Search Results (155)

Understanding the drivers of water yield (WY) changes in ecologically sensitive, data-scarce watersheds is crucial for sustainable management, particularly in the context of accelerating forest expansion and urbanization. This study focuses on the upper Yellow River Basin (UYRB), a critical headwater region that supplies 60% of the Yellow River’s flow and is undergoing rapid land use transitions from 1990 to 2100. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Future Land-Use Simulation (FLUS) model, we quantify historical (1990–2020) and projected (2025–2100) WY dynamics under three SSP scenarios (SSP126, SSP370, and SSP585). InVEST, a spatially explicit ecohydrological model based on the Budyko framework, estimates WY by balancing precipitation and evapotranspiration. The FLUS model combines cellular automata (CA) with an artificial neural network (ANN)-based suitability evaluation and Markov chain-derived transition probabilities to simulate land-use change under multiple scenarios. Results show that WY increased significantly during the historical period (1990–2020), primarily driven by increased precipitation, with climate change accounting for 94% and land-use change for 6% of the total variation in WY. Under future scenarios (SSP126, SSP370, and SSP585), WY is projected to increase to 217 mm, 206 mm, and 201 mm, respectively. Meanwhile, the influence of land-use change is expected to diminish, with its contribution decreasing to 9.1%, 5.7%, and 3.1% under SSP126, SSP370, and SSP585, respectively. This decrease reflects the increasing strength of climate signals (especially extreme precipitation and evaporative demand), which masks the hydrological impacts of land-use transitions. These findings highlight the dominant role of climate change, the scenario-dependent effects of land-use change, and the urgent need for integrated climate–land management strategies in forest-urbanizing watersheds. Full article

This study proposes an innovative method that integrates multi-source remote sensing technologies and artificial intelligence to meet the urgent needs of deformation monitoring and ecohydrological environment analysis in Great Wall heritage protection. By integrating synthetic aperture radar (InSAR) technology, low-altitude oblique photogrammetry models, and the three-dimensional Gaussian splatting model, an integrated air–space–ground system for monitoring and understanding the Great Wall is constructed. Low-altitude tilt photogrammetry combined with the Gaussian splatting model, through drone images and intelligent generation algorithms (e.g., generative adversarial networks), quickly constructs high-precision 3D models, significantly improving texture details and reconstruction efficiency. Based on the 3D Gaussian splatting model of the AHLLM-3D network, the integration of point cloud data and the large language model achieves multimodal semantic understanding and spatial analysis of the Great Wall’s architectural structure. The results show that the multi-source data fusion method can effectively identify high-risk deformation zones (with annual subsidence reaching −25 mm) and optimize modeling accuracy through intelligent algorithms (reducing detail error by 30%), providing accurate deformation warnings and repair bases for Great Wall protection. Future studies will further combine the concept of ecological water wisdom to explore heritage protection strategies under multi-hazard coupling, promoting the digital transformation of cultural heritage preservation. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

The surface of the Great Wall harbors a large number of non-vascular plants dominated by cyanobacteria, lichens and mosses as well as microorganisms, and form biocrusts by cementing with the soils and greatly alters the pore structure of the soil and the ecohydrological processes associated with the soil pore space, and thus influences the soil resistance to erosion. However, the microscopic role of the biocrusts in influencing the pore structure of the surface of the Great Wall is not clear. This study chose the Warring States Qin Great Wall in Weiyuan, Gansu Province, China, as research site to quantify thepore structure characteristics of the three-dimensional of bare soil, cyanobacterial-lichen crusts, and moss crusts at the depth of 0–50 mm, by using optical microscopy, scanning electron microscopy, and X-ray computed tomography and image analysis, and the precipitation infiltration process. The results showed that the moss crust layer was dominated by large pores with long extension and good connectivity, which provided preferential seepage channels for precipitation infiltration, while the connectivity between the cyanobacterial-lichen crust voids was poor; The porosity of the cyanobacterial-lichen crust and the moss crust was 500% and 903.27% higher than that of the bare soil, respectively. The porosity of the subsurface layer of cyanobacterial-lichen crust and moss crust was significantly lower than that of the biocrusts layer by 92.54% and 97.96%, respectively, and the porosity of the moss crust was significantly higher than that of the cyanobacterial-lichen crust in the same layer; Cyanobacterial-lichen crusts increased the degree of anisotropy, mean tortuosity, moss crust reduced the degree of anisotropy, mean tortuosity. Biocrusts increased the fractal dimension and Euler number of pores. Compared with bare soil, moss crust and cyanobacterial-lichen crust increased the isolated porosity by 2555% and 4085%, respectively; Biocrusts increased the complexity of the pore network models; The initial infiltration rate, stable infiltration rate, average infiltration rate, and the total amount of infiltration of moss crusted soil was 2.26 and 3.12 times, 1.07 and 1.63 times, respectively, higher than that of the cyanobacterial-lichen crusts and the bare soil, by 1.53 and 2.33 times, and 1.13 and 2.08 times, respectively; CT porosity and clay content are significantly positively correlated with initial soil infiltration rate (|r| ≥ 0.85), while soil type and organic matter content are negatively correlated with initial soil infiltration rate. The soil type and bulk density are directly positively and negatively correlated with CT porosity, respectively (|r| ≥ 0.52). There is a significant negative correlation between soil clay content and porosity (|r| = 0.15, p < 0.001). Biocrusts alter the erosion resistance of rammed earth walls by affecting the soil microstructure of the earth’s great wall, altering precipitation infiltration, and promoting vascular plant colonisation, which in turn alters the erosion resistance of the wall. The research results have important reference for the development of disposal plans for biocrusts on the surface of archaeological sites. Full article

