Search Results (895)

Wetland restoration plays a crucial role in enhancing hydrologic resilience amidst the challenges posed by climate change and evolving land uses. The historical reduction in beaver populations due to the fur trade and alterations to riparian zones have compromised the ecological stability of many landscapes. Presently beaver populations are increasing as there are now protections in place for them. In response, Beaver Dam Analogs (BDAs) have emerged as an effective restoration strategy, particularly in regions where natural beaver activity is limited due to inadequate habitat conditions. BDAs are a human-made structure that mimics the function and form of natural beaver dams. This paper focuses on a restoration project within the Fish Creek area between the year 2019 and 2021, which is a part of the Weber River watershed in northern Utah, where BDAs were installed to rehabilitate a degraded wetland and rectify an incised channel network. Over the initial two years following the installation (2019–2021), significant ecological transformations were observed. Notably, there was an increase in the areal coverage of sediments that sizes ranged from 1 to 256 mm within the stream channel, alongside a corresponding decrease in coarser substrates. These changes facilitated a reduced channel slope, indicating substantial sediment deposition above the installed BDAs. Concurrently, there was an expansion in riparian vegetation along an approximate stretch of 40 m, primarily grasses, reflecting an adjustment in habitat conditions favorable to riparian recovery. The preliminary outcomes from this study contribute to a broader understanding of the dynamics involved in BDA-driven restoration efforts in semiarid regions like the western United States, highlighting the potential shifts in riparian habitats prompted by such interventions. Full article

Evapotranspiration (ET) is a key component of the terrestrial water and energy cycle, and its long-term dynamics are essential for regional hydrological assessment in subtropical China. In this study, two widely used satellite-based ET products, MOD16 and PML-V2, were selected for intercomparison because they provide consistent spatial (500 m) and temporal (8-day) resolutions. Validation against flux observations showed that PML-V2 performed better than MOD16 and was therefore used for subsequent analysis. Based on the 500 m, 8-day PML-V2 dataset, the spatiotemporal variation in ET in subtropical China during 2001–2020 was examined using the Theil–Sen slope estimator, Mann–Kendall test, and Hurst exponent. To identify the most relevant controls on ET variation, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to screen environmental factors and rank their relative importance. Multiple linear regression (MLR) was then applied only to the selected dominant factors to quantify their contributions. Residual analysis was used to distinguish climate–vegetation effects from residual influences, which could arise from human activities and unmodeled natural processes. The results showed that annual ET averaged 669 mm and increased significantly at a rate of 2.03 mm yr⁻¹ from 2001 to 2020, with an accelerated increase after 2010. Spatially, ET exhibited clear gradients from south to north and from coastal to inland regions. Downward shortwave radiation (SWDown) and leaf area index (LAI) were the dominant drivers over most of the study area, although their controls varied geographically, with northern subregions being more energy-limited and southern subregions being jointly influenced by vegetation and temperature. Residual ET trends largely coincide with cropland and urbanising areas, indicating a partial influence of human activities, while in subregions such as XM, complex terrain and hydrological heterogeneity suggest that unmodeled natural processes may dominate. These findings enhance understanding of ET dynamics in subtropical China and demonstrate the value of high-resolution remote sensing products for regional hydrological monitoring and driver attribution. Full article

Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95, $\kappa$ = 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management. Full article

Cropland non-grain use has become an important challenge for food security and cropland governance in rapidly urbanising agricultural regions, yet its trajectory heterogeneity and the divergence between current spatial patterns and long-term-change mechanisms remain insufficiently understood. Taking the Changsha–Zhuzhou–Xiangtan (CZT) urban agglomeration in China as a case, this study quantified the cropland non-grain rate (NGR) on a 1 km grid for 2000, 2010, and 2020, classified grid-level transition trajectories, and developed three temporally structured eXtreme Gradient Boosting (XGBoost) models with spatial block cross-validation, Shapley additive explanations (SHAP) interpretation, and geographically explicit SHAP (GeoSHAP) local attribution. The results show that low-NGR and stable grids formed the dominant regional background, while recent NGR increases were mainly concentrated along the urban development corridor and metropolitan fringe. Current NGR status and long-term NGR change showed divergent explanatory structures. The current spatial pattern was mainly associated with terrain constraints and contemporary urban pressure, whereas long-term change was more strongly conditioned by baseline urbanisation and subsequent urban–environmental changes. Nonlinear dependence analysis further identified model-derived response zones related to slope, impervious surface conditions, hydrothermal change, and hydrological proximity. GeoSHAP mapping revealed that locally dominant mechanisms varied substantially across the study area, indicating that cropland non-grain use was shaped by spatially heterogeneous combinations of terrain, urbanisation, hydrothermal background, and hydrological context. These findings support a shift from aggregate status monitoring toward trajectory-specific and mechanism-differentiated cropland management in urban agglomerations. Full article

