Search Results (552)

Water scarcity, high non-productive soil evaporation, and poor vegetation establishment are major constraints on the sustainable ecological restoration of reconstructed open-pit mine dumps in arid and semi-arid regions. Conventional surface-applied water replenishment can result in rapid evaporative loss, thereby reducing the ecological benefits obtained from limited water resources. However, whether redistributing water into deeper reconstructed soil layers can simultaneously reduce non-productive evaporation, stabilize the root-zone hydrothermal environment, and improve vegetation growth remains insufficiently verified. In this study, a deep-profile soil water replenishment (DPSWR) device was tested in reconstructed mine-dump soil columns planted with locally adapted Stipa. Surface-applied water replenishment (CK) and DPSWR were compared using a single-run simulated rainfall comparison, soil water-retention and water-loss measurements, continuous temperature and moisture monitoring at 10 and 40 cm depths, and plant growth indicators. In the rainfall-simulation comparison, DPSWR showed lower cumulative water loss across the tested rainfall intensities and improved water-retention stability; the evaporation rate under CK was approximately 1.3 times that under DPSWR, whereas final soil water-holding capacity under DPSWR was approximately 2.4 times that under CK. Root fresh weight, plant fresh weight, and seedling number were significantly higher under DPSWR than under CK (p < 0.01), and maximum plant height and root length also increased significantly (p < 0.05). Under equal water-input conditions, DPSWR reduced non-productive water loss, prolonged soil water retention, and supported vegetation establishment. These findings suggest that DPSWR may provide a more water-efficient approach to the sustainable restoration of reconstructed mine dumps in water-limited regions. Full article

Waterlogging simulation is an important non-structural measure for flood-risk management; however, the heterogeneity of urban surfaces complicates reliable simulation. Urban drainage inlets and buildings strongly influence runoff routing, yet the effects of alternative modeling treatments remain uncertain. This study evaluated the impact of three inlet treatments and three building treatments on urban waterlogging simulation under different storms. Results show that (1) under rainfall pattern 1, the grate inlet produced 4–5.8% higher peak drainage discharge than curb-opening treatments, and point-scale water-level differences reached 0.49 m at hydraulically sensitive locations. Compared with the roof-to-drainage method, the roof-to-surface discharge method increased flood volume, flooded area, and average water depth by 43.5%, 21.3%, and 15.6%, respectively. (2) The effects of the two representation types responded differently to rainfall characteristics. Drainage inlet rankings were strongly rainfall-dependent: under rainfall pattern 2 at a 100-year return period, the hierarchy reversed, with the depressed curb-opening inlet slightly outperforming the grate inlet by 0.6%. By contrast, the building treatment methods (BTMs) ranking remained consistent across all rainfall scenarios, with the roof-to-surface discharge method producing the largest flood volume and extent regardless of rainfall pattern or return period. Overall, this study identifies urban drainage inlet and building representations as important sources of structural uncertainty, providing practical guidance for urban flood modeling and drainage planning. Full article

Flooding during the rice (Oryza sativa L.) jointing stage threatens yield stability in regions with concentrated rainfall and limited drainage. However, the extent to which the water regime before flooding modifies rice growth and yield formation remains poorly understood. A pot experiment was conducted over two years to compare controlled irrigation (CI) with conventional flooding irrigation (CF) across three flooding depths and two durations imposed at the jointing stage. Flooding increased tiller number, plant height, and total leaf area, but reduced net photosynthetic rate and grain yield. Yield loss increased with flooding depth and duration. Under corresponding flooding treatments, CI moderated vegetative expansion and maintained a higher net photosynthetic rate, greater root dry matter, and a higher root-to-shoot ratio than CF. Relative to the corresponding non-flooded controls, yield loss ranged from 4.11% to 39.33% under CI and from 5.63% to 52.50% under CF. The lower yield loss under CI was associated with greater effective panicle number, higher seed setting rate, and better maintenance of photosynthetic activity and root biomass. These findings indicate that the water regime before flooding can influence biomass allocation and yield formation during the jointing stage flooding. Controlled irrigation combined with timely drainage may help reduce yield risk in rice systems exposed to temporary flooding. Full article

