Search Results (71)

Dryland watersheds are governed by tightly coupled source–sink dynamics, in which expanding bare soil and declining vegetated patches amplify runoff, sediment transport, and land degradation. Identifying suitable locations for water harvesting structures remains challenging due to the limited scalability of field assessments and the inability of coarse DEM-based GIS methods to capture critical microtopographic features. This study evaluates whether high-resolution (0.44 m) topographic data, integrated with multi-criteria decision analysis (MCDA), can identify suitable locations for water harvesting structures in dryland watersheds and compares the model discrimination of the Analytical Hierarchy Process (AHP) and the Fuzzy AHP (FAHP). Eight geomorphic and ecological indicators were evaluated and validated using 565 practitioner-identified restoration practice locations across two watersheds in southern New Mexico. The results show that 78% (East Control) and 94% (West Restoration) of validation sites occur within the top two predicted suitability classes, with moderate to good model discrimination (AUC: 0.671–0.723) and strong ranking performance (Boyce Index: 0.945–0.983). AHP and FAHP produced nearly identical outputs (ΔAUC < 1%; ΔBoyce ≤ 0.005). These findings demonstrate that high-resolution topography, coupled with MCDA, provides a robust and transferable framework for the landscape-scale prioritization of nature-based water harvesting structures to support ecohydrological restoration in dryland watersheds. Full article

Dryland sustainability depends on how vegetation productivity and water-use processes respond to climatic variability and human intervention. Focusing on Xinjiang, China, this study assessed eco-hydrological change from 2000 to 2023 using multi-source remote sensing and climatic datasets. We integrated vegetation productivity and water-use efficiency into a composite EcoIndex, combined anomaly-based diagnostics with eco-hydrological synchrony analysis, and used pixel-level random forest attribution to identify dominant climatic and anthropogenic controls. The results show clear regional differentiation. Northern Xinjiang remained primarily climate-driven and maintained relatively stronger vegetation–water coupling, whereas Southern Xinjiang exhibited more pronounced human-induced restructuring, especially in oasis and cultivated areas. Eastern Xinjiang functioned as a transitional zone with weak coupling and high sensitivity to multiple pressures. Across Xinjiang, 63.27% of the area was classified as climate-dominated, 22.41% as human-dominated, and 14.32% as mixed influence. The results indicate that improvements in vegetation condition do not necessarily imply improved eco-hydrological coordination, and that mixed-influence zones may represent early-warning areas of sustainability risk. This study provides a spatial diagnostic framework for supporting sustainable land and water management, regional adaptation planning, and resilience-oriented governance in arid and semi-arid regions. Full article

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

Under the pronounced warming–wetting trend in Northwest China, understanding vegetation responses to the redistribution of hydrothermal resources is essential for interpreting regional ecohydrological processes. Here, we developed a bivariate Long Short-Term Memory (LSTM) model to simulate leaf area index (LAI) dynamics for four representative vegetation types (cold temperate forest, shrubland, grassland, and cropland), using air temperature and soil moisture as predictors. The model reproduces seasonal vegetation phenology well across vegetation types (R² > 0.9), indicating that LSTM effectively captures the cumulative and lagged effects of hydrothermal drivers. However, its performance diverges at the interannual scale. Interannual variability in grasslands in water-limited environments is reasonably represented (R² = 0.31), consistent with their sensitivity to short-term hydroclimatic variability under warming–wetting conditions. In contrast, the model fails to reproduce the observed long-term greening trend in forests when driven solely by hydrothermal variables. This contrast suggests distinct underlying mechanisms across ecosystem types. Grassland dynamics are closely linked to high-frequency hydroclimatic variability, whereas forest growth appears to be governed by slower processes and low-frequency drivers, including CO₂ fertilization, nitrogen deposition, and ecological inertia. As a result, hydrothermal variables alone are insufficient to explain long-term forest dynamics. Overall, these findings highlight a transition from water-limited to energy- and process-limited controls across vegetation types and underscore the limitations of purely climate-driven models. Integrating biogeochemical processes or process-based constraints into machine learning frameworks may therefore be necessary to improve predictions of long-term vegetation change under climate change. Full article

