Search Results (372)

Against the background of global warming, snow cover, as an extremely sensitive and active component of the cryosphere, plays an indispensable role in regulating regional water circulation, energy balance mechanisms and the climate system. To explore the dynamic variation characteristics and driving mechanisms of snow depth in southeastern Xizang, this study took Chamdo City as the research area. Based on multi-source datasets including snow depth, meteorology, vegetation, topography, and population density from 2000 to 2020, methods such as the coefficient of variation, Theil–Sen trend analysis, Mann–Kendall test, Hurst index, and geographical detector were adopted to systematically analyze the spatiotemporal patterns of snow depth variations and their influencing factors. The results indicate that, temporally, the overall snow depth in Chamdo City showed a fluctuating increasing trend over the past 20 years, with an annual growth rate of 0.03 cm. It exhibited distinct characteristics across three stages: snow depth increased at a rate of 0.12 cm·a⁻¹ from 2000 to 2005, decreased at 0.05 cm·a⁻¹ during 2005–2015, and rose rapidly from 2015 to 2020 at a growth rate of 0.52 cm·a⁻¹. Spatially, the distribution of snow depth varied significantly. The extremely shallow snow cover area (≤2 cm) accounted for 51.71% of the total area, primarily concentrated in low-altitude regions with intensive human activities. In contrast, the relatively deep (6–10 cm) and extremely deep (>10 cm) snow cover areas together constituted 14.34% of the total, mainly distributed in high-altitude regions with sparse populations. Hurst index analysis revealed that 61.71% of the study area exhibited persistent changes in snow depth, with a trend toward deepening snow cover in the future. The results from the geographical detector show that air temperature (X9, q = 0.90) was the core driving factor dominating the static spatial differentiation of multi-year average snow depth. Furthermore, the interactions between slope (X4) and air temperature (X9), vegetation type (X6) and air temperature (X9), and population density (X5) and aspect (X8) all demonstrated bivariate enhancement effects, with explanatory power significantly higher than that of individual factors. This study provides a scientific reference for water resource management, snowmelt runoff prediction and snow disaster prevention in Chamdo City. Full article

Hydrometeorological changes profoundly influence runoff generation and evolution in river basins. It is of great significance to carry out runoff prediction research to ensure water resources security and improve disaster prevention and mitigation capabilities. In this paper, the Ganhe River Basin in Northeast China was taken as the research object. Based on the hydrometeorological and runoff data from 1980 to 2022, a variety of statistical methods were used to systematically study the climate change, runoff evolution characteristics and driving mechanism of the basin. Combined with BP neural network model and CMIP6 climate scenario data, the future runoff changes were predicted. The results showed that the precipitation and relative humidity showed a downward trend, while the temperature, sunshine and evapotranspiration showed an upward trend during the study period. The runoff showed a non-significant upward trend, and an abrupt change occurred in 2009. After the abrupt change, the runoff increased by 38.7% compared with the baseline period. The change in land use was the most significant from 1990 to 2000, and the area of cultivated land increased significantly. Correlation analysis showed that precipitation was the dominant meteorological factor affecting runoff change, and the contribution rate of human activities was 88.51%, which was much higher than that of climate change. The BP neural network model demonstrated satisfactory simulation performance, and the training set and test set R2 reached 0.88 and 0.82, respectively. In the future, both temperature and precipitation will increase under different SSP scenarios. On this basis, the BP neural network prediction results show that the runoff of the basin is generally increasing, and the increase is the most significant under the high emission scenario, and the risk of extreme hydrological events may be further aggravated. These findings provide scientific support for water resources management and ecological conservation in the Ganhe River Basin. Full article

