Search Results (2,121)

The water yield (WY) service is a critical ecosystem service in arid regions, and understanding its spatiotemporal heterogeneity and controls is important for sustainable watershed management. Annual water yield (WY) in the Aksu River Basin (ARB), China, from 2000 to 2020 was simulated using the InVEST model, with validation against observed runoff (NSE = 0.840, R² = 0.846, RMSE = 1.787). The results revealed a decline in WY from 66.49 mm in 2000 to 43.15 mm in 2015, while retaining a clear north–south gradient, with higher values in the north. Areas showing decreasing and increasing trends accounted for 45.34% and 3.14% of the basin, respectively. WY exhibited strong spatial autocorrelation (global Moran’s I = 0.912–0.941), with high-value clusters in the north and low-value clusters in the south. GeoDetector identified precipitation, temperature, and potential evapotranspiration as key drivers (q = 0.889, 0.880, and 0.832, respectively), with precipitation-related interactions generally exceeding 0.9, indicating enhanced explanatory power through multi-factor coupling. After variable screening and collinearity control, MGWR revealed spatially varying effects of drivers and significant spatial non-stationarity. Overall, despite the declining trend, WY in the ARB maintained a relatively stable spatial structure, with its heterogeneity primarily driven by the coupling of climatic forcing and topographic constraints, providing a scientific basis for zonal water resource management in arid river basins. Full article

This study integrates 90 years of hydrometeorological observations (1930/31–2019/20) with end-century projections (2080–2099) to evaluate climate-driven changes in the water balance of the Nitra River basin (2094 km²), Slovakia. Despite a modest 2–3% increase in annual precipitation from 1930/31–1959/60 to 1990/91–2019/20, mean annual runoff declined from 229 mm to 201 mm (≈−12%), primarily due to enhanced evapotranspiration stemming from a +1.08 °C basin-wide temperature increase. An empirical regression from 90 hydrological years shows that +100 mm in precipitation boosts runoff by ≈41 mm, while +1 °C in temperature reduces it by ≈13 mm. The BILAN monthly water balance model was calibrated for 1930/31–2019/20 to decompose runoff components. Over the 90-year period, the modeled annual runoff averaged 222 mm, comprising a 112 mm baseflow (50.4%), a 91 mm interflow (41.0%), and a 19 mm direct runoff (8.6%), underscoring the key role of groundwater and subsurface flows in sustaining streamflow. In the second part of our study, the monthly water balance model BILAN was recalibrated for 1995–2014 to simulate future runoff under three CMIP6 Shared Socioeconomic Pathways. Under the sustainability pathway SSP1-1.9 (+0.88 °C; +1.1% precipitation), annual runoff decreases by 8.9%. The middle-of-the-road scenario SSP2-4.5 (+2.6 °C; +3.1% precipitation) projects a 17.5% decline in annual runoff, with particularly severe reductions in autumn months (September −32.3%, October −35.8%, December −40.4%). The high-emission pathway SSP5-8.5 (+5.1 °C; +0.4% precipitation) yields the most dramatic impact with a 35.2% decrease in annual runoff and summer deficits exceeding 45%. These results underline the extreme sensitivity of a mid-sized Central European basin to temperature-driven evapotranspiration and the critical importance of emission mitigation, emphasizing the urgent need for adaptive water management strategies, including new storage infrastructure to address both winter floods and intensifying summer droughts. Full article

Rapid urbanization and increasingly frequent extreme rainfall events have intensified stormwater challenges, underscoring the need for watershed-scale strategies that integrate blue-green infrastructure (BGI). This study evaluates the stormwater control performance of combined initial reservoir storage level regulation, river water level adjustment, and green infrastructure (GI) implementation in the 42.4 km² Baihuayong watershed of Guangzhou, China. A coupled stormwater model (SWMM) was developed, calibrated, and coupled with TELEMAC-2D to simulate schemes varying initial reservoir storage levels (30.6 m to 27.6 m), river water levels (11 m to 8 m), and GI proportions (0–45%) under 2- to 100-year rainfall events. Results show that lowering initial reservoir storage levels from 30.6 m to 27.6 m enhanced runoff reduction by ~40% and reduced discharged water volume by ~30%, though overflow mitigation remained limited. Decreasing river water levels from 11 m to 8 m reduced flooded areas by up to 8.3%, with diminishing benefits below 9 m. Increasing GI coverage from 0% to 45% reduced overflow nodes from 236 to 192 and flood extent from 10.76 ha to 9.20 ha under moderate storms, but improvements were modest during extreme events. A synergistic configuration, combining a low initial reservoir storage level (27.6 m), low river water level (8 m), and a high GI proportion (35–45%), yielded the most comprehensive improvements. These findings demonstrate the strong potential of integrated BGI for watershed-scale flood resilience and provide quantitative guidance for sponge city planning. Full article

