Search Results (3,998)

Urbanisation reshapes Land Cover and Land Use (LCLU) by driving deforestation, wetland loss, and the conversion of natural and agricultural areas into built environments. However, integrated analyses of LCLU change in response to climate variability in topographically complex, rapidly urbanising African cities remain limited. Therefore, this study examined 2000–2024 LCLU changes in hilly Gasabo District (Kigali, Rwanda) using 30 m Landsat imagery and a Random Trees classifier (92.7% accuracy, 70/30 train-test split), with 2032 projections via a population-driven hybrid trend model. Population estimates/projections 320,516 in 2002 to 967,512 in 2024, 1.41 million by 2032, were derived from Rwanda’s census data and exponential growth modelling (calibrated to 5.05% annual growth). Rapid population growth has driven a 539% expansion of Built-up Areas, accompanied by notable declines in cropland and Forest. Local climate trends (Meteo Rwanda stations) aligned with global datasets (ERA5-Land and CHIRPS): rainfall fluctuation and temperature rose, with strong correlations between population-driven Built-up Areas expansion. From 2024 to 2032, LCLU projections indicate that Built-up Areas will continue to expand by 29.5%. Cropland was forecast to decline to 15.9%, while Forest loss slowed to 5.7%. MLR analysis revealed strong correlations between population-driven expansion of Built-up Areas, cropland/forest loss, warming, and rainfall fluctuations in Gasabo. An ARDL model was applied to address multicollinearity among LCLU predictors, which limited the interpretation of individual coefficients, and confirmed the core MLR correlation trends, with statistically significant (p < 0.05) coefficients. The results highlight the need for data-driven spatial planning in Gasabo (stricter zoning, high-rise buildings, targeted reforestation, climate-resilient green infrastructure) to mitigate population and urbanisation-driven environmental degradation. Full article

As a major ecological safeguard in northwestern China and an important corridor for the Belt and Road Initiative, the Hexi Corridor holds strategic significance for improving landscape structure and enhancing regional ecological security. Focusing on the Hexi Corridor, this study develops a landscape ecological risk (LER) index based on land use (LU) data from 2000, 2010, and 2020. The study employs ArcGIS spatial analysis and XGBoost-SHAP, an interpretable machine learning method, to analyze the spatiotemporal evolution of LU and LERs, as well as their driving factors. Furthermore, the Markov-FLUS model is utilized to simulate and predict LU and LER spatial patterns under multiple scenarios for 2030. The results show that: (1) The dominant land type in the Hexi Corridor is unused land, accounting for 67.33%. During the research period, the extents of unused land, grassland, and forestland showed a steady decline, while built-up land and cropland increased. (2) LERs are categorized into five types, with high risk being the most prevalent, accounting for 52.02%. Between 2000 and 2020, the total area of higher and high risks decreased by 4312 km², indicating an overall decrease in LER across the region. (3) LER is primarily influenced by annual rainfall, population density, distance to main roads, and distance to rivers. (4) Marked variations in LU patterns and LER are observed across different development scenarios projected for 2030. Full article

