Search Results (3,887)

Waterfront wilderness landscapes in mountainous cities, such as Chongqing, play a vital role in sustaining urban biodiversity and human well-being amid steep topography and hydrological variations that create unique habitats. However, public recognition of their ecological values and potential ecological–aesthetic conflicts remain underexplored. This study investigated biodiversity features and public preferences in Chongqing’s central urban waterfront wilderness through field surveys of 218 quadrats for biodiversity assessment (e.g., Shannon–Wiener and Simpson indices, cluster analysis identifying 12 typical communities) and two questionnaire surveys (N = 260 and 306) evaluating spatial features and plant attributes, with correlation and regression analyses examining relationships between ecological indices and preference scores. Results recorded 116 plant species from 41 families, dominated by herbaceous plants (77.6%), with herbaceous, shrub-herbaceous, and tree-herbaceous communities prevalent. No significant correlations existed between objective diversity indices and preference scores; instead, structure (β = 0.444, p < 0.001) and color (β = 0.447, p < 0.001) drove preferences (explaining 96.7% variance), favoring accessible mid-successional shrub-herbaceous structures over dense, low-diversity evergreen types. These findings reveal ecological–aesthetic conflicts in mountainous settings where aesthetic dominance limits biodiversity recognition. Implications include user-centered zoning: restrict access in low-preference steep areas with buffers for conservation, while enhancing high-preference flat zones via selective pruning and native colorful species introduction, supplemented by educational signage. This research provides a mountainous city archetype, enriching global urban wilderness studies and informing sustainable management in rapidly urbanizing regions. Full article

Estimated groundwater recharge is considered the essential factor for groundwater management and sustainability, especially in arid lands such as the Kingdom of Saudi Arabia (KSA). Consequently, assessing groundwater recharge is a key process for forecasting groundwater accessibility to sustain safe withdrawal. So, this study focused on environmental isotopes, the chloride mass balance (CMB) method, and a SWAT model by integrating GIS with hydrological and hydrochemical techniques to detect the origin of coastal aquifer groundwater and to compute the recharging rate in the study area. This study is based on the results of chemical analysis of 78 groundwater samples and environmentally stable isotopes, including deuterium (²H) and oxygen-¹⁸O, in 29 representative samples. The results revealed that the origin of groundwater recharge comes through precipitation, where the ranges of δ¹⁸O and δ²H isotopes in the analyzed groundwater were from −1.10‰ to +1.03‰ and from −0.63‰ to 11.63‰, respectively. The CMB finding for estimating the average recharge is 3.57% of rainfall, which agrees with a previous study conducted in the wadi Qanunah basin (north of the study area), where the estimated average value of recharge was 4.25% of rainfall. Meanwhile, the estimated annual recharge using a SWAT model ranged between 1 mm and 16.5 mm/year at an average value of approximately 8.75 mm/year. The results obtained by the two techniques are different due to some reasons such as the presence of additional chloride sources, as well as evaporation. Outputs of this study will be valuable for the local community, officials, and decision-makers who are concerned with groundwater resources. Full article

As global climate change intensifies, hydrological processes in arid inland river basins are undergoing profound transformations, posing severe challenges to regional water security and ecological stability. This study aims to develop a coupled SWAT-LSTM model integrating glacier melt processes to simulate runoff dynamics in the Keria River basin under climate change, providing a basis for local water resource management. Based on natural monthly runoff observations from the Langgan hydrological station (1961–2015), glacier data extracted from Landsat 8 remote sensing imagery (2013–2019), and downscaled data from the CMIP6 Multi-Model Ensemble (MME), this study constructed a SWAT-LSTM coupled model to simulate future scenarios (2026–2100). Research indicates that this hybrid model significantly enhances the accuracy of hydrological simulations in high-altitude glacier-fed catchments. The Nash efficiency coefficient (NSE) during the validation period reached 0.847, representing a 15% improvement over the SWAT model. SSP5-8.5 is identified as a high-risk scenario, underscoring the urgency of emissions reduction; SSP1-2.6 represents the most desirable pathway, with its relatively stable pattern offering sustained advantages for long-term water resource management in the basin. The study further reveals a negative feedback mechanism between glacier ablation and runoff increase, validating the regulatory role of Jiyin Reservoir’s “store during floods to compensate for droughts” operation strategy in balancing basin water resources. This study explores the coupling path between the physical model and the deep learning model, and provides an effective integration scheme for the hydrological simulation of the global watershed with ice–snow meltwater as the main recharge runoff, especially for the adaptive management of water resources in inland river basins in arid areas. Full article