The impact of sewage discharge on water quality in reversing rivers has rarely received attention. This study simulated water quality changes in Maoergang River (a water body with counter flow conditions) affected by effluent discharge from Yangjiabu Sewage Treatment Plant. The results revealed that the diffusion patterns of COD, NH₄⁺-N, and TP in the study area were largely consistent; however, different hydrological conditions and discharge scenarios resulted in obvious differences in pollutant distribution. During the dry season, regardless of normal or counter folow conditions, the Maoergang and Xitiaoxi downstream were the primary affected segments. Regulated by hydrodynamic forces, under normal flow conditions, the Xitiaoxi downstream received a higher pollutant load while the Xitiaoxi upstream received minimal inputs. In the wet season, pollutant concentrations were generally lower due to the dilution effect of increased runoff; notably, the primary affected segments shifted to the downstream reaches of Maoergang and Huanchenghe. Under accidental discharge scenarios, excessive sewage release expanded the scope of pollution impacts, with elevated pollutant concentrations causing water quality non-compliance in parts of the upstream and downstream Xitiaoxi—both of which are within the germplasm resource protection zone. Predictive analysis indicated that when the sewage treatment plant’s discharge was reduced to 1.0 × 10⁴ t·d⁻¹, the receiving water bodies could still meet local water quality standards, even under the counter flow hydrological conditions, which pose the greatest threat to water quality during the dry season. Full article

In Long Short-Term Memory (LSTM)-based runoff forecasting models, the selection of input schemes is critically important. This study, using daily rainfall and runoff data from the Jingle Basin (2006–2014), investigated three input schemes to evaluate their forecasting efficacy and employed the Shapley Additive Explanation (SHAP) method to quantitatively analyze variable contributions. The results demonstrate that LSTM model performance deteriorates with increasing lead time, achieving optimal accuracy at a 1-day lead (MAE: 0.90 m³/s, RMSE: 3.09 m³/s, NSE: 0.84). The results, validated by significance testing, are reasonable; incorporating precipitation characteristics significantly enhances model performance compared to baseline schemes, reducing RMSE by 6–34% and improving NSE by 9–14%. SHAP analysis reveals antecedent runoff as the dominant influencing factor, accounting for 65.9–84.7% of total importance. Furthermore, the contributions of trend, seasonal, and residual components progressively increase with extended lead times, demonstrating non-negligible roles in forecast outcomes. These findings, confirmed by significance testing, provide quantitative insights into input variable contributions to target uncertainty and enhance the mechanistic understanding of precipitation-runoff relationships, offering valuable references for optimizing hydrological forecasting systems. Full article

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China. Full article

High-resolution satellite precipitation products are key inputs for basin-scale rainfall estimation, but they still exhibit substantial biases in complex terrain and during heavy rainfall. Recent multi-source fusion studies have shown that simply stacking multiple same-type microwave satellite products yields only limited additional gains for high-quality precipitation estimates and may even introduce local degradation, suggesting that targeted correction of a single, widely validated high-quality microwave product (such as IMERG) is a more rational strategy. Focusing on the mountainous, gauge-sparse Lüshui River basin with pronounced relief and frequent heavy rainfall, we use GPM IMERG V07 as the primary microwave product and incorporate CHIRPS, ERA5 evaporation, and a digital elevation model as auxiliary inputs to build a daily attention-enhanced CNN–LSTM (A-CNN–LSTM) bias-correction framework. Under a unified IMERG-based setting, we compare three network architectures—LSTM, CNN–LSTM, and A-CNN–LSTM—and test three input configurations (single-source IMERG, single-source CHIRPS, and combined IMERG + CHIRPS) to jointly evaluate impacts on corrected precipitation and SWAT runoff simulations. The IMERG-driven A-CNN–LSTM markedly reduces daily root-mean-square error and improves the intensity and timing of 10–50 mm·d⁻¹ rainfall events; the single-source IMERG configuration also outperforms CHIRPS-including multi-source setups in terms of correlation, RMSE, and performance across rainfall-intensity classes. When the corrected IMERG product is used to force SWAT, daily Nash-Sutcliffe Efficiency increases from about 0.71/0.70 to 0.85/0.79 in the calibration/validation periods, and RMSE decreases from 87.92 to 60.98 m³ s⁻¹, while flood peaks and timing closely match simulations driven by gauge-interpolated precipitation. Overall, the results demonstrate that, in gauge-sparse mountainous basins, correcting a single high-quality, widely validated microwave product with a small set of heterogeneous covariates is more effective for improving precipitation inputs and their hydrological utility than simply aggregating multiple same-type satellite products. Full article

