Search Results (1,031)

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

Even under a warming climate, winter sea ice in the Bohai Sea continues to threaten ships and offshore/coastal infrastructure. Reliable pre-season prediction of the overall wintertime sea ice condition in the Bohai Sea, as represented by the Bohai Sea Ice Grade (BSIG), is therefore important for disaster preparedness and mitigation. Based on the 1979–2024 BSIG record, this study compares seven statistical and AI-based seasonal prediction methods: analog year analysis, multiple linear regression, stepwise regression, Principal Component Regression, a cross-correlation-based regression model, support vector regression, and the Bayesian Ensemble Bohai Ice Grade Net (BE-BIGNet). As potential precursors, we considered sea ice extent in 14 Arctic regions together with 114 large-scale atmospheric and oceanic circulation indices. The results suggest substantial differences in predictive skill among the methods. Among the tested approaches, BE-BIGNet, which combines Bayesian regularization with bootstrap median ensembling, achieves strong full-period performance and stable skill during the independent test period, suggesting that it may provide a useful framework for operational BSIG forecasting in the Bohai Sea. Full article

Because high-precision DEMs are costly to obtain, while low-precision DEMs often fail to meet accuracy requirements for watershed flood simulation, this study proposes a multi-source DEM fusion method based on the Random Forest algorithm. This method combines K-Means slope clustering and Optuna hyperparameter optimization to realize adaptive weight allocation across eight slope zones. After multi-source DEM fusion, the fused DEM is applied to the flood simulation model of the Wei River Basin to simulate the catastrophic flood event in July 2021. The results show that the Mean Absolute Error (MAE) of the fused DEM ranges from 0.9855 to 1.7218, the Root Mean Square Error (RMSE) ranges from 1.0902 to 2.3953, and the Mean Error (ME) is close to 0 with no significant systematic bias. Compared with single-source DEM, the fused DEM reduces MAE by 21.32–85.32% and RMSE by 7.63–82.03%. In flood simulation, the peak discharge error based on the fused DEM is controlled within 0.013–0.059, and the coefficient of determination (R²) is not less than 0.9808. The simulated errors of inundation area and flood detention volume in flood detention areas are significantly lower than those using a single-source DEM. The proposed multi-source DEM fusion method can effectively improve terrain accuracy and the reliability of flood routing simulation, providing technical support for flood control scheduling in the Wei River Basin and watershed hydrological and flood simulation in data-scarce regions. Full article

The data-scarce Varzob River basin, Tajikistan, shows significant cold-season warming, an earlier spring runoff shift, and a sharp rise in avalanche frequency. We analyse long-term runoff (1940–2018), meteorological records (2000–2024), avalanche observations (2019–2026), field snow surveys (2025–2026), and satellite/UAV imagery (2024–2025). Annual runoff shows a 6.7% higher mean in 1991–2018 than in 1940–1990, but the long-term trend is not significant (p = 0.23). However, the centre of mass of spring runoff shifted significantly earlier by 3.7 days (p < 0.001). Cold-season temperature increased significantly (p = 0.016), while wind speed showed no significant trend (p = 0.061). Snow water equivalent at seven elevations (1930–2955 m) ranges from 200 to 440 mm, and melt-freeze crusts indicate a snowpack prone to wet-slab avalanches. Avalanche frequency increased from 81 events in 2019 to 430 in 2025 and 560 (partial) in 2026, coinciding with a ~70% higher snow water equivalent in 2026. Mapped avalanche paths terminate less than 50 m from the Varzob River, suggesting a potential, though unquantified, contribution of avalanche snow to spring runoff. The integration of long-term hydrology, high-resolution meteorology, field surveys, and remote sensing offers a replicable framework for cryospheric-hydrological studies in data-scarce mountain basins. Full article