Flood risks are escalating under climate change, necessitating advanced methods to improve runoff prediction and multivariate flood-risk assessment. In this study, a physics–XGBoost-based Bayesian model averaging with bivariate copulas (PXB-BVC) framework was developed by integrating the Soil and Water Assessment Tool (SWAT), the Hydrologiska Byråns Vattenbalansavdelning (HBV) model, Extreme Gradient Boosting (XGBoost), Bayesian model averaging (BMA), and bivariate copulas. Spatially detailed underlying surface parameters including 30 m land-use data derived from the 2000 China land-use remote sensing monitoring data were pre-processed and reclassified using ArcGIS to support spatially explicit hydrological simulation. The framework was applied to the Xiangxi River Basin (XXRB), China, under four general circulation models and three shared socioeconomic pathways. PXB-BVC improved daily runoff simulation by combining process-based hydrological information with nonlinear machine learning correction, achieving Nash–Sutcliffe efficiency (NSE) values of 0.95 during calibration and 0.89 during validation. Future runoff generally increased from the near-term to the late-century period, with stronger changes under SSP585 and Sen slopes reaching up to 0.46 m³ s⁻¹ yr⁻¹, although the magnitude and significance of trends varied among GCMs. The dependence structures among flood peak, flood volume, and flood duration showed non-stationary behavior under future climate forcing, with Kendall’s tau for peak–volume pairs mostly ranging from 0.6 to 0.8. The revised bivariate return-period analysis further indicates that inferred flood-risk changes depend on the joint risk definition. Under SSP245 and ACCESS-ESM1–5, OR-type joint return periods show that representative near-future 50-year events may become more frequent in 2061–2100, whereas AND-type return periods show weaker and less uniform changes among flood-characteristic pairs. Conditional probability analysis also indicates enhanced compound risk under high-emission conditions: given an extreme peak flow, the probability of accompanying high flood volume increases from 0.23 to 0.56, while the probability of prolonged duration increases from 0.18 to 0.45. These results demonstrate that the PXB-BVC framework can support non-stationary multivariate flood-risk assessment and provide useful information for climate-resilient water-resource management and infrastructure planning. Full article

Accurate daily runoff prediction is essential for flood control, reservoir operation, and scientific water resources management. However, runoff processes are increasingly affected by climate change and human activities, leading to pronounced nonlinearity and nonstationarity that limit the performance of single data-driven models. This study aims to improve the reliability and hydrological credibility of daily runoff prediction by systematically evaluating recurrent neural network (RNN) structures and explicitly modeling prediction residuals. Three commonly used RNN architectures—long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM)—are systematically compared for daily runoff prediction in the Andi Reservoir watershed under identical hydrometeorological conditions. Based on the comparative results, BiLSTM is selected as the base model to capture dominant temporal dependencies. To further address systematic prediction errors under complex hydrological conditions, a residual-learning framework is constructed by integrating BiLSTM with extreme gradient boosting (XGBoost), in which XGBoost is employed to model and correct the nonlinear residuals of BiLSTM predictions. In addition, the Shapley Additive Explanations (SHAP) method is applied to interpret the contributions of input variables and to examine the learning mechanisms of both the base model and the residual-correction stage. Results indicate that BiLSTM performs better than LSTM and GRU for daily runoff prediction and that residual correction using XGBoost further enhances prediction accuracy and robustness, particularly under nonstationary conditions and peak-flow scenarios. The contribution of this study lies in providing a systematic modeling framework that combines model comparison, residual learning, and interpretability analysis to support more reliable daily runoff prediction in complex watersheds. Full article

