Search Results (100)

This study presents a data-driven methodology to develop and close the continuous water balance of a cascaded hydrological system consisting of two upstream regulating reservoirs and a downstream lake. The analysis is based on monthly hydrometeorological and hydrographic data for the period 1998–2024. A baseline water balance model using raw measured data was first implemented, followed by two bias correction approaches based on multiple linear regression coefficient estimation. The first approach applies uniform coefficients across all months, while the second accounts for seasonal variability by estimating coefficients only for the December–May period, when systematic errors are most pronounced. Model performance was evaluated using calibration (1998–2017) and validation (2018–2024) periods, with Nash–Sutcliffe efficiency (NSE) and residual diagnostics used as evaluation metrics. The baseline model showed substantial cumulative deviations due to systematic errors (NSE: −0.28 and −5.13 for the two reservoirs and −18.70 for the lake), confirming the need for bias correction. Both correction approaches significantly improved model performance, with NSE values up to 0.89 for reservoirs and 0.83–0.86 for the lake during calibration. In validation, the seasonally adjusted model performed more stably in simulating water levels for Lake Velence (NSE = 0.72) than the uniform coefficient model (NSE = 0.60), particularly under extreme hydrological conditions. Residual analysis further indicated improved independence and homoscedasticity when seasonal structure was considered. The results demonstrate that water balance closure in the system is affected by distributed errors across multiple components rather than a single dominant source. The proposed methodology provides a practical, scalable framework for reconstructing consistent water balance time series in data-limited, regulated systems and supports the development of water management scenarios. Full article

Multi-source remote sensing is transforming landslide susceptibility assessment from static terrain-based zonation toward observation-driven spatiotemporal inference and dynamic map updating. Satellite precipitation products, interferometric synthetic aperture radar (InSAR) deformation time series, optical image sequences, land-cover products, and multi-temporal terrain observations provide complementary evidence of hydrometeorological forcing, slope kinematics, land-system regulation, and geomorphic reorganization. However, these observation streams differ substantially in spatial support, temporal resolution, physical meaning, and uncertainty structure and therefore cannot be reliably integrated as generic predictors without process-aware interpretation. This review synthesizes recent progress in remote sensing-enabled dynamic landslide susceptibility assessment by linking four key components: dynamic factor construction from Earth observation data, spatiotemporal representation and learning, susceptibility map updating, and validation under temporal and spatial independence. The reviewed literature is organized around four process roles: rainfall- and soil moisture-related forcing, kinematic state and response captured by InSAR, land-system and ecological regulation derived from optical time series, and geomorphic memory represented by multi-temporal digital elevation models (DEMs). We further examine how these signals are encoded and integrated through temporal models, graph-based representations, attention mechanisms, and hybrid frameworks, with particular emphasis on consistency among process role, data structure, mapping unit, inference target, and validation design. Current progress remains constrained by temporally coarse landslide inventories, cross-scale incompatibility among remote sensing products, uneven and insufficiently process-aware multimodal fusion, and limited physical interpretability. Future advances require event-resolved inventories, uncertainty-aware multimodal fusion, process-consistent spatiotemporal learning, and validation designs that explicitly test whether susceptibility maps can be updated in a scientifically defensible manner as new Earth observation data become available. Full article

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin. Full article

Sustainable small hydropower (SHP) is central to the clean-energy transition of mountainous Central Asia, yet its long-term reliability depends on a rapidly changing cryosphere. Winter river-ice dynamics—an underappreciated control on run-of-river generation—remain poorly characterized owing to the collapse of in situ hydrometeorological networks since 1991. We use a 112-month Sentinel-1 C-band SAR time series (February 2017–May 2026) over a 5320 km² headwater catchment of the Chu River basin, northern Tien Shan, Kyrgyzstan, to quantify river-ice phenology at 20 m resolution using a per-pixel summer-baseline anomaly approach. Mid-winter (December–February) ice cover declined significantly at −0.51%·yr⁻¹ (p = 0.013; Mann–Kendall p = 0.029), with the 2026 winter recording an unprecedented 2.6–2.8 σ departure from the 2017–2025 climatology. Contrasting the cold 2022 and warm 2026 winters revealed bidirectional climate sensitivity—early breakup versus persistent thin ice—posing distinct SHP hazards. ERA5-Land reanalysis (1992–2026) showed significant winter warming with no precipitation or snowfall trend, indicating thermally forced ice decline. Interpreted within a conceptual Peak Water scenario, this signals a closing window of opportunity for SHP generation, with direct relevance to sustainable water–energy management and the UN Sustainable Development Goals (SDG 7; SDG 13). Our results provide the first decadal, satellite-based evidence of river-ice loss for Central Asian mountain rivers and a transferable monitoring framework to support climate-resilient, sustainable hydropower planning in ungauged basins. Full article

