Search Results (506)

Global streamflow reanalyses such as GloFAS-ERA5 are available everywhere yet lose fidelity in small, snow- and glacier-fed headwaters that feed Central Asia’s transboundary rivers. We present an explainable, calibrated deep ensemble framework that corrects the GloFAS-ERA5 log-residual at gauges of the Syr Darya and Amu Darya systems using the CA-discharge archive. An entity-aware long short-term memory (LSTM) backbone drives a regime-gated mixture of experts trained under a closed-form mixture continuous ranked probability score (CRPS) and augmented with snow physics constraints and a regime-conditional (Mondrian) conformal layer; skill was assessed under temporal holdout, leave-one-basin-out and prediction in ungauged region protocols, with grouped Shapley value attribution. Correction rendered all 74 gauges skillful, raising the median modified Kling–Gupta efficiency (KGE′) from 0.386 (raw) to 0.825 (flagship); on temporal point skill the framework is statistically tied with gradient boosting (paired Wilcoxon p = 0.49). Under-dispersed raw intervals (90% coverage 0.68) were recalibrated to near-nominal coverage (~0.90), and high-flow exceedance decision skill was moderate (Q90 Brier skill score 0.34, ROC-AUC 0.92), while low-flow (Q10) exceedance showed no skill over climatology. Transfer to ungauged, more glacierized catchments was a measured limit that degraded with glacier fraction and basin area. The framework’s value is calibration, an inspectable (supervised) regime structure and regional physical insight, not point skill superiority. Full article

This study investigates the impacts of human activities and climate variability on hydrologic alterations in the Mobile River and Perdido River Basins of Alabama. The research uses a machine learning approach, specifically cubist models, to quantify and predict changes in flow duration curves (FDCs) under both historical (1980–2009) and future climate scenarios. Future climate projections include the Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and the Shared Socioeconomic Pathways (SSP2 4.5 and SSP5 8.5), evaluated for two future periods: 1980–2069 and 1980–2099. The models incorporate a wide range of covariates, including basin geomorphology, aquifer characteristics, land cover, water storage, environmental factors, solar radiation, census data, and water use data. Under the baseline period (1980–2009), most level 12 hydrologic unit codes (HUC12s) in both basins showed alterations, with substantial differences observed between pre- and post-alteration FDCs. The model performance varied, with a Nash–Sutcliffe Efficiency between 0.91 and 0.95 for testing and between 0.98 and 0.99 for training during the baseline period. Future projections under the RCP 4.5 and RCP 8.5 scenarios generally differed significantly from baseline conditions across all flow regimes (p < 0.05). In contrast, SSP2 4.5 showed comparatively limited statistical significance, while SSP5 8.5 exhibited significant departures from baseline conditions across all flow regimes, reflecting the greater influence of high-emissions climate forcing on projected hydrologic alterations. Overall, the RCP scenarios projected more widespread statistically significant changes than the corresponding SSP scenarios at the same forcing level, particularly when comparing RCP4.5 with SSP2-4.5, while both RCP8.5 and SSP5-8.5 consistently indicated greater hydrologic alterations than their moderate-emissions counterparts. These findings highlight the importance of considering different flow regimes when assessing the impacts of climate variability on streamflow. This study contributes to the understanding of hydrologic alterations in the Mobile River and Perdido River Basins, providing insights for water resource management and ecological conservation efforts in the region. Full article

