Tropical Geography

   

An XGBoost-Merging Method for High-Resolution Daily Precipitation Estimation for a Regional Rainstorm Event

Junmin Zhang1,2,3(), Huihua Ruan4(), Jianhui Xu2,3, Xiaoai Dai1, Yanping Zheng4, Jinbiao Zhang4   

  1. 1.School of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
    2.Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Engineering Laboratory for Geographic Spatio-temporal Big Data, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    3.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
    4.Guangdong meteorological observation data center, Guangzhou 510080, China
  • Received:2020-10-08 Revised:2021-03-05 Online:2021-08-09
  • Contact: Huihua Ruan E-mail:zhangjunmin130@163.com;ruanhuihua@163.com

Abstract:

Precipitation is a vital physical parameter of the earth surface system, and accurate estimation of spatiotemporal patterns of precipitation is essential for flood disaster monitoring, drought monitoring, and water management. However, regional precipitation, which is derived solely from rain gauges, remote sensing, and weather radar, is subject to large uncertainties, especially for topographically complex mountain areas. Multi-source precipitation data fusion is a practical method for achieving high-accuracy and high-resolution precipitation information. This study proposes an XGBoost-based geostatistical fusion method (XGBoost) for combining information from ground-based measurements, radar precipitation, and other auxiliary parameters, to improve the accuracy of the spatiotemporal distribution of precipitation in geographically complex mountain areas. In the XGBoost-based geostatistical fusion model, radar precipitation and terrestrial parameters, which include longitude, latitude, digital elevation model data, aspect, slope, enhanced vegetation index, and distance from the coastline, are considered as the independent variables. The XGBoost-based geostatistical fusion model was applied to a regional rainstorm event that lasted from August 26 to 30, 2018, in northern Guangdong using daily measurements from 206 rain gauges and 51 stations for model training and validation. The fused results were further compared with the results obtained from the multiple linear regression kriging method (LM). Validation using ground-based precipitation measurements was applied for different data fusion methods based on the coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE). The experimental results indicated that: (1) The ground-based precipitation data were positively associated with radar precipitation, and the correlation coefficient between the ground-based precipitation data and the terrestrial parameters varied significantly with measurement time over the regional rainstorm event. (2) The XGBoost produced 1-km precipitation prediction with higher accuracy than the LM before residual correction. (3) The accuracy of fused precipitation with the XGBoost-based geostatistical method was reduced after residual correction, but the accuracy of the LM was increased. The XGBoost-based geostatistical method produced 1-km precipitation with lower accuracy than the TsHARP utility on August 27 and 29; however, in general, the XGBoost-based geostatistical method outperformed the LM because the nonlinear relationships between the ground-based precipitation data and the independent variables were considered in XGBoost. (4) The XGBoost-based geostatistical method captured the differences in precipitation for different land cover patterns and produced the spatial details of fused precipitation over the complex mountain areas.

Key words: Multi-source precipitation data fusion, Radar precipitation, XGBoost, Ordinary kriging

CLC Number: 

  • P407