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries. Full article

The traditional tank model-based landslip early warning system (LEWS) calculates the soil water index (SWI) as a single time series driven by basin-averaged rainfall, which cannot capture spatial heterogeneity along linear highway infrastructures. To overcome this limitation, this study proposes an integrated model that couples the tank model with an ordinary differential equation (ODE) form stormwater runoff simulation model: namely, the distributed runoff model (DRM). The DRM-computed distributed surface water depth replaces the first-layer water height of the tank model to generate spatially varying SWI values. The proposed framework is validated against the 2016 Typhoon No. 10 event that triggered five landslides (L1–L5) along Highway 274 in Hokkaido, Japan. Quantitative results show the following: (1) at all five landslide locations, the peak SWI values exceed 225 mm, while at a non-landslide reference point (L0) the peak SWI is only 158 mm, demonstrating clear spatial differentiation; (2) the predicted landslide initiation times from the integrated model deviate by less than 1.5 h from the actual occurrence times, whereas the shallow water equations (SWEs) and tank-coupled model advances predictions by over 7 h (L3, L4 and L5); (3) after revising the critical line (CL) based on the event data, the proposed model demonstrates a 100% identification rate for the five landslide sites with zero false alarms at L0 in this case study, indicating its potential for practical application. Compared with the tank + SWEs, the proposed tank + DRM approach maintains comparable spatial resolution but significantly improves temporal accuracy and computational efficiency, making it practical for real-time early warning along elongated highway projects. This study provides a spatially differentiated and temporally reliable decision-support tool for rainfall-induced landslide risk assessment along transportation corridors. Full article

Having reliable tools for characterizing rainfall-induced soil erosion is fundamental to the effective management of agroforestry systems in order to increase resilience against climate change. Physics-based models provide a robust, comprehensive and widely applicable framework to quantify runoff generation and soil erosion during intense rainfall events in agroforestry catchments. In this work, we propose a novel hydro-erosive model to simulate hydrodynamical flow and bed mobilization, movement and deposition. This hydro-erosive model solves the two-dimensional shallow water equations (SWE-2D) with hydrological source terms for runoff generation, coupled with the 2D depth-averaged solid transport and the soil surface evolution equations. The partial differential system is solved using a finite volume method. Alternative Integral/Differential Bed Slope and explicit upwind/implicit pointwise friction term discretization options can be used to improve performance in terms of numerical stability and conservation. The behavior of different discretization options in this hydro-erosive model is evaluated through an analytical hillslope verification, a benchmark V-catchment rainfall–runoff test and a laboratory dam-break experiment over an erodible bed. The results show that the Differential Bed Slope formulation combined with the upwind friction discretization provides the most accurate and conservative predictions. Also, an Upwind Bed Updating method for integrating soil surface elevation change is compared with the cell-centered integration of the bed change term by suppressing non-physical oscillations without compromising computational efficiency. Overall, the proposed open-source hydro-erosive model provides a reliable and computationally efficient framework for high-resolution simulations of rainfall-induced soil erosion and represents a valuable tool for environmental and agroforestry applications, but appropriate calibration and mesh resolution are required to ensure reliable predictions. Full article

