Search Results (101)

This study evaluated the water supply and regulation of the San Pedro River basin, located in the municipality of Puerto Libertador (Córdoba, Colombia), under climate change scenarios, using the SWAT (Soil and Water Assessment Tool) hydrological model. The model was calibrated and validated in SWAT-CUP using the SUFI-2 algorithm, based on observed streamflow series and sensitive hydrological parameters. Observed and satellite climate data, CHIRPS for precipitation and ERA5-Land for temperature, radiation, humidity, and wind, were employed. Climatic data were integrated along with spatial information on soils, land use, and topography, allowing for an adequate representation of the basin’s heterogeneity. The model showed acceptable performance (NSE > 0.6; PBIAS < ±15%), reproducing the seasonal variability and the average flow behavior. Climate projections under RCP 4.5 and RCP 8.5 scenarios, derived from the MIROC5 model (CMIP5), indicated a slight decrease in mean streamflow and an increase in interannual variability for the period 2040–2070, suggesting a potential reduction in surface water availability and natural hydrological regulation by mid-century. The Water Regulation Index (WRI) exhibited a downward trend in most sub-basins, particularly in areas affected by forest loss and agricultural expansion. The WRI showed a downward trend in most sub-basins, especially those with loss of forest cover and a predominance of agricultural uses. These findings provide basin-specific evidence on how climate change and land-use pressures may jointly affect hydrological regulation in tropical Andean–Caribbean basins. These results highlight the usefulness of the SWAT model as a decision-support tool for integrated water resources management in the San Pedro River basin and similar tropical Andean–Caribbean catchments, supporting basin-scale climate adaptation planning. They also emphasize the importance of conserving headwater ecosystems and forest cover to sustain hydrological regulation, reduce vulnerability to flow extremes, and enhance long-term regional water security. Full article

Accurate precipitation estimation is essential for hydrological modeling and water resource management in arid regions; however, complex terrain and sparse meteorological station networks introduce substantial uncertainties into gridded precipitation datasets. This study evaluates the performance of nine widely used precipitation products in the arid region of Northwest China (ARNC) at both the meteorological station scale and the sub-basin scale, and applies the Bayesian Model Averaging (BMA) approach to merge multi-source precipitation estimates. The results reveal pronounced spatial heterogeneity and significant differences in performance among datasets, with the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement mission performing best at the station scale and the Famine Early Warning Systems Network Land Data Assimilation System performing best at the sub-basin scale. Compared with individual products, the BMA-merged precipitation demonstrates substantial improvements at both scales, providing higher coefficients of determination and agreement indices, and lower relative mean absolute error and relative root mean square error, indicating enhanced accuracy and robustness. The BMA-merged precipitation product generally exhibits superior and more spatially consistent performance than the individual datasets across the ARNC, thereby providing a more reliable basis for regional hydrological and climate-related applications. The merged dataset shows that the mean annual precipitation in the ARNC during 2000–2024 is approximately 230.4 mm, exhibiting a statistically significant increasing trend of 1.4 mm per year, with the strongest increases occurring in the Tianshan and Qilian Mountains. This study provides a reliable foundation for hydrological modeling and climate-change assessments in data-limited arid environments. Full article

The Gravity Recovery and Climate Experiment (GRACE) tracks drought events by detecting changes in the global gravitational field and capturing abnormal information on the reserves of surface water, soil water, and groundwater, which makes it possible for a more comprehensive and unified global and regional monitoring of groundwater drought. This study adopted the gravity satellite GRACE data and combined it with the hydrological model dataset. Additionally, we assessed the temporal evolution and spatial pattern of groundwater drought in the Yangtze River Basin (YRB) and its sub-basins from 2003 to 2022, determined the change points of the hidden seasonal and trend components in groundwater drought, and identified the direct/indirect driving contributions of the main climatic and circulation factors to groundwater drought. The results show that (1) as a normalized index, the groundwater drought index (GDI) can reflect direct evidence of any surplus and deficit in groundwater availability. During the study period, the minimum value (−1.66) of the GDI occurred in July 2020 (severe drought). (2) The average value of GDI in the entire basin ranged from −1.66 (severe drought) to 0.52 (no drought). (3) The average Z_s values (Mann–Kendall Z-statistic) of GDI were −0.23, −0.16, −0.43, and 0.14, respectively, and the proportions of areas with aggravated drought reached 65.21%, 61.05%, 89.70% and 43.67%, respectively. (4) Partial wavelet coherence analysis can simultaneously reveal the local correlations of time series at different time scales and frequencies. Based on partial wavelet analysis, precipitation was the best factor for explaining the dynamic changes in groundwater drought. (5) The North Pacific Index (NPI), the Pacific/North American Index (PNA), and the Sunspot Index (SSI) can serve as the main predictors that can effectively capture the drought changes in groundwater in the YRB. The GRACE satellite can provide a new tool for monitoring, tracking, and assessing groundwater drought situations, which is of great significance for guiding the development of the drought early warning system in the YRB and effectively preventing and responding to drought disasters. Full article