Accurate streamflow prediction is an important part of disaster management, as forecasting flow rates is an indispensable element of early warning systems, yet it remains challenging due to the complexity and nonlinearity of climatic inputs. This study evaluated the performances of Long Short-Term Memory (LSTM) and random forest (RF) machine learning algorithms for daily streamflow prediction across sixteen hydroclimatically diverse basins in the contiguous United States using the CAMELS dataset. The models were trained on five climatic features augmented with antecedent streamflow lag features at 7-, 14-, and 30-day intervals. The random forest algorithm demonstrated a better accuracy, achieving an average Nash–Sutcliffe efficiency (NSE) of 0.755 compared to 0.632 for the LSTM model. Both models performed well in the snowmelt-dominated basins and were least effective in flashy humid regimes. Additionally, both models exhibited high precision for flood detection, with accuracy rates exceeding 88% for distinguishing flood events and F1 scores of 0.734 and 0.797 for LSTM and RF, respectively. These results recommend RF for operational streamflow forecasting across hydroclimatically diverse settings and LSTM for perennial snowmelt- and groundwater-influenced catchments where long-range temporal dependencies govern runoff generation. Full article

Accurate streamflow forecasting is critical for water resource management and disaster mitigation, yet conceptual models often suffer from systematic biases while machine learning lacks physical interpretability. This study proposes a coupled XAJ-LSTM model to integrate the mechanistic strengths of the Xin’anjiang (XAJ) model with the predictive capability of Long Short-Term Memory (LSTM) networks. Applied in the Woluo River basin using an 11-year daily record from 2012 to 2022, the framework was evaluated with leave-one-year-out validation. Results indicate that XAJ-LSTM achieved the highest full-period NSE of 0.885 and the lowest full-period RMSE of 19.48 m³/s, compared with NSE values of 0.867 for XAJ and 0.724 for LSTM. XAJ-LSTM also performed best during the flood period, with NSE of 0.820 and RMSE of 28.81 m³/s. The SHAP framework indicated that XAJ-simulated flow and precipitation remained the dominant predictors, suggesting that the LSTM relied strongly on the conceptual-model baseline and rainfall information. These findings indicate that XAJ-LSTM can improve selected full-period and flood-period diagnostics for daily streamflow simulation in the Woluo River Basin, while further tests with longer records and additional catchments are needed before broader application. Full article

Climate change introduces severe non-stationarity and high-frequency noise into hydro-meteorological data. This noise degrades the predictive accuracy of traditional data-driven streamflow models. We propose a physics-inspired data enhancement framework coupling the CEEMDAN-based Hilbert-Huang Transform (HHT) with Stochastic Resonance (SR). We applied this framework to the Lanzhou section of the upper Yellow River. HHT isolates the dominant characteristic frequency of the basin’s streamflow system at 0.0026 cycles/day. Using this frequency as a target, we constructed a Bayesian-optimized SR system. The system converts the energy of high-frequency meteorological noise into low-frequency periodic components, facilitating frequency alignment between the meteorological inputs and the hydrological response. We evaluated the SR-enhanced meteorological inputs across three machine learning architectures: Random Forest, XGBoost, and LSTM. All algorithms demonstrated an improved performance. The SR-LSTM model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.91 ± 0.03. This represents a 19% improvement over the baseline LSTM score of 0.79 ± 0.02. The SR-LSTM demonstrated robust accuracy during extreme hydrological events; it achieved a high-flow NSE of 0.89 and effectively mitigated the common peak-underestimation issue by constraining relative peak magnitude errors to approximately −5.08%. Overall, this study presents a practical data enhancement approach for streamflow forecasting under complex climatic conditions. Full article

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

Accurate streamflow prediction in small catchments remains challenging due to their rapid response times, threshold-driven behaviors, and high spatial heterogeneity. This study develops and evaluates a novel modeling approach combining physics-informed feature selection with machine learning algorithms. Overall, 1825 model configurations were tested across fifteen algorithms (including Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines, and deep learning methods) using multiple physics-informed input structures based on classical rainfall–runoff theory and mass balance conservation. Models were evaluated for predicting minimum, average, and maximum daily water levels and discharge. Results demonstrate that models structured around Green-Ampt infiltration assumptions consistently outperformed alternative configurations, with Random Forest achieving good performance for water level predictions. Causal models outperformed autoregressive approaches while the residuals analysis showed limitations in predicting extreme values. Feature importance analysis revealed that channel and catchment morphology and initial soil moisture conditions were dominant predictors, aligning with hydrological process understanding. Full article

