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Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies, but are more difficult in high mountainous areas because of the high elevation and complex terrain. This study compares and evaluates two kinds of precipitation datasets, the reanalysis product downscaled by the Weather Research and Forecasting (WRF) output, and the satellite product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product, as well as their bias-corrected datasets in the Middle Qilian Mountain in Northwest China. Results show that the WRF output with finer resolution performs well in both estimating precipitation and hydrological simulation, while the TMPA product is unreliable in high mountainous areas. Moreover, bias-corrected WRF output also performs better than bias-corrected TMPA product. Combined with the previous studies, atmospheric reanalysis datasets are more suitable than the satellite products in high mountainous areas. Climate is more important than altitude for the ‘falseAlarms’ events of the TRMM product. Designed to focus on the tropical areas, the TMPA product mistakes certain meteorological situations for precipitation in subhumid and semiarid areas, thus causing significant ‘falseAlarms’ events and leading to significant overestimations and unreliable performance. Simple linear bias correction method, only removing systematical errors, can significantly improves the accuracy of both the WRF output and the TMPA product in arid high mountainous areas with data scarcity. Evaluated by hydrological simulations, the bias-corrected WRF output is more reliable than the gauge dataset. Thus, data merging of the WRF output and gauge observations would provide more reliable precipitation estimations in arid high mountainous areas.

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.

The agro-pastoral ecotone of Northwestern China (APENC) is one of the major agricultural production areas in China and a region where climate change is evident. Maize is a widely cultivated crop in the APENC, but the potential impact of climate change on maize, and potential adaptation strategies in response to this, are poorly understood. In this study, we used the Cropping System Model (CSM)-CERES-Maize to evaluate the impacts of climate change on maize yield, as well as the feasibility of 2 adaptation strategies; namely, adjusting the planting date and supplying irrigation. CSM-CERES-Maize was driven by an ensemble of 20 global climate models under 2 Representative Concentration Pathways (RCPs: RCP4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). CSM-CERES-Maize performed well in simulating phenology, leaf area index (LAI), maize yield, and soil water dynamics. The results showed that irrigated maize yield would change by +3.9, -16.3, and -20.4% under the RCP4.5 scenario and +0.1, -31.2, and -53.1% under the RCP8.5 scenario in the 2030s, 2060s, and 2090s, respectively. Rainfed maize yield during the 2030s, 2060s, and 2090s would change by +21.7, +16.4, and +12.6% under the RCP4.5 scenario and +25.1, +4.8, and -12.3% under the RCP8.5 scenario, respectively. Evaluation of adaptation strategies suggests that delaying planting dates and supplying irrigation at the tasseling and grain filling stages are the best strategies to increase maize yield under climate change. These results will provide comprehensive information for local policymakers to combat the adverse impacts of climate change.

From 1998 until now, the Chinese government has implemented numerous policies and programs, such as the Grain for Green Program, the Three-North Shelter Forest Program, and the Beijing-Tianjin Sand Control Program, to restore ecosystems and to improve environmental protection in the agro-pastoral ecotone of Northern China (APENC). However, it remains unclear how the large-scale vegetation restoration modulates the regional moisture cycle in the APENC. To fill this gap, we investigated the variations of observed precipitation and estimated evapotranspiration from 1995 to 2015. The evapotranspiration is estimated by the Priestley-Taylor Jet Propulsion Laboratory model with dynamic vegetation (DV). The precipitation recycling ratio calculated by the Dynamic Recycling Model is used to analyze the impacts of vegetation restoration on regional moisture recycling. Our results show that the precipitation and ET under the DV were significantly increased during the period of 1995-2015, with the increasing rate of 4.42 mm yr-1 and 2.13 mm yr-1, respectively. The precipitation recycling ratio was also significantly increased during the study period, showing positive feedback of vegetation restoration on precipitation. The atmospheric water budget analysis shows that vegetation restoration noticeably modifies the annual mean values of water transport terms in the regional water cycle, indicating an indirect effect on local precipitation. Our findings help better understand the impacts of land cover change on local water resources, which in turn supports local water resource management and decision making.

