Search Results (451)

The primary aim of this study is to identify the driving mechanisms behind long-term water-level changes and drought–flood transitions in Dongting Lake. To achieve this, we employed methods including the Improved Standardized Water Level Index (ISWI), Mann–Kendall test, Sen’s slope estimator, and a random forest–SHAP model to analyze hydro-meteorological data from 1992 to 2023. The results demonstrate a significant overall decline and spatial heterogeneity in water levels, alongside a systemic shift in the regional pattern from flood-dominated conditions to frequent droughts with intense drought–flood abrupt alternations. Crucially, during the critical autumn water recession period, runoff anomalies from the Yangtze River’s three outlets emerged as the dominant factor driving water-level changes, far exceeding the influence of local precipitation. Furthermore, a recent downward shift in the water level–discharge relationship indicates that under identical inflow conditions, water levels are now 1.5 to 2.0 m lower than in previous decades. These general findings highlight that critical-period inflow reductions and altered boundary hydrodynamic conditions mutually amplify low-water-level risks, providing a scientific reference for adaptive water resource management in complex river-connected lakes. Full article

This study presents a fire risk prediction framework applied to two conservation units within the Atlantic Forest biome (AFb): Serra da Gandarela National Park (PNSG), Minas Gerais, and Campos de Palmas Wildlife Refuge (RVSCP), Paraná. Daily climate data (2001–2023), remote sensing vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Multi Band Drought Index (NMDI), fire foci, and estimates of soil volumetric moisture were integrated to analyze the climatic and environmental drivers of fire occurrence and to develop predictive models. Sea Surface Temperature (SST) anomalies in the Niño 3.4 region revealed the influence of El Niño–Southern Oscillation (ENSO) variability on local hydrometeorological dynamics. Vegetation indices and soil moisture data reinforced this relationship, with NMDI values below 0.4 and sharp declines in volumetric moisture indicating water stress during the dry season. Kernel density maps identified clusters of fire foci during this period, confirming the strong seasonality of fire occurrence. Based on climatic predictors and environmental indicators, fire risk indices were developed for each conservation unit and validated using independent data. Model performance showed moderate explanatory capacity, with coefficients of determination ranging from 0.53 to 0.68 and high agreement between estimated and observed values. Validation stratified by ENSO phases (Neutral, El Niño, and La Niña) demonstrated stable performance across contrasting climatic regimes, indicating temporal resilience of the modeling framework. Overall, the integration of climate data, spectral indices, and soil moisture information improves the ability to anticipate fire risk in Atlantic Forest conservation units, providing a useful tool to support prevention, monitoring, and decision-making in protected areas. Full article

Urban cities have been intensely prone to floods during extreme rainfall events and water scarcity issues during dry periods in recent years. In this context, identifying rainwater harvesting potential (RWHP) regions in urban environments provides a sustainable approach to mitigate both urban flooding and water security, thereby improving urban stormwater management. Geospatial mapping of RWHP has tried to consider various hydrometeorological, topographical and other geospatial datasets, but integrating socio-economic factors over urban environments has not been explored much. The present study integrated remote sensing and hydrological-based information, such as slope, soil type, drainage density, geomorphology, topographic wetness index (TWI), land use land cover (LULC), rainfall, runoff coefficient, proximity to roads, and proximity to settlements for geospatial mapping of RWH potential zones for Hyderabad city using multi-criteria decision analysis (MCDA) and weighted overlay analysis (WOA). The resulting RWH potential map indicates that 80.20% of the area falls within the “low” potential category, 17.53% as “moderate”, 2.0% as “very low”, and only 0.25% as “high” potential, mainly in the southeastern portion near the Hussain Sagar outlet. These categories are spatially verified using Sentinel-2 LULC and Google Earth imagery to assess the qualitative plausibility of the mapped RWH potential zones. Northwestern areas, with loamy soils and mild slopes, demonstrate suitability for rooftop collection and percolation structures, highlighting the effectiveness of the proposed modelling framework for sustainable stormwater management for urban environments. Full article