Seasonal streamflow predictability in alpine catchments is governed by the interplay between initial hydrological conditions (IC) and climate forcing (CF), with catchment geology exerting a modulating influence. This study applied the Ensemble Streamflow Prediction (ESP)/reverse ESP (revESP) framework, which isolates predictability arising from IC and CF, respectively, to two alpine catchments of differing geology and subsurface storage, resulting in markedly different hydrological behavior. The results were contrasted with End Point Blending (EPB) experiments that quantify the relative contributions of IC and CF to the forecast skill. The two approaches exhibited strong agreement under well-defined hydrological regimes but diverged during transitional periods, highlighting implications for operational seasonal forecasting and reservoir management in snow-dominated regions. Full article

Understanding how climate variability is reflected in streamflow is essential for reservoir management and hydropower planning. This study investigated how temporal scale influences climate–streamflow relationships, persistence characteristics, and predictability in two Brazilian reservoirs: Três Marias (São Francisco Basin) and Serra da Mesa (Tocantins Basin). Monthly streamflow and climate-index records (Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Antarctic Oscillation (AAO)) from 1979–2020 were analyzed using a 12-month moving average (MA12) filter to emphasize low-frequency variability. Temporal filtering strengthened climate–streamflow relationships, particularly for PDO and AAO, revealing signals that were less apparent in the original monthly series. Lagged-correlation analyses identified contrasting persistence structures between the reservoirs. Três Marias exhibited multi-year persistence timescales (22–27 months), whereas Serra da Mesa showed shorter and more heterogeneous response timescales, ranging from an immediate PDO response to approximately 14–19 months for ENSO and AAO. Forecasting experiments using benchmark models (Persistence and Linear Regression) and deep learning architectures (LSTM and TCN) showed limited predictive skill on the raw monthly series but substantially improved performance after temporal filtering. For the MA12-filtered series, the benchmark models achieved the highest performance in both reservoirs ($R^2\approx 0.95$ in Três Marias and $R^2\approx 0.93$ in Serra da Mesa). Overall, the results indicate that temporal scale strongly influences the detectability of climate signals, the persistence of streamflow variability, and the predictability of reservoir inflows. Full article

Quantifying the impact of land use changes on the threat of flash-floods is a critical consideration in flood hazard planning and risk reduction, and is an area of active research. Here, a coupled Weather Research and Forecasting model hydrological extension package (i.e., WRF-Hydro) modeling approach is applied to simulate flash-flooding processes for short-duration, localized, intense precipitation events. To better understand the effect of urbanization on flash floods, a series of numerical experiments is performed surrounding Ellicott City, Maryland, a location which has experienced both significant heavy rainfall events and suburban development over the past several decades. Two intense rainfall events occurring on 30 July 2016 and 27 May 2018 are investigated, respectively, to first calibrate the hydrologic model performance and then quantify the sensitivity of flash flooding to varying degrees of urbanization. Performing the same experiments using observed historical land use states is of more limited insight, as the thrust of suburban development in the Ellicott City region significantly predates satellite-derived land use datasets. Results confirm that urbanization produces larger river streamflow, higher water stages, faster hydrologic responses to achieve peak flow discharge, and shorter recession limbs, even for very intense, short-duration events. The collective findings suggest that WRF-Hydro is applicable for both watershed flash flood prediction and hypothesis testing, and demonstrates potential utility to urban development decision-makers in locations such as Ellicott City, which could face future increases in catastrophic flooding. 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