Profile soil moisture (SM) in mountainous areas are significant in water resources management and ecohydrological studies of downstream arid watersheds. Satellite products are useful in providing spatially distributed SM information, but only have limited penetration depth (e.g. top 5 cm). In contrast, in situ observations can provide multi-depth measurements, but only with limited spatial coverage. Spatially continuous estimates of subsurface SM can be obtained from surface observations using statistical methods, but this requires sufficient coupling strength among surface and subsurface SM. This study evaluates methods to calculate subsurface SM from surface SM and an application to the satellite SM product based on a SM observation network in the Qilian Mountains (China) established since 2013. First, we used cross-correlation to analyze the coupling strength among surface (0–10 cm) and subsurface (10–20, 20–30, 30–50, 50–70 cm, and profile of 0–70 cm) SM. Our results indicated an overall strong coupling among surface and subsurface SM in this study area. Afterwards, three different methods were tested to estimate subsurface SM from in-situ surface SM: the exponential filter (ExpF), artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods. The results showed that both ANN and ExpF methods were able to provide accurate estimates of subsurface soil moisture at 10–20 cm, 20–30 cm, and for the profile of 0–70 cm using surface (0–10 cm) soil moisture only. Specifically, the ANN method had the lowest estimation error (RSR) of 0.42, 0.62 and 0.49 for depths of 15 and 25 cm and profile SM, respectively, while the ExpF method best captured the temporal variation of subsurface soil moisture. Furthermore, it could be shown that the performance of the profile SM estimation was not significantly lower with using an area-generalized Topt (optimum T) compared to the station-specific Topt. In a final step, the ExpF method was applied to the satellite SM product (Soil Moisture Active Passive Level 3: SMAP_L3) to estimate profile SM, and the resulting profile SM was compared to in situ observations. The results showed that the ExpF method was able to estimate profile SM from SMAP_L3 surface products with reasonable accuracy (median R of 0.718). It was also found that the combination of ExpF method and SMAP_L3 surface product can significantly improve the estimation of profile SM in the mountainous areas in comparison to the SMAP_L4 root zone product. Overall, it was concluded that the ExpF method is able to estimate profile SM using SMAP surface products in the Qilian Mountains.

Accurate monitoring of soil moisture is crucial in hydrological and ecological studies. Cosmic-ray neutron sensors (CRNS) measure area-average soil moisture at field scale, filling a spatial scale gap between in-situ observations and remote sensing measurements. However, its applicability has not been assessed in the agricultural-pastoral ecotone, a data scarce semi-arid and arid region in Northwest China (APENC). In this study, we calibrated and assessed the CRNS (the standard N 0 method) estimates of soil moisture. Results show that Pearson correlation coefficient, R P, and the root mean square error (RMSE) between the CRNS soil moisture and the gravimetric soil moisture are 0.904 and less than 0.016 m3 m–3, respectively, indicating that the CRNS is able to estimate the area-average soil moisture well at our study site. Compared with the in-situ sensor network measurements (ECH2O sensors), the CRNS is more sensitive to the changes in moisture in its footprint, which overestimates and underestimates the soil moisture under precipitation and dry conditions, respectively. The three shape parameters a 0, a 1, a 2 in the standard calibration equation ( N 0 method) are not well suited to the study area. The calibrated parameters improved the accuracy of the CRNS soil moisture estimates. Due to the lack of low gravimetric soil moisture data, performance of the calibrated N 0 function is still poor in the extremely dry conditions. Moreover, aboveground biomass including vegetation biomass, canopy interception and widely developed biological soil crusts adds to the uncertainty of the CRNS soil moisture estimates. Such biomass impacts need to be taken into consideration to further improve the accuracy of soil moisture estimation by the CRNS in the data scarce areas such as agricultural-pastoral ecotone in Northwest China.

Soil hydraulic properties (SHPs) and soil moisture (SM) are fundamental to describing and predicting water and energy cycles at the land surface, and for regulating evapotranspiration, infiltration and runoff. However, information about these soil properties from existing datasets is often scarce and inaccurate for high and cold mountainous areas such as the Qinghai-Tibet Plateau (QTP), which hampers our understanding of hydrological and energy cycle processes over large mountainous areas like the QTP. Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 SM monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a SHP and SM dataset for a high and cold mountainous area, Northeastern QTP. We used this dataset to explore the large-scale spatial and temporal variability of SHPs and of SM across the study area. Our evaluation of several existing SHP datasets, SM datasets derived from remote-sensing, reanalysis and data assimilation, showed that SHPs (soil texture, bulk density, and soil saturated hydraulic conductivity) in these datasets are biased, and do not capture the spatial variability recorded in the in-situ observations. When comparing with the in-situ SM observations, the SM product derived from remote-sensing was more reliable than the SM product derived from reanalysis data (which had a higher bias), and than the data assimilation product (which did not capture SM temporal variability). The in situ observation dataset presented here provides unique and important information about the SHP variability and long-term SM trends at a large-scale, high and cold mountainous area, and thus offers opportunity for further understanding of water cycle and energy exchange processes over the QTP.

