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"He, Chansheng"
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Watershed science: Linking hydrological science with sustainable management of river basins
Over the past decades, a number of water sciences and management programs have been developed to better understand and manage the water cycles at multiple temporal and spatial scales for various purposes, such as ecohydrology, global hydrology, sociohydrology, supply management, demand management, and integrated water resources management (IWRM). At the same time, rapid advancements have also been taking place in tracing, mapping, remote sensing, machine learning, and modelling technologies in hydrological research. Despite those programs and advancements, a water crisis is intensifying globally. The missing link is effective interactions between the hydrological research and water resource management to support implementation of the UN Sustainable Development Goals (SDGs) at multiple spatial scales. Since the watershed is the natural unit for water resources management, watershed science offers the potential to bridge this missing link. This study first reviews the advances in hydrological research and water resources management, and then discusses issues and challenges facing the global water community. Subsequently, it describes the core components of watershed science: (1) hydrological analysis; (2) water-operation policies; (3) governance; (4) management and feedback. The framework takes into account water availability, water uses, and water quality; explicitly focuses on the storage, fluxes, and quality of the hydrological cycle; defines appropriate local water resource thresholds through incorporating the planetary boundary framework; and identifies specific actionable measures for water resources management. It provides a complementary approach to the existing water management programs in addressing the current global water crisis and achieving the UN SDGs.
Journal Article
Characterization of Preferential Flow Occurrence During Freeze‐Thaw Cycles
Preferential flow (PF) critically influences water and energy dynamics in frozen soils, yet its quantification and mechanisms remain poorly understood due to observational challenges. This study proposes a novel method to identify PF by analyzing soil temperature response times across depths. The method detects thermal anomalies—such as earlier or synchronized temperature peaks across depths—indicate rapid, advective heat transfer driven by PF and phase‐change latent heat. We conducted our investigation in the Qilian Mountain areas using a network of soil temperature stations. Our findings reveal that PF significantly enhances magnitude and speed of energy transfer to deeper soil. Furthermore, the frequency of PF varies significantly across the partially frozen, frozen and partially thawing phases, as well as across different sites and depths, driven by shifts soil properties and meteorological forcing. Using random forest analysis, we identified key spatial drivers related to soil pore structure: soil organic carbon (partially frozen phase), residual soil moisture (frozen phase), and wilting point (partially thawed phase). Furthermore, classification and regression tree analysis revealed that the snowmelt rates and maximum near‐surface (5 cm) soil temperatures are the primary temporal drivers of PF. Our study demonstrates that PF can be effectively identified by analyzing soil temperatures at various depths. By utilizing temperature‐based detection during the frozen phase and moisture monitoring in the unfrozen phase, we can better correlate PF with soil hydrothermal conditions, ultimately elucidating the complex mechanisms governing water and energy dynamics during the freeze‐thaw cycle.
Journal Article
Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method
Vegetation is the major component of the terrestrial ecosystem. Understanding both climate change and anthropogenically induced vegetation variation is essential for ecosystem management. In this study, we used an ensemble empirical mode decomposition (EEMD) method and a linear regression model to investigate spatiotemporal variations in the normalized difference vegetation index (NDVI) over the agropastoral ecotone of northern China (APENC) during the 1982–2015 period. A quantitative approach was proposed based on the residual trend (RESTREND) method to distinguish the effects of climatic (i.e., temperature (TEM), precipitation (PRE), total downward solar radiation (RAD), and near surface wind speed (SWS)) and anthropogenic effects on vegetation variations. The results showed that the NDVI exhibited a significant greening trend of 0.002 year−1 over the entire study period of 1982–2015 and that areas with monotonous greening dominated the entire APENC, occupying 40.97% of the region. A browning trend was also found in the central and northern parts of the APENC. PRE presented the highest spatial correlation with the NDVI and climate factors, suggesting that PRE was the most important factor affecting NDVI changes in the study area. In addition, the RESTREND results indicated that anthropogenic contributions dominated the vegetation variations in the APENC. Therefore, reusing farmland for grass and tree planting made a positive contribution to vegetation restoration, while deforestation, overgrazing, and the reclamation of grasslands were the opposite. In addition, with the continuous implementation of national ecological engineering programs such as the Grain to Green Program, positive human activity contributions to vegetation greening significantly increased. These results will support decision- and policy-making in the assessment and rehabilitation of ecosystems in the study region.
Journal Article
Contrasting streamflow regimes induced by melting glaciers across the Tien Shan – Pamir – North Karakoram
The glacierized Tien Shan – Pamir – Karakoram mountain complex supplies water to about 42 million people. Yet, the knowledge about future glacial runoff in response to future climate is limited. Here, we address this issue using a hydrological model, that includes the three components of glacial runoff: ice melt, snowmelt and the runoff of rainfall over ice. The model is forced by climate projections of the CMIP5 models. We find that the three components exhibit different long-term trajectories, sometimes opposite in sign to the long-term trend in glacier impacts. For the eastern slope basins, streamflow is projected to increase by 28% (ranging from 9 to 44%, from climate model variation (CMV)) by the late 21
st
century, under the representative concentration pathway, RCP8.5. Ice melt contributes 39% (25 to 65%, CMV) of the total streamflow increase. However, streamflow from the western slopes is projected to decrease by 5% (−24 to 16%, CMV), due to the smaller contribution of ice melt, less precipitation and higher evapotranspiration. Increasing water supply from the eastern slopes suggests more water availability for currently degraded downstream ecosystems in the Xinjiang province of China, while the likely decreasing streamflow in Central Asian rivers on the western slopes indicates new regulations will be needed.
