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Lake area, water level, and water storage changes of terminal lakes are vital for regional water resource management and for understanding local hydrological processes. Nevertheless, due to the complex geographical conditions, it is difficult to investigate and analyze this change in ungauged regions. This study focuses on the ungauged, semi-arid Gahai Lake, a typical small terminal lake in the Qaidam Basin. In addition to the scant observed data, satellite altimetry is scarce for the excessively large fraction of outlier points. Here, we proposed an effective and simple algorithm for extracting available lake elevation points from CryoSat-2, ICESat-2 and Sentinel-3. Combining with the area data from Landsat, Gaofen (GF), and Ziyuan (ZY) satellites, we built an optimal hypsographic curve (lake area versus water level) based on the existing short-term data. Cross-validation was used to validate whether the curve accurately could predict the lake water level in other periods. In addition, we used multisource high-resolution images including Landsat and digital maps to extract the area data from 1975 to 2020, and we applied the curve to estimate the water level for the corresponding period. Additionally, we adopted the pyramidal frustum model (PFM) and the integral model (IM) to estimate the long-term water storage changes, and analyzed the differences between these two models. We found that there has been an obvious change in the area, water level, and water storage since the beginning of the 21st century, which reflects the impact of climate change and human activities on hydrologic processes in the basin. Importantly, agricultural activities have caused a rapid increase in water storage in the Gahai Lake over the past decade. We collected as much multisource satellite data as possible; thus, we estimated the long-term variations in the area, water level, and water storage of a small terminal lake combining multiple models, which can provide an effective method to monitor lake changes in ungauged basins.
Chuanhui Zhang; Aifeng Lv; Wenbin Zhu; Guobiao Yao; Shanshan Qi. Using Multisource Satellite Data to Investigate Lake Area, Water Level, and Water Storage Changes of Terminal Lakes in Ungauged Regions. Remote Sensing 2021, 13, 3221 .
AMA StyleChuanhui Zhang, Aifeng Lv, Wenbin Zhu, Guobiao Yao, Shanshan Qi. Using Multisource Satellite Data to Investigate Lake Area, Water Level, and Water Storage Changes of Terminal Lakes in Ungauged Regions. Remote Sensing. 2021; 13 (16):3221.
Chicago/Turabian StyleChuanhui Zhang; Aifeng Lv; Wenbin Zhu; Guobiao Yao; Shanshan Qi. 2021. "Using Multisource Satellite Data to Investigate Lake Area, Water Level, and Water Storage Changes of Terminal Lakes in Ungauged Regions." Remote Sensing 13, no. 16: 3221.
As a fundamental natural resource and a strategic economic resource, water resources play a key supporting role in the process of social and economic development. In this paper, risk factors influencing the water resources carrying capacity (WRCC) were investigated based on risk theory and resources carrying capacity theory, and a method for the risk assessment of the WRCC was proposed. The vulnerability of WRCC system was evaluated by using the Fuzzy Comprehensive Evaluation method, the hazard of WRCC system was calculated by using the comprehensive hazard index, and then the risk of WRCC was obtained by combining the vulnerability with the hazard. The risk of WRCC in China due to climate change, urbanization, and industrialization was thoroughly investigated. The results demonstrated that the risk of WRCC was higher in Northern China than in Southern China and was higher in developed areas than in developing areas. The risk was observed to be highest in the Beijing–Tianjin–Hebei region. Meanwhile, the results also suggested that the risk of WRCC can be reduced in two ways: (1) Reducing the pressure on the water resources carrying system caused by economic and social activities; and (2) increasing the water resources supply in certain areas. The risk assessment of the WRCC presented here provides a valuable reference for the management and sustainable utilization of water resources in China.
Aifeng Lv; Yan Han; Wenbin Zhu; Shifeng Zhang; Weihua Zhao. Risk Assessment of Water Resources Carrying Capacity in China. JAWRA Journal of the American Water Resources Association 2021, 57, 539 -551.
AMA StyleAifeng Lv, Yan Han, Wenbin Zhu, Shifeng Zhang, Weihua Zhao. Risk Assessment of Water Resources Carrying Capacity in China. JAWRA Journal of the American Water Resources Association. 2021; 57 (4):539-551.
Chicago/Turabian StyleAifeng Lv; Yan Han; Wenbin Zhu; Shifeng Zhang; Weihua Zhao. 2021. "Risk Assessment of Water Resources Carrying Capacity in China." JAWRA Journal of the American Water Resources Association 57, no. 4: 539-551.