Karst aquifers present unique challenges for groundwater level prediction due to their dual-porosity structures and highly nonlinear hydrological responses. This study systematically evaluates nine machine learning and deep learning models (RF, XGBoost, LSTM, CNN, Transformer, N-BEATS, CNN-LSTM, Seq2Seq-LSTM, and Attention-Seq2Seq-LSTM) for rainfall-driven groundwater level forecasting in the Maocun subterranean river catchment, Guilin, Guangxi, China. Two years of hourly high-frequency data from three monitoring sites representing distinct hydrogeological zones (recharge, flow, and discharge) were employed within a multidimensional evaluation framework integrating single-step accuracy, multi-step stability, and computational efficiency. Results indicate that the Transformer achieved the highest single-step prediction accuracy, attaining the lowest RMSE (0.130–0.606 m) and highest R² (0.813–0.965) across all three sites. CNN-LSTM offered the best balance between predictive performance and computational cost, requiring an average training time of only 27.97 s and 28.0 convergence epochs. N-BEATS demonstrated superior long-term stability in 12-steps-ahead forecasting, achieving R² = 0.914 at ZK1, outperforming all other architectures. More fundamentally, hydrogeological complexity exerted a dominant control on predictive skill that systematically outweighed differences arising from model architecture. All models yielded R² below 0.813 at the geologically complex ZK2 site, whereas R² exceeded 0.950 across all models at ZK1, indicating that aquifer complexity, rather than algorithm selection, constitutes the primary constraint on prediction feasibility. This study presents the first application of N-BEATS to karst groundwater level forecasting and proposes a replicable multidimensional evaluation framework, providing a scientific reference for intelligent modelling of complex karst systems. Full article

The Galápagos Islands, located in the eastern equatorial Pacific approximately 1000 km west of mainland Ecuador, are highly sensitive to the El Niño–Southern Oscillation. However, the mechanisms linking sea surface temperature (SST) variability to daily rainfall extremes remain poorly understood. Focusing on Santa Cruz Island, one of the main islands of the archipelago, we analyzed the response of daily rainfall to four El Niño events (1991–1992, 1997–1998, 2015–2016 and 2023–2024) and their relationship with SST spatial patterns. Our approach followed three steps: (1) Daily rainfall observations were classified using percentile thresholds; (2) SST spatial clusters were identified using Local Indicators of Spatial Association (LISA), which explicitly incorporates spatial autocorrelation to distinguish warm and cold SST spatial clusters; and (3) SST cluster metrics (mean temperature, spatial extent, and persistence) were extracted and related to rainfall intensification. Results show that El Niño can increase daily extreme rainfall (>P95) in frequency and in totals, with the strongest and most persistent signal during 1997–1998; in contrast, the 2015–2016 event, despite being classified as very strong by the Oceanic Niño Index (ONI), exhibited a limited and short-lived >P95 rainfall response in Santa Cruz. The link between SST clusters and extreme rainfall strengthened during El Niño (r from ~0.40 to 0.70). Correspondingly, SST clusters underwent significant spatial reorganization in their extent and persistence. Contrasts were most evident in the central–southern domain, where 1997–1998 showed strong warm incursion and persistent ≥28 °C coverage, while 2015–2016 remained more spatially constrained and less coherent. The area where clusters reached mean SST ≥ 28 °C became widespread in 1997–1998 (98.55%), whereas it remained more localized in 1991–1992 (30.28%), 2015–2016 (27.02%), and 2023–2024 (26.55%) and was absent in neutral years (0%). Persistent warm-cluster coverage increased from neutral conditions (38.53%) in 1991–1992 (47.49%), 1997–1998 (53.42%), and 2023–2024 (42.97%), but was lower in 2015–2016 (34.53%). Overall, these results provide a process-oriented link between SST cluster organization and event-to-event differences in Galápagos rainfall extremes, highlighting the value of local SST metrics beyond basin-scale ENSO indices. Full article

Climate change and variable rainfall are pushing the wine industry to assess grapevine adaptability, as water deficit alters volatile compounds and understanding these processes is key to maintaining wine quality. A total of 64 compounds, free and glycosidically bound fractions, were analyzed using HS-SPME-GC×GC/ToFMS in Macabeo and Chardonnay grapes under two water irrigation regimes. Results showed that water availability significantly influenced aroma composition. Macabeo showed a strong response to rainfed conditions, with higher levels of monoterpenes, norisoprenoids and sesquiterpenes, mainly in the bound fraction, suggesting a metabolic adaptation to preserve aromatic potential. Chardonnay showed a more stable bound fraction and moderate changes in specific volatiles. These findings indicate that this advanced chromatographic technique allows a detailed evaluation of aroma precursors and their modulation by water availability. Full article