Water resources are critical for the existence and development of oases in endorheic basins. Thus, to enable sustainable development, it is fundamentally important to understand how to allocate and use these resources in a reasonable way. We therefore simulated and analyzed changes in water consumption pattern within the Dunhuang Oasis of China under three scenarios using a system dynamic model that corresponds to different water consumption pattern. This was done to assess the impacts of regional water resource planning (comprehensive planning of the rational use of water resource and protection of ecosystem services in the Dunhuang Basin) on water consumption pattern within the Dunhuang Oasis. The first of these, Scenario 1, is a baseline in which the status quo is maintained, while Scenario 2 incorporates the comprehensive effects of agricultural water-saving irrigation measures with an inter-basin water diversion project, and Scenario 3 focuses on ecological rehabilitation. In the baseline Scenario 1, the total water consumption within the Dunhuang Oasis increased progressively while agricultural water consumption remained extremely high and threatened overall ecological security. In contrast, Scenario 2 would decrease agricultural water consumption by almost 5.30×107 m3 following the implementation of water-saving practices. The additional water allocated from an inter-basin water diversion project would play an important role in alleviating ecological strain on the oasis. Finally, in Scenario 3, the total irrigated land must be decreased to 20.6×103 hm2 by 2025 assuming that water supply for ecosystem restoration would be at least 50% of the total consumption. Although water resource planning plays a very important role in alleviating the ecological water crisis within the oasis, it is necessary to consider the suitable scale of oasis with regard to current water consumption pattern.

Understanding the dynamic response of soil moisture to rainfall is critical for hydrological modelling in arid and semi-arid basins. However, little is known about rainfall-related soil moisture dynamics in arid high-altitude mountainous areas due to the absence of long-term high-resolution soil moisture observations. In this study, we investigated the dynamic response processes of profile soil moisture using data from a soil moisture monitoring network in the Qilian Mountains established in 2013 covering altitudes from 2,000–5,000 m a.s.l. To investigate the effects of different land covers on soil moisture response, we selected data from eight soil moisture stations with the same soil textural class and slope, but different land covers (scrubland, meadow, high coverage grassland (HCG), medium coverage grassland (MCG) and barren land). Several indices were evaluated to quantitatively describe soil moisture dynamics during the growing seasons of 2014–2016 based on soil wetting events. In addition, HYDRUS-1D simulations were used to further analyze the effect of land cover on soil moisture dynamics. Our results showed that soil moisture response amplitudes along profile are similar under MCG and barren land, but significantly different under scrubland, meadow and HCG. The rate of soil moisture increment decreased significantly with depth for all land covers, except for the HCG. The temporal pattern of soil moisture increase was highly variable along the soil profiles depending on land cover type. In particular, the difference of response time between the adjacent layers varied from negative values to 280 h with depth. Preferential flow occurred mostly in soils covered by scrubland. Water transferability was higher in deeply rooted soil. Furthermore, sensitivity analysis indicated that soil hydraulic properties are key factors in regulating profile soil wetting events. Our results show that the soil moisture response indices are useful to quantitatively characterize patterns in profile soil moisture dynamics, and provide new insights into the soil moisture profile wetting process (e.g. occurrence of preferential flow etc.), which helps for effective model parameterization and validation, in turn improving hydrological modelling in arid high-altitude mountainous areas.

Assessment of the suitability of satellite soil moisture products at large scales is urgently needed for numerous climatic and hydrological researches, particularly in arid mountainous watersheds where soil moisture plays a key role in land-atmosphere exchanges. This study presents evaluation of the SMOS (L2) and SMAP (L2_P_E and L2_P) products against ground-based observations from the Upstream of the Heihe River Watershed in situ Soil Moisture Network (UHRWSMN) and the Ecological and Hydrological Wireless Sensor Network (EHWSN) over arid high mountainous watersheds, Northwest China. Results show that all the three products are reliable in catching the temporal trend of the in situ observations at both point and watershed scales in the study area. Due to the uncertainty in brightness temperature and the underestimation of effective temperature, the SMOS L2 product and both the SMAP L2 products show “dry bias” in the high, cold mountainous area. Because of the more accurate brightness temperature observations viewing at a constant angle and more suitable estimations of single scattering albedo and optical depth, both the SMAP L2 products performed significantly better than the SMOS product. Moreover, comparing with station density of in situ network, station representation is much more important in the evaluation of the satellite soil moisture products. Based on our analysis, we propose the following suggestions for improvement of the SMOS and SMAP product suitability in the mountainous areas: further optimization of effective temperature; revision of the retrieval algorithm of the SMOS mission to reduce the topographic impacts; and, careful selection of in situ observation stations for better representation of in situ network in future evaluations. All these improvements would lead to better applicability of the SMOS and SMAP products for soil moisture estimation to the high elevation and topographically complex mountainous areas in arid regions.

