Search Results (1,216)

A spatiotemporal, multi-task learning (MTL) model for simulating surface water–groundwater (SW-GW) dynamics is developed and applied to the Heihe River Basin, Northwest China. The Transformer-based model (MT-TFT) jointly forecasts surface runoff and groundwater levels, outperforming MTL models built on gated recurrent unit (GRU) and long short-term memory (LSTM) architectures. Compared with single-task learning, adding a coupled groundwater-level task markedly improves surface runoff prediction, achieving a Nash–Sutcliffe efficiency (NSE) of 0.73 and a coefficient of determination (R²) of 0.75. Attention-based interpretability shows that the model assigns the highest weights to time steps with elevated precipitation; as lead time shortens, attention further concentrates on these periods, improving the accuracy of near-term, multi-step forecasts. These results highlight the value of inductive transfer across hydrologic targets and demonstrate that MT-TFT provides an effective, interpretable framework for SW–GW coupling. Full article

As global climate change intensifies, understanding drought mechanisms is crucial for managing water resources and agriculture. This study employs the Standardized Precipitation–Actual Evapotranspiration Index (SPAEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI) to analyze meteorological, hydrological, and agricultural droughts in the lower Songhua River basin. The PLUS model was used to predict future land types, with model accuracy validated using four evaluation metrics. The projected land cover was integrated with CMIP6 data into the SWAT model to simulate future runoff, which was used to calculate future SRI. Drought events were extracted using run theory, while drought occurrence probability and return period were calculated via a Copula-based joint distribution model. Bayesian conditional probability was employed to explore propagation mechanisms. The results indicate a significant increase in multidimensional drought risk, particularly when the cumulative frequency of univariate droughts reaches 25%, 50%, or 75%. Although increased duration and intensity enhance the likelihood of combined droughts, extremely high values cause a decline in joint probability under “OR” and “AND” conditions. Under different climate scenarios, the recurrence intervals of meteorological, hydrological, and agricultural droughts in the lower reaches of the Songhua River exhibit increased sensitivity with severity, demonstrating consistent propagation patterns across the meteorological–hydrological–agricultural system. Meteorological drought was found to propagate to hydrological and agricultural drought within ~6.00 months and ~3.67 months, respectively, with severity amplifying this effect. Propagation thresholds between drought types decreased with increasing intensity. This study combined SWAT and CMIP6 models with PLUS-based land-use scenarios, highlighting that land-use changes significantly influence spatiotemporal drought patterns. Model validation (Kappa = 0.83, OA = 0.92) confirmed robust predictive accuracy. Overall, this study proposes a multidimensional drought risk model integrating Copula and Bayesian networks, offering valuable insights for drought management under climate change. Full article

Runoff simulation and forecasting is of great significance for flood control, disaster mitigation, and water resource management. Alpine regions are characterized by complex terrain, diverse precipitation patterns, and strong snow-and-ice melt influences, making accurate runoff simulation particularly challenging yet crucial. To enhance predictive capability and model applicability, this study takes the Upper Jinsha River as a case study and comparatively evaluates the performance of a physics-based hydrological model BTOP and the data-driven deep learning models LSTM and BiLSTM in runoff simulation and short-term forecasting. The results indicate that for daily-scale runoff simulation, the LSTM and BiLSTM models demonstrated superior simulation capabilities, achieving Nash–Sutcliffe efficiency coefficients (NSE) of 0.82/0.81 (Zhimenda Station) and 0.87/0.86 (Gangtuo Station) during the test period. These values are significantly better than those of the BTOP model, which achieved a validation NSE of 0.57 at Zhimenda and 0.62 at Gangtuo. However, the hydrology-based structure of the BTOP model endowed it with greater stability in water balance and long-term simulation. In short-term forecasting (1–7 d), LSTM and BiLSTM performed comparably, with the bidirectional architecture of BiLSTM offering no significant advantage. When it came to flood events, the data-driven models excelled at capturing peak timing and hydrograph shape, whereas the physical BTOP model demonstrated superior stability in flood peak magnitude. However, forecasts from the data-driven models also lacked hydrological consistency between upstream and downstream stations. In conclusion, the present study confirms that deep learning models achieve superior accuracy in runoff simulation compared to the physics-based BTOP model and effectively capture key flood characteristics, establishing their value as a powerful tool for hydrological applications in alpine regions. Full article