Journal Article
Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China
Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.
Journal Article
Tracking Sustainable Restoration in Agro-Pastoral Ecotone of Northwest China
Large-scale ecological restoration (ER) projects have been implemented in northwest China in recent decades as a means to prevent desertification and improve ecosystem services. However, previous studies have demonstrated adverse impacts in the form of widespread soil water deficit caused by intensive ER activities. Understanding the role of climate change and ER efforts in vegetation dynamics and soil moisture consumption is essential for sustainable ecosystem management. Here, we used the break for additive season and trend (BFAST) method to analyse spatial patterns in the normalized difference vegetation index (NDVI) variation over the agro-pastoral ecotone of northwest China (APENC) for 2000–2015. From the combined use of generalized additive modelling (GAM) and residual-trend analysis (RESTREND), we distinguished and quantified the effects of climate and human management on vegetation and soil water dynamics. Approximately 78% of the area showed vegetation variations representing a significant change in NDVI, of which more than 68% were categorized as abrupt changes. Large areas of the abrupt change type, interrupted increase and monotonic increase in NDVI were observed before 2006, and small areas of the change type of negative reversals were observed after 2012. Anthropogenic activity was found to be the major driving factor of variation in vegetation (contribution rate of 56%) and soil moisture (contribution rate of 78%). The vegetation expansion, which was mainly related to the large number of ER programs that started in 2000, was found to increase soil moisture depletion. By comparing areas where anthropogenic activities had a high contribution rate to vegetation increase and areas where soil moisture consumption was severely increased, we identify and discuss hotspot areas of soil moisture consumption caused by the ER programs. The current methodological workflow and results represent a novel foundation to inform and support water resource management and ecological-restoration-related policy making.
Journal Article
Three‐Dimensional Characterization of Lateral Subsurface Flow in an Alpine Forested Hillslope on the Tibetan Plateau Using Time‐Lapse Electrical Resistivity Tomography
Lateral subsurface flow (LSF) is a key component of rainfall‐runoff generation on alpine forested slopes, but the influence of weathered bedrock on LSF remains poorly understood due to methodological constraints in monitoring three‐dimensional flow dynamics. This study addresses this gap by proposing a standardized morphological interpretation framework for time‐lapse 3D electrical resistivity tomography (3D‐ERT) inspired by dye‐tracing studies, and applying this framework to investigate LSF mechanisms in soil‐weathered bedrock systems on an alpine forested slope (NE Tibetan Plateau). During a controlled infiltration experiment, we conducted seven 3D‐ERT measurements. The inverted resistivity distributions were temperature‐corrected and converted to moisture content. The developed framework was then applied to quantify LSF patterns observed with ERT. Results show that the weathered bedrock layer acts as a permeable conduit, promoting downward leakage of water and limiting storage at the soil‐bedrock interface. LSF penetrated vertically into deeper weathered bedrock but remained laterally constrained within the monitoring range, with greater flow distances observed in deeper layers. The soil and weathered‐bedrock horizons exhibited contrasting moisture retention and drainage behavior during the initiation and development of LSF events. The emergence of two discrete flow pathways and the different maximum flow distance indicates the heterogeneity of LSF in weathered bedrock layers. Collectively, findings underscore the hydrological role of weathered bedrock in runoff generation on alpine slopes. The proposed ERT interpretation framework complements existing hydrogeophysical approaches by providing standardized morphological descriptors of ERT‐inferred wetting patterns, offering a transferable way to characterize the spatiotemporal organization of subsurface flow in data‐scarce mountain regions.
Journal Article
Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands
In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (
Journal Article
Assessing the Effects of Spatial Scales on Regional Evapotranspiration Estimation by the SEBAL Model and Multiple Satellite Datasets: A Case Study in the Agro-Pastoral Ecotone, Northwestern China
Evapotranspiration (ET) estimation is important for understanding energy exchanges and water cycles. Remote sensing (RS) is the main method used to obtain ET data over large scales. However, owing to surface heterogeneities and different model algorithms, ET estimated from RS products with different spatial resolutions can cause significant uncertainties, whose causes need to be thoroughly analyzed. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) model was selected to explore spatial resolution influences on ET simulations. Three satellite datasets (Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High-Resolution Radiometer (AVHRR)) were selected to independently estimate ET in SEBAL model to identify the influence of the spatial scale on ET estimation, and analyze the effects and causes of scale aggregation. Results indicated that: (1) the spatial distributions of ET estimated from the three satellite datasets were similar, with the MODIS-based ET having the largest uncertainty; and (2) aggregating input parameters had limited changes in the net radiation and soil heat fluxes. However, errors in the sensible heat and latent heat fluxes were relatively larger, which were caused by changes in the selection of hot and cold pixels and the NDVI and surface albedo parameters during scale aggregation. The scale errors caused by the model mechanisms were larger than those caused by the land use/cover pattern in the SEBAL model. Overall, this study highlights the impact of spatial scale on ET and provides a better understanding of the scale aggregation effect on ET estimation by RS.
Journal Article
Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China
As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperature TBp and effective temperature Teff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m3/m3 in mountainous area. These uncertainties also result in the “dry bias” of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m3/m3 and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient bp result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature TBp and effective temperature Teff; determine coefficient bp and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future.
Journal Article