Mexico City experiences severe water stress driven by aquifer overexploitation and recurrent droughts. Effective water management requires operational spatial monitoring systems capable of spatially reconstructing meteorological anomalies across multiple temporal scales. In this work we developed an explainable deep learning framework using Long Short-Term Memory (LSTM) networks to spatially reconstruct three drought indices—the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI)—across five accumulation scales (3, 6, 12, 18, and 24 months). To strictly isolate genuine meteorological deviations, we adopted a hybrid statistical approach: SPI was computed following the standard WMO methodology using Gamma distribution fitting, while SPEI and RDI were computed using empirical monthly standardized anomalies to ensure robustness in non-stationary urban climates without forcing distributional assumptions. Model generalization was evaluated using a leave-one-microsite-out validation strategy, training on two stations and testing on a spatially isolated third station, with inter-station distances ranging from 1.8 to 6.7 km, sufficient to capture urban microclimatic heterogeneity while remaining within the same regional climate zone. We quantified feature importance using SHapley Additive exPlanations (SHAP) to provide mathematical transparency. The LSTM achieved predictive performance at long-term scales by effectively capturing deep sequential memory, while short-term reconstructions reflected the inherent noise of urban convective precipitation. The framework demonstrates reliable intra-urban spatial generalization capacity, supporting the development of diagnostic tools for metropolitan water stress assessment. Full article

Marine heatwaves (MHWs) in the Eastern China Seas exert profound ecological and economic impacts, highlighting the need for reliable indicators to support early prediction. Based on observations from 1982 to 2022, this study identifies three characteristic patterns linking annual maximum sea surface temperature (T_max) with summer MHWs: in July, the northern region follows a pattern where earlier T_max favors more frequent MHWs; in August, the whole study area is dominated by a mode where T_max coincides with the seasonal threshold peak, driving widespread MHWs; and in September, the southern region exhibits a pattern where later T_max favors more frequent MHWs. A threshold-based method integrating both T_max and its timing demonstrates strong skill in assessing MHW occurrence and exhibits practical utility when validated with independent observations from 2023 to 2024. Long-term warming of T_max, together with regionally divergent trends in its seasonal timing, closely aligns with observed increases in MHW days. Significant correlations between T_max and preceding monthly mean SST suggest that T_max integrates accumulated thermal conditions and carries seasonal memory, offering a potential pathway from seasonal SST prediction to early MHW risk assessment. These findings clarify the structured and regionally differentiated T_max–MHW relationship, demonstrate the feasibility of a T_max-based assessment framework, and provide a scientific basis for improving seasonal monitoring and early warning of MHWs under sustained climate warming. Full article

Plastic film mulching combined with raised bed–furrow irrigation is an effective technique for enhancing the seed yield, oil contents, and plant-level water use efficiency of sunflower cultivation, while also optimizing water footprint. In this study, a field experiment was carried out at the experimental station of the Department of Irrigation and Drainage during 2023–2024. The trial involved three types of raised bed–furrow irrigation (raised beds with 60, 45, and 30 cm ridges and 30 cm furrows) with and without mulching practices. The results revealed that the treatments combining mulching with raised beds showed higher soil temperature and moisture contents compared to non-mulching treatments. The highest seed yield and oil content were recorded in furrow irrigation with mulching, representing a 35% increase in yield and a 28% increase in oil content compared to the control treatment. Seed yield was positively correlated with oil content. Additionally, the highest plant-level water use efficiency was observed in a raised bed 45 cm in size with mulching, while the highest total water footprints were recorded in a raised bed with a 60 cm ridge and non-mulch treatment, both exceeding the control treatment. It is concluded that sunflower cultivation under mulching combined with raised bed–furrow irrigation significantly enhances crop and water productivity. Full article