Accurate quantification of ecohydrological processes is essential for effective water and carbon management in terrestrial ecosystems. Traditional simulations mainly rely on mechanistic models, yet their accuracy is often limited by inconsistencies in representing physical processes and uncertainties in parameterization. Integrating remote sensing signals offers a promising way to reduce these uncertainties and enhance model applicability. In this study, in-situ observations from a wheat cropland in the Guanzhong Plain were used to simulate gross primary productivity (GPP) and latent heat flux (LE) by comparing a forward model (STEMMUS-SCOPE) with a remote sensing-driven inverse model (STEMMUS-MLR). We further examined the role of solar-induced chlorophyll fluorescence (SIF), an emerging proxy for photosynthesis, as an input to improve mechanistic modeling of GPP and LE. Results show that STEMMUS-MLR outperformed STEMMUS-SCOPE in estimating water and carbon fluxes, demonstrating that incorporating SIF effectively reduces bias associated with uncertainties in parameters and forcing data. The contribution of SIF was quantified using Random Forest regression and Shapley additive explanations (SHAP), revealing that SIF markedly reduced the dependence of GPP and LE simulations on shortwave radiation (SW), air temperature (Ta), and leaf area index (LAI). These findings highlight the critical role of SIF in ecohydrological modeling of semi-arid cropland ecosystems and provide a scientific basis for advancing process understanding and improving the precision management of water and carbon budgets in terrestrial ecosystems. Full article

Forest canopy water storage capacity is a critical component of ecohydrological research. However, because most current studies focus on the plot or stand scale, upscaling these fine-scale measurements to regional spatial scales remains a major challenge. Taking the forest in southern Jiangxi province as a case study, we integrated water immersion experiments, Handheld Laser Scanning (HLS), Unmanned Aerial Vehicle LiDAR (UAV-LiDAR), and optical remote sensing data to construct a spatial upscaling model. This model aims to quantify regional canopy water storage capacity and delineate its spatial patterns. The results indicate that: (1) the water storage capacity of branches and leaves per unit surface area of coniferous trees was significantly higher than that of broad-leaved trees, and the water storage capacity of branches was 6.0–10.7 times that of leaves. The mean canopy water storage capacities of coniferous forests, mixed coniferous and broad-leaved forests, and broad-leaved forests were 1.41 ± 0.27 mm, 1.30 ± 0.45 mm, and 1.26 ± 0.36 mm, respectively. (2) The canopy water storage capacity was significantly positively correlated with canopy volume ($V_C$) and average canopy area ($A_C$) extracted from UAV-LiDAR data, and vegetation structure factors such as normalized difference vegetation index (NDVI) and vegetation cover (FVC) extracted from optical remote sensing, and significantly negatively correlated with altitude and slope. Among them, canopy closure (C), average canopy area ($A_C$), and altitude were key factors affecting canopy water storage capacity. (3) The upscaling prediction models based on UAV-LiDAR data and optical remote sensing factors, respectively, show reliable prediction performance, with R² values of 0.884 and 0.815, RMSE of 0.951 and 0.116 mm, respectively. (4) The canopy water storage in the study area ranged from 0 to 1.76 mm, with a prediction uncertainty ranging from 0.12 to 0.49 mm. Canopy water storage is higher in the continuous middle and low mountain and hill areas within the region, while it is relatively lower in the high elevation ridge areas along the western, eastern, and southern margins. The results provide baseline structural information for understanding the spatial patterns of regional forest canopy interception potential. Full article

Urbanization alters the hydrological and structural functioning of tropical urban streams, influencing organic matter transport and retention processes. This study investigated leaf litter retention dynamics in the Bindá Stream in central Amazonia. A six-month leaf release experiment (100 leaves per 12 trial; 1200 leaves total) was conducted alongside hydrological monitoring and floristic surveys of riparian vegetation (adult and regeneration strata). Leaf retention remained consistently low (<33%) across sampling periods. Generalized linear models indicated that flow velocity and discharge were the primary predictors of retention probability, with higher hydrodynamic intensity significantly reducing in-stream storage. Riparian vegetation exhibited moderate structural complexity (Shannon H′ = 1.80; Structural Complexity Index = 3.80), yet limited channel roughness and physical obstructions constrained retention efficiency. Anthropogenic debris locally increased retention, but represents a structurally altered retention mechanism. Hydrodynamic forcing, rather than precipitation totals alone, governed organic matter transport dynamics. Reduced retention capacity suggests limited buffering of downstream material export under high-flow conditions. Although direct water-quality or epidemiological indicators were not measured, findings align with ecohydrological frameworks linking structural simplification and flow flashiness to diminished ecosystem regulation. These results inform riparian restoration and urban stormwater management strategies aimed at enhancing ecosystem regulation and water-quality buffering in tropical cities. Full article

