Search Results (723)

Data-driven models can be used to understand basin-wide hydrological processes and generate predictions for future conditions, particularly in cases of scarce data availability related to basin characteristics. Although they have long been applied in hydrological modeling, there is still limited information regarding their ability to produce reliable long-term projections under climate change conditions. This study evaluates the long-term predictive performance of data-driven models by employing a hybrid deep learning architecture combining Wavelet Transform (WT) and Deep Neural Network (DNN). The dataset used in this study was obtained from the Yamula Reservoir Basin, a semi-arid agricultural basin in Türkiye. Monthly streamflow was simulated based on climate projection data from the HadGEM2-ES model under the RCP4.5 and RCP8.5 scenarios. Results showed that the WT–DNN framework was successful in learning the system dynamics and reproducing observed streamflow behavior. The model produced continuous projections for the future period; however, these projections should be interpreted with caution due to the increasing uncertainty associated with long-term climate forcing and the sensitivity of data-driven approaches to shifts in climatic and hydrological regimes. Full article

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year public hydrochemical database from 10 monitoring nodes with SAR-derived discharge estimates and thermodynamic metal modeling (PHREEQC). To validate the structural integrity of the mass load estimates against hydrometric uncertainties, a deterministic boundary-sensitivity analysis was conducted. Results empirically refute the classical dilution paradigm, introducing the “Anthropogenic Pulse” to describe the non-linear acceleration of pollutant export during high-flow events (discharge Q surging from 36.62 to 286.13 m³/s). While climate-driven parameters follow seasonal cycles, industrial stressors (COD, Pb, Cd) remain in a chronic steady state, decoupling from volumetric dilution. Based on coupled × CQ × C (discharge × concentration) estimates, this dynamic induces a synchronized flushing of toxic burdens, exporting monthly peak loads exceeding 51,000 kg of Zinc, 6500 kg of Lead, and 3100 kg of Cadmium. Thermodynamic simulations reveal that this hydrological flushing functions as a chemical activator; the seasonal dilution of natural Alkalinity and Hardness suppresses the river’s theoretical buffered pH (from 8.5 to 7.0), maintaining metals in their uncomplexed free-ion states (Me²⁺). Modeling indicates that nearly 90% of the exported Cadmium remains in this highly labile, toxic form due to a dual coupling with both river Discharge (r_s = 0.87) and pH (r_s = 0.79). The identification of stochastic arsenic peaks 100 times above regulatory limits at Paso de Guadalupe (RS-08) underscores the failure of concentration-based monitoring. Our findings suggest that restoration strategies should shift toward mass-loading-based regulatory frameworks and targeted sediment management at critical nodes to mitigate the chronic export of bioavailable industrial waste. Full article

Large-scale urbanization in China has altered land surface characteristics, affected climate and hydrological cycles, and changed the spatial and temporal distribution of precipitation. The combined effects of global warming and the urban heat island effect have further intensified changes in urban rainfall patterns. Therefore, it is essential to clarify the spatiotemporal evolution of precipitation under China’s rapid urbanization process in order to reduce multiple disaster risks. To achieve this, historical precipitation data and multisource remote sensing imagery were integrated to construct a spatiotemporal coupling model for analyzing the relationship between urbanization patterns and precipitation distribution in China. In addition, combined with the background of global climate change, the spatiotemporal evolution characteristics of annual, monthly, and seasonal precipitation were investigated. The main conclusions are as follows: (1) China still has great potential for urbanization and economic development and is currently in a new stage of rapid growth; (2) During 1992–2020, the national area proportion receiving annual precipitation of (200, 400] mm decreased by approximately 0.12 percentage points per year, whereas the area proportion receiving (400, 800] mm increased by approximately 0.11 percentage points per year, indicating a measurable shift toward wetter precipitation conditions; (3) Heavy rainfall events in China are expected to increase in the future, mainly occurring from June to August, with a maximum monthly precipitation reaching 1137.9 mm; (4) Urbanization may be one of the important factors associated with precipitation changes in China, with 2008 identified as a key turning point, when the urbanization rate approached 50% and began to exhibit a preliminary scale effect. Full article