Flooding in the Kathmandu Valley has intensified in recent years due to rapid urbanization, unregulated land-use change, and insufficient drainage infrastructure. Existing flood hazard assessments are often based on low-resolution datasets and lack proper field validation. This study presents an integrated flood modeling framework that combines Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs), field-based flood measurements, and hydrological simulations to assess urban flood hazards in the Bagmati-Nakkhu confluence, Nepal. High-resolution UAV-derived DEM and field survey data, including flood marks and high-water levels, were used as the foundation for the analysis. Hydrological modeling was conducted using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) to estimate the peak discharges of the Nakkhu River (2000–2024), which were then used to derive design flows for return periods of 5 to 150 years using the Gumbel distribution. These flows were used as boundary condition inputs for the Hydrologic Engineering Center—River Analysis System (HEC-RAS) to simulate flood depth and inundation extent under different scenarios. Flood extents for the 27 September 2024 event were derived from Sentinel-2 imagery and validated against surveyed flood marks. Additionally, land use/land cover (LULC) mapping based on UAV data was used to support flood impact analysis. The results show that flood depths ranged from approximately 0.5 m to 2.8 m, with inundation areas increasing by 35–50% under extreme rainfall. Model validation demonstrated strong agreement with simulated results, with deviations generally within ±0.3–0.5 m. Scenario analysis further indicates that urban expansion significantly increases runoff and flood extent, particularly in low-lying areas near the river confluence. Socio-economic exposure analysis for the 27 September 2024 event indicates that approximately 2569 residents (56.4% of the study zone population) and 4.011 km (77.42%) of the local road network were exposed to inundation. Overall, the results demonstrate that integrating high-resolution UAV data, field observations, and hydrological modeling greatly improves the accuracy and reliability of flood hazard assessments in data-scarce urban environments. Full article

This study established a refined, distributed SWAT modeling framework that integrates elevation-band and snowmelt modules to reconstruct the alpine hydrological and sediment cycles of the Koshi River Basin (KRB) over the period 1990–2024, with climate scenarios constructed using the delta change approach. The KRB, a major transboundary watershed traversing China, Nepal, and India, was selected owing to its critical hydro-climatic role under the destabilizing “Asian Water Tower”; it generates substantial sediment yield, hosts the densest concentration of hydropower potential within the Ganges system, and spans an extreme vertical gradient from Mount Everest to the southern alluvial plains. Results reveal accelerated warming at a rate of 0.21 °C per decade and an overall warming–wetting trend, punctuated by an abrupt interdecadal shift around 2015. Precipitation dominated interannual streamflow variability, with enhanced rainfall triggering basin-wide sediment surges that overwhelmed the natural buffering capacity of the land surface. Conversely, rising temperatures intensified actual evapotranspiration, markedly depleting soil water and reducing total water yield and monsoon runoff, although sustained snow and glacier melt effectively elevated the dry-season low-flow baseline. The integrated climate forcing reshaped the disparity between hydrological extremes, imposing severe seasonal eco-hydrological stress that manifested as a pre-monsoon deficit in terrestrial green water and acute summer sediment outbursts for aquatic habitats. Furthermore, the flood regime exhibited an altered distribution, with mid-to-high frequency floods enhanced while low-frequency extreme flood peaks declined. The hydro-sedimentological regime consequently exhibits pronounced nonlinear responses to climate change, providing a critical, threshold-based scientific foundation for adaptive transboundary water resource management. Full article

Sustainable Urban Drainage Systems (SuDSs) represent a contemporary and eco-friendly method for managing surface water, with the goal of reducing flooding impacts while preserving the environment and enhancing water quality and biodiversity. In Bari, recurrent flooding stemming from water stagnation, extreme weather, and urban development poses challenges to sustainable growth. This study applies the ‘Sponge city’ concept to address these issues through an evaluation of current urban permeability and the implementation of Nature-Based Solutions (NBSs) to reduce runoff and manage underground flows. By assessing the climatic conditions and hydrological factors contributing to urban stagnation, this project seeks to create a resilient urban environment capable of adapting to climate change and effectively mitigating both significant and minor rainfall events. It aims to reduce runoff, while also promoting groundwater recharge and alleviating saline contamination effects in coastal areas, ultimately enhancing the safety and livability of urban landscapes. Full article