The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field observations (2022–2025) across 24 plots with four burn severities. The Penman–Monteith–Leuning model provided physically based benchmarks. Results revealed three distinct recovery phases: destruction/stagnation (0–7 months, ET at 6%–10% of pre-fire levels), rapid recovery (8–19 months), and stabilization (20–37 months, reaching 100% ET recovery). The coupled LSTM–Transformer ensemble achieved superior performance (RMSE = 0.10 mm·day⁻¹, NSE = 0.98), outperforming single models by 31% in uncertainty reduction. SHAP analysis identified phase-dependent factor shifts: soil water content dominated Stage I (42.5%), while leaf area index (LAI) controlled Stages II–III (>48%). A bimodal LAI time-lag effect emerged: 4–7 days (leaf water potential equilibrium, 27.7% contribution) and 8–14 days (root uptake compensation, 21.7%). Burn severity significantly extended time-lags (severe burns: 12/21 days vs. unburned: 5/12 days), indicating hydraulic system reconstruction requirements. Despite equivalent LAI recovery, severe burns maintained 12%–15% ET reduction, suggesting lasting hydraulic limitations. This study demonstrates that physics-constrained AI models effectively capture complex post-fire ecohydrological dynamics while providing mechanistic interpretability, advancing understanding of vegetation–water coupling reconstruction under increasing fire frequency. Full article

The Soil and Water Assessment Tool (SWAT) model has been widely used to simulate ecohydrological processes in watersheds. However, the SWAT model uses a simplified Environmental Policy Impact Climate (EPIC) model to simulate the leaf area index (LAI), creating a critical gap in accurately simulating evapotranspiration (ET) and runoff in semi-arid regions. This work aims to fill this gap by modifying the SWAT source code to integrate high-resolution Global Land Surface Satellite (GLASS) leaf area index (LAI) data. The modified version was applied to the semi-arid Wuding River Basin and calibrated using a Fortran-based dynamic dimension search (DDS) algorithm. The results show a relatively significant improvement in the accuracy of the daily-scale runoff simulation (R² from 0.52 to 0.71 and NSE from 0.52 to 0.7 for the calibration period, and R² from 0.21 to 0.58 and NSE from 0.2 to 0.51 for the validation period). The improved version also corrects the unrealistic default LAI peak (from >5.0 to 1.5–3.0), correcting the multi-year average ET from 251.7 mm to 341.8 mm. The improved vegetation growth module of the SWAT model effectively improved the accuracy of hydrologic simulation in the semi-arid region and enhanced the structural robustness of SWAT for water management. Full article

The Hailiutu River Basin in northern China represents a semi-arid area where groundwater-dependent ecosystems (GDEs) play a critical role in maintaining regional vegetation structure and ecological stability. This study investigated the spatiotemporal dynamics of GDEs and their relationship with water conditions using trend analysis, partial correlation, and Random Forest models over the period of 2002–2022. The results show that vegetation activity (NDVI) increased at a rate of 0.0052/yr in GDEs. Precipitation exhibited a basin-wide upward trend of 0.735 mm/yr, while SPEI increased at 0.0207/yr. In contrast, groundwater storage declined markedly at −11.19 mm/yr, highlighting a persistent reduction in water availability that poses a significant risk to the stability of GDEs. Both partial correlation analysis and the random forest model consistently showed strong ecohydrological interactions between vegetation and groundwater. Vegetation dynamics are primarily driven by groundwater availability, especially in groundwater-dependent ecosystems. Conversely, groundwater variations are most strongly influenced by vegetation. The results indicate that precipitation and the standardized precipitation–evapotranspiration index (SPEI) are the primary positive drivers of interannual NDVI variability, whereas groundwater plays a critical role in sustaining GDEs. Field observations of key species confirm the dependence of GDEs on groundwater, and vegetation dynamics are regulated by climate and groundwater; however, ongoing groundwater decline may threaten ecosystem stability. These findings demonstrate that vegetation transpiration exerts the dominant influence on groundwater variations, while groundwater simultaneously constrains vegetation growth, particularly in areas where declining groundwater storage anomalies (GWSAs) coincide with reduced NDVI. The results emphasize that continuous groundwater depletion threatens vegetation–groundwater sustainability, highlighting the need for balanced groundwater and vegetation management in arid regions. Full article

