Application of water resource multi-objective allocation service based on digital water network

The rational allocation of water resources plays an important role in alleviating disparities between supply and demand in areas with water shortages. With the continuous development of modern information technology, the pace of digitization is accelerating. Digital water networks provide a means of technical support, and their application is becoming more extensive. Based on the traditional study of water resource allocation combined with the development of modern information technology, this paper proposes a new operational application model of multi-objective water resource allocation based on a digital water network and applies this model to allocate water resources in the Heihe River basin in Xi'an, Shaanxi Province. First, a topological digital water network is constructed based on the connectivity criterion of water systems, and a cooperative configuration model with social, economic and ecological objectives is established. Second, the model and its solution method are componentized, and the water resource allocation business system is constructed based on the comprehensive integration platform to integrate the digital water network and the water resource multi-objective allocation business. Finally, to verify the scientificity and feasibility of the new model, the new model was applied to allocate water resources in the Heihe River basin of Xi'an city, Shaanxi Province.

Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images

Soil property monitoring is useful for sustainable agricultural production and environmental modeling. It is possible to automatically predict soil properties in a wide range based on remote sensing images. Heihe River Basin was chosen as the research area. Measurements on three soil properties, which were pH, organic carbon, and bulk density, were available there. Two kinds of attributes were extracted, which were the remote sensing index and terrain attributes. The prediction models were constructed by random forest algorithms. The features were determined by combining correlation statistics with prediction error, and different features were selected for each of the three properties. The validation experimental results are presented. The error results were as follows: pH (MAE = 0.28, RMSE = 0.39, R2 = 0.41), organic carbon (MAE = 4.75, RMSE = 8.26, R2 = 0.75), and bulk density (MAE = 0.11, RMSE = 0.13, R2 = 0.70). Through the analysis and comparison of the experimental results, it was proven that the algorithm in this paper had a good performance in the prediction of organic carbon and bulk density.

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale.

Zoning of Ecological Restoration in the Qilian Mountain Area, China

Ecosystem restoration has been widely concerned with the damage and degradation of ecosystems worldwide. Scientific and reasonable formulations of ecological restoration zoning is the basis for the formulation of an ecological restoration plan. In this study, a restoration zoning index system was proposed to comprehensively consider the ecological problems of ecosystems. The linear weighted function method was used to construct the ecological restoration index (ERI) as an important index of zoning. The research showed that: (1) the ecological restoration zones of the Qilian Mountains can be divided into eight basins, namely the headwaters of the Datong River Basin, the Danghe-Dahaerteng River Basin, the northern confluence area of the Qinghai Lake, the upper Shule River to middle Heihe River, the Oasis Agricultural Area in the northern foothills of the Qilian Mountain, the Huangshui Basin Valley, Aksay (corridor region of the western Hexi Basin), and the northeastern Tsaidam Basin; (2) the restoration index of the eight ecological restoration zones of the Qilian Mountains was between 0.34–0.8, with an average of 0.61 (the smaller the index, the more prominent the comprehensive ecological problem representing the regional mountains, rivers, forests, cultivated lands, lakes, and grasslands, and thus the greater the need to implement comprehensive ecological protection and restoration projects); and (3) the ecological problems of different ecological zones are frequently numerous, and often show the phenomenon of multiple overlapping ecological problems in the same zone.

Estimating Regional Evapotranspiration Using a Satellite-Based Wind Speed Avoiding Priestley–Taylor Approach

Wind speed (u) is a significant constraint in the evapotranspiration modeling over the highly heterogeneous regional surface due to its high temporal-spatial variation. In this study, a satellite-based Wind Speed Avoiding Priestley–Taylor (WAPT) algorithm was proposed to estimate the regional actual evapotranspiration by employing a u-independent theoretical trapezoidal space to determine the pixel Priestley–Taylor (PT) parameter Φ. The WAPT model was comprehensively evaluated with hydro-meteorological observations in the arid Heihe River Basin in northwestern China. The results show that the WAPT model can provide reliable latent heat flux estimations with the root-mean-square error (RMSE) of 46.0 W/m2 across 2013–2018 for 5 long-term observation stations and the RMSE of 49.6 W/m2 in the growing season in 2012 for 21 stations with intensive observations. The estimation by WAPT has a higher precision in the vegetation growing season than in the non-growing season. The estimation by WAPT has a closer agreement with the ground observations for vegetation-covered surfaces (e.g., corn and wetland) than that for dry sites (e.g., Gobi, desert, and desert steppe).