As one of the key water systems in Bosnia and Herzegovina, the Bosna River Basin plays a vital role in sustaining agricultural production, industrial development, and water supply for municipalities. Accurate streamflow forecasting is fundamental to optimising water resource planning. This study explores streamflow forecasting using long-term data (1961–2020) from five meteorological stations and one hydrological station distributed across various sections of the basin. For precise streamflow forecasting, the study employs several machine-learning models: Random Forest, LSTM, and XGBoost. Model performance is evaluated using widely used metrics, including mean absolute error, root mean square error, Nash–Sutcliffe efficiency (NSE), and Kling–Gupta efficiency (KGE). Among the tested models, Random Forest proved to be the most accurate for streamflow forecasting, confirming its effectiveness in capturing the complex dynamics of hydrological processes. During the testing phase, the Random Forest model achieved an NSE of 0.591 and a KGE of 0.591, demonstrating good generalisation and reliable predictions. The results demonstrate the strength of Random Forest in capturing nonlinear hydrological patterns and supporting reliable streamflow forecasting for national water management. Moreover, as a novel approach, explainable AI was applied using SHAP analysis to go beyond the regular predictions of the models, thereby providing a deeper understanding of the model’s performance described by the magnitude and direction of influence of each problem feature. Full article

Numerical simulations quantify the transient impacts of implementing green infrastructure (GI) grass swales on unconfined aquifer storage and groundwater-surface water interactions around the Red Butte Creek (RBC) of Utah, USA. The Red Butte Creek Watershed (RBCW) transitions from undeveloped mountainous National Forest land to downstream urbanized areas within Salt Lake Valley (SLV). This reconnaissance-level study demonstrates that increasing stormwater infiltration in urbanized areas during the rainy months (April-June) can, until at least the subsequent March, (a) enhance aquifer recharge and support sustainable groundwater yields; and (b) improve surface water availability. Simulations predict hydrologic impacts of aquifer recharge resulting from hypothetical grass-swale implementation within a 704-acre area located around RBC. The employed model, HyperRBC, is an adaptation of a United States Geological Survey (USGS) transient numerical flow, MODFLOW, model implementation for SLV. Adaptations involved (a) uniformly refined horizontal discretization of seven aquifer layers within a sub-area encompassing parts of RBCW and an adjacent watershed; (b) updated input data; and (c) MODFLOW’s Streamflow-Routing (SFR) package to simulate RBC flow and aquifer-stream seepage. Model predictions indicated that by the end of next March: (a) about 3% of the GI-induced recharge would remain within the unconfined aquifer in the HyperRBC area; (b) 66.6% of the recharge would flow northward into the downgradient continuation of the unconfined aquifer; and (c) 30.3% would discharge to nearby stream and river. In summary, predicted hydrologic changes due to the short-term GI-induced recharge highlight increased groundwater availability within and outside the study area for at least the subsequent 12 months, including high-water-demand summer. These findings show the importance of GI in interim environmental management and in enhancing the effective use of water resources. Full article

Climate change poses a significant threat to water resources and agricultural sustainability, particularly in semi-arid and socio-economically vulnerable regions such as South Africa. This review synthesizes empirical, modelling, and policy-based evidence on the impacts of climate change on water availability, crop production, and adaptation strategies in the country, drawing on approximately 162 peer-reviewed studies and institutional reports published between 2010 and 2025. The findings indicate that rising temperatures, shifting rainfall patterns, and an increasing frequency of extreme events, such as droughts and floods, are intensifying water stress and disrupting agricultural systems. Hydrological models consistently project declines in runoff, soil moisture, and streamflow, while crop simulation models predict reductions in the yields of major staple crops, including maize, wheat, and sorghum, particularly under high-emission scenarios. Although localized improvements in water availability and crop productivity may occur, these tend to be limited and highly context-specific. In response, South Africa has implemented a range of adaptation strategies, including climate-smart agriculture, water-efficient irrigation, ecosystem-based approaches, and policy-driven interventions. However, their effectiveness remains constrained by institutional fragmentation, limited financial capacity, and persistent socio-economic inequalities, particularly among smallholder farmers. The review underscores the need for integrated, inclusive, and context-specific adaptation strategies that strengthen governance, enhance the science–policy interface, and improve access to climate finance. The insights provided offer valuable guidance for advancing climate resilience in South Africa and other vulnerable regions across the Global South. Full article