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R²: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost. 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

Soil moisture is a core climate variable in land surface processes and has a strong influence on the energy balance and water exchange between the land surface–vegetation–atmosphere columns. However, the low spatial resolution of soil moisture remote sensing products cannot satisfy the requirements of research and applications based on hydro-meteorological and eco-hydrological simulations and the management of water resources at the watershed scale. A feasible solution is to downscale soil moisture products derived from microwave remote sensing, which often requires the support of auxiliary variables. Meanwhile, during the validation process of remote sensing products, the spatial scales between in situ observations and remote sensing pixel retrievals are inconsistent; thus, in situ observations should be translated to ground truths at a pixel scale via reasonable upscaling methods. Many auxiliary variables can serve as proxies in the scaling of soil moisture, although few studies have analyzed their feasibility and application conditions. In this paper, a SiB2 (Simple Biosphere Model-II) simulation for the Daman superstation from 1 May to 30 September 2013, was employed to calculate seven auxiliary variables related to soil moisture: ATIs and ATIc (Apparent Thermal Inertias based on surface soil temperature and canopy temperature), E (Evaporation), E/ET_a (Ratio of Evaporation and Actual Evapotranspiration), E/ET_p (Ratio of Evaporation and Potential Evapotranspiration), EF (Evaporative Fraction) and AEF (Actual Evaporative Fraction). The applicability of these variables was then evaluated via a correlation analysis between the variables and soil moisture. The results indicated that E is highly sensitive to soil moisture at Phase I (R² ≥ 0.67), whereas ATIs is the greatest indicator of soil moisture at Phase II (R² ≥ 0.51). Considering both the correlation and computability of these auxiliary variables, the EF (R² ≥ 0.56) and AEF (R² ≥ 0.54) are recommended as proxies for Phase I, while ATIs (R² ≥ 0.51) is also recommended for Phase II. Full article

Rainfall partitioning, a crucial process in shaping the local hydrological cycle, governs canopy interception and subsequent soil water recharge. While canopy structure and meteorological conditions fundamentally regulate this process, the role of plant self-organization and its interactions with meteorological drivers (non-precipitation variables in particular) remain underexplored. To address this gap, we investigated rainfall partitioning components, including the amount, intensity, efficiency, and temporal dynamics of throughfall and stemflow, in clumped and scattered Vitex negundo L. var. heterophylla (Franch.) Rehder shrubs in the Yangjuangou catchment of the Chinese Loess Plateau during the 2021–2022 rainy seasons. Despite comparable net precipitation (clumped: 83.5% vs. scattered: 84.2% of incident rains), divergent rainfall partitioning strategies emerged. Clumped V. negundo exhibited greater stemflow (8.6% vs. 5.2%), characterized by enhanced intensity, efficiency, and favorable temporal dynamics. Conversely, scattered shrubs favored throughfall generation (79.0% vs. 74.9%). Consistent with previous research, rainfall amount was recognized as the primary control on partitioning rains. Furthermore, our integrated analysis, combining machine learning with variance decomposition, highlighted the critical roles of antecedent canopy wetness (4 h pre-event leaf wetness) and wind speed thresholds (e.g., low wind vs. gust) in regulating partitioning efficiency and temporal dynamics. These findings advance the mechanistic understanding of the interplay between plant self-organization and hydrological processes, demonstrating how morphological adaptations in V. negundo optimize water harvesting in semi-arid ecosystems. This addressed the need to incorporate dynamic interplays between plant structure (specifically, self-organized patterns) and meteorological factors (particularly non-precipitation variables) into ecohydrological models, especially for improved predictions in water-limited regions. Full article