The impact of climate change on watershed hydrological processes has become increasingly significant, with the frequent occurrence of extreme flood events posing a severe challenge to the water resource security of the upper Yangtze River and the Three Gorges Reservoir. To enhance the understanding of runoff evolution under future climate scenarios, this study focuses on the upper Yangtze River Basin, integrating CMIP6 climate model data with the WRF-Hydro model to systematically assess the effects of climate change on runoff projections. Firstly, using CMFD data as a benchmark, the systematic biases in CMIP6 simulation results were evaluated and corrected. Precipitation and temperature data accuracy were improved through Local Intensity Correction (LOCI) and Daily Bias Correction (DBC). Secondly, a large-scale WRF-Hydro model suitable for the upper Yangtze River was developed and calibrated. Finally, based on the corrected CMIP6 data, the climate and runoff changes under the SSP2-4.5 and SSP5-8.5 scenarios for the period 2021–2050 were projected. The results show that: (1) the corrected CMIP6 data significantly improved issues of overestimated precipitation and underestimated temperature, providing a more realistic reflection of regional climate characteristics; (2) the sub-basin calibration strategy outperformed the overall calibration strategy at key control sites, with high runoff simulation accuracy during the validation period; (3) future temperatures exhibit a continuous rising trend, while precipitation changes are not significant—however, the magnitude and uncertainty of extreme events increase—and (4) under the SSP5-8.5 scenario, the inflow to the Three Gorges Reservoir during the wet season significantly increases, raising flood risk. The findings provide a scientific basis for understanding the hydrological response mechanisms in the upper Yangtze River Basin under climate change and offer decision-making support for flood control scheduling and water resource management at the Three Gorges Reservoir. Full article

The Bekasi River Basin is highly vulnerable to severe and recurrent flooding, as evidenced by significant infrastructure and environmental damage during major events. This study investigates the catastrophic floods of 2016, 2020, 2022, and 2025 by implementing the Rainfall-Runoff-Inundation (RRI) model to simulate key hydrological processes. After validation using historical water level data, the model performed effectively, achieving the highest coefficient of determination (R² = 0.75) and lowest root mean square error (RMSE = 0.66) at Cileungsi Station. In contrast, the lowest R² = 0.02, and the highest RMSE = 3.74 at Pondok Gede Permai (PGP) Station. The results reveal a concerning trend of worsening 5-year flood events, with the 2025 flood reaching a peak inundation depth exceeding 3 m and affecting an area of 2.97 km², caused by a rainfall threshold of more than 180 mm/day. Furthermore, the model shows a rapid hydrological response, with a time lag of approximately 7 h or less between peak rainfall and flood onset across three monitoring stations. Analysis indicates these severe floods were primarily triggered by heavy rainfall combined with significant land cover changes. The findings provide valuable insights for flood prediction and mitigation strategies in this vulnerable region. Full article

In the context of intensifying climate change, it is particularly important to assess the transformation of spring floods as a key phase of the hydrological regime of rivers. This study provides a comprehensive analysis of the characteristics of maximum runoff in the Zhaiyk–Caspian basin for the modern period and projected changes for 2030, 2040, and 2050 based on CMIP6 climate scenarios (SSP3-7.0 and SSP5-8.5). Analysis of observations at 34 hydrological stations showed a reduction in spring runoff by up to 35%, a decrease in the duration of high water and a reduction in maximum water discharge on some rivers by up to 45%. It has been established that those rising temperatures, more frequent thaws, and reduced autumn moisture lead to lower maximum water discharge and a redistribution of the seasonal flow regime. Scenario projections revealed significant spatial heterogeneity: some rivers are expected to experience an increase in maximum discharge of up to 72%, while others will see a steady decline in maximum discharge of up to 35%. The results obtained indicate the need to transition to an adaptive water management system focused on the regional characteristics of river basins and the sensitivity of small- and medium-sized watercourses to climate change. Full article