Under complex and changing environmental conditions, hydrological drought in the upper Hanjiang River (UHR) is becoming increasingly severe, so investigating the variation characteristics and influencing factors of hydrological drought in this basin can provide favorable support for drought prevention and water resources management. In this study, based on monthly runoff data from the Ankang Hydrological Station of the UHR, the mutation change year at the Ankang Station was first identified using the Pettitt mutation test and the B-G segmentation algorithm. Subsequently, the ABCD hydrological model coupled with eight machine learning algorithms was employed to simulate the runoff variation process in the Ankang Station. Finally, we used the Standardized Runoff Index to describe the hydrological drought conditions and quantitatively analyzed the impacts of human activities and climate change on the seasonal hydrological drought in the UHR. The results showed that (1) the coupled machine learning–hydrological model can effectively improve the simulation accuracy of the runoff change process. (2) The coupled ABCD–Random Forest model has the highest accuracy. (3) Hydrological drought exhibits a significant increasing trend in spring and autumn, a significant decreasing trend in winter, and a non-significant increasing trend in summer. (4) Climate change serves as the primary driver of hydrological drought variations across four seasons in the UHR. Full article

This study adapted and applied a spatially distributed analytical model to estimate the annual representative leached fraction and the annual potential leached mass of atrazine in the Cauquenes catchment in Chile under contrasting Mediterranean hydroclimatic conditions. The model was based on van der Zee and Boesten and Rakonjac et al. and was modified to account for the strong seasonality of precipitation and evapotranspiration by using representative daily hydrological conditions derived from monthly averages. Spatially distributed soil, climate, land-cover, and atrazine application data were integrated at the pixel scale, including locally corrected soil organic carbon, hydraulic properties, precipitation, evapotranspiration, leaf area index, and annual atrazine dose. The model was applied to two contrasting years, 2018 and 2023, and outputs were aggregated at the pixel, land-cover, hotspot, and catchment scales. The results showed a marked hydroclimatic control on potential atrazine leaching. In the drier year, 2018, both the annual representative leached fraction and the annual potential leached mass were generally very low across the catchment, whereas in the wetter year, 2023, moderate-to-high leaching values became much more spatially extensive, and hotspot areas expanded substantially. At the catchment scale, potential leached mass increased from 0.088 kg in 2018 to 179.784 kg in 2023, while the percentage of applied mass potentially leached increased from 5.50 × 10⁻⁵% to 0.112%. Land-cover classes influenced the results both through the spatial allocation of atrazine application and through LAI-dependent partitioning of evapotranspiration. Global sensitivity analysis using the Morris method identified K_OC and DT50 as the dominant controls on annual potential leached mass, and spatial uncertainty propagation was performed. Overall, the proposed framework provides a potential annual screening estimate and may serve as a preliminary screening tool to prioritize areas for targeted monitoring and future model benchmarking in Chile. Full article

Droughts are complex extreme phenomena that severely impact regional development and water availability. Although the influence of interannual and decadal macroclimatic patterns, such as the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on precipitation alteration is widely recognized, current water management systems lack multivariate predictive approaches to anticipate their phases with sufficient operational lead time. This study developed a predictive framework to project ENSO and PDO phases, establishing an optimal temporal window to forecast drought-triggering conditions. Using monthly historical records, teleconnections were evaluated through cross-correlation and Granger causality. Subsequently, Vector Autoregression (VAR) models and machine learning algorithms (Random Forest) were implemented to project anomalies and classify climatic phases. The Granger causality test demonstrated that ENSO variations statistically precede PDO phase shifts, establishing an optimal forecasting window of three to four months. The VAR model exhibited robust joint explanatory capacity for a continuous four-month projection, while the Random Forest algorithm achieved a predictive accuracy of 52.2% specifically for categorical phase classification at a three-month lead time. It is concluded that this lagged interaction allows for reliable mathematical anticipation, providing an essential analytical framework for exploring regional hydrological dynamics and supporting local preventive water management. Full article