Lake eutrophication exhibits pronounced spatial heterogeneity at the watershed scale, yet a systematic and quantitative understanding of how landscape characteristics drive these variations remains limited. In this study, a long-term and internally consistent trophic state dataset for the Liangzi Lake Basin was constructed by integrating Landsat imagery from 1990 to 2022 with a semi-analytical water color inversion method. A multi-scale landscape feature system incorporating both land use composition and landscape pattern metrics was developed at the sub-basin level to elucidate the mechanisms by which landscape characteristics influence eutrophication dynamics. The XGBoost model was employed to characterize the nonlinear relationships between landscape attributes and trophic conditions, while the SHAP interpretability approach was applied to quantify the relative contribution of individual landscape components and their interaction pathways. The analytical framework demonstrates that landscape pattern attributes—such as fragmentation, diversity, and connectivity—play essential roles in shaping the spatial variability of eutrophication by modulating hydrological processes, nutrient transport, and ecological buffering capacity. By integrating remote sensing observations with interpretable machine learning, the study reveals the complexity and scale dependence of landscape–water interactions, providing a methodological foundation for advancing the understanding of eutrophication drivers. The findings offer theoretical guidance and practical references for optimizing watershed landscape planning, controlling non-point source pollution, and supporting ecological restoration efforts in lake basins. Full article

Satellite-based rainfall products (SRPs) are indispensable for hydro-climatological research, particularly in data-limited environments such as Ethiopia. This study systematically evaluates the performance of three widely used SRPs: Climate Hazards Group InfraRed Precipitation with Station data version 2 (CHIRPS), Tropical Applications of Meteorology using Satellite and ground-based observations version 3.1 (TAMSAT), and Multi-Source Weighted Ensemble Precipitation version 2.8 (MSWEP) across the North and South Gojjam sub-basins of the Abbay Basin. Using ground observations as benchmarks, spatial and temporal accuracy was assessed under varying elevation and rainfall intensity conditions, employing bias decomposition, error analysis, and detection metrics. Results show that rainfall variability in the region is shaped more by the local climate and topography than elevation, with elevation alone proving a weak predictor (R² < 0.5). Among the products, MSWEP v2.8 demonstrated the highest daily rainfall detection skill (≈ 87–88%), followed by TAMSAT (≈78%), while CHIRPS detected only about half of rainfall events (≈54%) and tended to overestimate no-rain days. MSWEP’s error composition is dominated by low random error (~52%), though it slightly overestimates rainfall and rainy days. TAMSAT provides finer-resolution data that capture localized variability and dry conditions well, with the lowest false alarm rate and moderate random error (~59%). CHIRPS exhibits weaker daily performance, dominated by high random error (~66%) and missed bias, though it improves at monthly scales and better captures heavy and violent rainfall. Seasonally, SRPs reproduce MAM rainfall reasonably well across both sub-basins, but their performance deteriorates markedly in JJAS, particularly in the south. These findings highlight the importance of sub-basin scale analysis and demonstrate that random versus systematic error composition is critical for understanding product reliability. The results provide practical guidance for selecting and calibrating SRPs in mountainous regions, supporting improved water resource management, climate impact assessment, and hydrological modeling in data-scarce environments. Full article