This study presents a comprehensive analysis of a landslide that occurred in February 2024 in the Tau-Samal district of Almaty, Kazakhstan. Characterized by rapid onset and anthropogenic influence, this event resulted from a complex interaction of environmental and anthropogenic factors. Specifically, the landslide was triggered by seasonal temperature fluctuations leading to multiple freeze–thaw cycles, localized microseismicity (magnitude 3.5 on 4 February 2024), and a major water main break resulting in localized flooding of loess soils. The study utilizes an integrated landslide susceptibility index (LSI) model, which combines the analytic hierarchy process (AHP) for factor weighting. Validation was conducted by comparing the spatial distribution of high-susceptibility zones derived from the LSI model with the actual location of the landslide. Geotechnical studies highlight the susceptibility of Almaty loess, focusing on parameters such as cohesion, internal friction angle, and liquefaction potential. The findings highlight the need for climate-adapted urban policies and improved geotechnical monitoring in high-risk loess areas. This study contributes to a regional understanding of Tien Shan geohazards by placing the Tau-Samal event within the broader context of seismically and hydrologically driven slope processes. Full article

Climate change remains among the most pressing environmental challenges confronting the world, exerting profound pressure on both ecological systems and socio-economic development. To advance understanding of the evolution patterns and driving mechanisms governing hydroclimatic systems in arid and semi-arid regions, this study employed an integrated framework encompassing trend testing, change-point detection, periodicity and persistence analysis, and machine learning-based attribution. Focusing on the Mu Us Sandy Land from 1982 to 2023, we systematically investigated the spatiotemporal evolution, periodic characteristics, and driving mechanisms of hydroclimatic factors. Furthermore, future climate risks were assessed using CMIP6 multi-model data. The results showed that: (1) All four variables exhibited positive slopes, but only soil moisture showed a statistically significant long-term wetting trend (β = 0.025 × 10⁻³, p = 0.0008) and a clear global abrupt change in 2011; the upward tendencies of precipitation (p = 0.3946), potential evapotranspiration (p = 0.4970), and surface runoff (p = 0.1097) did not reach the 0.05 significance level. (2) Meteorological elements showed weak periodicity and strong anti-persistence (mean Hurst index = 0.379 for precipitation and 0.222 for PET), whereas hydrological elements exhibited clear seasonal–interannual periods and more random future variability with greater spatial heterogeneity (mean Hurst index = 0.436 for runoff and 0.414 for soil moisture). (3) Monthly changes were mainly associated with local surface processes. Vegetation dynamics were key predictors of precipitation, runoff, and soil moisture, while potential evapotranspiration was dominated by atmospheric demand, with limited influence from large-scale climate indices. (4) Under high-emission scenarios, imbalanced water–heat increases may lead to a higher likelihood of drought conditions. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

The upper Sebou River occupies a strategic territory draining varied mountain reaches in northern Morocco. As such, it is rich in surface water resources and karst springs with important downstream uses. However, the variability of rainfall threatens its water potential, making it highly vulnerable and at risk of desiccation. This study explores rainfall trends and their effects on streamflow and water resource availability. Data from three stations representing the upstream section of the watershed, along with two streamflow series—one for the upper Sebou River (Pont Medz) and the other for the Aïn Timdrine karst spring—cover the period from 1956 to 2018. The methodology employs Mann–Kendall trend tests, Sen’s Slope test, and the Standardized Precipitation Index (SPI) for rainfall series, as well as the Streamflow Drought Index (SDI) for hydrological series. The results demonstrate a decline in rainfall since 1979, significant at the 5% threshold. This trend has an immediate impact on the flow rates of the area’s rivers and karst springs, which have also tended to decline, with a succession of dry years and seasons since 1980. This observation highlights the depletion of water resources of the fragile upper Sebou region in the face of decreasing rainfall and snowfall, compounded by the rampant and unsustainable exploitation of groundwater resources linked to the development of irrigated cash crops in the Middle Atlas Mountains. Full article