Water scarcity and climate variability severely constrain rainfed millet production in semi-arid areas. Supplementary irrigation (SI) and foliar selenium application (FS) are effective strategies for enhancing rainfed millet productivity, yet their interactive effects on millet growth and yield formation remain unclear. A 2-year field experiment was conducted in 2022 and 2023 to investigate the effects of four SI levels (0, 30, 50, and 80 mm, referred to as SI0, SI1, SI2, and SI3, respectively) and four FS concentrations (0, 40, 60, and 80 g Se/ha, designated as FS0, FS1, FS2, and FS3, respectively) on millet growth, yield, water use efficiency (WUE), Relative Chlorophyll Content (SPAD), and photosynthesis. The results show the following: (1) The crop water consumption (CWC), yield, dry matter weight, SPAD, and photosynthetic parameters of millet were lower in 2023 (normal-rainfall year) than in 2022 (high-rainfall year), whereas the WUE was higher in 2023. (2) Moderate SI at the late jointing stage improved the photosynthesis–transpiration processes of millet, while FS at the heading stage enhanced chlorophyll retention and delayed physiological senescence. (3) Excessive SI and FS reduced the yield, WUE, SPAD, and photosynthetic performance at different growth stages. (4) A significant SI × FS interaction was detected for the WUE in both years (p < 0.01) and for the CWC, yield, and SPAD in 2023 (p < 0.01), whereas effects on the CWC and yield in 2022 and on the leaf area index in both years were non-significant. The physiological effects of FS were strongly dependent on the hydrological year type and SI level. The highest yield and WUE were achieved under SI1FS2 in 2022 and SI2FS2 in 2023. Compared with SI0FS0, the yields increased by 7.84% and 43.46% and the WUEs by 2.33% and 25.96%, respectively. These findings provide a scientific theoretical basis for integrated water and nutrient management in semi-arid regions. Full article

Progressive climate change and rapid urbanization are placing increasing pressure on water resources, highlighting the need for local stormwater retention and reuse in residential areas. This study aimed to evaluate the influence of geological-engineering and geotechnical conditions on the design of stormwater management systems and to develop a conceptual solution for a residential property in Kraków, Poland. Geological, hydrogeological, hydrological, and topographic conditions were assessed using archival data verified through field investigations. Rainfall data from the Kraków-Balice meteorological station (2014–2023) were used to estimate rainwater harvesting potential and evaluate system performance. The proposed system consists of surface and subsurface drainage, two storage tanks, and an infiltration well. The first tank collects roof runoff for non-potable domestic use, whereas the second stores water from the drainage system and paved surfaces for irrigation. The annual rainwater harvesting potential was comparable to the non-potable water demand of a five-person household, while water collected from the drainage system and paved surfaces was sufficient for irrigation. Monthly precipitation analysis revealed pronounced seasonal variability, with winter shortages and summer surpluses. Integrating the storage tanks with the infiltration well enabled effective management of excess stormwater while supporting groundwater recharge. The results demonstrate that consideration of geological-engineering conditions is essential for the effective and sustainable design of residential stormwater management systems. Full article

Morocco, located at the southern margin of the Mediterranean climate-change hotspot, is exposed to a rapidly evolving precipitation regime whose national-scale characterization remains incomplete. This study delivers an integrated assessment of the spatio-temporal variability and trends of precipitation and its extremes over the country during the most recent World Meteorological Organization (WMO) climate-normal period (1991–2020), based on daily observations from 31 synoptic stations operated by the Direction Générale de la Météorologie (DGM). Trends in annual, seasonal and monthly precipitation were quantified using the non-parametric Mann–Kendall test combined with Sen’s slope estimator, while the structural transformation of the rainfall regime was characterized through three indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI): the Consecutive Dry Days (CDDs), the Simple Daily Intensity Index (SDII) and the amount of precipitation from very wet days (R95pTOT). The results reveal an apparent tendency toward a negative trend, with a predominance of negative precipitation trends in winter and early spring, most pronounced in February, that reach statistical significance at only a limited number of stations, partly offset by a spatially coherent wetting in November over central and eastern Morocco. The joint analysis of the three ETCCDI indices indicates a north–south contrasted reorganization: northern stations exhibit longer dry spells coexisting with intensified extreme rainfall, whereas southern stations show a generalized weakening of both intensity and extremes. These findings point to a structural shift toward more episodic and contrasted precipitation regimes, with the wet season starting later, ending earlier and concentrating rainfall into fewer but more intense events. The analysis provides an updated observational baseline for the validation of CMIP6 based regional projections and for the design of climate-resilient water and agricultural strategies in Morocco. Full article