As a representative large shallow freshwater lake in China, Lake Taihu has suffered from persistent cyanobacterial blooms for a long time. Although intensive restoration actions have been carried out and caused visible improvements, the long-term evolution path and inner driving mechanisms of its ecosystem are still not fully made clear. Based on long-term monitoring data during 2011 to 2024, this study aims to characterize temporal dynamics of the aquatic environment, find out key drivers that shape community succession, and offer a scientific foundation for effective lake management. A series of data about hydrometeorological factors, physicochemical water quality indexes, and biological community data was analyzed by using the Mann–Kendall trend test, Pettitt change-point test, Redundancy Analysis, and correlation heatmaps. The results show that the Taihu ecosystem has experienced a notable regime shift in the past 14 years. First, nitrogenous nutrients reacted quickly to external emission reductions, showing a notable monotonic decline; in comparison, Total Phosphorus and Cyanobacterial Density followed a non-linear “U-shaped” path, with a notable shift happening in 2020, which marks the change from a “deterioration phase” to a “recovery phase.” Second, correlation analysis has confirmed that the lake is mainly phosphorus-limited, and a clear “decoupling” between nitrogen levels and algal outbreaks has taken place. Third, the “10-year Fishing Ban” (initiated in 2020), together with sustained phosphorus control, reduced the competitive exclusion of phytoplankton by cyanobacteria, promoting the recent rebound in biodiversity. This study points out that Lake Taihu has passed a tipping point of ecological restoration, shifting from a turbid “algae-dominated state” to a stable state with higher biodiversity. Future management strategies should put first the mitigation of internal phosphorus loading and adaptive management against extreme climatic events. 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

Current climatic conditions are drying peatland ecosystems, compromising carbon storage through increased decomposition and vegetation shifts. Large-scale monitoring is essential to quantify climate change impacts on vegetation and hydrology. PlanetScope high-resolution imagery (3 m pixel) over seven years (2017–2023) served as proof-of-concept for a central European peatland (Rzecin, Poland). The enhanced vegetation index (EVI) was selected based on ground validation (R = 0.9 vs. 0.8 for NDVI-normalised vegetation index). Phenological metrics (SOS—start of the season; EOS—end of the season; LOS—length of the season; POS—peak of the season; EVImax; amplitude; area) were derived via DATimeS from snow-free EVI time series. Trends were analysed using pixel-wise slopes, change-point detection (break ~2020–2021), paired correlations, subarea (P1–P4) behaviour, and PCA, alongside air temperature (Tair), precipitation, and water table depth (WTD). Results revealed LOS and peak EVI increased until 2020, a 2021 break, and a 2022–2023 recovery, signalling nonlinear vegetation reorganisation. Transitional mire floating mats (Sphagnum spp.–Carex spp.–Vaccinium oxycoccus) showed the longest seasons/highest greenness but weakest hydrometeorological links, implying rising internal dynamics. Phragmites mats, fern–sedge edges, and riparian willow differed in tolerance or sensitivity to WTD and precipitation oscillations. Tair dominated EVI seasonality across types, while WTD and precipitation controlled phenology and greenness in edges, showing better results with phase-aligned means. Vascular plants outpaced mosses in peak EVI and persistence, with patch-specific shifts. Full article

This study evaluates temporal variability and algorithm differences in soil moisture estimates over Europe using the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis and the passive Soil Moisture Active Passive (SMAP) soil moisture product. While models and satellite retrievals have improved in capturing the timing of soil moisture dynamics, absolute accuracy and temporal variability magnitudes still diverge. This study compares the representation of short-term and seasonal variability of soil moisture in absolute and normalized terms over two different hydrometeorological growing periods (2021 and 2022). Both datasets exhibit intermediate to high temporal correlations with in situ measurements at selected stations (median Pearson correlation coefficients of all stations range between 0.65 and 0.79), confirming previous studies. However, they overestimate the magnitude of absolute soil moisture variability at most stations (median interquartile range of all stations at 0.085 (0.10) m³m⁻³ for ECMWF and 0.072 (0.079) m³m⁻³ for SMAP opposed to 0.063 (0.072) m³m⁻³ for in situ in 2021 (2022)) due to an overestimation of short-term fluctuations, especially at dry stations in southern France and Eastern Europe. The soil wetness index is underestimated, particularly within SMAP estimates. The performance of both is sensitive to hydrometeorological conditions, with the 2022 European drought causing strong seasonal and weak short-term fluctuations. This is easier to capture than conditions with pronounced short-term and weaker seasonal fluctuations, as in 2021. Overall, SMAP and ECMWF time series show considerable coincident timing, whereas the magnitude of temporal variability and accuracy depend on site-specific characteristics and the pre-processing of the data. Full article