Precipitation is a fundamental element in terrestrial water circulation and ecosystem hydrological balance. The occurrence of concentrated precipitation is closely linked to vegetation growth and soil fertility rather than accumulated or averaged precipitation. Despite its importance, the characteristics of continuous precipitation and its specific effects on vegetation cover remain uncertain. In this study, we formulated a new continuous precipitation index system, including $C{P}_d$ (continuous precipitation days); $AC{P}_t$ (annual continuous precipitation times); $C{P}_a$ (continuous precipitation amount); and $FCP$ (frequency in different ranges of $AC{P}_a$). We utilized daily precipitation data from 467 meteorological stations across China, which were divided into eight vegetation type regions. We observed that the spatial distribution of continuous precipitation differed to varying degrees from accumulated precipitation. The national average of $MAC{P}_a$ for a single event was 16.7 mm, ranging from 3.8 mm in the temperate desert region to 37.1 mm in the tropical monsoon forest and rainforest region. Similarly, the national average of $MC{P}_d$ ($MMC{P}_d$) for a single event was approximately 2.3 or 9 days. At the regional level, the tropical monsoon forest and rainforest region experienced the longest $MMC{P}_d$. Furthermore, the national average of $MAC{P}_t$ occurrences for 1 year was 57.7 times, varying from 29.8 times in the temperate desert region to 77.9 times in the tropical monsoon forest and rainforest region. Vegetation responses to precipitation regimes exhibit significant regional heterogeneity across China. Our analysis reveals that $MAC{P}_t$ and $M{P}_a$ show markedly positive correlations with vegetation growth. In subtropical monsoon climate zones, particularly the Yunnan–Guizhou Plateau and Qinling Mountains, $MAC{P}_t$ demonstrates strong positive correlations (r = 0.6–1.0) with NDVI, where sustained rainfall provides stable moisture availability for vegetation. While a positive correlation between vegetation (NDVI) and mean annual consecutive precipitation is observed in some arid northern regions, in ecosystems such as the Loess Plateau (TG/TM), vegetation growth shows greater dependence on $M{P}_a$, highlighting the crucial role of total precipitation amount in water-limited ecosystems. Notably, extreme precipitation events display dual effects on vegetation dynamics. Prolonged heavy rainfall ($MMC{P}_d$/$MMC{P}_a$) exhibits significant negative impacts on NDVI (r = −1.0 to −0.6) in topographically complex regions, including the Hengduan Mountains and Yangtze River Basin (SE), likely due to induced soil erosion and waterlogging stress. Our findings underscore the importance of incorporating continuous precipitation indices to evaluate and forecast the influence of precipitation on ecosystem stability. This understanding is vital for developing informed conservation and management strategies to address current and future climate challenges. Full article

Heavy precipitation in Northeast China is frequently modulated by the Northeast China Cold Vortex (NCCV), although the microphysical processes within squall lines under such conditions remain insufficiently understood. This study presents a comprehensive analysis of an NCCV-influenced squall line in Liaoning Province, utilizing coordinated S-band polarimetric radar and surface disdrometer observations. The raindrop size distribution (DSD) characteristics and three-dimensional microphysical structure are systematically examined for both convective and stratiform regimes. A comparative analysis of DSD and warm-rain microphysical mechanisms is also conducted with a Mei-yu event. Results show that convective rain in the NCCV squall line exhibits a continental-type DSD, characterized by fewer but larger raindrops compared to other heavy rainfalls in China. In contrast, the Mei-yu frontal convection under NCCV influence exhibits a transitional DSD pattern between the maritime and continental types, with raindrops smaller and denser than those in the NCCV squall line. Vertical structure of the mature squall line shows prominent differential reflectivity (Z_DR) and specific differential phase (K_DP) columns above the melting level within the convective region, indicating vigorous riming growth of graupel and hail driven by strong updrafts. Meanwhile, the stratiform region is characterized by ice crystals and aggregates, formed primarily through deposition and aggregation processes. The subsequent melting of ice-phase particles followed by collision–coalescence and evaporation-driven size sorting shapes the large but sparse raindrops in the NCCV squall line. Comparison with Mei-yu convection demonstrates that surface DSD is shaped by environmental conditions and vertical microphysics. The drier, more unstable environment in the NCCV squall line favors deep convection with active ice-phase processes, while the relatively moist and stable environment of the Mei-yu convection supports shallower convection dominated by warm-rain processes. Future multi-case studies with integrated observations are needed to quantify how environmental and aerosol conditions modulate these heavy precipitation processes. Full article