The Yellow River Basin faces severe challenges in water security and ecological protection: at the basin scale, complex hydrological processes and fragile ecosystems undermine the water security pattern; at the local scale, waterlogging risks have intensified in Zhengzhou—a core city in the lower reaches—impacting the city itself and also exerting negative effects on the basin’s water security. To address this, mapping the scientific layout of green infrastructure (GI) is urgent. However, existing studies on GI layout at the basin-urban scale have certain limitations: neglect of underlying surface spatial heterogeneity, insufficient integration of natural, hydrological and social factors’ synergies, and lack of research on large-scale basins and cities, especially ecologically sensitive areas with complex hydrological processes. To fill these gaps, this study proposes an integrated framework (SCS–GIS–MCDM) combining the SCS hydrological model, GIS spatial analysis, and multi-criteria decision making (MCDM). The SCS hydrological model is refined via localized parameter calibration for better accuracy; indicator weights are determined through the MCDM framework; and green infrastructure (GI) suitability maps are generated by integrating ArcGIS spatial analysis with fuzzy logic. Results show that (1) 6.8% of Zhengzhou is highly suitable for GI, mainly in riparian areas and the Yellow River alluvial plain; (2) sensitivity analysis confirms flooded areas and runoff corridors as key drivers; (3) spatial validation against government-issued ecological control zone plans demonstrates the model’s value in balancing flood safety and socio-economy. This framework provides a replicable application model for GI construction in cities along the Yellow River Basin, thereby supporting urban planners in making evidence-based decisions for sustainable blue–green space planning. Full article

This study assesses the impact of extreme droughts on the surface and atmospheric water balance of the Peruvian Amazon basin during 2003–2024. It extends previous work by incorporating multiple datasets for precipitation (CHIRPS, MSWEP, and ERA5) and evapotranspiration (ERA5, GLDAS, Amazon-Paca, and observations from the Quistococha flux tower) and comparing three drought indices: Maximum Cumulative Water Deficit (MCWD), Standardized Precipitation Evapotranspiration Index (SPEI), and self-calibrated Palmer Drought Severity Index (scPDSI). The study focuses on the Peruvian–Ecuadorian Amazon basin, particularly on the Amazon and Madre de Dios river basins, closing at Tamshiyacu and Amaru Mayu stations, respectively. The results confirm four extreme drought years (2004–2005, 2009–2010, 2022–2023, and 2023–2024) with major precipitation deficits in dry seasons and significant reductions in runoff and total water storage anomalies (TWSAs), physically manifesting as negative surface balances indicating net terrestrial water depletion and negative atmospheric balances reflecting reduced moisture convergence, with residuals signaling hydrological uncertainties. The study highlights significant imbalances in the water cycle during droughts and underscores the need to use multiple indicators and datasets to accurately assess hydrological responses under extreme climatic conditions in the Amazon basin. Full article