Water demand has increased due to population growth and rapid socioeconomic development, creating conflicts between human activities and water resources and having a substantial impact on the balance between blue and green water supplies. Existing study lacks a spatial perspective to examine the inherent relationship between blue and green water supply and demand, particularly in terms of geographical differentiation characteristics and rational allocation of blue and green water supply–demand balance in inland river basins. Using the Taolai River Basin as a case study, this research uses the distributed hydrological model SWAT from a blue–green water resources viewpoint to simulate the spatiotemporal distribution features of blue and green water resources at the sub-basin scale from 2002 to 2021. The supply and demand balance relationship of blue and green water resources within the basin was investigated, an assessment index system for water resource security was developed, and the realizable potential of blue water resources was quantified using various indicators. The findings show that during the study period, the average annual green water resources in the Taolai River Basin were 1.95 times greater than blue water resources, making green water the most abundant component of regional water resources. Spatially, both blue and green water resources showed considerable latitudinal zonality, with a declining tendency from south to north and very consistent distribution patterns. Blue water resources showed high geographic variability, with a safety index more than one, suggesting that supply–demand imbalances were most concentrated in the upper and intermediate ranges of the irrigated region, as well as the desert zone, where safety levels were relatively low. In contrast, green water resources had a safety score ranging from 0.7 to 1.0, indicating great overall safety and negligible regional variability. During the research period, the average annual theoretical transferable blue water resources were 4.06 × 108 m³, based on cross-regional water resource allocation potential analysis. This reveals tremendous potential for enhancing regional water resource allocation, hence providing substantial support for effective water consumption within the Taolai River Basin and regional economic growth. In conclusion, the assessment method developed in this work provides a solid foundation for improving water resource allocation and sustainable management in river basins. It provides technical assistance in the construction of water network systems in inland river basins, which is critical in establishing reasonable water resource distribution across various areas within these basins. Full article

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article

The reducing streamflow is a major concern in the Yilong Lake Basin (YLB), which supplies water for agriculture and the growing population in the basin and to maintain the health of the regional ecosystem. The YLB has experienced remarkable land use/land cover change (LUCC) and climate change (CC) in recent years. To understand the drivers of the streamflow change in this basin, the effects of the land use change and climate variation on the temporal flow variability were studied using the Soil and Water Assessment Tool (SWAT). The calibration and validation results indicated that the SWAT simulated the streamflow well. Then the streamflow responses to the land use change between 2010 and 2020 and climate change with future climate projections (SSP245, SSP370, and SSP585) were evaluated. Results showed that the LUCC in the YLB caused a marginal decline in the annual streamflow at the whole basin scale but significantly altered rainfall–runoff relationships and intra-annual discharge patterns; e.g., monthly streamflows decreased by up to 3% in the dry season under the surface modification, with subbasins of the YLB exhibiting divergent responses attributed to spatial heterogeneity in land surface transitions. Under future climate scenarios, streamflow projections revealed general declining trends with significant uncertainties, particularly under high-emission pathways, e.g., SSP370 and SSP585, in which the streamflow could be projected to reduce by up to 5.9% in the mid-future (2031–2045). In addition, droughts were expected to intensify, exacerbating seasonal water stress in the future. It suggests that integrated water governance should synergize climate-resilient land use policies with adaptive infrastructure to address regional water resources challenges. Full article

Arid regions face multiple challenges such as population expansion, water scarcity, land degradation, and biodiversity reduction. Understanding temporal and spatial patterns of ecosystem service trade-offs and synergies is critical for sustainable development and effective ecosystem service management in arid regions under environmental stress. Taking the Tarim River Basin in China as an example, five ecosystem services (carbon sequestration, water yield, sediment delivery ratio, habitat quality, and food production) were studied at different scales in 1990, 2000, 2010, and 2020 in the inland arid region. Spearman correlation, geographical weighted regression, and self-organizing mapping were used to analyze the ecosystem service trade-offs and synergies. The results showed that the ecosystem services in the basin increased gradually; in particular, the water yield increased from 15.38 × 10⁹ m³ to 29.8 × 10 m³, and the food production increased from 11.03 × 10⁶ t to 29.26 × 10⁶ t. There was a significant positive correlation between carbon sequestration, water yield, and habitat quality, but a negative correlation between sediment delivery ratio and food production. The spatial distribution of trade-offs and synergies of ecosystem services varies in different years and on different scales. The area change in ecosystem service bundles at the pixel scale is relatively small, while the area change at the sub-basin scale is relatively large. This paper provides policy suggestions for the ecological management and sustainable development of the Tarim River Basin through the analysis of ecosystem service trade-offs and synergies. Full article