The Upper Indus Basin (UIB) plays a crucial role in water security and socio-economic development in Pakistan. Under changing climatic conditions, the sustainable management of the water resources of the UIB needs accurate and reliable projections of hydroclimatic variables. Given the limited assessments of hydroclimatic simulations from CMIP6 models in the region, this study assessed the uncertainties associated with the historical simulations of 16 CMIP6 GCMs in the UIB. The observations of 34 in situ weather stations were used as reference, while the performances of GCMs were assessed based on widely used evaluation indices, including correlation coefficient (CC), bias, relative bias (rBIAS), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Taylor diagram, and the performance diagram. Results of the evaluation indices indicated that most of the considered GCMs failed to represent the observed precipitation in the UIB. Correlations between the simulations of GCMs and the reference observations were generally low; CCs ranged from −0.24 to 0.16. All GCMs exhibited negative NSE values (ranging between −2.79 and −0.51). The values of RMSE (59.36 to 98.43 mm/month) and rBIAS (9 to 96%) were also very high. Among the considered GCMs, INM-CM4-8 and EC-Earth3-Veg-LR showed comparatively lower RMSE values, moderate rBIAS, and higher CC values. Three GCMs (MRI-ESM2-0, GFDL-ESM4, and CNRM-CM6-1) performed very poorly, with high negative NSE and significant overestimations. Among the 16 GCMs, EC-Earth3-Veg-LR had the highest composite score and better performance across all considered indices. The overall findings of this study indicated that none of the CMIP6-based GCMs (in their raw form) performed satisfactorily in the UIB of Pakistan; therefore, the application of bias-correction techniques is strongly recommended before direct application of these projections for climate impact and adaptation studies in this mountainous region. The results will be useful for the hydroclimatic data users and algorithm developers of global climate models. Full article

Intensifying hydroclimatic extremes have heightened the need for basin-scale indicators of antecedent wetness that are relevant to flood responses. However, ground-based soil-moisture observations are spatially sparse, and satellite products frequently exhibit temporal gaps. To address this limitation, this study integrated satellite- and ground-soil-moisture observations, hydro-meteorological variables, and observed streamflow data from 2018 to 2024 across 26 standard basins (SBs) within three dam basin regions in South Korea: the Nam River Dam (NGD) and the upstream and downstream regions of the Seomjin River Dam (SJD). Using this integrated dataset, we quantified the relationships among precipitation, basin wetness, and rapid discharge increases, subsequently deriving composite thresholds for flood early warnings. For each SB, we trained a Random Forest regression model using satellite-soil-moisture and basin-representative hydro-meteorological inputs—including 1-day accumulated precipitation (P_1d), 7-day accumulated precipitation (P_7d), the antecedent precipitation index (API), and related meteorological variables—to estimate a continuous, daily basin-representative soil-moisture series (SM_RF). Validation results indicated that the coefficient of determination (R²) ranged from 0.6 to 0.7 for most SBs. Extreme event days were consistently associated with elevated values of SM_RF, P_1d, P_7d, and API, demonstrating that antecedent wetness significantly influences the likelihood of rapid discharge events. Finally, composite threshold scanning yielded candidate rules characterized by high precision, moderate hit rates, and low false-alarm rates, confirming the efficacy of the proposed framework for developing flash-flood early-warning thresholds in South Korean dam basins. Full article

Urban floods have become a major systemic risk to sustainable urban development under climate change and increasingly frequent extreme hydro-meteorological events. Yet evidence on the long-term evolution of urban flood resilience (UFR) and its structural constraints at the provincial scale remains limited. This study develops a PSR-based framework to assess UFR and diagnose its dominant obstacles using data for 21 prefecture-level cities in Sichuan Province from 2011 to 2023, including meteorological, geomorphological, socioeconomic, infrastructure, environmental, and public service indicators. A combined AHP–EWM is used to integrate subjective and objective information, TOPSIS is applied to derive a composite UFR index and subsystem scores, and an obstacle degree model is employed to identify key constraints and their temporal evolution. Results show that: (1) UFR in Sichuan Province fluctuated but increased overall during 2011–2023, reaching its highest level in 2023; (2) resilience improvement was driven mainly by the response subsystem, while the pressure subsystem showed the greatest interannual variability; and (3) the annual top five obstacles were highly persistent and insufficient response capacity was the dominant long-term constraint on resilience enhancement. These findings underscore that improving the adequacy, institutional robustness, and operational stability of response systems is central to enhancing UFR. This study provides empirical support for the assessment of provincial-scale resilience and policy-oriented flood risk governance. Full article