Land degradation and declining groundwater availability threaten the sustainability of rural livelihoods across semi-arid regions. This study evaluates the hydrological performance of contour-based trenches as a low-cost and replicable nature-based solution (Nbs) for soil restoration, runoff regulation, and potential distributed managed aquifer recharge (MAR) in Guerrero, Mexico. The structures were installed on 12% slopes and designed using a simplified water balance criterion based on trench storage capacity, runoff coefficient, and representative rainfall events. Each trench was constructed along contour lines with overflow notches and connecting micro-trenches to improve hydraulic continuity, reduce erosion, and enhance infiltration opportunities under degraded field conditions. After one year of field monitoring, the trenches reached an average filling efficiency of approximately 90% per effective rainfall event, with estimated infiltration rates ranging from 0.0069 to 0.011 L·s⁻¹. Soil moisture in the upper soil layer showed a relative increase of approximately 10–18% compared to adjacent untreated areas, while visible reductions in runoff velocity, sediment transport, and surface erosion were observed across the treated plot. Based on trench storage capacity, observed infiltration behavior, and assumed deep percolation fractions, the potential induced recharge was estimated between 216 and 360 m³·yr⁻¹ (43–72 mm·yr⁻¹). These values represent indicative plot-scale estimates rather than direct measurements of aquifer recharge, since no tracer studies or piezometric validation were performed. The results demonstrate that contour-based trenches contribute not only to infiltration enhancement and runoff control, but also to short-term soil restoration and improved water availability in rainfed agricultural systems. Their low-cost implementation, combined with community-based maintenance and adaptation to local environmental conditions, makes them a viable complementary strategy for strengthening decentralized water management, soil resilience, and climate adaptation in semi-arid rural landscapes. However, long-term effectiveness remains dependent on maintenance continuity, institutional support, and local governance conditions. Further multi-year monitoring and direct hydrogeological validation are recommended to improve the design and replicability of decentralized MAR systems. Full article

Accurately assessing and predicting vegetation dynamics is of great significance for evaluating regional hydrological and ecological environments. This study focuses on the climate-sensitive Qinghai-Tibetan Plateau (QTP), aiming to reveal the spatiotemporal patterns, underlying driving mechanisms, and future trends of vegetation dynamics. The historical turning points of greening trends were identified using the running slope difference method, and the SHapley Additive exPlanations (SHAP) method was employed to analyze the key driving factors. An Xtreme Gradient Boosting (XGBoost) prediction model was constructed and validated, and then coupled with Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble data to project seasonal vegetation changes under different Shared Socioeconomic Pathways (SSP). The main conclusions are as follows: (1) Vegetation on the QTP showed an overall greening trend with significant spatial heterogeneity. Approximately 47.25% of the area exhibited no trend shift (NS), while 29.42% experienced a shift from greening to browning (GB), with most shifts occurring between 1990 and 2010. (2) Soil moisture and precipitation were the dominant driving factors, with contributions significantly higher than those of temperature, wind speed, and other variables, and they exhibited nonlinear interactive effects with the Normalized Difference Vegetation Index (NDVI). (3) In the future, vegetation is projected to show an overall increasing trend, with stronger responses in spring and autumn. The regional average rate of change is highest in spring, especially under the SSP5-8.5 scenario (17.8% for 2030–2060 and 26.4% for 2061–2100); in autumn, although the regional average rate of change is small, the internal spatial variability is significant. The humid regions in the eastern and southeastern parts of the QTP demonstrated more active greening across all seasons except winter, and high-emission scenarios are expected to exacerbate regional and seasonal differences. This study systematically reveals the adaptive dynamics and future scenarios of vegetation dynamics on the QTP, providing scientific support for the adaptation of alpine ecosystems to global change and the management of regional ecological security barriers. Full article

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

Groundwater is one of the key factors affecting the changes and evolution of surface processes in arid regions, determining the direction and scope of the evolution of surface eco-hydrological processes. To achieve sustainable water resource management in arid areas, this study aims to systematically explore the dynamic changes in groundwater level and their ecological effects on the basis of multi-source remote sensing data by multivariate statistical methods. The results show that groundwater levels in the Bayin River Basin increased from 2895.35 m in 2005 to 2906.75 m in 2022 at a rate of 6.7 m/decade, driven by increased runoff and irrigation. Conversely, groundwater levels in urbanized areas near Delingha City slightly decreased by approximately 0.3 m/decade, with a general west-to-east declining spatial gradient. These changes have generated cascading ecological effects. Overall, rising groundwater has coincided with increased vegetation index, wetland extent, and soil moisture. Annual average NDVI rose from 0.18 in 2000 to 0.23 in 2022, an increase of 27.7%, and wetland area expanded from 349.25 km² in 2005 to 355.25 km² in 2022. Soil moisture content showed an insignificant upward trend form 0.14% in 2003 to 0.15% in 2022, with the slope of 0.01%/yr. However, soil salinization has exhibited an aggravating trend, with salinization index (SI) values of 0.25, 0.26, and 0.31 in 2000, 2010, and 2020, respectively. Affected by human activities and geological constraints, the ecological effects associated with groundwater level changes display pronounced regional heterogeneity. This study provides a solid basis for regional water resource regulation and further quantification of water conveyance benefits. Full article