Soil moisture plays a critical role in the water and energy cycle within the soil–vegetation–atmosphere system and is a primary limiting factor in dryland ecosystems. Given the ongoing vegetation restoration in drylands, understanding the impact of vegetation changes on soil moisture is crucial for maintaining ecosystem stability and ensuring the sustainability of restoration efforts. This study combined long-term satellite data with eco-hydrological modeling to investigate the interannual and seasonal responses of soil moisture to vegetation changes in the Yinshanbeilu region during 1982–2018. The results indicated that vegetation in the region predominantly exhibited a greening trend, with 60.43% of the area experiencing significant increases in LAI. In areas with vegetation greening, soil moisture declined, with the effect being more pronounced at deeper soil profiles. Furthermore, the soil moisture trends shifted from wetting to drying, or, in more cases, from drying to intensified drying. The influence of vegetation greening on soil moisture exhibited seasonal variations, with more significant effects found in summer and autumn. This study highlights the complex responses of soil moisture to vegetation changes in grassland ecosystems in northern China’s drylands and provides a scientific guidance for ecological restoration and water management in these regions. Full article

Vegetation dynamics significantly influence watershed ecohydrological processes. Physically based hydrological models often have general plant development descriptions but lack vegetation dynamics data for ecohydrological simulations. Solar-induced chlorophyll fluorescence (SIF) and the Normalized Difference Vegetation Index (NDVI) are widely used in monitoring vegetation dynamics and ecohydrological research. Accurately predicting long-term SIF and NDVI dynamics can support the monitoring of vegetation anomalies and trends. This study proposed a SWAT-ML framework, combining the Soil and Water Assessment Tool (SWAT) and machine learning (ML), in the Jinsha River Basin (JRB). The lag effects that vegetation responds to using hydrometeorological elements were considered while using SWAT-ML. Based on SWAT-ML, SIF and NDVI series from 1982 to 2014 were reconstructed. Finally, the spatial and temporal characteristics of vegetation dynamics in the JRB were analyzed. The results showed the following: (1) the SWAT-ML framework can simulate ecohydrological processes in the JRB with satisfactory results (NS > 0.68, R² > 0.79 for the SWAT; NS > 0.77, MSE < 0.004 for the ML); (2) the vegetation index’s mean value increases (the Z value, the significance indicator in the Mann–Kendall method, is 1.29 for the SIF and 0.11 for the NDVI), whereas the maximum value decreases (Z value = −0.20 for SIF and −0.42 for the NDVI); and (3) the greenness of the vegetation decreases (Z value = −2.93 for the maximum value and −0.97 for the mean value) in the middle reaches. However, the intensity of the vegetation’s physiological activity increases (Z value= 3.24 for the maximum value and 2.68 for the mean value). Moreover, the greenness and physiological activity of the vegetation increase in the lower reaches (Z value = 3.24, 2.68, 2.68, and 1.84 for SIF_max, SIF_ave, NDVI_max, and NDVI_ave, respectively). In the middle and lower reaches, the connection between the SIF and hydrometeorological factors is stronger than that of the NDVI. This research developed a new framework and can provide a reference for complex ecohydrological simulation. Full article

Urban green spaces, including green roofs (GRs), are vital infrastructure for climate resilience, retaining water in city landscapes and supporting ecohydrological processes. Quantifying the hydrologic performance of GRs in the urban environment for future climate scenarios is the original contribution of this research developed within the URCA! project. For this purpose, a continuous modelling approach is undertaken to evaluate the hydrological performance of GRs expressed by means of the runoff volume and peak flow reduction at the event scale for long data series (at least 20 years). To investigate the prediction of GRs performance in future climates, a simple methodological approach is proposed, using monthly projection factors for the definition of future rainfall and temperature time series, and transferring the system parametrization of the current model to the future one. The proposed approach is tested for experimental GR sites in Genoa and Rende, located in Northern and Southern Italy, respectively. Referring to both the Genoa and Rende experimental sites, simulation results are analysed to demonstrate how the GR performance varies with respect to rainfall event characteristics, including total depth, maximum rainfall intensity and ADWP for current and future scenarios. Full article