The overexploitation of groundwater in China has raised concern, as it has caused a series of environmental and ecological problems. However, far too little attention has been paid to the relationship between groundwater use and the spatial distribution of water users, especially that of manufacturing factories. In this study, a factory scatter index (FSI) was constructed to represent the spatial dispersion degree of manufacturing factories in China. It was found that counties and border areas between neighboring provinces registered the highest FSI increases. Further non-spatial and spatial regression models using 205 provincial-level secondary river basins in China from 2016 showed that the scattered distribution of manufacturing plants played a key role in groundwater withdrawal in China, especially in areas with a fragile ecological environment. The scattered distribution of manufacturing plants raises the cost of tap water transmission, makes monitoring and supervision more difficult, and increases the possibility of surface water pollution, thereby intensifying groundwater withdrawal. A reasonable spatial adjustment of manufacturing industry through planning and management can reduce groundwater withdrawal and realize the protection of groundwater. Our study may provide a basis for water-demand management through spatial adjustment in areas with high water scarcity and a fragile ecological environment.
Yanting Zheng; Huidan Yang; Jinyuan Huang; Linjuan Wang; Aifeng Lv. The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China. Water 2021, 13, 1158 .
AMA StyleYanting Zheng, Huidan Yang, Jinyuan Huang, Linjuan Wang, Aifeng Lv. The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China. Water. 2021; 13 (9):1158.
Chicago/Turabian StyleYanting Zheng; Huidan Yang; Jinyuan Huang; Linjuan Wang; Aifeng Lv. 2021. "The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China." Water 13, no. 9: 1158.
Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.
Aifeng Lv; Zhilin Zhang; Hongchun Zhu. A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province. Remote Sensing 2021, 13, 1583 .
AMA StyleAifeng Lv, Zhilin Zhang, Hongchun Zhu. A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province. Remote Sensing. 2021; 13 (8):1583.
Chicago/Turabian StyleAifeng Lv; Zhilin Zhang; Hongchun Zhu. 2021. "A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province." Remote Sensing 13, no. 8: 1583.
The mixture of agricultural and non-agricultural land-use represents a new pattern of urbanization in the Global South. This mixture has hindered the improvement of land-use productivity and makes it difficult to achieve the centralized disposal of pollutants, which has resulted in the waste of land resources and serious environmental problems. Although many studies have investigated land-use mixture, most of them remain descriptive and lack quantitative examination and an in-depth mechanism analysis. Using raster land-use data, this paper examines the spatiotemporal pattern of the land-use mixture in China between 1990 and 2015 by calculating join counts values supplemented by landscape metrics, and attempts to explain the regional variations in land-use mixtures in recent years. The results show that, between 2000 and 2010, land-use was more mixed in fast-growing regions such as Zhejiang, Fujian, Chongqing, Guangdong, and some major metropolises and mining cities, and that, between 2010 and 2015, land-use was more mixed in Central China. Additionally, the results of econometric models reveal that mixed land-use can be alleviated in regions with strict land planning and management, such as urban agglomerations in the Yangtze River Delta and the Pearl River Delta, as well as in areas with high levels of urbanization. Furthermore, the results of a spatial heterogeneity analysis show that strict land management has played an important role in reducing the land-use mixture in Eastern China; however, it has not played a significant role in Central China. The findings of this study suggest that land-use should be appropriately planned and managed to ensure sustainable development.
Yanting Zheng; Sai Zhao; Jinyuan Huang; Aifeng Lv. Analysis of the Spatiotemporal Pattern and Mechanism of Land Use Mixture: Evidence from China’s County Data. Land 2021, 10, 370 .
AMA StyleYanting Zheng, Sai Zhao, Jinyuan Huang, Aifeng Lv. Analysis of the Spatiotemporal Pattern and Mechanism of Land Use Mixture: Evidence from China’s County Data. Land. 2021; 10 (4):370.
Chicago/Turabian StyleYanting Zheng; Sai Zhao; Jinyuan Huang; Aifeng Lv. 2021. "Analysis of the Spatiotemporal Pattern and Mechanism of Land Use Mixture: Evidence from China’s County Data." Land 10, no. 4: 370.
In the Qaidam Basin, meteorological stations are sparsely distributed, and the observational precipitation data are therefore not comprehensive, which significantly hampers the accurate assessment and optimal allocation of regional water resources. This study aims to evaluate the accuracy of three precipitation products (MSWEP V2, GPM IMERG V6, and TRMM 3B43) against gauged-based precipitation data from nine meteorological stations and 12 hydrologic stations in the Qaidam Basin from 2001 to 2016. The results reveal the following: (1) At the annual and monthly scales, the MSWEP product has the highest accuracy, followed by the GPM product. However, the TRMM product only reveals a correlation at the monthly scale; (2) the MSWEP product performs better in the wet season than the dry season, and the TRMM product has an abnormally high value of precipitation in the wet season. Moreover, it was shown that the accuracy of the GPM product is superior to that of the TRMM product; however, it has a low detection capability in some mountainous areas; and (3) the average error of each precipitation product at the meteorological stations is smaller than that at the hydrological stations.
Shanshan Qi; Aifeng Lv. Applicability analysis of multiple precipitation products in the Qaidam Basin, Northwestern China. Environmental Science and Pollution Research 2021, 1 -17.