Climate change remains among the most pressing environmental challenges confronting the world, exerting profound pressure on both ecological systems and socio-economic development. To advance understanding of the evolution patterns and driving mechanisms governing hydroclimatic systems in arid and semi-arid regions, this study employed an integrated framework encompassing trend testing, change-point detection, periodicity and persistence analysis, and machine learning-based attribution. Focusing on the Mu Us Sandy Land from 1982 to 2023, we systematically investigated the spatiotemporal evolution, periodic characteristics, and driving mechanisms of hydroclimatic factors. Furthermore, future climate risks were assessed using CMIP6 multi-model data. The results showed that: (1) All four variables exhibited positive slopes, but only soil moisture showed a statistically significant long-term wetting trend (β = 0.025 × 10⁻³, p = 0.0008) and a clear global abrupt change in 2011; the upward tendencies of precipitation (p = 0.3946), potential evapotranspiration (p = 0.4970), and surface runoff (p = 0.1097) did not reach the 0.05 significance level. (2) Meteorological elements showed weak periodicity and strong anti-persistence (mean Hurst index = 0.379 for precipitation and 0.222 for PET), whereas hydrological elements exhibited clear seasonal–interannual periods and more random future variability with greater spatial heterogeneity (mean Hurst index = 0.436 for runoff and 0.414 for soil moisture). (3) Monthly changes were mainly associated with local surface processes. Vegetation dynamics were key predictors of precipitation, runoff, and soil moisture, while potential evapotranspiration was dominated by atmospheric demand, with limited influence from large-scale climate indices. (4) Under high-emission scenarios, imbalanced water–heat increases may lead to a higher likelihood of drought conditions. Full article

Quantifying changes in groundwater storage (GWS) remains a fundamental challenge in hydrology, given the sparsity of long-term in situ monitoring networks and the inherent difficulty of direct subsurface observation. Although GRACE and GRACE-FO satellite missions provide a means of tracking total terrestrial water storage at the continental scale, their coarse spatial resolution (~300 km) and monthly temporal sampling limit their direct applicability to regional groundwater studies. Here, we present a spatio-temporal disaggregation and data fusion framework for reconstructing daily GWS anomalies (GWSAs) across the Korean Peninsula, integrating GRACE/GRACE-FO Mascon solutions with the GLDAS Catchment Land Surface Model (CLSM). The approach leverages satellite-derived mass variations to constrain the model’s long-term anomaly structure while retaining the high-frequency temporal dynamics of land-surface modeling. The framework is evaluated against in situ bedrock monitoring well records from five sites: Seoul, Cheongyang, Uiseong, Imsil, and Wonju. Raw time-series correlations range from R = 0.14 to 0.70; upon removal of the monthly climatology to isolate non-seasonal variability, R improves to 0.49–0.72 across all sites, reaching 0.718 in Seoul and 0.707 in Cheongyang, with Cheongyang’s RMSE declining from 8.847 to 7.574 cm. These results indicate that the GRACE-CLSM fusion framework captures genuine sub-monthly groundwater dynamics beyond the dominant seasonal cycle. To our knowledge, this represents the first reconstruction of daily GWS changes for the Korean Peninsula with explicit preservation of spatial mass conservation, and the resulting dataset has direct utility for operational groundwater monitoring in a region subject to hydroclimatic variability. Full article

This study presents an updated Hybrid Hydrological Forecasting System (HHFS) for streamflow prediction at the Santa Branca outlet, located in the upper Paraíba do Sul River Basin in southeastern Brazil, aiming to support hydropower-oriented water resources management. This paper is explicitly framed as a companion paper which introduced the original HHFS framework and demonstrated the feasibility of combining deterministic and probabilistic machine-learning approaches for monthly streamflow forecasting. Building upon that foundation, the present study develops and validates a substantially enhanced and operationally oriented version of the system. The upgraded HHFS replaces the original BR-DWGD forcing strategy—a Brazilian gridded meteorological dataset useful for research applications but not routinely updated for sustained operations—with ERA5, the fifth-generation global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which provides temporally consistent and operationally updated meteorological fields. This transition renders the framework fully operational while preserving the original dual-stage architecture, composed of a deterministic forecasting module (GA₁) and a hydro-adaptive uncertainty module (GA₂). In addition, the study introduces a daily short-term forecasting extension using a single multi-output XGBoost 2.1.1 model to predict streamflow from D+1 to D+10. Predictive uncertainty is quantified using split conformal prediction, a distribution-free uncertainty method that provides valid prediction intervals with empirical coverage guarantees. Coverage represents the proportion of observed values falling within the prediction intervals and is used here as a reliability metric. For the monthly product, the ERA5-based methodology maintained and slightly improved deterministic skill relative to the original BR-DWGD benchmark, with independent-test NSE increasing to 0.798, KGE to 0.878, and RMSE decreasing to 18.778 m³/s. The probabilistic component preserved a high hit rate and similar relative width, although coverage declined modestly to 0.838, indicating slight undercoverage relative to the previous reliability target. For the daily forecasts, predictive skill decreased progressively with lead time, from NSE = 0.881 at D+1 to 0.394 at D+10, accompanied by coherent widening of the uncertainty intervals. Taken together, these results demonstrate that ERA5 is a robust and operationally practical forcing source for the HHFS, preserving monthly forecasting skill while enabling a promising multi-day extension for anticipatory streamflow prediction across multiple temporal scales. Full article