Floods are among the most devastating natural hazards, causing widespread damage to lives, livelihoods, and infrastructure, particularly in vulnerable river basins. The Cagayan River Basin (CRB), the largest and most flood-prone basin in the Philippines, remains a significant challenge for disaster risk management. This study developed an event-based hydrologic–hydraulic modeling framework by coupling HEC-HMS rainfall–runoff simulations with HEC-RAS 2D unsteady flow routing to produce validated flood hazard maps. Inputs included rainfall from 41 gauge stations and observed inflows from the Magat Dam, processed in HEC-DSS. Validation utilized 137 surveyed flood marks collected from post-flood surveys, community reports, government archives, and household RTK measurements, with a concentration in Tuguegarao City. The coupled model reproduced key hydrograph peaks with moderate accuracy (R² = 0.56, Bias = +0.32 m, RMSE = 1.61 m, MAE = 1.43 m), although NSE (−2.30) reflected the limits of daily rainfall inputs. Simulated hazard maps identified 767.97 km² of inundated area (approximately 2.77% of CRB), concentrated along the floodplain and at the Magat confluence. Unlike previous scenario-based or localized efforts, this study delivers the first basin-wide, event-validated flood hazard maps for the CRB using integrated depth and depth–velocity criteria. The resulting hazard layers provide a scientific basis for strengthening evacuation planning, guiding land-use and infrastructure decisions, and supporting long-term resilience strategies in one of the Philippines’ most flood-prone rivers. Full article

Karst aquifers, vital freshwater resources, are highly vulnerable to agricultural pollution, yet their hydro-geochemical responses remain poorly understood due to high spatial heterogeneity. This study aimed to unravel these complex responses in a subtropical karst agricultural catchment to provide a basis for its sustainable management. We employed high-frequency monitoring at a headwater spring (background), a depression well (hotspot), and the catchment outlet (integrated) in Southwest China. Using hydrological and geochemical data from 2017, we applied Principal Component Analysis (PCA) to apportion natural and anthropogenic sources. The main findings revealed significant spatial heterogeneity, with the depression well acting as a contamination hotspot characterized by rapid hydrological responses and elevated SO₄²⁻ and Cl⁻ concentrations. PCA successfully decoupled an “anthropogenic factor” (PC1, 40.5%) from a “natural weathering factor” (PC2, 25.2%). Critically, agricultural SO₄²⁻ at the hotspot was counter-intuitively higher during the wet season than the dry season, opposing the typical dilution pattern of background ions and revealing that depressions act as contaminant-concentrating pathways, whose risks are severely underestimated by traditional outlet monitoring. The anomalous sulfate dynamics reveal a cross-seasonal “storage-and-release” mechanism (legacy effect) within the karst Critical Zone, demonstrating that these systems can buffer and “remember” contaminants. Full article

Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in semi-arid regions such as the Guadalupe Basin in northern Baja California. In this study, we explored the predictive capability of several hybrid deep learning architectures—Long Short-Term Memory (LSTM), Convolutional Neural Network combined with LSTM (CNN–LSTM), and Bidirectional LSTM with Attention (BiLSTM–Attention)—to model the temporal evolution of wind speed, wind direction, temperature, relative humidity, and atmospheric pressure using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1980 to 2020. Model performance was evaluated using RMSE, MAE, and R² metrics and compared against persistence and climatology baselines. The BiLSTM–Attention model achieved the best overall performance, showing particularly high accuracy for temperature (R² = 0.95) and relative humidity (R² = 0.76), while maintaining angular errors below 35° for wind direction. The results demonstrate the potential of hybrid deep learning models to capture nonlinear temporal dependencies in meteorological time series and provide a methodological framework to enhance hydrometeorological understanding and water resource management in the Guadalupe Basin under Santa Ana wind conditions. Full article