Understanding the spatial variability in soil hydraulic properties (SHPs) and their influencing variables is critical for ecohydrological and biogeochemical studies in coastal wetlands, where complex landscapes make it challenging to accurately delineate the spatial patterns of SHPs. In this study, soil samples were collected from two transects covered by Suaeda salsa (S. salsa) and Phragmites australis (P. australis) from the Beidagang Wetland Nature Reserve in northern China, and a comprehensive dataset on soil physical properties and SHPs was obtained by laboratory experiments. The results showed that soil physical properties (e.g., soil particle size, bulk density (BD), and soil organic matter (SOM)) displayed significant spatial variability, which was related to the physiological characteristics of S. salsa and P. australis and to soil depth. As a result, SHPs, including saturated hydraulic conductivity (K_s) and parameters of the van Genuchten model (θ_s-saturated soil water content, including α, the reciprocal of the air-entry value, and n, the pore size distribution index) varied considerably along the two transects. Specifically, K_s, θ_s, and α were negatively correlated with BD and pH, while positively correlated with SOM, which promoted soil aggregation to enlarge soil pores. Soil depth was shown to significantly affect SHPs, whereas the differences in SHPs between the two transects were not statistically significant, suggesting vegetation type did not directly impact SHPs. Soil water retention capacities were noticeably higher in surface soils, especially when soil suctions were less than 1000 cm, whereas their differences between depths largely diminished with further increasing soil suctions. This study highlights the complex interplay of SHPs with surrounding environments, providing critical insight for characterizing the spatial patterns of SHPs in coastal wetlands. Full article

The determination of ecological water requirements (EWRs) is of critical significance for maintaining watershed sustainable development and river health. However, the estimation of instream and off-stream EWRs remains uncertain due to the complicated and competitive interaction between off-stream EWR resources (mainly vegetation water requirements in low-intensity human-use basins) and instream EWR resources (runoff), especially in arid watersheds. In this study, instream and off-stream EWRs are determined by considering the interaction between vegetation variations and hydrological processes, as well as their climate impact, using a two-way ecohydrological model in a representative semi-arid basin. The increased infiltration capacity of the substrate, resulting from continuous vegetation growth without mortality, enhances deep soil water return flow, thereby boosting baseflow to streams. Lateral flow is shown to contribute up to 39.50% of the instream runoff. While downstream grassland growth is dependent on vertical water input, upstream forests experience energy-limited transpiration despite increased water storage, regardless of lateral flow distribution. Changes in precipitation (either an increase or decrease) simultaneously affect (i.e., increase or decrease) both basin instream and off-stream EWRs. In contrast, temperature increases of up to 3 °C generally enhance instream EWRs by raising evapotranspiration (ET). However, this effect may be diminished or even reversed when plants become water-stressed under higher temperatures, resulting in a reduction of off-stream EWRs. The findings of this research provide a scientific foundation for water resource management in semi-arid basins. Full article

Girdling is a crucial technique for promoting the close-to-nature transformation of plantation forests in Hainan Tropical Rainforest National Park (HNNP). It has shown effectiveness in aspects such as community structure and biodiversity restoration. However, its impacts on ecological functions like eco-hydrology still require further in-depth investigation. This study analyzes the impact of girdling on the eco-hydrological indices of three plantations—Acacia mangium, Pinus caribaea, and Cunninghamia lanceolata—through field investigations and laboratory tests. The data was evaluated using a game theory combination weighting-cloud model. The results show that the eco-hydrological indicators of leaf litter in A. mangium increased by 5.77% while those of P. caribaea and C. lanceolata decreased by 11.86% and 5.29%, respectively. Soil bulk density decreased slightly across all plantations while total porosity increased, with A. mangium showing the highest increase of 20.31%. Organic carbon content increased by 76.81% in A. mangium and 7.24% in C. lanceolata, whereas it decreased in P. caribaea. Saturated hydraulic conductivity increased by 33.32% in P. caribaea and 20.91% in A. mangium but decreased in C. lanceolata. Based on the cloud model, the eco-hydrological function of A. mangium improved from ‘medium’ to ‘good’, while that of P. caribaea and C. lanceolata declined towards the ‘poor’ level. In summary, during the process of close-to-nature transformation of tropical rainforests, girdling is an effective method to enhance the ecohydrological functions of broadleaf planted forests. However, for coniferous species, the ecohydrological functions of the planted forests weaken in the short term following the transformation. Full article