Dynamic Patterns of the Vertical Distribution of Vegetation in Heihe River Basin since the 1980s

The vertical distribution of vegetation in Heihe River Basin has presented a significant dynamic change in the different elevation zones since the 1980s. To explore the dynamic patterns of vegetation types located in the different elevation zones of Heihe River Basin, this study collected 440 field sampling datapoints of vegetation types, remote sensing images, climatic observation data, and DEM and preprocessed them. On the basis of the vegetation distribution and the terrain characteristics of Heihe River Basin, this study classified the vertical distribution of vegetation in Heihe River Basin into six vegetation zones, namely, the oasis farmland and desert zone, desert-steppe zone, dry scrub-steppe zone, mountain forest-steppe zone, subalpine scrub-meadow zone, and alpine cold desert-meadow zone. Moreover, the mean annual biotemperature (MAB) and total annual average precipitation (TAP) were used to analyze the relationship between vegetation change and climate change in the different elevation zones. The results show that the change rate of vegetation was up to 25.75% in Heihe River Basin. The area of vegetation that changed in the oasis farmland and desert zone was the largest (7224 km2), and the rate of vegetation that changed in the mountain forest-steppe zone was up to 56.93%. The mean annual biotemperature (MAB) and total annual average precipitation (TAP) in the six elevation zones showed an increasing trend, in which the increased rate of TAP presented a downward trend with the increase of elevation, and that of MAB showed a continuous upward trend with the increase of elevation. The change rate of vegetation was generally higher than that of MAB and TAP in the low and middle vegetation zones. The influence intensity of human activities on vegetation change in the lower and middle elevation zones of Heihe River Basin was greater than that in the high elevation zone between the 1980s and the 2010s. MAB is the major impact factor to vegetation change in the alpine cold zone of Heihe River Basin.

Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China

Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate the suitability of satellite SSM products, especially in alpine mountain areas where soil moisture plays a key role in terrestrial atmospheric exchanges. Aiming to overcome this limitation, a downscaling method based on random forest (RF) was proposed to disaggregate satellite SSM products. We compared the ability of the downscaled soil moisture active passive (SMAP) SSM and soil moisture and ocean salinity satellite (SMOS) SSM products to capture soil moisture information in upstream of the Heihe River Basin by using in situ measurements, the triple collocation (TC) method and temperature vegetation dryness index (TVDI). The results showed that the RF downscaling method has strong applicability in the study area, and the downscaled results of the two products after residual correction have more details, which can better represent the spatial distribution of soil moisture. The validation with the in situ SSM measurements indicates that the correlation between downscaled SMAP and in situ SSM is better than downscaled SMOS at both point and watershed scales in the Babaohe River Basin. From the TC method, the root mean square error (RMSE) of the CLDAS (CMA land data assimilation system), downscaled SMAP and downscaled SMOS were 0.0265, 0.0255 and 0.0317, respectively, indicating that the downscaled SMAP has smaller errors in the study area than others. However, the soil moisture distribution in the study area shown by the SMOS downscaled results is closer than the downscaled SMAP to the degree of drought reflected by TVDI. Overall, this study suggests that the proposed RF-based downscaling method can capture the variation of SSM well, and the downscaled SMAP products perform significantly better than the downscaled SMOS products after the accuracy verification and error analysis of the downscaled results, and it should be helpful to facilitate applications for satellite SSM products at small scales.