Climate change is expected to significantly alter hydrological regimes in high-altitude tropical basins, where water availability strongly depends on precipitation variability and groundwater processes. The Ramis River basin, a major tributary of Lake Titicaca in the Peruvian Altiplano, is particularly vulnerable to hydroclimatic variability due to its dependence on seasonal water resources. This study evaluates the impacts of climate change on runoff, groundwater recharge, percolation, and renewable water resources using the SWAT hydrological model calibrated and validated for the period 1981–2024. Future projections were developed using the MPI-ESM-MR and ACCESS1-0 global climate models under RCP 4.5 and RCP 8.5 scenarios for the period 2025–2100, applying bias correction through CMhyd. The results indicate a strong sensitivity of basin hydrology to climate forcing. Under the MPI-ESM-MR model, runoff decreases by up to 68% under RCP 4.5, while extreme increases exceeding 130% are projected under RCP 8.5. In contrast, ACCESS1-0 shows moderate reductions in most scenarios. Renewable water resources exhibit a general declining trend (−23% to −41%), suggesting increasing water scarcity conditions. Additionally, the Standardized Precipitation Index (SPI) reveals a higher frequency and persistence of drought events toward the end of the century, particularly under high-emission scenarios. Overall, the findings indicate that the Ramis River basin may face a dual hydroclimatic risk characterized by reduced water availability and increased hydrological extremes. These results highlight the need to integrate climate projections into water resource management and to implement adaptive strategies to reduce future water vulnerability in high-Andean basins. Full article

Human-induced climate change has rendered urban areas highly vulnerable to extreme events such as heatwaves, droughts, and floods. This study conducts a scoping review of extreme and compound climate hazards in Iranian urban areas under global warming conditions. Mapping the available literature, 92 authoritative scientific works published between 1999 and 2025 were analyzed. The review synthesizes evidence on the spatiotemporal patterns of heatwaves, drought, torrential rainfall, sea-level rise, and compound hazards across Iran. The results indicate that central, northwestern, eastern, and southern Iran experience the highest heatwave intensity and frequency, with short-duration heatwaves being more common than prolonged ones. Western Iran faces a high risk of torrential rainfall, but urbanization amplifies flood consequences by expanding impervious surfaces and accelerating surface runoff. Coastal areas show high vulnerability to compound flooding due to sea-level rise and storms. The review further reveals that Iran is experiencing hydroclimate whiplash (abrupt transitions between drought and flood) driven by global warming. The study concludes by presenting management suggestions and future research directions for integrated compound hazard management in Iran. Full article

Climate change and rapid urbanization increasingly threaten water security in large river basins, yet existing assessments often fail to capture the multi-scale interactions between hydroclimatic extremes and human activities. To address this gap, we developed an integrated framework combining risk assessment, multi-method driver diagnosis (Geodetector, Multi-Scale Geographically Weighted Regression (MGWR), and Structural Equation Modeling (SEM)), and Zoned Management. Using a landscape-derived Ecological Risk Index (ERI) as a proxy indicator of runoff and non-point source potential, based on established empirical linkages between landscape metrics and hydrological processes, we applied the framework to three major urban agglomerations in the Yangtze River Basin from 2000 to 2020. Our results reveal three distinct risk mechanisms: in the Chengdu–Chongqing area (CYUA), a 165.8% increase in impervious surfaces drives altered runoff; in the Middle Reaches (MRC), the q-value of the Standardized Precipitation Index (SPI) rose from 0.017 in 2000 to 0.146 in 2020, corresponding to a 759% relative increase. Although the absolute q-value of SPI remains moderate at around 0.15, its rapid rise suggests increasing hydrological sensitivity of the MRC’s river–lake system to precipitation extremes; in the Yangtze River Delta (YRD), socioeconomic activities exert overriding pressure. Based on these diagnostics, we propose tailored strategies for water environment management, adaptive planning, and disaster mitigation. This framework offers a scientific basis for differentiated water governance in large river basins facing coupled anthropogenic and hydroclimatic pressures. Full article