The sediment yield resulting from storm erosion has become a focal point of research and a significant area of interest in the upper reaches of the Yangtze River amid changing environmental conditions. The issue of numerous types of erosivity factors (R) in storm erosion sediment yield models, with unclear applicability. This study examines two classical types of erosivity factors: the rainfall erosivity factor (EI₃₀, Zhang Wenbo empirical formula, etc.) and runoff erosivity power. Four combinatorial forms of erosion dynamic factors, encompassing rainfall and runoff elements, were developed. Based on the rainfall, runoff and sediment data of four stations along the Fu River basin–Pingwu station, Jiangyou station, Shehong station and Xiaoheba station from 2008 to 2018, the correlation between different R factors and sediment transport in different watershed areas was studied, and the semi-monthly sediment transport model of heavy rainfall in the Fu River basin was constructed and verified. The results revealed a weak correlation between the rainfall erosivity factor and the sediment transport modulus, making it unsuitable for developing a sediment transport model. In smaller basin areas, the correlation between the combined erosivity factor and sediment transport modulus was strongest; conversely, in larger basins, the relationship between runoff erosivity power and the sediment transport model was most pronounced. The power function relationship between the erosivity factor and sediment transport modulus yielded a more accurate simulation of sediment transport during the verification period, particularly during rainstorms, surpassing that of SWAT. These findings provide a scientific basis for predicting sediment transport during storms and floods in small mountainous basins. Full article

The continuous growth of the social economy and the accelerated urbanisation process have led to a rising increase in the demand for water resources in river basins. The uneven temporal and spatial distribution of water resources has further exacerbated the contradiction between supply and demand. The traditional extensive water resource allocation model is no longer suitable for the diverse demands of sustainable development in river basins. Therefore, there is an urgent demand to determine how to reconcile the supply and demand of water resources in river basins to achieve a rational allocation. Taking the Yellow River Basin as an example, an optimal water allocation framework based on multi-objective robust optimisation method was proposed in this study. A robust constraint boundary conditions for the industrial, agricultural, construction and service, ecological, and social water demand were selected from the perspective of the economy–society–ecology nexus. Then, Latin hypercube sampling was adopted to modify the Monte Carlo method to improve the dispersion of sampling values for quantifying the uncertainty of water allocation parameters. Furthermore, a multi-dimensional spatial equilibrium optimal allocation combining adjustable robust optimisation and multi-objective optimisation was established. Finally, a multi-objective particle swarm optimisation algorithm based on a crossover operator was constructed to obtain the Pareto-optimal solution for multi-dimensional spatial equilibrium optimal allocation. The primary findings were as follows: (1) Parameter uncertainty had a significant effect on the provincial/regional revenues of water resources but has no obvious effect on basin revenue. (2) The uncertainty in runoff and parameters had a significant influence on decisions for optimal water allocation. The optimal volume of water purchased by different provinces (regions) varied greatly under different scenarios. Full article

In semi-arid regions, flood events are often characterized by rapid runoff and high hydrological variability, posing significant challenges for infrastructure safety and flood risk assessment. Traditional flood frequency analysis methods, typically based on univariate models using annual flood peak, may fail to capture the full complexity of such events. This study investigates the limitations of the univariate approach through the analysis of the 2004 flood event in the Jaguaribe River basin (Brazil), which caused the Castanhão Reservoir to receive a discharge of more than 5 hm³ and fill from 4.5% to over 70% of its capacity in just 55 days. Although the peak discharge in 2004 was not an exceptional record, the combination of high flood volume and short duration revealed a much rarer event than suggested by peak flow alone. To improve compound flood risk assessment, a bivariate frequency analysis based on copula functions was applied to jointly model flood peak and average flood intensity. The latter is a variable newly proposed in this study to better capture the short-duration but high-volume flood until peak that can strongly influence dam safety. Specifically, for the 2004 event, the univariate return period of flood peak was only 35 years, whereas the joint return period incorporating both peak flow and average flood intensity reached 995 years—underscoring a potential underestimation of flood hazard when relying solely on peak flow metrics. Our bivariate return periods and the average flood intensity metric provide actionable information for climate adaptation, supporting adaptive rule curves and risk screening during initial impoundment and high-inflow events in semi-arid reservoirs. Collectively, the proposed methodology offers a more robust framework for assessing extreme floods in intermittent river systems and offers practical insights for dam safety planning in climatically variable regions such as the Brazilian Semi-Arid. Full article