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction methods. To address this, a hybrid CNN-LSTM deep learning model is constructed. This model is designed to extract multivariate coupled features and capture temporal dependencies from multi-variable time series data, while simultaneously simulating the nonlinear and delayed responses of aquifers to groundwater abstraction. Specifically, the convolutional neural network (CNN) component extracts the multivariate coupled features of hydro-meteorological driving factors, and the long short-term memory (LSTM) network component models the temporal dependencies in groundwater level fluctuations. This integrated architecture comprehensively represents the combined effects of natural recharge–discharge processes and anthropogenic pumping on the groundwater system. Utilizing monitoring data from 2021 to 2024, the model was trained and tested using a rolling time-series validation strategy. Its performance was benchmarked against traditional models, including the autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN), and standalone LSTM. The results show that the CNN-LSTM model delivers superior performance across diverse hydrogeological conditions: at the upstream well AJC-7, which is dominated by natural recharge and discharge, the Nash–Sutcliffe efficiency (NSE) coefficient reached 0.922; at the downstream well AJC-21, which is subject to intensive pumping, the model maintained a robust NSE of 0.787, significantly outperforming the benchmark models. Further sensitivity analysis reveals an asymmetric response of the model’s predictions to uncertainties in pumping data, highlighting the role of key hydrogeological processes such as delayed drainage from the vadose zone. This study not only confirms the strong applicability of the hybrid deep learning model for groundwater level prediction in data-scarce arid regions but also provides a novel analytical pathway and mechanistic insight into the nonlinear behavior of aquifer systems under significant human influence. Full article

Saltwater intrusion increasingly jeopardizes groundwater in low-lying coastal plains worldwide, where the combined effects of sea-level rise, land subsidence, and hydraulic regulation further exacerbate aquifer vulnerability and threaten the long-term sustainability of freshwater supplies. To move beyond sparse and fragmented piezometric observations, we propose “integrated coastal supersites”: wells equipped with multiparametric sensors and multilevel piezometers that couple high-resolution vertical conductivity–temperature–depth (CTD) profiling with continuous hydro-meteorological time series to monitor the hydrodynamic behavior of coastal aquifers and saltwater intrusion. This study describes the installation of two supersites and presents early insights from the first monitoring period, which, despite a short observation window limited to the summer season (July–September 2025), demonstrate the effectiveness of this approach. Two contrasting supersites were deployed in the coastal plain between the Brenta and Adige Rivers (Italy): Gorzone, characterized by a thick, laterally persistent aquitard, and Buoro, where the aquitard is thinner and discontinuous. Profiles and fixed sensors at both sites reveal a consistent fresh-to-saline transition in the phreatic aquifers and a secondary freshwater lens capping the confined systems. At Gorzone, the confining layer hydraulically isolates the deeper aquifer, preserving low salinity beneath a saline, tidally constrained phreatic zone. Groundwater heads oscillate by about 0.2 m, and rainfall events do not dilute salinity; instead, pressure transients—amplified by drainage regulation and inland-propagating tides—induce short-lived EC increases via upconing. Buoro shows smaller water-level variations, not always linked to rainfall, and, in contrast, exhibits partial vertical connectivity and faster dynamics: phreatic heads respond chiefly to internal drainage and local recharge, with rises rapidly damped by pumping, while salinity remains steady without episodic peaks. The confined aquifer shows buffered, delayed responses to surface forcings. Although the monitoring window is currently limited to 2025 through the summer season, these results offer compelling evidence that coastal supersites are reliable, scalable, and management-critical relevance platforms for groundwater calibration, forecasting, and long-term assessment. Full article