Rainfall-induced instability of gravelly silt slopes is strongly affected by infiltration, runoff erosion, pore water pressure evolution, and particle-scale degradation. In this study, laboratory rainfall model tests were conducted on gravelly silt slopes under three extreme rainfall intensities of 80, 120, and 160 mm/h, and an FVM-DEM coupled model was developed to investigate the associated hydromechanical response and failure mechanism. The tested soil was obtained from the Shanghai East Railway Station project, and the 30% gravel content was selected to represent the typical field condition. Pore water pressure gauges and laser displacement sensors were used to monitor the infiltration response and slope deformation. The results show that all three slopes developed shallow instability, but the deformation rate and failure mode changed with rainfall intensity. Under the tested infiltration-excess conditions, the additional rainfall mainly increased surface runoff, toe erosion, and failed mass mobility rather than proportionally increasing the infiltration depth. The numerical results further indicate that failure evolved through equivalent fine matrix mobilization, gravel destabilization, skeleton collapse, and matrix-entrained gravel movement. These findings clarify the progressive instability mechanism of gravelly silt model slopes under extreme rainfall and provide experimental evidence for slope protection under short-duration, high-intensity rainfall. Full article

Against the backdrop of global warming and the ‘warming and wetting’ trend in north-western China, changes in seasonal snowpack and glacial ice in high-altitude cold regions directly impact water security in inland river basins. At present, there is a paucity of systematic research concerning the long-term evolution of snow and ice cover, multi-scale climate responses and future trends in the source region of the Keriya River on the northern slope of the Kunlun Mountains. To address this, this study utilised Landsat remote sensing imagery and meteorological station data from 2005 to 2024. Employing a multi-model fusion framework that integrates various machine learning and time-series models—including random forests, gradient boosting trees and ARIMA—the research incorporated trend factors, climate cycle identification and probabilistic modelling of extreme events to systematically analyse the spatiotemporal variability of snow/ice coverage and its multiscale coupling relationships with air temperature and precipitation. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. The results indicate that: (1) the annual average snow/ice cover percentage in the study area shows a non-significant decreasing trend (−0.69%/year, p > 0.1); within the year, it exhibits a pattern of accumulation in winter and melting in summer, with a peak in January (average 63.2%) and a trough in August (average 11.6%); (2) snow/ice cover percentage increases significantly with altitude; the annual average SICP in the <2000 m elevation zone is 5.2%; in the 2000–3000 m and 3000–4000 m altitude ranges, this rises to 5.7% and 8.3%, respectively, representing the primary seasonal snow/ice distribution zones; in areas above 6000 m, the annual average reaches 70.3%, constituting a zone of perennial stable snow/ice cover; (3) the relationship between snow/ice and temperature and precipitation exhibits significant time-scale dependence: correlations are weak on an annual scale (temperature R = −0.25, precipitation R = −0.14), but significantly strengthen on a monthly scale and exhibit seasonal differentiation; during the melting season, temperature exerts a dominant negative influence (August R = −0.35), whilst during the accumulation season, solid precipitation provides a positive supplement (February R = 0.34), with the strongest correlation with temperature occurring in September (R = −0.50); (4) it is projected that between 2025 and 2044, snow and ice cover will follow a fluctuating downward trend (averaging an annual decrease of roughly −0.12%), falling to approximately 29% by 2044; at the same time, temperatures are expected to continue rising (+0.035 °C per year), whilst precipitation will increase slightly (+0.4% per year). The results of this study provide a sound scientific basis for formulating sustainable water resource management strategies for the northern flank of the Kunlun Mountains and optimising measures to regulate snowmelt runoff. They are of great importance for safeguarding the stability of the oasis ecological systems in the Keriya River basin and ensuring the sustainable development and utilisation of water resources. Full article