This article analyzes the state of water resources in the Guercif Plain (Morocco) under the combined effects of drought and increasing consumption pressures. The study adopts a quantitative and analytical approach based on climatic and hydrological data, demographic information, and Landsat satellite imagery. The main findings reveal pronounced rainfall variability with an overall declining tendency, with drought years accounting for approximately 58% of the observation period. This climatic context has been accompanied by strong interannual fluctuations in the discharge of Oued Melloulou, with a slight long-term declining trend, along with a continuous and accelerating groundwater decline in the Tafrata aquifer at an average rate of 0.98 m per year. The analysis also indicates an estimated urban water deficit approaching 77% under peak demand conditions in 2025. Furthermore, NDVI-based analysis of satellite imagery highlights a marked expansion of irrigated areas in the Guercif Plain, increasing from about 2% of the total plain area in 1985 to approximately 9% in 2020. This vegetation expansion is largely associated with irrigation development, suggesting increasing pressure on groundwater resources rather than recovery linked to rainfall conditions. Overall, the findings raise critical concerns regarding the long-term sustainability of water resources and underscore the need for integrated and adaptive water-management strategies under persistent drought conditions. Full article

Across Africa, drought seldom occurs alone. Rainfall deficits often coincide with heat, rapid soil moisture loss and reduced streamflow, producing compound events whose impacts exceed those of any single driver. This review synthesises station observations, satellite and reanalysis products, and climate model simulations to clarify where such events are most common, how they form, how they affect societies and ecosystems, and how risks are changing. A practical tiered definition tailored to African conditions is outlined and applied to identify five recurrent hotspots: the Sahel, the Greater Horn of Africa, southern Africa, the margins of the Congo Basin and the Guinea Coast. The review sets out a physically consistent sequence that links basin-scale sea surface temperature anomalies to shifts in monsoon circulation, and then to land processes that amplify and prolong heat and dryness through reduced evapotranspiration and soil-moisture memory. Documented impacts include lower crop and pasture productivity, pressure on rivers, reservoirs and groundwater, stress on hydropower and wider consequences for food and energy security. Compound drought frequency across these hotspots has risen by 18–55% since 1980, with the probability of the most severe events roughly doubling at 1.5 °C of global warming and tripling at 3 °C. The review highlights near-term priorities, including compound-aware monitoring, sub-seasonal-to-seasonal early warning and conjunctive water management. Full article

Climate change poses significant challenges to the operation and safety of dam and reservoir (D&R) systems, particularly in regions characterized by water scarcity and high climate variability. This study presents a structured methodology for climate risk assessment that integrates regional climate projections, system-specific thresholds, and a semi-quantitative risk matrix approach. A key innovation is the explicit linkage between climate indicators and system performance through physically based thresholds, combined with empirically derived exceedance probabilities from high-resolution climate projections. The methodology is applied to the Almopeos D&R system in northern Greece, using an ensemble of statistically downscaled CMIP6 simulations under two emission scenarios (SSP2-4.5 and SSP5-8.5) and two future periods (2041–2060 and 2081–2100). Three climate indicators are analyzed: TX35 (temperature extremes), CDD (consecutive dry days), and Rx1day (extreme precipitation). Results indicate that temperature increase is the dominant climate risk hazard, leading to increased irrigation demand and reduced system reliability, with risks classified as high to very high. Drought conditions represent a secondary but important risk, becoming critical during prolonged dry periods affecting reservoir storage, while extreme precipitation events exhibit low likelihood but potentially high consequences for dam safety. Adaptation measures are prioritized using a qualitative multi-criteria approach, highlighting the effectiveness of operational measures, while structural and monitoring interventions remain essential for ensuring system safety. The proposed methodology provides a transparent and transferable framework for climate-resilient planning of water infrastructure systems. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