Sediment connectivity is a key indicator of whether eroded sediment can be efficiently transported within a catchment. Landslides are a major form of rainfall-induced erosion on the steep slopes of the Loess Plateau and contribute substantially to overall catchment sediment yield. However, evaluating the connectivity of landslide-derived sediment and its implications for sediment transport risk remains challenging. Therefore, field investigations were conducted in three watersheds (R1, R2, and R3) on the Loess Plateau to examine landslides triggered by rainstorms. We analyzed the characteristics of landslide erosion and its influencing factors, applied graph theory to investigate sediment connectivity after landslides occurred, and assessed the risk of sediment transport to the catchment outlet. The results showed that the landslide number densities in the catchments R1, R2, and R3 were 9, 155, and 214 km⁻², respectively. The average erosion intensities were 25,153, 53,074, and 172,153 t km⁻², respectively. The network analyses indicated that the locations of landslides within the catchments were primarily concentrated in areas with high transport networks and high sediment accessibility to the catchment outlets. The sediment connectivity index further showed that 59%, 43%, and 51% of landslides in the three watersheds, respectively, were at high risk of delivering sediment to the catchment outlet. Accordingly, measures such as slope drainage and gully dam construction may help reduce both landslide occurrence and sediment transport. These findings provide new insights into the transport risk of eroded sediment from a connectivity perspective, identify hotspot areas of sediment connectivity and landslide erosion, and support the targeted prevention and control of catchment erosion. Full article

Soil erosion in mountain environments is governed by the interaction of climatic drivers, surface conditions, and geomorphic connectivity. Recently, disturbance by large herbivores has been recognized as a potentially important but poorly quantified geomorphic driver. However, the combined effects of freeze–thaw processes and ungulate disturbance on sediment production remain unclear. This study provides quantitative field-based evidence linking deer activity to hillslope sediment flux in a montane forest catchment in central Japan. A six-year dataset (2019–2025), including climatic conditions, deer detections from camera traps, understory vegetation cover, and hillslope sediment flux (<9.5 mm) was analyzed. Multiple regression analysis was conducted using daily sediment flux as the response variable and maximum 1 h rainfall, freeze–thaw frequency, and daily deer detections as explanatory variables. The results showed that deer detections had a significant positive effect on sediment flux, whereas rainfall intensity and freeze–thaw frequency did not exhibit strong independent effects. Particle-size analysis further indicated that eroded sediment was markedly coarser than the surface soil, suggesting that short-term climatic drivers alone did not control sediment transport. These findings demonstrate that biotic disturbance by large herbivores can play a dominant role in hillslope sediment flux under cold, high-elevation conditions by modifying surface conditions and sediment connectivity. From a sustainability perspective, these results highlight the importance of managing deer populations to maintain ecosystem stability, prevent land degradation, and support sustainable forest and watershed management under changing environmental conditions. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. 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