Landslide susceptibility mapping (LSM) is an essential tool for hazard assessment and land-use planning in landslide-prone areas. This study compares three statistical models—Frequency Ratio (FR), Weight of Evidence (WoE), and Logistic Regression (LR)—with six machine-learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), and Decision Tree (DT), for regional landslide susceptibility assessment in Hiroshima Prefecture, Japan. A balanced dataset comprising 1936 landslide and 1936 non-landslide samples was developed from the 2018 rainfall-induced landslide inventory, utilizing seven conditioning factors: slope angle, profile curvature, aspect, elevation, lithology, soil water index, and 24 h cumulative rainfall. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. Among the statistical models, WoE exhibited the highest performance, while SVM provided the most balanced results among the machine-learning models. Both modeling approaches consistently identified lithology and slope angle as the primary controls on landslide occurrence. Independent validation demonstrated comparable predictive performance for both models; however, spatial validation showed that WoE assigned 96.72% of observed landslides to the High and Very High susceptibility classes, compared to 72.54% for SVM. These findings underscore the importance of integrating conventional classification metrics with spatial validation to enhance the evaluation and interpretation of landslide susceptibility models for regional hazard assessment. Full article

To elucidate the condensation characteristics and ecological effects of dew in semi-humid regions, this study took Yangzhuang Town of Tengzhou in the semi-humid zone of southwestern Shandong as the research area. Field observations of dew yield and environmental factors were carried out at four near-surface heights (0.2 m, 0.6 m, 1 m, and 2 m) from June to November 2025. The condensation patterns and driving factors of dew were analyzed, a ridge regression model was developed for dew yield simulation, and the ecological effects of dew were systematically expounded. The results indicate that dew exhibits a unimodal pattern characterized by nocturnal accumulation and diurnal dissipation. Both dew duration and dew yield are higher in autumn (September–November) than in summer (June–August), with the maximum dew yield observed at 1 m height. Dew yield shows a significantly positive correlation with relative air humidity (p < 0.05), and significantly negative correlations with vapor pressure deficit, dew point temperature, and air–dew point temperature difference (p < 0.05). It also presents a nonlinear relationship with wind speed, and the wind speed for dew condensation in this area ranges from 0.1 to 0.6 m/s, whereas cloud cover has no significant effect. The established ridge regression model performs satisfactorily in dew yield simulation. In addition, dew may provide continuous minor water supply during dry periods and potentially alleviate mild drought stress in plants. The results provide a scientific reference for the sustainable development and exploitation of dew water resources, the improvement in non-rainfall water theories, and the mitigation of drought. Full article

Precipitation use efficiency (PUE) links ecosystem carbon uptake to precipitation input, but endpoint ratios alone cannot show where carbon–water coupling differs along ecohydrological pathways. This limitation is especially relevant in karst landscapes, where thin soils and heterogeneous hydrological pathways can decouple rainfall, soil moisture, evapotranspiration, and plant carbon gain. Here, we developed a PUE chain decomposition framework based on gross primary productivity (GPP), transpiration (T), evapotranspiration (ET), soil moisture (SM), and precipitation (PRE): PUE = GPP/T × T/ET × ET/SM × SM/PRE. In this framework, GPP/T represents carbon fixation per unit transpiration, T/ET the transpiration fraction of evapotranspiration, ET/SM evapotranspiration output relative to soil moisture, and SM/PRE soil moisture status relative to precipitation input. We used multi-source remote-sensing and reanalysis data from 2003 to 2022 to compare karst and non-karst landscapes in Southwest China, applied variance decomposition to quantify the contributions of chain terms and their interactions, and used Stacking ensemble learning with Shapley additive explanations (SHAP) to interpret model-inferred environmental associations. Mean PUE was 1.16 g C m⁻² mm⁻¹ in non-karst areas and 1.08 g C m⁻² mm⁻¹ in karst areas, and all four chain components differed significantly between landform types. Variance decomposition identified SM/PRE and its interaction terms as the largest contributors to PUE variability, mainly reflecting a precipitation-sensitive diagnostic signal and soil moisture status relative to precipitation input. Machine learning interpretation showed that solar radiation, leaf area index, aridity, and groundwater storage were associated with different chain components; karst areas showed stronger groundwater-storage signals and lower model-inferred response thresholds. These findings indicate that PUE differences in Southwest China arise from multiple linked diagnostic stages rather than from endpoint carbon uptake or precipitation alone. The framework can help locate water-use constraints and support landform-specific ecological restoration and water management. Full article

