cold season and are expressed in a statistically significant increase in winter runoff, while no significant long-term trend in annual runoff is observed. Structural shifts in winter runoff are predominantly associated with the late 1990s, whereas changes in the temperature regime are detected earlier and exhibit spatial coherence. The findings indicate that the contemporary transformation of river runoff is primarily driven by rising air temperatures and the associated intra-annual redistribution of flow. Full article

The rapid growth of cities and expansion of impervious surfaces have intensified surface runoff problems and urban flooding risk. This scenario, exacerbated by the effects of climate change, demands sustainable and integrated solutions. Thus, this study evaluates the pre-feasibility of implementing sustainable urban drainage systems (SUDS) in the Monteverde neighborhood in Montería, Colombia; an area that is critically affected by floods during rainfall events. Using the storm water management model (SWMM) and hydrological simulations based on design hyetographs for different return periods, the performance of a conventional drainage system was compared with five scenarios using SUDS. To determine the modeling scenarios, a decision-making method through the analytic hierarchy process, AHP, was used to select the most appropriate SUDS. The results showed that implementing storage tanks reduces peak flows at outlets 1 and 2 up to 50%, while bioretention zones and rain gardens in isolation showed reduced effectiveness (<6%). Combining strategies slightly improves overall efficiency, although the impact keeps being dominated by tanks. This study demonstrates that the incorporation of SUDS in vulnerable urban areas lessens water risks, strengthens urban resilience, promotes rainwater harvesting, and eases the transition to a more sustainable infrastructure. In addition, it proposes a methodology that can be replicated in other similar Latin American cities. Full article

Given the increasing frequency and intensity of floods, which are mostly caused by continuous climate change and growing human pressures on the environment, accurately identifying areas that are susceptible to flooding is a crucial priority for risk reduction and long-term land use planning. Thus, this research examines multilevel flood susceptibility mapping across North Macedonia, using 328 past flood occurrences, 14 conditioning variables derived from a digital elevation model, simplified lithology, and calculated direct runoff. The methodology integrates fuzzy set theory (Fuzzy), analytic hierarchy process (AHP), weighted linear combination (WLC), and random forest (RF) approaches. The two-stage process employs distinct sets of conditioning factors in sequential flood susceptibility mapping: first, generating Fuzzy/AHP/WLC predictions and pseudo-absence data, and second, producing five RF predictions by varying pseudo-absences and binary cutoffs. Validation results indicate that the very high susceptibility class (0.8–1.0) of the Fuzzy/AHP/WLC model predicted 46.6% of flood pixels within 31.6% of the total area. In comparison, the very high susceptibility class of the RF models predicted 88.5%, 78.3%, 60.6%, 48.5%, and 28.3% of flood pixels within 54.7%, 42.2%, 30.5%, 27.0%, and 25.1% of the total area, respectively. The RF models achieved area under the curve (AUC) values exceeding 0.850, with a maximum of 0.966. Additionally, areas of high and low uncertainty were highlighted using a standard deviation map created from the RF models, highlighting agreement/disagreement and potential locations for methodological improvement and focused sampling. The findings also highlight the potential of the multilevel technique for mapping flood susceptibility and call for more research into its potential for future studies and practical uses. Full article

Freshwater accumulation is one of the most striking observations in the Beaufort Gyre (BG) region in the Arctic Ocean. A 39-year simulation, using the validated high-resolution, geometrical-fitting, unstructured grid Finite-Volume Community Ocean Model for the Arctic Ocean, aimed to investigate the contributions of coastal currents and their interannual variability to this phenomenon. The model reasonably reproduced the interannual variability of freshwater content (FWC) in the BG region. Analysis revealed the constructive role of Ekman pumping in supplying FWC, while the lateral flux generally acts to remove FWC from the region. The disparity between Ekman pumping and lateral flux drives the interannual variability of total FWC, with accumulation occurring when the downward Ekman FWC flux surpasses the net outflow-induced lateral FWC flux. Since 2007, there has been a significant increase in downward Ekman pumping, accompanied by a rise in net outflow lateral flux, indicating heightened variability of FWC in the BG region. The model results suggested that the coastal flow over the Arctic continental shelf underwent dramatic changes, especially during summer, and these changes were partially due to increased freshwater and sea ice melting. Increased lateral FWC flux during summer has become a competitive source for unprecedented seasonal freshwater accumulation in the BG region. Flow intensification over the North American coast is influenced by increased freshwater runoff, including the Firth, Kobuk, and Mackenzie Rivers. Interannual FWC variation in the Beaufort Sea could be influenced by the changes in slope flow, with the water originating in part from the Barents and Kara Seas. Full article