Urban lakes, as critical components of urban ecosystems, provide essential ecological services but face water quality deterioration due to rapid urbanization and associated land use changes. This study investigated the temporal and spatial characteristics and evolution mechanisms of water quality in Wuhan city lakes, with a focus on the Great East Lake basin (GELB), a typical urban lake cluster in the middle Yangtze River basin. By integrating monthly water quality monitoring data (2017–2023) with high-resolution land use data (2020), we employed the Water Quality Index (WQI), Spearman correlation analysis, and Redundancy Analysis (RDA) to assess water quality and the impact of land use on major pollutants. The results revealed significant spatial heterogeneity: Sha Lake (SL) exhibited the best water quality, while Yangchun Lake (YCL) and North Lake (NL) showed the worst conditions. Seasonal variations in water quality were observed, influenced by the ecological functions of lakes and surrounding land use. Notably, understanding these seasonal dynamics provides insights into nutrient cycle operations and their effective management under varying climatic conditions. In addition, the correlation between chlorophyll-a concentration and nutrient elements in urban lakes was not consistent, with some lakes showing significant negative correlations. The water quality of urban lakes is influenced by both land use and human management. Land use analysis indicated high impervious surfaces in East Lake (EL), SL, and YCL exacerbated runoff-driven nutrient loads, the nitrogen elevation from agricultural runoff of Yan East Lake (YEL) and NL’s pollution from historical industrial discharge. This study highlights the urgent need for targeted water management strategies to mitigate the impact of urbanization on water quality and provide a scientific basis for effective governance and ecological restoration in rapidly urbanizing areas around the world. By adopting an integrated approach combining water quality assessments with land use data, this research offers valuable insights for sustainable urban lake management. Full article

Agricultural nonpoint source pollution is emerging as one of the increasingly serious environmental concerns all over the world. This study conducted field experiments in Zengcheng District, Guangzhou City, from 2019 to 2023 to explore the mechanisms by which different crop types, fertilization modes, and meteorological conditions affect the loss of nitrogen and phosphorus in agricultural nonpoint source pollution. In rice and corn, the CK and PK treatment groups showed significant fitting advantages, such as the R² of rice-CK reaching 0.309. MAE was 0.395, and the R² of corn-PK was as high as 0.415. For compound fertilization groups such as NPK and OF, the model fitting ability decreased, such as the R² of rice-NPK dropping to 0.193 and the R² of corn-OF being only 0.168. In addition, the overall performance of the model was limited in the modeling of total phosphorus. A relatively good fit was achieved in corn (such as NPK group R² = 0.272) and in vegetables and citrus. R² was mostly below 0.25. The results indicated that fertilization management, crop types, and meteorological conditions affected nitrogen and phosphorus losses in agricultural runoff. Cornfields under conventional nitrogen, phosphorus, and potassium fertilizer (NPK) and conventional nitrogen and potassium fertilizer treatment without phosphorus fertilizer (NK) treatments exhibited the highest nitrogen losses, while citrus fields showed elevated phosphorus concentrations under NPK and PK treatments. Organic fertilizer treatments led to moderate nutrient losses but greater variability. Organic fertilizer treatments resulted in moderate nutrient losses but showed greater interannual variability. Meteorological drivers differed among crop types. Nitrogen enrichment was mainly associated with high temperature and precipitation, whereas phosphorus loss was primarily triggered by short-term extreme weather events. Linear regression models performed well under simple fertilization scenarios but struggled with complex nutrient dynamics. Crop-specific traits such as flooding in rice fields, irrigation in corn, and canopy coverage in citrus significantly influenced nutrient migration. The findings of this study highlight that nutrient losses are jointly regulated by crop systems, fertilization practices, and meteorological variability, particularly under extreme weather conditions. These findings underscore the necessity of crop-specific and climate-adaptive nutrient management strategies to reduce agricultural nonpoint source pollution. By integrating long-term field observations with machine learning–based analysis, this study provides scientific evidence to support sustainable fertilizer management, protection of water resources, and environmentally responsible agricultural development in subtropical regions. The proposed approaches contribute to sustainable land and water resource utilization and climate-resilient agricultural systems, aligning with the goals of sustainable development in rapidly urbanizing river basins. Full article