Biochar has recently been widely used as a soil amendment. However, the interaction effects of biochar with irrigation management on soil water leakage and water use efficiency of paddy black soil remain unclear, which seriously restricts the production potential of black soil. Therefore, the purpose of this paper was to explore the response rule of water loss and water use efficiency of black soil under the coupling effects of biochar, irrigation amounts, and irrigation methods through column experiment, field experiment, and HYDRUS-AquaCrop coupling simulation. Biochar application rates, irrigation amounts, and irrigation methods were set at five levels (B = 0, 1.5, 3, 4.5, 6 kg·m⁻²), seven levels (I = 0, 60, 120, 180, 240, 300, 360 mm), and two levels (M, conventional irrigation and drip irrigation), respectively. The results showed that B and M had a significant coupling effect on water leakage loss (p < 0.05). Single factor B promoted water loss, but B and M inhibited water loss, which helps reduce water waste and environmental pollution. Compared with a single effect, the synergistic effect of B, I, and M on water consumption (ET), yield (Y), and water use efficiency (WUE) was better, increasing Y by 18.2%–57.9% and WUE by 17.1%–34.9%. Additionally, ET, Y, and WUE were also correlated with hydrological years, and this correlation works best in dry years. The maximum of Y and WUE in wet and normal years occurred in the ‘BDI6, 0 mm’ treatment (saving water and high yield), while that in dry years occurred in the ‘BDI6, 360 mm’ treatment (a stable yield). Therefore, the interaction effects of biochar and irrigation management should be comprehensively considered in black soil agricultural production to improve the agricultural potential of black soil and ensure food security. Full article

Predicting slope deformation is pivotal for reservoir safety management; however, quantitative attribution to hydrologic–temporal factors with interpretable and hyperparameter-robust models under multi-point temporal dependence is still rare. Hence, we develop an interpretable hybrid framework that couples a Light Gradient Boosting Machine (LightGBM) with a Newton–Raphson-based optimizer (NRBO) for hyperparameter tuning. Unsupervised clustering is first employed to capture intrinsic temporal associations among multiple monitoring points. Subsequently, the NRBO–LightGBM framework is proposed to enhance prediction accuracy and model robustness in slope deformation prediction. Finally, SHAP analysis is integrated to quantify the contribution of influencing factors, thereby strengthening the physical interpretability and credibility of the model. The proposed framework is validated using long-term deformation monitoring data from the Lijiaxia Hydropower Station. Comparative experiments indicate that the NRBO–LightGBM model achieves a 22.8% reduction in RMSE and an 11.4% increase in R² relative to conventional statistical models, improving prediction accuracy with a 21.5% lower RMSE and a 15.5% higher R² compared with the baseline LightGBM. Furthermore, SHAP interpretability analysis elucidates the internal predictive mechanism, revealing that deformation evolution is primarily governed by temporal accumulation and seasonal variations represented by the time variable t and periodic components. Overall, the NRBO–LightGBM model provides high-precision and interpretable deformation prediction for reservoir slopes, effectively bridging predictive performance with mechanistic understanding and offering actionable insights for landslide early warning and risk management. Full article

Mining-induced subsidence has significantly altered the structure of the vadose zone in coal mining areas, where soil cracks act as preferential pathways controlling water infiltration and redistribution. In this study, a Hydrus-2D dual-domain seepage model incorporating geometric parameterization of cracks was developed to simulate water migration in the vadose zone of a typical subsidence area in the Ordos Basin. The model integrates field-measured crack geometry, soil texture, and rainfall characteristics to quantitatively analyze preferential flow formation under twelve combinations of crack width, soil type, and rainfall intensity. The results show that (i) crack width dominates preferential flow behavior, with wider cracks (≥5 cm) deepening the wetting front from approximately 107 cm to 144 cm within 120 h and sustaining high conductivity after rainfall; (ii) soil texture governs infiltration pathways, as sandy soils promote deeper wetting fronts (up to 99 cm, ~40% deeper than loam) and layered soils induce interface retention or “jump” infiltration; and (iii) rainfall intensity controls infiltration depth, with storm events producing wetting fronts more than four times deeper than those under light rain. Overall, this study demonstrates the feasibility and significance of integrating crack parameterization into vadose-zone hydrological modeling using Hydrus-2D, providing a quantitative basis for understanding rapid infiltration–migration–recharge processes and supporting ecological restoration and water resource management in arid and semi-arid mining regions. Full article