Rain-on-snow (ROS) is a hydrometeorological phenomenon in which liquid precipitation falls onto an existing snowpack, augmenting runoff through the combined effects of rainfall and accelerated snowmelt. Anthropogenic climate change is progressively shifting the rain-to-snow partitioning of precipitation and altering land-surface conditions across mid- to high-latitude mountainous regions, thereby heightening flood potential. Most previous work, however, has addressed ROS at regional scales and over historical periods; hemispheric-scale assessments of future ROS dynamics and their implications for flood hazard and societal exposure remain scarce. Here we apply 10 bias-corrected CMIP6 models together with ERA5-Land reanalysis data to project changes in ROS days across the Northern Hemisphere under four Shared Socioeconomic Pathway (SSP) scenarios. ROS days are coupled with flood frequency analysis to quantify changes in ROS flood occurrence, and gridded population and Gross Domestic Product (GDP) data are integrated to evaluate future population-socioeconomic exposure. Under low-to-medium emission scenarios, ROS days increase substantially over historical hotspots, whereas under high-emission scenarios they decline at mid- to high latitudes yet expand into previously unaffected high-latitude and inland cold regions. ROS flood days respond nonlinearly to ROS frequency because progressive snow water equivalent loss limits runoff generation, causing ROS floods to decrease in some mountainous areas even as ROS events become more frequent. Population-socioeconomic exposure exhibits a corresponding polarization: it declines in mid-latitude regions where snow cover is disappearing but rises sharply at high latitudes, with high-emission pathways accelerating the northward migration of disaster risk. These findings bridge critical gaps in large-scale ROS climatology and shed light on future changes in ROS-induced hydrological extremes. Besides, the findings facilitate the creation of regionally focused adaptation strategies and provide useful references for integrating climate model projections with remote sensing observations to improve future monitoring and risk assessment of ROS-related floods. Full article

Land degradation caused by soil erosion is a major challenge in Mediterranean sloping agroecosystems, where extreme weather events and conventional land management practices accelerate soil loss and threaten long-term sustainability. This study evaluates and compares three complementary approaches to estimate soil erosion in an olive orchard in Messenia, Greece. Field-based runoff plots provided direct measurements of sediment yield, drone-based Light Detection and Ranging (LiDAR) surveys enabled soil surface change detection through the Difference of Digital Elevation Models (DoD) method, and the Revised Universal Soil Loss Equation (RUSLE) was applied to model erosion risk using site-specific parameters. Results indicate that field measurements and RUSLE estimates are broadly consistent, particularly when the model is calibrated with empirical data, offering reliable insights into soil loss dynamics. In contrast, the LiDAR-DoD analysis identified patterns of soil surface displacement, which reflected spatial variation in surface change across the olive orchard. Overall, the integration of field monitoring, remote sensing, and modeling highlights the strengths and limitations of each method and demonstrates the value of multi-method approaches for improving erosion assessment and supporting sustainable land management in vulnerable Mediterranean landscapes. Full article

Global sediment reduction threatens deltaic sustainability and channel stability. While climatic and anthropogenic drivers are recognized, their cross-scale interactions remain poorly understood. This study investigated area-specific sediment yield (SSY) and its driving mechanisms across 14 stations (1.9 × 10⁴ to 1.0 × 10⁶ km²) in the Upper Yangtze River Basin (UYRB) from 1960 to 2018 using PLS-SEM and power-law scaling. Results show that by 2018, reservoir capacity reached 165.5 billion m³, regulating 38% of annual runoff. SSY significantly declined at 12 of 14 stations, with abrupt change points clustering around 1985. We found that intensive human interventions have fundamentally restructured the natural scale dependency of SSY, with the scaling exponent (β) shifting from a stable near-zero value to violent fluctuations (−0.2 to 0.5). Temporally, the dominant driver transitioned from hydro-climatic factors to dam-induced regulation. Spatially, the “filtering effect” of dams intensified with increasing drainage area, whereas smaller watersheds remained disproportionately sensitive to extreme precipitation. This scale-based divergence reveals a critical vulnerability: while mega-dams mitigate sediment at the basin scale, smaller catchments face elevated risks of high sediment delivery under intensifying climate extremes. These findings provide evidence of human-induced scaling instability in a large river system and highlight the necessity of scale-sensitive governance to ensure geomorphic and ecological resilience worldwide. Full article