As key headwater regions of the upper Yangtze River, the Yalong and Dadu River basins are expected to experience highly uncertain hydrological responses under climate warming. However, the nonlinear and spatially heterogeneous evolution of streamflow across multiple time-frequency scales remains insufficiently understood. In this study, a SWAT model driven by CMIP6 climate projections under four shared socioeconomic pathways (SSP1-2.6 to SSP5-8.5) was coupled with multivariate wavelet coherence, spatial wavelet transform, and change-point detection methods to investigate the spatiotemporal evolution of streamflow and extreme risks during 2017–2100. Results indicate that precipitation is the primary driver of streamflow variability, with streamflow responding rapidly, while air temperature mainly regulates seasonal intensity via snowmelt. Streamflow seasonal intensity exhibits a northwest-southeast gradient, with low variability upstream and high sensitivity downstream, reflecting precipitation-concentrated, forested canyons where rapid lateral flow and dry-season evapotranspiration amplify flow contrasts. Moreover, hydrological nonstationarity and extreme risks are projected to intensify, with structural regime shifts emerging in the 2040s–2050s and extreme high-flow magnitudes doubling under SSP5-8.5, accompanied by more frequent drought-flood alternations. These findings highlight an upstream buffering-downstream sensitivity pattern, emphasizing the need for spatially differentiated water resources management under nonstationary climate conditions. Full article

Nutrient inputs from human activities, such as agriculture and sewage discharge, influence algal blooms in water bodies. In Ecuador, the Daule River receives wastewater discharges. In addition, poor agricultural practices, including the unsuitable use of fertilisers in combination with soil erosion and surface runoff processes, increase the nutrient load to the river. Considering this, the objective of this study was to evaluate environmental and biological variables using statistical analysis to identify the parameters that influence algal blooms in the main stem of the Daule River. The methodology consisted of two phases: (i) data collection, including water sampling and laboratory work for the analysis of nutrients and phytoplankton, and (ii) statistical analysis, which includes univariate, bivariate, inferential and multivariate analysis (STATICO technique). The results showed that pH and dissolved oxygen were the main drivers of diatoms (Polymyxus coronalis and Aulacoseira granulate) and the charophyte Mougeotia sp. Similarly, ammonium-N was the main driver of the diatom Ulnaria ulna and the cyanobacteria Planktothrix cf. agardhii. The outcomes of this study identified the main environmental variables driving blooms of the five most abundant species, providing a basis for the development of ecological models in the context of land use and climate change. Full article

Vegetation restoration in water-limited regions typically increases evapotranspiration (ET) while reducing runoff. Over the past four decades, Daihai Lake in China’s northwest inland river basin has experienced significant shrinkage. Previous studies attribute this primarily to climate change and water resource exploitation, yet the impact of vegetation dynamics remains insufficiently examined. This study analyzed changes in the water budget across different vegetation types in the Daihai Lake Basin, based on remote sensing-derived precipitation and ET data, and employed correlation analysis to examine the relationships between environmental factors (such as climate change, afforestation projects, and water-saving irrigation) and lake shrinkage. Our findings revealed that afforestation has expanded forest cover by 69.42 km² since 2000, accounting for 73.95% of the total forest area. Notably, forest ET demonstrated the strongest negative correlation (r = −0.89, p < 0.001) with lake area among all vegetation types. Grasslands emerged as the primary water-surplus vegetation, contributing 81.34% to the basin’s total water surplus. The synergistic effects of precipitation reduction, temperature increase, and enhanced ET from forest expansion drove the shrinkage of the lake. These results highlight the need for science-based vegetation management in arid and semi-arid regions, where we recommend adopting shrub-grass combined restoration approaches to enhance the sustainability of ecological restoration. Full article