This article examines the effects of climate change on the 32 million inhabitants of the Megalopolis of Central Mexico (MCM), which is threatened by chaotic urbanization, land-use changes, the deforestation of the Forest of Water by organized crime, unsustainable agriculture, and biodiversity loss. Expensive hydraulic management extracting water from deep aquifers, long pipes exploiting water from neighboring states, and sewage discharged outside the endorheic basin result in expensive pumping costs and air pollution. This mismanagement has increased water scarcity. The overexploitation of aquifers and the pollution by toxic industrial and domestic sewage mixed with rainfall has increased the ground subsidence, damaging urban infrastructure and flooding marginal neighborhoods with toxic sewage. A system approach, satellite data, and participative research methodology were used to explore potential water scarcity and weakened water security for 32 million inhabitants. An alternative nature-based approach involves recovering the Forest of Water (FW) with IWRM, including the management of Natural Protected Areas, the rainfall recharge of aquifers, and cleaning domestic sewage inside the valley where the MCM is found. This involves recovering groundwater, reducing the overexploitation of aquifers, and limiting floods. Citizen participation in treating domestic wastewater with eco-techniques, rainfall collection, and purification filters improves water availability, while the greening of urban areas limits the risk of climate disasters. The government is repairing the broken drinking water supply and drainage systems affected by multiple earthquakes. Adaptation to water scarcity and climate risks requires the recognition of unpaid female domestic activities and the role of indigenous people in protecting the Forest of Water with the involvement of three state authorities. A digital platform for water security, urban planning, citizen audits against water authority corruption, and aquifer recharge through nature-based solutions provided by the System of Natural Protected Areas, Biological and Hydrological Corridors [SAMBA] are improving livelihoods for the MCM’s inhabitants and marginal neighborhoods, with greater equity and safety. Full article

Floods, especially in urbanised areas, incur enormous economic and social losses. The structural flood management is often limited by urbanization and environmental issues. Following the catastrophic flood events of 1997 and 2010, a relatively large dry polder was constructed in Racibórz Dolny, Poland, with the highest flood retention capacity in Central Europe. During the 2024 flood in Czechia and Poland, the polder was filled to 80%, which significantly reduced the floodwave crest on the Odra River (by 1.65 m), halved the peak discharge, and delayed the floodwave passage by two days according to hydrological calculations. The operation of the polder enables multifunctional use of the river valley—ranging from agriculture and mineral extraction to environmental protection—without the need for permanent water impoundment. Aggregate extraction carried out within the basin contributed to shaping the reservoir, reducing the demand for transport and construction materials, while the overburden was reused for engineering and reclamation purposes. Mining activities between 2007 and 2023 increased the retention capacity of the polder by 13%, providing an example of rational environmental resource management combined with effective flood protection. The findings demonstrate that integrating retention functions with mineral resource management represents an efficient and sustainable approach to mitigating flood impacts in large European river valleys. Full article

Monitoring groundwater dynamics and basin-scale water budget closure is critical for sustainable water resource management, especially in regions facing climate stress and overexploitation. This study examines the temporal variability of total water storage and groundwater trends in Türkiye’s Kızılırmak River Basin by integrating GRACE/GRACE-FO satellite gravimetry, GLDAS-Noah land surface model outputs, ERA5-Land reanalysis products, and local meteorological observations. Groundwater storage anomalies (GWSAs) were derived from the difference between GRACE-based total water storage anomalies (TWSAs) and GLDAS-modeled surface storage components, revealing a long-term groundwater depletion trend of −9.55 ± 2.6 cm between 2002 and 2024. To investigate the hydrological drivers of these changes, lagged correlation analyses were performed between GRACE TWSA and ERA5-Land variables (precipitation, evapotranspiration, runoff, soil moisture, and temperature), showing time-shifted responses from −3 to +3 months. The strongest correlations were found with soil moisture (CC = 0.82 at lag −1), temperature (CC = −0.70 at lag −3), and runoff (CC = 0.71 at lag 0). A moderate correlation between GRACE TWSA and ERA5-based water storage closure (CC = 0.54) indicates partial alignment. These findings underscore the value of satellite gravimetry in tracking subsurface water changes and support its role in basin-scale hydrological assessments. Full article