Groundwater in arid and semi-arid regions is increasingly stressed by low rainfall, high evaporation, population growth, agricultural expansion, and climate change. A critical question is whether non-renewable aquifers can sustain rising water demand without irreversible decline. This study addresses that question for the Al Kufrah Basin in southeastern Libya, part of the Nubian Sandstone Aquifer System, the world’s largest fossil aquifer. A three-dimensional groundwater flow model (MODFLOW-2000) was calibrated using data from more than 1000 production wells and 32 piezometers spanning 1968–2022. The model was applied to simulate groundwater behavior under five scenarios extending to 2050, including the planned development of 150 new wells. The results indicate that over 85% of withdrawals are derived from aquifer storage rather than boundary inflows. While regional water levels remain relatively stable over the 25-year horizon, localized drawdowns of up to 11 m are expected near new well fields. These findings highlight short-term resilience but point to long-term vulnerability, as continued reliance on non-renewable reserves without recharge will ultimately lead to depletion. The study underscores the need for adaptive management, climate-resilient water strategies, and regional cooperation to ensure the sustainable use of this transboundary aquifer under increasing environmental and socio-economic pressures. Full article

The carbonate massifs of the southern Italian Apennines host extensive karst aquifers, which represent the principal drinking water resources. This study focuses on the Dragone Plain polje, a vast closed karst depression located in the main recharge sector of the Terminio–Tuoro carbonate massif. The polje drains a ~55 km² endorheic catchment and may be flooded during the cold and wet season, forming a temporary lake. We employed continuous hydroclimatic time series (rainfall, groundwater level, spring discharge, and river level) together with sparse Sentinel-2 true color satellite images for the period 2020–2024 to analyze the flooding process in the polje and its hydraulic connection with the saturated zone of the karst aquifer. Results indicate that lake formation depends on the balance among soil moisture, rainfall intensity, and runoff development, which were modeled on a daily scale. Daily recharge was also estimated and compared with groundwater level time series from the deep karst aquifer. The modeling was integrated with cross-correlation analysis of the time series, providing insights into the propagation of precipitation pulses through the hydrogeological system. This case study represents an important example for understanding the relationship between karst polje hydrological functioning and climate in a Mediterranean area. Full article

Due to ongoing global warming, the frequency and intensity of extreme weather events have increased substantially. Compared to individual extremes, compound drought and high temperature (CDHT) events represent a major climate risk in China. However, their spatiotemporal characteristics remain insufficiently understood, particularly at fine temporal scales. To address this gap, this study systematically investigated CDHT events across China from 1982 to 2023. Methodologically, CDHT events were identified at the raster level by combining an improved daily Standardized Precipitation Evapotranspiration Index (SPEI) with daily maximum temperature using a quantile relative dynamic threshold. The results show strong spatial heterogeneity: the longest event durations are primarily observed in Xizang, while higher event severity is concentrated in regions south of 30° N. Trend analysis reveals a widespread increase in the duration, frequency, and severity of CDHT events across most of China, with the most pronounced intensification detected in Xinjiang, Inner Mongolia, and Yunnan. Overall, these findings highlight a clear climate-driven intensification of CDHT events, offering new insights into their spatiotemporal dynamics. The results offer a robust scientific basis for improving risk assessment and developing targeted adaptation strategies to mitigate the impacts of compound climate extremes in China. Full article

This study investigates the transformation of river runoff and its sensitivity to changes in large-scale atmospheric circulation in the Zhaiyk–Caspian water management basin during the period of 1951–2023. The analysis is based on hydrometeorological observations data, the Vangengeim–Girs classification of macro-circulation patterns, and the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices. Correlation analysis, the Mann–Kendall test, Sen’s slope estimator, and the Pettitt test were applied to identify trends, structural shifts, and the spatial coherence of hydroclimatic changes. The results show that interannual variability in river runoff is characterized by a degree of spatial coherence, with correlation coefficients between annual streamflow records at most gauging stations reaching up to 0.95. It is demonstrated that the most pronounced changes in the hydrological regime occur during the cold season and are expressed in a statistically significant increase in winter runoff, while no significant long-term trend in annual runoff is observed. Structural shifts in winter runoff are predominantly associated with the late 1990s, whereas changes in the temperature regime are detected earlier and exhibit spatial coherence. The findings indicate that the contemporary transformation of river runoff is primarily driven by rising air temperatures and the associated intra-annual redistribution of flow. Full article