Accurate precipitation nowcasting plays an important role in disaster prevention and hydrometeorological applications, yet it remains highly challenging due to the complex spatiotemporal variability and multi-scale structural characteristics of precipitation systems. Existing deep learning methods are largely data-driven and often struggle to effectively exploit multi-source observations or learn physically meaningful representations. To address these limitations, this study proposes a Multi-Scale Frequency–Temporal Network (MS-FTNet) for precipitation nowcasting. The framework leverages Fourier transform-based frequency-domain modeling to achieve an interpretable multi-scale decomposition of precipitation dynamics. Specifically, low-frequency components capture large-scale stratiform patterns and their temporal evolution, while high-frequency components represent localized convective structures and abrupt variations. Building on this, a Global Feature Collaboration (GFC) module integrates global frequency-domain representations with multi-scale convolutional features, and an Adaptive Temporal Fusion (ATF) module enhances temporal dependency modeling. Experiments on the SEVIR dataset demonstrate that MS-FTNet consistently outperforms representative baseline models in terms of MSE, CSI, and LPIPS, particularly for heavy precipitation events and longer forecast lead times. Full article

In recent years, the Mediterranean basin has been characterized as a climate change hotspot due to its rapid transition to warmer conditions and the strong agreement among most climate models predicting a significant decrease in precipitation by the end of the 21st century. These robust signals of climate change highlight the region’s high susceptibility to hydrometeorological extremes, such as droughts, which are expected to become more frequent, prolonged, and intense. In this context, the study focuses on Greece, where rising water scarcity threatens critical sectors such as food security, energy production, public health, and, more broadly, the resilience of ecosystems. Future drought conditions were assessed using the 12-month Standardized Precipitation Index (SPI-12) for 58 meteorological stations during 2071–2100, based on high-resolution regional climate simulations under RCP4.5 and RCP8.5. Spatial drought variability was examined using Principal Component Analysis, while drought severity and duration were quantified through Run Theory. The results indicate increasingly prolonged and severe droughts by the late 21st century, particularly in eastern Crete and southeastern Peloponnese, highlighting the urgent need for targeted adaptation measures. Full article

Groundwater is a critical natural and strategic economic resource, and the accurate prediction of groundwater depth dynamics is essential for the rational development and utilization of water resources. However, under the combined influence of climate variability, human activities, and complex hydrogeological conditions, groundwater level time series exhibit strong nonlinear and non-stationary characteristics, posing great challenges to the accurate prediction of groundwater level dynamics. Most existing prediction models rely on sufficient hydro-meteorological and exploitation data that are difficult to obtain in water-scarce regions, or fail to effectively decouple the multi-scale features of non-stationary groundwater level signals, resulting in limited prediction accuracy and insufficient generalization ability. To address these research gaps, this study takes Zhengzhou, a typical water-deficient city in the Yellow River Basin, as the study area, and proposes a hybrid deep learning framework combining Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) neural network for predicting shallow and intermediate-deep groundwater level changes. Kolmogorov–Arnold Networks (KANs) and Gated Recurrent Units (GRUs) are selected as benchmark models to verify the superior performance of the proposed framework. In this framework, the non-stationary groundwater level signal is adaptively decomposed into Intrinsic Mode Functions (IMFs) with distinct frequency characteristics via VMD. An independent LSTM model is constructed for each IMF to capture its unique temporal variation pattern, and the final groundwater level prediction is obtained by linearly reconstructing the predicted results of all IMFs. The results show that the coefficient of determination (R²) of the VMD-LSTM model exceeds 0.90 for all monitoring datasets, with low Mean Absolute Error (MAE) and Mean Squared Error (MSE). It significantly outperforms the benchmark models in handling nonlinear and non-stationary time series features. Using only historical groundwater level data as input, the proposed framework effectively overcomes the limitation of insufficient driving variables in data-scarce regions and fully explores the multi-scale evolution of groundwater dynamics through the synergistic effect of multi-scale decomposition and deep learning. The method presented in this study provides a novel and reliable technical approach for groundwater level prediction in water-deficient and data-limited areas, and also offers scientific support for the rational management and sustainable utilization of regional groundwater resources. Future research will incorporate driving factors such as meteorology and exploitation to further improve the model’s ability to capture abrupt changes in groundwater level dynamics. Full article