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.

From 1998 to the present, the Chinese government has implemented numerous large-scale ecological programs to restore ecosystems and improve environmental protection in the agro-pastoral ecotone of Northern China (APENC). However, it remains unclear how vegetation restoration modulates intraregional moisture cycles and changes regional water balance. To fill this gap, we first investigated the variation in precipitation (P) from the China Meteorological Forcing Dataset and evapotranspiration (ET) estimated using the Priestly-Taylor Jet Propulsion Laboratory model under two scenarios: dynamic vegetation (DV) and no dynamic vegetation (no-DV). We then used the dynamic recycling model to analyze the changes in precipitation recycling ratio (PRR). Finally, we examined how vegetation restoration modulates intraregional moisture recycling to change the regional water cycle in APENC. Results indicate P increased at an average rate of 4.42 mm yr-2 from 1995 to 2015. ET with DV exhibited a significant increase at a rate of 1.57, 3.58, 1.53, and 1.84 mm yr-2 in the four subregions, respectively, compared with no-DV, and the annual mean PRR values were 10.15%, 9.30%, 11.01%, and 12.76% in the four subregions, and significant increasing trends were found in the APENC during 1995-2015. Further analysis of regional moisture recycling shows that vegetation restoration does not increase local P directly, but has an indirect effect by enhancing moisture recycling process to produce more P by increasing PRR. Our findings show that large-scale ecological restoration programs have a positive effect on local moisture cycle and precipitation.

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.

International audience; 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. The Tien Shan-Pamir-Karakoram (TPK) mountain complex is the "water tower" of the Central Asian countries east of the Aral Sea (

Topography and spatial patterns of landscape significantly affect spatial distribution of precipitation and, in turn, hydrological modelling, especially in high elevation, mountainous watersheds of arid regions. This study incorporates a physically based inverse distance and elevation weighted (PBIDEW) method into a distributed conceptual hydrological model, distributed large basin runoff model, and compared with an inverse distance weighted (IDW) method to assess the performances of both methods in precipitation estimation for hydrological modelling at watershed scale. The PBIDEW method considers the impacts of topography using month-dependent parameters in its interpolation of meteorological variables while the IDW method does not. Both the IDW and the PBIDEW methods are evaluated and compared in hydrological modelling at different spatial resolutions in the upper reach of the Heihe River Watershed, Northwest China. Results show that the IDW method underestimated the areal precipitation, and the PBIDEW method produced more realistic precipitation estimations in the study area. Both methods have some limitations, the performance of the IDW method was mainly influenced by the availability of observation data, while that of the PBIDEW method was mainly influenced by the representation of topographical information. Considering more detailed information for precipitation estimates, the PBIDEW method performed better at finer spatial resolution. Overall, the PBIDEW method, using month-dependent physical interpolation parameters, seems more suitable for precipitation estimation in hydrological simulations in data-scarce, high elevation and topographically complex mountainous watersheds in arid area. Copyright (c) 2017 John Wiley & Sons, Ltd.

Understanding the variability in soil hydraulic conductivity in the mountainous headwaters is critical to the modeling of mountainous runoff and the water resources management of river basins, especially in the arid and semiarid areas. In this study, a total of 32 soil profiles with five layers within 0-70cm were sampled under different land cover types: forest, meadow, high coverage grassland (HCG), medium coverage grassland (MCG), and barren land in the upper stream of the Heihe river watershed, Northwest China. Saturated hydraulic conductivity (K-S) was measured for each sample. The vertical variation of K-S and soil hydrological response under different land covers were analyzed. Results show that K-S value in layer 5 was significantly lower than the values of above four layers. K-S decreased in the order of forest, meadow, HCG, MCG, and barren land, corresponding to the degree of vegetation degradation. The K-S decreased with depth under forest, HCG, and barren land, but increased first and then decreased under meadow and MCG. The dominant stormflow paths for different land covers were different: forest was dominated by deep percolation, HCG was dominated by subsurface flow (SSF), meadow was prevailed by Hortonian overland flow and had no SSF, while MCG and barren land were also dominated by Hortonian overland flow, but still formed SSF. This result provides important information for improving the accuracy of mountainous hydrological modeling and, in turn, leading to sustainable management of water resources in the study watershed. Copyright (c) 2016 John Wiley & Sons, Ltd.