Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

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