AMA StyleShanshan Qi, Aifeng Lv. Applicability analysis of multiple precipitation products in the Qaidam Basin, Northwestern China. Environmental Science and Pollution Research. 2021; ():1-17.
Chicago/Turabian StyleShanshan Qi; Aifeng Lv. 2021. "Applicability analysis of multiple precipitation products in the Qaidam Basin, Northwestern China." Environmental Science and Pollution Research , no. : 1-17.
The overexploitation of groundwater in China has raised concern as it has caused a series of environmental and ecological problems. However, far too little attention has been paid to the relationship between groundwater use and the spatial distribution of water users, especially that of manufacturing factories. This study proposed a factory scatter index (FSI) that incorporates the latitude and longitude of each plant and calculates the distance between factories to characterize the degree to which manufacturing plants are scattered in China. It is found that counties and border areas between neighboring provinces registered the highest FSI increase. It seems that the degree of scattering of manufacturing plants is closely related to land planning and management of local governments. Further non-spatial and spatial regression models using 205 provincial-level secondary river basins in China from 2016 show that the scattered distribution of manufacturing plants played a key role in groundwater withdrawal in China, especially in fragile ecological-environment areas. The scattered distribution of manufacturing plants raises the cost of tap water transmission, makes monitoring and supervision more difficult, and increases the possibility of surface water pollution, thereby intensifying groundwater withdrawal. A reasonable spatial adjustment of manufacturing industry through planning and management can reduce groundwater withdrawal and realize the protection of groundwater. Our study may provide a basis for water-demand management through spatial adjustment in areas with high water scarcity and fragile ecological environment.
Yanting Zheng; Huidan Yang; Jinyuan Huang; Linjuan Wang; Aifeng Lv. The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China. 2020, 1 .
AMA StyleYanting Zheng, Huidan Yang, Jinyuan Huang, Linjuan Wang, Aifeng Lv. The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China. . 2020; ():1.
Chicago/Turabian StyleYanting Zheng; Huidan Yang; Jinyuan Huang; Linjuan Wang; Aifeng Lv. 2020. "The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China." , no. : 1.
Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57).
Hongchun Zhu; Zhilin Zhang; Aifeng Lv. Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation. Water 2020, 12, 1292 .
AMA StyleHongchun Zhu, Zhilin Zhang, Aifeng Lv. Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation. Water. 2020; 12 (5):1292.
Chicago/Turabian StyleHongchun Zhu; Zhilin Zhang; Aifeng Lv. 2020. "Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation." Water 12, no. 5: 1292.
The spatial distribution of manufacturing plants has a significant impact on resource use and the environment. This paper examines the impacts of the scattered geographic distributions of manufacturing plants on groundwater withdrawal in Hebei Province, China, which is a region with severe groundwater overexploitation. Instead of using traditional methods to measure urban sprawl, we proposed a factory scatter index (FSI) that incorporates the latitude and longitude of each plant to characterize the degree to which plants are scattered throughout Hebei Province. We also conducted a regression analysis using groundwater withdrawal as the dependent variable, FSI as the independent variable, and other traditional variables as controls. The results reveal that FSI is large in developed regions and has a significant impact on groundwater withdrawal that exceeds impacts from the number of high water-consuming factories in a region, total population, and urbanization. This study highlights the impact of geographically scattered manufacturing plants on water use and may provide a new geographic perspective for regional sustainable development.
Yanting Zheng; Linjuan Wang; Hao Chen; Aifeng Lv. Does the Geographic Distribution of Manufacturing Plants Exacerbate Groundwater Withdrawal? -A case study of Hebei Province in China. Journal of Cleaner Production 2018, 213, 642 -649.
AMA StyleYanting Zheng, Linjuan Wang, Hao Chen, Aifeng Lv. Does the Geographic Distribution of Manufacturing Plants Exacerbate Groundwater Withdrawal? -A case study of Hebei Province in China. Journal of Cleaner Production. 2018; 213 ():642-649.
Chicago/Turabian StyleYanting Zheng; Linjuan Wang; Hao Chen; Aifeng Lv. 2018. "Does the Geographic Distribution of Manufacturing Plants Exacerbate Groundwater Withdrawal? -A case study of Hebei Province in China." Journal of Cleaner Production 213, no. : 642-649.
The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km2, and 8 km2, respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m3, and RMSE = 0.5 billion m3, which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data.
Wenbin Zhu; Shaofeng Jia; Aifeng Lv. Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data. Remote Sensing 2014, 6, 10457 -10482.
AMA StyleWenbin Zhu, Shaofeng Jia, Aifeng Lv. Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data. Remote Sensing. 2014; 6 (11):10457-10482.
Chicago/Turabian StyleWenbin Zhu; Shaofeng Jia; Aifeng Lv. 2014. "Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data." Remote Sensing 6, no. 11: 10457-10482.