The construction and operation of reservoirs have made hydrological processes complex, posing challenges to flood modeling. While many hydrological models have incorporated reservoir operation schemes to improve discharge estimation, the influence of reservoir representation on model calibration has not been sufficiently evaluated—an issue that fundamentally affects the spatial reliability of distributed modeling. Additionally, the limited availability of reservoir regulation data impedes dam-inclusive flood simulation. To overcome these limitations, this study proposes a synergistic modeling framework for data-scarce dammed basins. It integrates a satellite-based reservoir operation scheme into a distributed hydrological model and incorporates reservoir processes into the model calibration procedure. The framework was tested using the coupled version of the DRIVE flood model (DRIVE-Dam) in the Nandu River Basin, southern China. Two calibration configurations, with and without dam operation (CWD vs. CWOD), were compared. Results show that reservoir dynamics were effectively reconstructed by combining satellite altimetry with FABDEM topography, successfully supporting the development of the reservoir scheme. Multi-site comparisons indicate that, while CWD slightly improved streamflow estimation (NSE and KGE > 0.75, similar to CWOD) on the calibrated outlet gauge, it enhanced basin-internal process representation, as evidenced by the superior peak discharge and flood event capture with reduced bias, boosting flood detection probability from 0.54 to 0.60 and reducing false alarms from 0.28 to 0.15. The improvements stem from refined parameterization enabled by a physically complete model structure. In contrast, CWOD leads to subdued flood impulses and prolonged recession due to spurious parameters that distort baseflow and runoff response. The proposed methodology provides a practical reference for flood forecasting in dam-regulated basins, demonstrating that reservoir representation enhances model parameterization and underscoring the strong potential of satellite observations for hydrological modeling in data-limited regions. Full article

Glaciers in the Northern Tien Shan are a major source of Ile River runoff, supplying water to Kazakhstan’s largest city, Almaty. Under ongoing climate warming, their degradation alters the magnitude and seasonality of river discharge, increasing water-resource vulnerability. This study quantifies long-term changes in glacier area, firn-line elevation, and glacier runoff in the northern Tien Shan from 1955 to 2021. The analysis uses multi-decadal meteorological observations, hydrological records, multi-temporal Landsat-7/8 and Sentinel-2 imagery, and DEMs combined with empirical and semi-empirical runoff estimation methods. The glacier area has declined by more than 45–60% since 1955, accompanied by a rise in firn-line altitude from ~3400 to 3700 m. At the Mynzhylky station, mean summer air temperature increased by 0.88 °C, reflecting persistent warming in glacierized elevations. The contribution of glacier meltwater to annual discharge decreased from ~32% in 1955–1990 to ~25% in 1991–2021, while total and vegetation-period runoff increased due to modified seasonal hydrological conditions. These results demonstrate the impact of climate warming on glacier-fed runoff in the Northern Tien Shan and highlight the need to integrate glacier degradation into water-resource management and long-term water-security assessments. Full article

