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Jiabin Peng
Sino-Belgian Joint Laboratory of Geo-information, 9000 Ghent, Belgium

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Journal article
Published: 11 January 2021 in Remote Sensing
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Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: (1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. (2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. (3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. (4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. (5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.

ACS Style

Jiabin Peng; Tie Liu; Yue Huang; Yunan Ling; Zhengyang Li; Anming Bao; Xi Chen; Alishir Kurban; Philippe De Maeyer. Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia. Remote Sensing 2021, 13, 221 .

AMA Style

Jiabin Peng, Tie Liu, Yue Huang, Yunan Ling, Zhengyang Li, Anming Bao, Xi Chen, Alishir Kurban, Philippe De Maeyer. Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia. Remote Sensing. 2021; 13 (2):221.

Chicago/Turabian Style

Jiabin Peng; Tie Liu; Yue Huang; Yunan Ling; Zhengyang Li; Anming Bao; Xi Chen; Alishir Kurban; Philippe De Maeyer. 2021. "Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia." Remote Sensing 13, no. 2: 221.

Journal article
Published: 18 July 2020 in Remote Sensing
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Human activities are mainly responsible for the Aral Sea crisis, and excessive farmland expansion and unreasonable irrigation regimes are the main manifestations. The conflicting needs of agricultural water consumption and ecological water demand of the Aral Sea are increasingly prominent. However, the quantitative relationship among the water balance elements in the oasis located in the lower reaches of the Amu Darya River Basin and their impact on the retreat of the Aral Sea remain unclear. Therefore, this study focused on the water consumption of the Nukus irrigation area in the delta of the Amu Darya River and analyzed the water balance variations and their impacts on the Aral Sea. The surface energy balance algorithm for land (SEBAL) was employed to retrieve daily and seasonal evapotranspiration (ET) levels from 1992 to 2018, and a water balance equation was established based on the results of a remote sensing evapotranspiration inversion. The results indicated that the actual evapotranspiration (ETa) simulated by the SEBAL model matched the crop evapotranspiration (ETc) calculated by the Penman–Monteith method well, and the correlation coefficients between the two ETa sources were greater than 0.8. The total ETa levels in the growing seasons decreased from 1992 to 2005 and increased from 2005 to 2015, which is consistent with the changes in the cultivated land area and inflows from the Amu Darya River. In 2000, 2005 and 2010, the groundwater recharge volumes into the Aral Sea during the growing season were 6.74×109 m3, 1.56×109 m3 and 8.40×109 m3; respectively; in the dry year of 2012, regional ET exceeded the river inflow, and 2.36×109 m3 of groundwater was extracted to supplement the shortage of irrigation water. There is a significant two-year lag correlation between the groundwater level and the area of the southern Aral Sea. This study can provide useful information for water resources management in the Aral Sea region.

ACS Style

Zhibin Liu; Yue Huang; Tie Liu; Junli Li; Wei Xing; Shamshodbek Akmalov; Jiabin Peng; Xiaohui Pan; Chenyu Guo; Yongchao Duan. Water Balance Analysis Based on a Quantitative Evapotranspiration Inversion in the Nukus Irrigation Area, Lower Amu River Basin. Remote Sensing 2020, 12, 2317 .

AMA Style

Zhibin Liu, Yue Huang, Tie Liu, Junli Li, Wei Xing, Shamshodbek Akmalov, Jiabin Peng, Xiaohui Pan, Chenyu Guo, Yongchao Duan. Water Balance Analysis Based on a Quantitative Evapotranspiration Inversion in the Nukus Irrigation Area, Lower Amu River Basin. Remote Sensing. 2020; 12 (14):2317.

Chicago/Turabian Style

Zhibin Liu; Yue Huang; Tie Liu; Junli Li; Wei Xing; Shamshodbek Akmalov; Jiabin Peng; Xiaohui Pan; Chenyu Guo; Yongchao Duan. 2020. "Water Balance Analysis Based on a Quantitative Evapotranspiration Inversion in the Nukus Irrigation Area, Lower Amu River Basin." Remote Sensing 12, no. 14: 2317.