Urban flood risk management in southern Chile is often constrained by fragmented territorial information, discontinuous hydrological records, and weak integration between hazard assessment and formal land-administration systems. These limitations are particularly evident in persistently cloudy cities such as Temuco, where optical satellite imagery is frequently unusable. This study examines how satellite-derived flood observations can be incorporated into municipal land-administration practices. Flood-prone areas were identified using multitemporal Sentinel-1 SAR imagery (2018–2025) and integrated into a municipal multipurpose cadastre structured according to the ISO 19152 Land Administration Domain Model (LADM). Rather than remaining as standalone GIS maps, detected inundation areas were translated into standardized cadastral entities representing spatial units and hazard-related planning constraints. The analysis identified recurrent flooding along the Cautín River floodplain, characterized by strong winter seasonality and increasing exposure linked to urban expansion. More importantly, the results demonstrate that satellite-based hazard observations can be structured as interoperable administrative information with defined semantics, temporal validity, and traceable data sources. The proposed framework enables flood information to support territorial planning, emergency preparedness, and municipal risk management without altering property legal status. By linking Earth observation data with cadastral information infrastructures, the study provides a replicable approach for integrating environmental observations into land-administration systems in regions affected by institutional fragmentation and recurring hydrometeorological hazards. Full article

Water resources management in arid and semi-arid regions has become increasingly challenging due to climate change impacts and upstream water policies, particularly for strategic reservoirs. This study evaluates the applicability of the HEC-HMS model for simulating inflow hydrographs and supporting reservoir operation in data-scarce arid environments, focusing on Haditha Reservoir, the only major dam on the Euphrates River within Iraqi territory. An integrated hydro-meteorological and GIS-based framework was developed using 20 years of data (2004–2024), incorporating basin characteristics and reservoir operation records into the HEC-HMS model. Rainfall–runoff processes were simulated using SCS-based methods and routing techniques, followed by calibration and validation against observed inflows. The results demonstrated satisfactory model performance, with an accurate reproduction of inflow hydrographs during both calibration and validation periods. Subsequently, three reservoir operation scenarios were developed and compared with the actual operating policy (outflow curve operation, outflow structure routing operation and rule-based operation scenarios). The rule-based operation scenario showed superior performance by maintaining higher reservoir storage and water levels during dry periods compared to the existing operation, despite higher supply deficits. Overall, the findings confirm that the HEC-HMS model can be reliably applied as a decision-support tool for evaluating reservoir operation in arid and semi-arid regions under water scarcity conditions. Full article

Rapid land-cover transformations associated with urban expansion have increasingly altered hydrological processes, modifying runoff generation and flood response at the catchment scale. This study applied the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to examine rainfall–runoff dynamics in the Prosna River catchment (Poland) and the Morava e Binçës River catchment (Kosovo) for 2006–2021. Land-use changes were quantified using CORINE Land Cover (CLC) data from 2006, 2012, and 2018, and their hydrological effects were evaluated through changes in the Curve Number (CN) parameter. The model was calibrated and validated for the Prosna catchment, achieving satisfactory performance (NSE = 0.72 during calibration and 0.56 during validation), confirming its reliability under varying hydrometeorological conditions. Due to the lack of continuous discharge data in Kosovo, a parameter-transfer approach was used, applying calibrated parameters from the Prosna to the Morava e Binçës. Scenario-based simulations assessed the combined effects of urban growth and meteorological variability. Under wetter conditions, increased precipitation and expanded impervious surfaces markedly amplified simulated discharge, with maximum daily differences reaching 86.9 m³ s⁻¹. These findings underscore the sensitivity of catchment response to interacting land-use and precipitation changes and highlight the need for improved hydrological monitoring in data-scarce regions. Full article