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article

Geothermal water from different orogenic belts, surrounding rock weathering, and salt-forming elements sourced from surface basins jointly shape the hydrochemical characteristics, evaporation evolution sequences, and prospects for subsequent development and utilization of terminal salt lakes. In view of the lack of research on the metallogenic model of a single salt lake in the Qinghai–Tibet Plateau, this paper selects the Nie’er Co Salt Lake, a terminal lake in Northern Tibet, and systematically samples the water, river sediments, and surrounding rocks of the upper reaches of the recharge river, the Xiangqu. The Piper, Gibbs, and Durov, combined with ion ratio analysis, correlation analysis, PHREEQC, quantitative calculations of surrounding rock weathering and tributary contributions to salt-forming elements, were applied to comprehensively characterize groundwater hydrochemistry and surface water system runoff, and clarify the evolution of salt-forming elements in the terminal lake. The driving mechanism of surface runoff and surrounding rock weathering on ion enrichment in the terminal lake was revealed. The Nie’er Co Salt Lake in Tibet evolves from Ca/Na-HCO₃ springs to Na-SO₄²⁻ via dilution, rock leaching, and evaporation. Tributaries contribute 39.6%, 8.2%, and 52.3% of the major ions. Silicate weathering dominates (75%–80%), shifting to evaporite–carbonate inputs. The overall performance is dominated by silicate weathering. The contribution rate of silicate weathering decreases, and the trend of evaporite–carbonate weathering increases. The evolution of surface runoff can be divided into a tributary ion concentration growth section, a mixed ring section (evaporation concentration–TDS increase), and a terminal lake sedimentary section (enrichment evaporation to form the salt lake), revealing that multi-branch superposition and surrounding rock weathering synergistically affect the formation of salt lake hydro-chemical types. Full article

Understanding the local-scale impacts of climate change is critical for protecting water resources and ecosystems in vulnerable agricultural regions. This study investigates the Canagagigue Creek Watershed (CCW) in Southern Ontario, Canada, which is an area vital to the Grand River Basin yet threatened by sediment runoff, making it an ecologically sensitive area. We applied an integrated Statistical Downscaling Model (SDSM) and Soil and Water Assessment Tool (SWAT) (version 2012) approach under the IPCC A2 scenario to project impacts for the period 2025–2044. The results reveal a fundamental hydrological shift, and evapotranspiration is projected to claim nearly 70% of annual precipitation, leading to a ~30% reduction in total water yield. Seasonally, the annual streamflow peak is projected to shift from March to April, indicating a transition from a snowmelt-dominated to a rainfall-influenced system, while extended low-flow periods increase drought risk. Crucially, sediment yield at the watershed outlet is projected to decrease by 7.9–10.5%. The concomitant reduction in streamflow implies a weakened sediment transport capacity. However, this points to a heightened risk of increased in-stream deposition, which would pose a dual threat, (a) elevating flood risk through channel aggradation and (b) creating a long-term sink for agricultural pollutants that degrades water quality. By linking SDSM and SWAT, this study moves beyond generic predictions, providing a targeted blueprint for climate-resilient land and water management that addresses the complex, interacting challenges of water quantity. Full article

The nitrogen and phosphorus pollution in water is highly related to the land use pattern in the watershed. The impacts of the land use patterns on total nitrogen (TN) and total phosphorus (TP) exports in an agricultural watershed of the Lijiang River Basin were studied using the Soil and Water Assessment Tool (SWAT). The SWAT model performed well in simulating runoff, TN, and TP exports, and the R² values were all above 0.67. The model simulation results showed that the total nitrogen (TN) and total phosphorus (TP) outputs in the wet season were 13.97 tons and 1.37 tons, respectively, approximately three times those in the dry season, highlighting that outputs of TN and TP predominantly occurred in the wet season in the basin. The correlation analysis showed that the forest land and water in the sub-basin had negative impacts on TN and TP exports, while the orchard, cultivated land, and building land had a positive correlation with TN and TP exports. Then, scenario simulations were conducted using the calibrated and validated SWAT model. A total of 55 scenarios were set up, involving five land use types with five conversion ratios (10%, 20%, 30%, 40%, and 50%), to analyze the impacts of dynamic land use changes on TN and TP exports. The results showed that the TN and TP exports significantly increased under the conversion of the other land use types into building land, cultivated land, and orchards, and the increasing rate decreased in order, while the TN and TP exports declined with the rising forest and water body area. Generally, the changing rates of TN exports under land use conversion were higher than those of TP exports, except for the orchard conversion. This study revealed that the reasonable planning of land use could alleviate nitrogen and phosphorus pollution, which was helpful for aquatic ecosystem restoration. It provided scientific references for land use planning, aquatic ecosystem restoration, and the achievement of sustainable development goals related to water environment protection in similar karst basins. Full article