Indicators of Hydrologic Alteration (IHA) are commonly screened with the Range of Variability Approach (RVA), which captures frequency shifts but can miss changes in central tendency, dispersion, distributional shape, and trend. We propose a Comprehensive Degree (CD) index that integrates RVA with these four statistical dimensions and apply it to daily discharge at the Xining station on the Huangshui River (1954–2022). Using conventional RVA, the overall alteration was 61.16% (moderate). After integration, alteration increased by 7.59% to 68.75%, reclassifying the regime as high. Across 32 Indicators, 15 showed larger alteration and 12 moved up one class, whereas 17 decreased and 2 moved down. Distributional shape and trend dominated the signal, revealing strongly altered ecohydrological indicators—most notably low-pulse frequency/duration and 3-day minimum—and, additionally, flagging indicators that RVA downplays (e.g., April–August monthly flows) via high trend and distributional shape shifts. The framework addresses RVA’s frequency-only blind spots, is more robust to short-term or episodic fluctuations, and improves diagnostic precision and ecological interpretability. These results provide a decision-ready basis for adaptive environmental flow management in climatically sensitive, topographically complex plateau basins. Full article

Plant roots influence soil infiltration by altering its properties like porosity and bulk density, which are essential for ecohydrological cycles. Moso bamboo (Phyllostachys edulis), using its well-developed underground root system, invades neighbor forest communities, thereby influencing root characteristics and soil properties. Although Moso bamboo invasion may alter soil hydrology, its specific impact on soil infiltration capacity and water flow connectivity remains unclear. This work took a fir forest (Cunninghamia lanceolata), mixed fir and bamboo forest, and a bamboo forest which represent three different degrees of invasion: uninvaded, partially invaded, and completely invaded, respectively, as study objects, using double-ring dyeing infiltration method to measure soil infiltration capacity and calculating water flow connectivity index for the root zone. To assess the effects of soil properties and root characteristics on soil infiltration capacity and water flow connectivity, we employed random forest and structural equation modeling. The analysis revealed that Moso bamboo invasion significantly enhanced soil infiltration capacity. Specifically, in partially invaded forests, the initial infiltration rate, stable infiltration rate, and average infiltration rate increased by 31.5%, 26.1%, and 28.5%, respectively. In completely invaded forests, the corresponding increases were 6.6%, 35.6%, and 28.5%. Also, Moso bamboo invasion increased water flow connectivity of root zone, compared to the uninvaded forest, the water flow connectivity index increased by 29.4% in the completely invaded forest and by 15.6% in the partially invaded forest. The marked increase in fine root biomass density (RBD1), fine root length density (RLD1), soil organic carbon (SOC), and non-capillary pores (NCP) and the decrease in soil bulk density (SBD) followed by Moso bamboo invasion effectively improved water flow connectivity and soil infiltration capacity. The analysis identified that RBD1, RLD1, NCP, and SBD as the key drivers of soil infiltration capacity, whereas the water flow connectivity index was controlled mainly by SOC, NCP, RLD1, and RBD1. These findings help clarify the mechanistic pathways of Moso bamboo’s effects on soil infiltration. Full article

This study investigates the paradoxical water security challenges in western Chilean Patagonia, where the regional abundance of water resources masks significant vulnerabilities of drinking water systems. We conducted an integrated socio-hydrological analysis over rural (APR) and urban (APU) drinking water systems, which provide water to approximately 846,000 people. We georeferenced 343 drinking water intake points, from which 51.6% are sourced from groundwater, and 45.8% from surface waters (2.6% other sources). An eco-hydrological characterization was conducted on the 147 watersheds supplying water to the surface intake points. Watersheds were characterized by their main hydrological, morphological, and land cover features, as well as by their level of anthropization (AI) and water stress index (WSI). Social dimensions were captured through structured interviews with 117 APR directorate leaders regarding their perceptions of infrastructure, governance, climate change, and local water management challenges. Our findings suggest that water availability in Patagonia creates a mirage of water security. AI and WSI indicate high variability in the status of water sources, with 25% of watersheds showing high levels of anthropization and 33% with medium to high levels of water stress, making it relevant to explore the results through a combination of hydroclimatic, longitudinal, and latitudinal gradients. A novel analysis linking WSI and AI to governance perceptions was conducted, finding significant inverse correlations between WSI and both technical capacity and users’ participation. Despite the region’s evident abundance of water resources, rural communities consistently express concerns regarding supply sustainability, infrastructure deficiencies, insufficient technical support, and climate change risks to current and future water availability, all of which constrain water security in Chilean Patagonia. Full article