While at the global level, water stress does not seem to present a serious threat to the sustainability of freshwater withdrawal and use, the situation appears much grimmer if a closer look is given to the status of the freshwater resources at basin and sub-basin levels. Unfortunately, such information is often not available to water managers and decision-makers, due both to the scarcity of sufficient data and also to the lack of methods capable of transforming the existing data into usable information. Hence, disaggregating water stress at basin and sub-basin levels is fundamental to provide a finer view of both its causes and effects, allowing the targeting of interventions at areas with high water stress and sectors with high water use. The spatial disaggregation of SDG indicator 6.4.2 by major river basin already implemented at a global scale showed the existence of a water stress belt running across the globe approximately between 10 and 45 degrees north, with a few other areas above and below it. The value of SDG indicator 6.4.2 at the country level is influenced by its size: the larger the country, the more the national average masks local variability. When the disaggregation is performed at sub-basin level, there is the possibility that the same amount of water is counted twice or even more (double counting), as it flows from one sub-basin to the neighbouring ones. Current water accounting methods do not allow this issue to be overcome. This causes an underestimation of water stress and an overestimation of the water resources available for human use in a given area. This paper presents a new methodology to assess SDG indicator 6.4.2 (water stress) seasonally and at the sub-basin level, addressing double counting by factoring in water demands between upstream and downstream sub-basins. This approach supports more informed water management. A corresponding plugin for the WEAP tool was developed, tested in the Senegal River basin countries, and is available online with a user manual in English, French, and Spanish. Full article

Water–sediment evolution and attribution analysis in watersheds is one of the research focuses of hydrogeology. An in-depth investigation into the spatiotemporal variation of water and sediment at multiple spatial scales within the basin, along with a systematic assessment of the respective impacts of climate change and human activities, provides a scientific foundation for formulating effective soil and water conservation practices and integrated water resource management strategies. This research holds significant implications for the sustainable development and ecological management of the basin. In this study, the Mann–Kendall nonparametric test method, double cumulative curve method, cumulative anomaly method, and cumulative slope change rate analysis method were used to quantitatively study the effects of climate change and human activities on runoff and sediment load changes at different spatial scales in the Huangfuchuan River basin. The results show that (1) from 1966 to 2020, the annual runoff and annual sediment load discharge in the Huangfuchuan River basin showed a significant decreasing trend. Among them, the reduction in runoff and sediment in the control sub-basin of Shagedu Station in the upper reaches was more obvious than that in the whole basin. The mutation points of runoff and sediment load in the two basins were 1979 and 1998. The water–sediment relationship exhibits a power function pattern. (2) After the abrupt change, in the change period B (1980–1997), the contribution rates of climate change and human activities to runoff and sediment load reduction in the Huangfuchuan River basin were 24.12%, 75.88% and 20.05%, 79.95%, respectively. In the change period C (1998–2020), the contribution rates of the two factors to the runoff and sediment load reduction in the Huangfuchuan River basin were 18.91%, 81.09% and 15.61%, 84.39%, respectively. Among them, the influence of precipitation in the upper reaches of the Huangfuchuan River basin on the change in runoff and sediment load is higher than that of the whole basin, and the influence on the decrease of sediment load discharge is more significant before 1998. There are certain stage differences and spatial scale effects. (3) Human activities such as large-scale vegetation restoration and construction of silt dam engineering measures are the main reasons for the reduction in runoff and sediment load in the Huangfuchuan River basin and have played a greater role after 1998. Full article