Accurate runoff forecasting is essential for effective water resource management, hydropower operation, and flood risk mitigation. In this study, daily inflow runoff in the Xin’an River Basin, eastern China, was simulated using four ensemble learning models: Gradient Boosting Decision Tree (GBDT), XGBoost, CatBoost, and Stacking. Among them, the CatBoost model achieved the best performance, with a correlation coefficient (CC) exceeding 0.97, Nash–Sutcliffe efficiency (NSE) above 0.95, and reduced RMSE and MAE compared with the currently operational hydrological model. To extend the forecast lead times, two hydro–meteorological coupled models were developed by integrating the CatBoost model with a single numerical weather prediction model (EC) and a dynamically weighted multi-model ensemble precipitation forecast system (OCF). The coupled models were evaluated for lead times up to 240 h. The forecast skill value was highest within 96 h, with CC values above 0.80 and NSE around 0.50. The OCF-coupled model demonstrated improved reliability for lead times of 48–96 h, whereas the EC-driven forecasts performed better within the first 48 h. Case studies during the 2021–2022 flood seasons confirmed that the coupled framework accurately reproduced flood evolution and peak discharge dynamics, demonstrating its practical value for medium-range runoff forecasting in humid river basins. Full article

Extreme precipitation events (EPEs), a key class of hydrometeorological extremes, are intensifying globally under climate change; however, their effects on water-table dynamics across varying soil textures remain poorly understood. To better understand the impacts of EPEs, we conducted one-dimensional modeling to evaluate water-table response time, displacement, recession time, and total recharge under EPEs of 0.20 m, 0.40 m, and 0.60 m amounts, applied over 1-, 7-, and 20-day durations across twelve soil textures. The results show that coarse soils (i.e., sand) respond within days, while fine soils (i.e., clay) may take over 200 days. Water-table displacement ranged from 0.30 to 1.64 m and increased with EPE magnitude. The time it took for water tables to recede ranged from 1.2 to 3.0 years. A first-order estimate of total possible recharge, calculated from porosity and displacement, ranged from 17% (clay) to 97% (sand), averaging ~63% across soil textures. These findings highlight that recharge is primarily governed by EPE magnitude and soil properties, not event duration. This modeling effort provides new insight into how soil texture modulates groundwater response to extreme precipitation, informing future water budget and resilience assessments. Full article

Revealing the spatiotemporal driving mechanisms of landslide activity is fundamental to improving long-term landslide hazard management and risk mitigation in mountainous cities. Focusing on landslide events in Hong Kong from 1990 to 2024, this study develops an integrated framework at the slope-unit scale that combines rainfall index optimization with multi-factor spatiotemporal driving analysis. First, Grey Relational Analysis (GRA) is employed to systematically evaluate the spatiotemporal associations between landslide occurrences and six commonly used rainfall indices, aiming to obtain a consistent and robust representation of rainfall triggering conditions. Subsequently, the Optimal-Parameter Geographical Detector (OPGD) model is introduced to quantitatively assess the explanatory power of individual factors—covering geological, topographic, hydro-meteorological, and human-related variables—as well as their pairwise interactions, thereby revealing the spatiotemporal evolution of landslide driving factors and their multi-factor coupling mechanisms over a 35-year period. The results indicate that the maximum 3-day cumulative rainfall index (RX3day) consistently exhibits the strongest association across different resolution parameter settings and is identified as the dominant rainfall indicator representing dynamic landslide triggering. Geological conditions and topographic factors constitute a stable background controlling the spatial heterogeneity of landslides throughout the entire study period, whereas the explanatory power of RX3day increases markedly after around 2000, gradually emerging as a primary dynamic driving factor of landslide activity. Interaction detection further demonstrates that landslide occurrence is mainly governed by nonlinear enhancement effects among multiple factors, with “geology–topography” and “rainfall–topography/geology” interactions showing the highest explanatory power, and rainfall-related interactions exhibiting continuous strengthening over time. Overall, the spatiotemporal distribution of landslides in Hong Kong is jointly controlled by long-term stable geological–topographic conditions and increasingly intensified extreme rainfall forcing. Full article