Hydropower-dominated electricity systems are increasingly exposed to hydroclimatic variability, making anticipatory streamflow information essential for energy security, operational resilience, and sustainable planning. This study develops a transparent monthly early-warning framework for the Paute River basin, Ecuador, a strategically important hydrological system for national hydropower generation. Using a 42-year series of observed and compiled monthly streamflow records from 1984 to 2025 (n = 504), the framework derives seasonal low-flow thresholds (P20 warning and P10 critical) and fits a Seasonal Autoregressive Integrated Moving Average model to log-transformed flows. The resulting lognormal predictive distribution provides point forecasts, prediction intervals, and probabilities of low-flow events. Predictive skill was assessed through a 2016–2025 rolling-origin validation with 120 one-step-ahead forecasts and benchmarks against Error–Trend–Seasonal Holt–Winters and seasonal naive models. The SARIMA-log specification achieved the best point accuracy (MAE = 38.80 m³/s, RMSE = 47.62 m³/s, sMAPE = 32.63%) and modest but useful probabilistic skill (CRPSS = 0.069; Brier Skill Score = 0.169 for Q < P20 and 0.274 for Q < P10). A threshold-sensitivity analysis showed that the 0.15 and 0.30 alert thresholds represent a deliberate trade-off between early detection and false-alarm reduction. For 2026, August displayed the highest low-flow probability (P(Q < P20) = 0.303), triggering a moderate Hydropower Low-Flow Risk Traffic-Light category. The contribution is not a new forecasting algorithm but an operationally auditable integration of seasonal thresholds, probabilistic forecasting, verification, and risk communication for hydropower energy-security governance in the tropical Andes. Full article

Accurate temperature prediction is essential for understanding climate variability and hydrological extremes. In this context, Long Short-Term Memory (LSTM) networks have become a widely adopted tool for temperature forecasting; however, their performance strongly depends on hyperparameter selection. This study proposes a combinatorial optimization framework that integrates the Simulated Annealing (SA) algorithm with LSTM networks to enhance long-term temperature forecasting performance. To evaluate the proposed approach, monthly temperature data (1927–2024) from the Turkish State Meteorological Service (MGM) were used. A spatial hold-out strategy (57 training and 24 testing provinces) was employed to assess generalization performance. Model performance was evaluated using MAE, RMSE, R², and NSE. Results indicate that the SA-LSTM model significantly improves prediction accuracy compared with the conventional LSTM configuration. The optimized model achieved lower prediction errors (MAE = 2.56; RMSE = 3.42) and higher agreement metrics (R² = 0.856; NSE = 0.848) on the independent testing dataset. These findings demonstrate that combinatorial hyperparameter optimization enhances the robustness and predictive capability of deep learning models for large-scale temperature forecasting and provides a robust and reliable tool for climate and hydrological modeling. Full article

The Pinios River Basin, located in the water district of Thessaly in central Greece, is one of the most water-stressed agricultural regions in the country. This study investigates the spatio-temporal characteristics of drought in the basin using combined ground observations and remote sensing data over the common period October 1981–September 2002. Meteorological drought is assessed through the Standardized Precipitation Index (SPI) and the Standardized Precipitation–Evapotranspiration Index (SPEI), while hydrological drought is analyzed using the Standardized Runoff Index (SRI) in the Ali Efenti sub-basin of the Pinios River Basin. Ground-based station precipitation and temperature data were interpolated to a 5 km × 5 km grid using a multiple linear regression (MLR) approach and compared with CHIRPS satellite precipitation and ERA5 reanalysis temperature on the same grid. SPI and SPEI were calculated at multiple accumulation periods (1–12 months) from both ground-based and satellite-based datasets. Three major multi-year drought episodes (1988–1989, 1989–1990 and 2000–2001) were identified, with long duration, large spatial extent and of severe to extreme intensity. Satellite-based indices reproduced the timing and main spatial patterns of these events but tended to yield stronger drought magnitudes than ground-based indices. In the Ali Efenti sub-basin, SRI derived from simulated runoff using the calibrated University of Thessaly monthly water Balance model (UTHBAL) showed a clear propagation of meteorological deficits into streamflow drought with a short time lag. In the Ali Efenti sub-basin, the strongest linkage between meteorological and hydrological drought occurs at seasonal time scales (SPI-3/SPEI-3), with SRI-1 correlating best with SPI-3 (r = 0.67) and SPEI-3 (r = 0.63), indicating rapid drought propagation and supporting the use of 3-month indices for early warning of streamflow drought. Full article