Nepal is known for its complex terrain, climate, and vegetation dynamics, resulting in tremendous hydrologic variability and complexity. Accurately quantifying the water balances at the national level in Nepal is extremely challenging and is currently not available. This study constructed long-term (2000–2022) water balances for 358 watersheds across Nepal by integrating watershed hydrometeorological monitoring data, remote sensing products including Leaf Area Index and land use and land cover data, with an existing ecohydrological model, Water Supply Stress Index (WaSSI). The WaSSI model’s performance is assessed at both watershed and national levels using observed water yield (Q) and evapotranspiration (ET) products derived from remote sensing (ETMonitor, PEW, SSEBop) and eddy flux network (i.e., FLUXCOM). We show that the WaSSI model captured the seasonal dynamics of ET and Q, providing new insights about climatic controls on ET and Q across Nepal. At the national scale, the simulated long-term (2000–2020) mean annual Q and ET was about half of the precipitation (1567 mm), but both Q and ET varied tremendously in space and time as influenced by a monsoon climate and mountainous terrain. We found that watersheds in the central Gandaki River basin had the highest Q (up to 1600 mm yr⁻¹) and ET (up to 1000 mm yr⁻¹). This study offers a validated ecohydrological modeling tool for the Himalaya region and a national benchmark dataset of the water balances for Nepal. These products are useful for quantitative assessment of ecosystem services and science-based watershed management at the national scale. Future studies are needed to improve the WaSSI model and remote sensing ET products by conducting ecohydrological research on key hydrologic processes (i.e., forest ET, streamflow generations of small watersheds) across physiographic gradients to better answer emerging questions about the impacts of environmental change in Nepal. Full article

How can the shape of biodiversity inform us about cities’ ecoclimatic fitness and guide their development? Can we use species as the harbingers of climatic extremes? Eco-climatically sensitive species carry information about hydroclimatic change in their distribution, fitness, and preferential gradients of habitat suitability. Conversely, environmental features outside of the species’ fitness convey information on potential ecological anomalies in response to extremes to adapt or mitigate, such as through urban parks. Here, to quantify ecosystems’ fitness, we propose a novel computational model to extract multivariate functional ecological networks and their basins, which carry the distributed signature of the compounding hydroclimatic pressures on sentinel species. Specifically, we consider butterflies and their habitat suitability (HS) to infer maximum suitability gradients that are meaningful of potential species networks and flows, with the smallest hydroclimatic resistance across urban landscapes. These flows are compared to the distribution of urban parks to identify parks’ ecological attractiveness, actual and potential connectivity, and park potential to reduce hydroclimatic impacts. The ecosystem fitness index (EFI) is novelly introduced by combining HS and the divergence of the relative species abundance (RSA) from the optimal log-normal Preston plot. In Shenzhen, as a case study, eco-flow networks are found to be spatially very extended, scale-free, and clustering for low HS gradient and EFI areas, where large water bodies act as sources of ecological corridors draining into urban parks. Conversely, parks with higher HS, HS gradients, and EFIs have small-world connectivity non-overlapping with hydrological networks. Diverging patterns of abundance and richness are inferred as increasing and decreasing with HS. HS is largely determined by temperature and precipitation of the coldest quarter and seasonality, which are critical hydrologic variables. Interestingly, a U-shape pattern is found between abundance and diversity, similar to the one in natural ecosystems. Additionally, both abundance and richness are mildly associated with park area according to a power function, unrelated to longitude but linked to the degree of urbanization or park centrality, counterintuitively. The Preston plot’s richness–abundance and abundance-rank patterns were verified to reflect the stationarity or ecological meta-equilibrium with the environment, where both are a reflection of community connectivity. Ecological fitness is grounded on the ecohydrological structure and flows where maximum HS gradients are indicative of the largest eco-changes like climate-driven species flows. These flows, as distributed stress-response functions, inform about the collective eco-fitness of communities, like parks in cities. Flow-based networks can serve as blueprints for designing ecotones that regulate key ecosystem functions, such as temperature and evapotranspiration, while generating cascading ecological benefits across scales. The proposed model, novelly infers HS eco-networks and calculates the EFI, is adaptable to diverse sensitive species and environmental layers, offering a robust tool for precise ecosystem assessment and design. Full article