This study evaluates the impact of assimilating the Crustal Movement Observation Network of China (CMONOC) global navigation satellite system (GNSS) tropospheric products on heavy-rainfall simulation over the complex terrain of the Sichuan Basin. Using the Weather Research and Forecasting model with the WRF Data Assimilation (WRF/WRFDA) three-dimensional variational (3DVar) system, we conducted a control (CTRL) experiment and a data-assimilation (DA) experiment for a primary heavy-rainfall event during 10–12 August 2020. The DA experiment applied 6 h cycling assimilation of station-based zenith total delay (ZTD) and precipitable water vapor (PWV). Compared with CTRL, DA improved the placement of the primary rainband and the depiction of peak rainfall. On 10 August, the observed rainfall core (~40 mm) over the northwestern basin was underestimated in CTRL (~15 mm) but was strengthened in DA (~25 mm). Hourly verification at a threshold of 2 mm h⁻¹ showed a higher maximum Threat Score (TS) in DA (0.292) than in CTRL (0.250), and the largest instantaneous gain reached 0.061. For 72 h accumulated precipitation, TS was higher in DA across multiple thresholds (≥10, ≥25, ≥50, and ≥100 mm), with the most pronounced improvement for heavier rainfall categories. Diagnostic analysis indicates that GNSS assimilation introduces dynamically consistent low-level moistening and strengthened convergence at 850 hPa, together with a better-aligned vertical ascent structure during the key stage of the event. An additional heavy-rainfall event during 21–23 August 2021 was further examined as a compact robustness test, and the results showed a generally consistent improvement in precipitation distribution and TS after GNSS assimilation. Overall, the present results suggest that cycling assimilation of CMONOC GNSS ZTD/PWV products can provide effective moisture constraints and improve heavy-rainfall simulation over the Sichuan Basin in the examined cases. Full article

Satellite-based precipitation estimates (SPE) provide essential spatial coverage and near real-time availability for hydrological applications but often exhibit systematic biases in regions characterized by complex terrain and strong climatic variability, limiting their reliability for flood-related studies. To address these limitations, this study proposes an Adaptive Regularization framework integrated within a Long Short-Term Memory (LSTM) model to enhance satellite–gauge rainfall fusion beyond conventional optimization strategies. The framework dynamically adjusts learning rate and weight decay during training based on validation performance and overfitting indicators, improving training stability, data efficiency, and model generalization across diverse precipitation regimes. The proposed approach was applied to refine Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) daily rainfall estimates over the flood-prone east coast of Peninsular Malaysia. Model performance was assessed against ten optimization algorithms using correlation coefficient (CC), mean absolute error (MAE), normalized root mean squared error (NRMSE), percentage bias (PBias), and Kling–Gupta efficiency (KGE). Results show that the Adaptive Regularization framework consistently outperforms all benchmark optimizers, achieving an MAE of 6.87, CC of 0.68, NRMSE of 1.84, and KGE of 0.56. Overall, the proposed framework enhances spatial consistency and robustness across monsoon seasons, offering a scalable solution for improving SPE in flood-prone regions. Full article

Climate change and rapid urbanization are intensifying urban pluvial flooding and threatening sustainable urban development. This study proposes a three-stage, four-dimensional framework (TSFD-UPFR) to assess urban pluvial flood resilience across resistance, response, and recovery phases that integrate natural, infrastructural, social, and economic dimensions. Using a representative urban catchment affected by a typical extreme rainfall event, we couple hydrological–hydrodynamic simulations with multi-source remote sensing and socio-economic indicators at a 100 m grid resolution to enable spatially explicit assessment. The results indicate moderate overall resilience with pronounced spatial heterogeneity. Resistance is primarily constrained by drainage capacity and impervious surfaces, response is shaped by road connectivity and public service accessibility, and recovery is determined by essential facility restoration and economic support. Low-resilience clusters are concentrated in dense built-up areas and transport hubs, revealing structural weaknesses in adaptive capacity. By linking flood processes with socio-economic recovery dynamics, the framework captures cross-stage interactions within urban systems. The findings support climate-adaptive planning, targeted infrastructure investment, and resilience-oriented governance, contributing to sustainable and equitable urban transformation in megacities facing intensifying extreme rainfall. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article