Wildfire-induced disturbances to soil and vegetation can significantly impact streamflows for years, depending upon the degree of burn severity. Accurately predicting the effects of wildfire on streamflow at the watershed scale is essential for effective water budget management. This study presents a novel approach to generating a burn severity map on a small scale by integrating unmanned aerial vehicle (UAV)-based thermal imagery with Landsat-derived Differenced Normalized Burn Ratio (dNBR) and upscaling burned severity to the entire burned area. The approach was applied to the Thompson Ridge Fire perimeter, and the upscaled UAV-Landsat-based burn severity map achieved an overall accuracy of ~73% and a kappa coefficient of ~0.62 when compared with the Burned Area Emergency Response’s (BAER) fire product as a reference map, indicating moderate accuracy. We then tested the transferability of burn severity information to a Beaver River watershed by applying Random Forest models. Predictors included topography, spectral bands, vegetation indices, fuel, land cover, fire information, and soil properties. We calibrated and validated the Distributed Hydrology Soil Vegetation Model (DHSVM) against observed streamflow and Snow Water Equivalent (SWE) data within the Beaver River watershed and measured model performance using Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS) metrics. We adjusted soil (maximum infiltration rate) and vegetation (fractional vegetation cover, snow interception efficiency, and leaf area index) parameters for the post-fire model setup and simulated streamflow for the post-fire years without vegetation regrowth. Streamflow simulations using the upscaled and transferred UAV-Landsat burn severity map and the Burned Area Emergency Response’s (BAER) fire product produced similar post-fire hydrologic responses, with annual average flows increasing under both approaches and the UAV-Landsat-based simulation yielding slightly lower values, by less than 6% compared to the BAER-based simulation. Our results demonstrate that the UAV-satellite integration method offers a cost- and time-effective method for generating a burn severity map, and when combined with the transferability method and hydrologic modeling, it provides a practical framework for predicting post-fire streamflow in both burned and unburned watersheds. Full article

In light of urbanization, climate change, and the escalation of extreme weather events, flood management is becoming more and more important. Improving community resilience and reducing flood risks require prompt decision-making and effective communication. This study investigates how flood management systems can incorporate Large Language Models (LLMs), especially those that use Retrieval-Augmented Generation (RAG) architectures. We suggest a multimodal framework that uses a Flood Knowledge Graph to aggregate data from various sources, such as social media, hydrological, and meteorological inputs. Although LLMs have the potential to be transformative, we also address important drawbacks like governance issues, hallucination risks, and a lack of physical modeling capabilities. When compared to text-only LLMs, the RAG system significantly improves the reliability of flood-related decision support by reducing factual inconsistency rates by more than 75%. Our suggested architecture includes expert validation and security layers to guarantee dependable, useful results, like flood-constrained evacuation route planning. In areas that are vulnerable to flooding, this strategy seeks to strengthen warning systems, enhance information sharing, and build resilient communities. Full article

The 2011 Great Flood in Thailand exposed critical deficiencies in water management across the Chao Phraya River Basin, particularly in controlling inflows and discharges from major reservoirs such as Sirikit and Bhumibol. Inadequate rainfall monitoring at the Nakhon Sawan station further intensified the disaster’s impact. As climate change continues to amplify extreme weather events, this study aims to improve flood forecasting accuracy and promote sustainable water resource management aligned with the Sustainable Development Goals (SDGs 6, 11, and 13). Advanced climate data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were spatially refined and integrated with hydrological models to enhance regional accuracy. The Discrete Wavelet Transform (DWT) was applied for feature extraction to capture hydrological variability, while the Nonlinear Autoregressive Model with Exogenous Factors (NARX) was employed to model complex temporal relationships. A multi-model ensemble framework was developed to merge climate forecasts with real-time hydrological data. Results demonstrate significant model performance improvements, with DWT-NARX achieving 55–98% lower prediction errors (RMSE) compared to baseline methods and correlation coefficients exceeding 0.91 across all forecasting scenarios. Marked seasonal variations emerge, with higher inflows during wet periods and reduced inflows during dry seasons. Under RCP8.5 climate scenarios, wet-season inflows are projected to increase by 15.8–17.4% by 2099, while dry-season flows may decline by up to 33.5%, potentially challenging future water availability and flood control operations. These findings highlight the need for adaptive and sustainable water management strategies to enhance climate resilience and advance SDG targets on water security, disaster risk reduction, and climate adaptation. Full article