Background: Melioidosis correlates strongly with rainfall, and there is substantial diversity in climate between melioidosis-endemic locations. The Northern Territory of Australia epitomises the “wet/dry” tropics, with a prolonged dry season from May to October. We analysed dry season cases of melioidosis during 35 consecutive years and compared these with wet season cases. We aimed to provide insights into how dry season cases of melioidosis may occur in this region and explore non-rainfall exposures that are usually not considered in the wet season. Methods: Case epidemiological and clinical data were extracted from the Darwin Prospective Melioidosis Study. Weather parameters, including daily rainfall, were analysed using generalised additive models and conditional logistic regressions to assess associations between dry season cases and preceding rainfall. Results: Of 1520 melioidosis cases between 1989 and 2024, there were 325 (21%) in the dry season. While the well-recognised clinical diversity of melioidosis was also seen amongst dry season cases, pneumonia was proportionally less common and cutaneous melioidosis was more common than in the wet season. A total of 23% of dry season patients had no identified clinical risk factors for melioidosis, compared to 14% in the wet season. Mortality was 8% in the dry season and 11% in the wet season. There was a range of plausible explanations for many of the dry season cases, including unseasonal rainfall prior to infection. Infections in urban settings were notable, with anthropogenic factors such as irrigation and construction resulting in persistence of Burkholderia pseudomallei in the environment during the dry season. A total of 3% of cases remained unexplained. Conclusions: Not all dry season cases are explained by infection occurring the previous wet season or by unseasonal rainfall in the dry. Identification of cases in the dry season support the need for year-round prevention strategies during potential exposure to contaminated water or soil. Further prospective studies are needed to better define the infecting events resulting in melioidosis, especially in the dry season. These studies should include timely history taking from the case and their family and selected environmental sampling for B. pseudomallei. Full article

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development. Full article

Total ammonia nitrogen (TAN) is a ubiquitous and critical pollutant in global surface waters. In China, regulatory oversight largely relies on static standard limits, often overlooking the influence of environmental factors on ammonia toxicity. Based on large-scale monitoring data from seven major river basins across China from 2021 to 2024, this study employed pH- and temperature-dependent Local Water Quality Criteria (LWQC) to identify the spatiotemporal decoupling between TAN concentrations and Risk Quotients (RQs). The results reveal a “double-peak” seasonal pattern in TAN concentrations nationwide, namely a primary peak in winter (December to February) and a secondary peak in summer (June to August), driven by low flow during the dry season and rainfall-induced non-point source runoff, respectively. Crucially, the study confirms a significant “concentration-risk paradox”: while TAN concentrations are highest in winter, ecological risk remains at an annual low due to the protective effect of low temperatures on toxicity. Conversely, despite lower total concentrations in summer, high temperatures and elevated pH trigger a sharp decline in LWQC and a surge in the proportion of highly toxic un-ionized ammonia (NH₃), marking summer as the peak period for ecological risk. Comparative analysis indicates that approximately 61.43% of river sections meeting the current Grade III water quality standards remain in a high-risk state. This underscores the inadequacy of static standards in providing sufficient protection during sensitive seasons. We suggest that water environmental management should shift from “concentration-based compliance” to “risk-based management,” implementing differentiated TAN control strategies specifically targeting the sensitive summer window. Full article