Unlike most other measures in flood risk management, flood forecasting stands out because it is not designed to address a pre-defined return period. In principle it is applicable to a whole range of possible events and can be operated continuously in real time. This makes flood forecasting an effective non-structural measure for saving lives and property, even in the face of increased hydro-meteorological variability and extremes. In Germany, a series of regional and transregional flood forecasting centres and services have been established that cover the entire national territory. For large basins, the existing forecasting centres are well equipped to provide accurate real-time forecasts. Nevertheless, there are remaining challenges that need to be met when the focus is on small to medium-sized catchments. This study focuses on discussing the capabilities of six state-of-the-art flood forecasting centres and derives the most important requirements for significant improvements in flood forecasting capabilities for small to medium-sized catchment areas. We emphasise that future research must focus on flood-driven predictions, including the prediction of flood inundation and consequences for buildings and infrastructure, as well as geotechnical failure mechanisms in Germany. Full article

Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in semi-arid regions such as the Guadalupe Basin in northern Baja California. In this study, we explored the predictive capability of several hybrid deep learning architectures—Long Short-Term Memory (LSTM), Convolutional Neural Network combined with LSTM (CNN–LSTM), and Bidirectional LSTM with Attention (BiLSTM–Attention)—to model the temporal evolution of wind speed, wind direction, temperature, relative humidity, and atmospheric pressure using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1980 to 2020. Model performance was evaluated using RMSE, MAE, and R² metrics and compared against persistence and climatology baselines. The BiLSTM–Attention model achieved the best overall performance, showing particularly high accuracy for temperature (R² = 0.95) and relative humidity (R² = 0.76), while maintaining angular errors below 35° for wind direction. The results demonstrate the potential of hybrid deep learning models to capture nonlinear temporal dependencies in meteorological time series and provide a methodological framework to enhance hydrometeorological understanding and water resource management in the Guadalupe Basin under Santa Ana wind conditions. Full article

Temporal and spatial variations in water temperature were analyzed from 1998 to 2023 across two contrasting southeast Basque coast estuaries: the deeper, stratified estuary of Bilbao and the shallower, mixed estuary of Urdaibai. We assessed long-term trends, seasonality, intra- and inter-estuary differences, and links to hydro-meteorological drivers using time-series decomposition, clustering, cumulative sum, regression, and correlation analyses. The largest differences in interannual and seasonal patterns occurred between outer neritic and shallow transitional waters. Most water masses warmed overall, with increases until 2003–2006, followed by cooling until 2013–2015, and sharp warming in 2020–2023. The strongest trends (0.24–0.25 °C decade⁻¹) occurred in middle-estuary waters, while inner above-halocline waters of the stratified estuary showed no trend or slight cooling. The strongest warming occurred in spring, particularly in the easternmost mixed estuary (0.49 °C decade⁻¹), especially in May (0.88 °C decade⁻¹). Seasonal minima and maxima occurred earlier in surface transitional waters than in neritic and deep transitional waters of the stratified system. Over time, temperature maxima advanced, minima were delayed, shortening the warming phase, and springs became warmer, extending the warm season. Air temperature was the main driver of water temperature trends, while river flow modulated patterns at annual and seasonal scales, with negative correlations with temperature, mainly in spring. Full article

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics and drivers remain poorly understood. Here, utilizing active fire and land cover products alongside climate and human footprint datasets, we explored the spatiotemporal distribution and dynamics of active fire counts (FC) over 20 years (2003–2022) in Asia, quantifying the effects of climate and human management. Results analyzed over 10 million active fires, with cropland fires predominating (25.6%) and Southeast Asia identified as the hotspot. FC seasonal dynamics were governed by temperature and precipitation, while spring was the primary burning season. A continental inter-annual FC decline (mean slope: −8716 yr⁻¹) was identified, primarily attributed to forest fire reduction. Subsequently, we further clarified the drivers of FC dynamics. Time series decomposition attributed short-term FC fluctuations to extreme climate events (e.g., 2015 El Niño), while long-term trends reflected cumulative human interventions (e.g., cropland management). The trend analysis revealed that woody vegetation fires in the Indochina Peninsula shifted to herbaceous fires, Asian cropland FC primarily increased but were restricted in eastern China and Thailand by strict policies. Spatially, hydrometeorological factors dominated 58.1% of FC variations but exhibited opposite effects between arid and humid regions, followed by human factor, where human activities shifted from fire promotion to suppression through land-use transitions. These driving mechanism insights establish a new framework for adaptive fire management amid escalating environmental change. Full article

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m³/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. Full article