(1) Background: Aiming at low-accuracy and unclear parameter differentiation of snowmelt ice melting, rainstorm and mixed flood simulation in Northwest Chinese arid inland river basins, this study aimed to improve complex flood simulation ability and support arid area flood prediction via HEC-HMS model optimization and classification standard innovation. (2) Method: A distributed HEC-HMS model was built using topography, soil and land use data. A “meteorology, hydrology, underlying surface” flood classification method was developed, and runoff generation-concentration parameters were calibrated via trial-and-error and Latin hypercube sampling for 36 historical floods (12 each type) to verify model applicability. (3) Result: The classification accuracy reached 92%. All three flood types met simulation standards: flood peak and runoff depth error ≤ ±20%, peak time error < 3 h, average NSE = 0.76 (snowmelt: 0.82, rainstorm: 0.76, mixed: 0.70). Parameters showed gradient differences: snowmelt (CN = 65, I_a = 20 mm, k = 0.3), rainstorm (CN = 80, I_a = 10 mm, k = 0.5), mixed (parameters in between). (4) Conclusions: After parameter optimization, the HEC-HMS model is suitable for multi-source flood simulation in arid areas, and the revealed parameter laws provide a quantitative basis for flood forecasting in similar basins. Full article

Reliable long-term runoff prediction for Wudongde and Three Gorges reservoirs, two major reservoirs in the upper Yangtze River basin, is crucial for optimal operation of cascade reservoirs and hydropower generation planning. This study develops a data-driven model that integrates large-scale climate factors with a Gated Recurrent Unit (GRU) neural network to enhance runoff forecasting at lead times of 7–18 months. Key climate predictors were systematically selected using correlation analysis and stepwise regression before being fed into the GRU model. Evaluation results demonstrate that the proposed model can skillfully predict the variability and magnitude of reservoir inflow. For Wudongde Reservoir, the model achieved a mean correlation coefficient (CC) of 0.71 and Kling–Gupta Efficiency (KGE) of 0.57 during the training period, and values of 0.69 and 0.53 respectively during the testing period. For Three Gorges Reservoir, the CC was 0.67 (training) and 0.66 (testing), and the KGE was 0.52 and 0.49 respectively. The model exhibited robust forecasting capabilities across a range of lead times but showed distinct seasonal variations, with superior performance in summer and winter compared to transitional months (April and October). This framework provides a valuable tool for long-term runoff forecasting by effectively linking large-scale climate signals to local hydrological responses. Full article

Given significant water scarcity events in the recent past, water resources management in the Piracicaba River Basin, São Paulo, Brazil, has intensified the adoption of complex measures to meet the population’s water supply demands. This study presents a methodology to optimize reservoir water release while adhering to restrictive rules, aiming to also conserve water. A rainfall–runoff model was utilized alongside a hydrological routing model, incorporating meteorological forecasts for simulation over ten consecutive years. The results demonstrated significant water savings when comparing the optimization scenario with the actual reservoir operation during the same period. The applied methodology reduced water releases up to 66% in comparison to the observed scenario. Overall, the study introduces tools to improve reservoir operation with computational techniques, enriching local water resources management, water security, and decision-making processes, ensuring water security for the São Paulo Metropolitan Region, the most populous region in Brazil. Full article

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 and Kling–Gupta Efficiency (KGE) of 0.84 in the multivariate setting at the critical downstream node, Lees Ferry. Compared to the univariate setup, SWE-enhanced predictions reduced Root Mean Square Error (RMSE) by 12.8%. Seasonal and spatial analyses showed the greatest improvements at high-elevation and mid-network stations, where snowmelt dynamics dominate runoff. These findings demonstrate that spatio-temporal learning frameworks, especially STGNNs, provide a scalable and physically consistent approach to streamflow forecasting under variable climatic conditions. Full article