Lake Vrana on Cres Island (northern Adriatic Sea) is a rare hydrogeological system consisting of a large freshwater body located within a karst cryptodepression with its bottom below sea level and surface above it. This study investigates long-term hydroclimatological changes using daily records of lake water level (1978–2024), water temperature (1979–2024), precipitation, and air temperature (1981–2024). Linear regression, the Mann–Kendall trend test, Sen’s slope estimator, and day-to-day variability metrics were applied to quantify long-term trends and system responses. A multi-index approach was used to enable a robust assessment of drought dynamics in this unique karst system: the Standardized Precipitation Index (SPI), representing meteorological conditions based on precipitation; the Standardized Hydrological Index (SHI), reflecting hydrological response derived from lake levels; and the New Drought Index (NDI), integrating precipitation and temperature to account for evapotranspiration effects. Results indicate a statistically significant decline in lake water levels (−4.5 to −5.2 cm yr⁻¹), while precipitation shows no significant trend. In contrast, both air and water temperatures exhibit a significant increase (~0.5 °C per decade) and are strongly correlated (R² = 0.767). The lake demonstrates pronounced thermal inertia and delayed response to atmospheric forcing. Day-to-day analysis reveals increasing variability in water temperature and decreasing variability in air temperature, suggesting changes in system energy dynamics. Drought indices (SHI and NDI) show significant negative trends, whereas SPI does not, indicating that drought intensification is primarily driven by rising temperatures and enhanced evapotranspiration rather than precipitation deficits. These findings demonstrate that Lake Vrana acts as a sensitive integrator of climatic forcing. Full article

Digital terrain models (DTMs) are required in many hydrologic, geomorphic, and ecological applications, yet widely used global elevation products often retain above-ground elevation contributions, particularly from vegetation canopies. This study investigates whether useful bare-earth terrain can be reconstructed from DSM-derived information alone at inference time. Rather than regressing terrain elevation directly, the proposed framework predicts the residual $\mathrm{DH}=\mathrm{DSM}-\mathrm{DTM}$ and reconstructs the DTM by subtraction. The model uses Copernicus DEM GLO-30 as the input source and augments it with CSF-derived priors and DSM-derived terrain features, including slope, aspect encoding, curvature, and local relief. Unlike multi-source terrain correction products that rely on external auxiliary datasets, all inference-time inputs in the proposed framework are generated from the DSM itself. A residual U-Net is trained with a weighted Huber loss together with gradient-consistency and DTM-slope-consistency constraints. Experiments across multiple regions in the central and southeastern United States show that the proposed method outperforms the compared public DEM products and baseline methods under a unified evaluation protocol. Relative to FathomDEM, it reduces the mean absolute error from 1.0445 m to 0.8538 m and the root mean square error from 1.6969 m to 1.4697 m on the study region test split, while also improving NMAD, P99, and Recall@5m. Performance on the geographically separate Arkansas region is similar to that on the in-region test split. Remaining errors are concentrated mainly in extremely steep terrain, densely vegetated areas, and cases with large residual heights. Full article

Understanding the relationship between the processes involved in hydrology and changesin land use becomes more urgent amid the accelerated development of urban areas. In this regard, this paper proposes the application of a spatio-temporal analysis of flood vulnerability through multi-criteria analysis (Analytical Hierarchy Process), integrated with GIS and modeling of multidimensional urban development processes within Cizre, Turkey. Important hydrological factors for the formation of flood risks, such as elevation, slope, land use/cover, rainfall, drainage density, and proximity to the river, were considered when preparing the flood susceptibility map. It was revealed that high- and very-high-risk zones are mainly located near the Tigris River and in urbanized areas, which occupy more than half of the territory under consideration. Multidimensional analysis showed that unplanned development increases flood risks in the area because of the increased area of impervious surfaces and the violation of natural water flows. As a way to overcome the limitations of traditional methods of static analysis of flood risks, the Flood Risk Transfer Index (FRTI) has been developed to describe the process of spatial redistribution of risks resulting from the impact of the increase in urbanization rates. The indicator of spatial redistribution of flood risk reached a value of 0.72, showing that flood pressures increased in existing cities instead of reducing them. Thus, this study provides a breakthrough in understanding flood risks through the introduction of a new methodology. Full article