This study investigated spring water in the core area and buffer zone of the Shibing Dolomite Karst World Heritage Site using one-year monthly monitoring, hydrochemistry, nitrate dual isotopes, and the MixSIAR model. The buffer zone spring exhibits shallow fissure-conduit flow with rapid hydrological response, anthropogenic nitrate dominance (>62%), nitrification as the main process, and limited denitrification. Its nitrate concentration shows seasonal peaks. In contrast, the core area spring is recharged by deep fissure water, with natural nitrate sources (>80%), stable nitrate levels (5–7.4 mg/L), and potential local denitrification. Nitrogen export in the buffer zone increases 4.5 times in the rainy season (NO₃⁻ accounting for 93% of TN). The core area shows higher TN export flux per unit area (3.34 vs. 0.4 g/m²/a) and greater DON proportion. Nitrogen export far exceeds that from rocky desertified areas, suggesting that dissolved nitrogen leaching drives karst rocky desertification evolution. Full article

Assessing hydropower potential in ungauged basins requires consistent derivation of key hydrological variables from heterogeneous geospatial and climatic data. Conventional GIS-based approaches often rely on fragmented, user-dependent workflows, limiting reproducibility and comparability. This study presents HYDROPOT, a web-based geospatial framework for the automated and reproducible extraction of hydrologically relevant basin descriptors for regional-scale hydropower screening. The platform integrates centralized datasets with server-side geoprocessing to delineate upstream catchments and compute quantitative basin descriptors, including drainage area (2–400 km²), Curve Number (CN), concentration time, and spatially aggregated monthly thermo-pluviometric variables derived from 95 stations over the 2004–2022 period. These descriptors provide essential inputs for rainfall–runoff modeling and preliminary discharge estimation, thereby supporting (although not directly performing) the assessment of water availability in ungauged basins. By eliminating manual preprocessing, HYDROPOT ensures consistent and reproducible analyses, reducing user-induced variability and improving comparability across applications, without implying increased predictive accuracy. The framework, applied to the Lazio Region (Central Italy) over the 2004–2022 period, enables rapid and transparent screening of river reaches, offering a scalable decision-support tool for preliminary, input-based screening in early-stage small hydropower planning. Full article

Under the influence of climate change and human activities, hydrologic regime and runoff in the upper Yangtze River basin (UYRB) have exhibited significant alterations. This study aims to address the primary drivers of runoff change and the destination of runoff reduction. Based on hydro-meteorological data from 1980 to 2022 and other related datasets, the temporal trend in hydro-meteorological variables was analyzed, and the impacts of climate change and human activities on runoff were quantified using the SWAT model. The destination of runoff reduction was also addressed based on the water balance equation. The SWAT model was calibrated using a top-down sequential strategy at five hydrological stations. The results show that despite a slight increase in precipitation and a pronounced rise in potential evapotranspiration, the annual average runoff at Yichang station is decreased by 22.3 billion m³. The SWAT model can simulate the monthly runoff hydrograph well with the NSE exceeding 0.85 during calibration and validation periods in the UYRB. Attribution analysis reveals that the contribution rate of climate change and human activities on runoff are 36.21% and 63.79% at the Yichang station, respectively. The annual average runoff change can be attributed to four pathways: (1) actual evapotranspiration increases due to land use and land cover (LULC) change and basin greening (−12.85 billion m³); (2) water intake and consumption increase (−2.94 billion m³); (3) reservoir dead storage impoundment (−3.34 billion m³); and (4) ground water storage variations (−3.21 billion m³). These findings highlight the impact of human water abstraction and land use change on runoff, providing a scientific basis for water resource management in the UYRB. Full article