Recent climate change has intensified extreme rainfall events, exacerbating soil erosion and agricultural nonpoint source pollution in South Korea’s steeply sloped farmlands. This study assessed soil erosion reduction measures by applying individual Best Management Practices (BMPs) in cropland and expanding upon existing management efforts through the implementation of additional BMPs aimed at further reducing soil erosion. Furthermore, priority management areas were identified based on soil erosion reduction efficiency within subbasins. For this evaluation, the Soil and Water Assessment Tool (SWAT) was employed, with a spatially distributed Hydrological Response Unit (SD-HRU) module and calibrated Modified Universal Soil Loss Equation (MUSLE) parameters tailored to Korean watershed conditions. Scenarios 1 and 2 were implemented in the study area to evaluate BMP effectiveness in controlling soil erosion and suspended sediment (SS) loads. Scenario 1 applied a set of BMPs already in place, while Scenario 2 involved the addition of supplementary BMPs to enhance soil erosion control. Scenario 1 resulted in a 34.6% reduction in annual soil erosion and a 35.0% decrease in SS concentration, whereas Scenario 2 achieved a 59.3% reduction in soil erosion and a 57.3% decrease in SS concentration. Subbasin-scale evaluations revealed considerable spatial variability in erosion control efficiency, ranging from 1.3% to 70.5%, highlighting the necessity for spatially targeted management strategies. These results underscore the importance of employing spatially adaptive BMP approaches and offer practical guidance for enhancing watershed sustainability, particularly in regions vulnerable to extreme hydrometeorological events. Full article

The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article

Neglecting ecosystem services has impeded sustainable urban–rural development, particularly in terms of the efficient flow of water supply services between urban and rural areas. This study focuses on the Fenhe River Basin, evaluating water supply and demand at the sub-basin, as well as county levels. Using the InVEST model to analyze basin-level geographic, meteorological, hydrological, and socio-economic data, the study reveals significant spatial and temporal mismatches between water supply and demand from 2010 to 2020. Through the calculated ecosystem services supply and demand ratio (0.3731 in 2010, −0.1555 in 2015, and −0.1063 in 2020), it is found although both supply and demand increased over the period, persistent deficits emerged, with water supply concentrated in upstream areas and demand primarily in downstream regions. The improved network connectivity by 2020, supported by water-saving policies and technological advancements, partially alleviated earlier imbalances. This research contributes a multi-scale framework to analyze ecosystem service flows and compensation mechanisms across grid, sub-basin, and county scales. Overall, the study underscores that research into ecological compensation plays a crucial role in enabling efficient resource flow, enhancing governance systems, and fostering an ecologically friendly urban–rural development model. Full article

In-depth analysis of the evolution of ecosystem services (ESs) in the basin at different spatial scales, scientific identification of ecosystem service clusters, and revelation of their spatial and temporal characteristics as well as coupling mechanisms of interactions are the key prerequisites for effective implementation of ES management. This paper assessed the spatial and temporal changes of six key ESs covering food provisioning (FP), water yield (WY), soil retention (SR), water conservation (WC), habitat quality (HQ), and carbon sequestration (CS) in the Xijiang River Basin (XRB), China, between 2000 and 2020. Given that the scale effects of ESs and their spatial heterogeneity in the XRB are still subject to large uncertainties, a combination of Spearman correlation analysis and geographically weighted regression (GWR) modelling systematically revealed the trade-offs and synergistic relationships between ESs and the scale effects from a grid, watershed, and county perspective. Additionally, we applied the self-organizing mapping (SOM) method to identify multiple ecosystem service bundles (ESBs) and propose corresponding sustainable spatial planning and management strategies for each cluster. The results reveal the following key findings: (1) Spatial distribution and heterogeneity: The six ESs demonstrated pronounced spatial variability across the study area during the two-decade period from 2000 to 2020. The downstream areas had higher levels of ESs, while the upstream regions showed comparatively lower levels. This trend was particularly evident in areas with extensive arable land, higher population density, and more developed economic activity, where ESs levels were lower. (2) Trade-offs/synergies: The analysis highlighted the prevalence of synergistic effects among ESs, with food provisioning-related services exhibiting notable trade-offs. Trade-off/Synergistic effects were weaker at the grid scale but more pronounced at the sub-basin and county scales, with significant spatial heterogeneity. (3) Identification of ESBs: We identified five distinct ESBs: the HQ-CS synergy bundle (HCSB), the integrated ecological bundle (IEB), the agricultural bundle (AB), the key synergetic bundle lacking HQ (KSB), and the supply service bundle (SSB). These clusters suggest that the overall ecological environment of the study area has significantly improved, the supply functions have strengthened, and ecosystem vulnerability has been effectively mitigated. Building upon the identified multi-scale spatiotemporal heterogeneity patterns of ESBs in the XRB, this study proposes an integrated framework for territorial spatial planning and adaptive land management, aiming to optimize regional ecosystem service provisioning and enhance socio-ecological sustainability. Full article