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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (2) : 66-79     DOI: 10.6046/zrzyyg.2023352
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Downscaling of precipitation products based on the random forest and assessment of their hydrologic applicability
CHEN Duoyan(), SHI Lan()
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Abstract  

Precipitation products, including the Global Precipitation Measurement (GPM) mission, have been widely used in river basin studies due to their advantages like continuous distributions and broad spatial ranges. However, they are limited by insufficient accuracy and low spatial resolution. Based on the random forest (RF), this study integrated multisource influencing factors to generate two daily precipitation products with high spatial resolution: RF1 and RF2. The two daily precipitation products were input to the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model to simulate daily runoff changes in the Xinjiang River basin. Finally, this study assessed the contributions of RF1 and RF2 to the improvement of GPM’s hydrologic applicability. The results show that both RF1 and RF2 improved the accuracy and distribution details of GPM data. RF2 exhibited a higher correlation and lower error, whereas RF1 manifested superior performance in detecting precipitation events. The RF1-simulated runoff curves resembled GPM-derived curves, showing significant improvements. RF2 corrected partial GPM’s overestimates and more accurately revealed the peak values of real flow curves in some periods. However, the uneven distribution of monitoring stations affected RF2’s prediction in complex terrain areas, limiting its simulation accuracy. Overall, both RF1 and RF2 can effectively reflect daily precipitation changes in the Xinjiang River basin, improving GPM’s hydrologic applicability to varying degrees.
Keywords downscaling      GPM      random forest      runoff simulation      Xinjiang River basin     
ZTFLH:  P412.27  
Issue Date: 09 May 2025
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Duoyan CHEN
Lan SHI
Cite this article:   
Duoyan CHEN,Lan SHI. Downscaling of precipitation products based on the random forest and assessment of their hydrologic applicability[J]. Remote Sensing for Natural Resources, 2025, 37(2): 66-79.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023352     OR     https://www.gtzyyg.com/EN/Y2025/V37/I2/66
Fig.1  Overview map of Xinjiang Basin
计算模块 计算方法 方法原理与输入参数
产流计算 SCS曲线数法 根据累积降雨量、土地利用方式、土壤类型以及前期土壤含水量等条件模拟计算产流量,由土壤最大蓄水能力与流域特征的关系引入参数CN
直接径流计算 斯奈德单位线法 通过收集某集水区的降雨和径流资料,算出单位线,单位线峰现时间与流量滞时tlag有关
基流计算 指数衰退法 假设集水区任意时刻的基流量和初始基流存在某种关系,由流量成分取值衰减常数k
河道汇流计算 马斯京根法 将马斯京根槽蓄曲线方程和水量平衡方程联合演算出马斯京根流量演算方程,需要输入蓄量常数K和流量比重因子x
Tab.1  HEC-HMS calculation method selection
地形因子 海拔 坡度 地形起伏度 地形切割度 地形位置指数 地形曲率
海拔 1.000**① 0.718** 0.819** 0.858** 0.314** 0.544**
坡度 1.000** 0.920** 0.886** 0.011** 0.614**
地形起伏度 1.000** 0.958** 0.044** 0.670**
地形切割度 1.000** 0.088** 0.614**
地形位置指数 1.000** -0.079**
地形曲率 1.000**
Tab.2  Correlation coefficients between topographic factors
Fig.2  Spatial distribution of average monthly precipitation in flood season for GPM, RF1 and RF2
Fig.3  Scatter diagram of the predicted value distribution of falling water in different rainfall classes
产品 精度评价指标 日降水
事件		 探测
能力		 评价
指标
CC MAE/mm RMSE/mm POD FAR ACC
GPM 0.696 6.833 14.894 0.772 0.448 0.523
RF1 0.717 6.766 14.141 0.970 0.438 0.564
RF2 0.779 5.480 11.991 0.871 0.451 0.530
Tab.3  Evaluation of daily precipitation accuracy and precipitation event detection ability of products
Fig.4  Comparison of monthly precision of daily precipitation products
Fig.5  Spatial distribution of evaluation indexes of GPM, RF1 and RF2
Fig.6  Schematic diagram of sub watershed division and meteorological station control range
场次 参数 GPM RF1 RF2 场次 参数 GPM RF1 RF2
率
定
期		 20180410 RNS 0.697 0.689 0.499 20190423 RNS 0.806 0.851 0.546
RCC 0.892 0.870 0.754 RCC 0.915 0.938 0.854
20180511 RNS 0.870 0.909 0.751 20190516 RNS 0.509 0.643 -0.459
RCC 0.943 0.959 0.937 RCC 0.877 0.904 0.855
20180526 RNS 0.427 0.495 0.304 20190531 RNS 0.703 0.791 0.822
RCC 0.764 0.779 0.874 RCC 0.887 0.898 0.907
20180610 RNS 0.414 0.433 0.631 20190617 RNS 0.218 0.363 -0.196
RCC 0.836 0.836 0.936 RCC 0.800 0.854 0.724
20180630 RNS 0.644 0.667 0.367 20190703 RNS 0.377 0.435 0.523
RCC 0.965 0.970 0.883 RCC 0.788 0.790 0.746
20190401 RNS 0.712 0.788 -0.117
RCC 0.910 0.923 0.848
验
证
期		 20200418 RNS -0.169 0.062 -0.658 20200608 RNS 0.289 0.773 0.110
RCC 0.727 0.709 0.746 RCC 0.956 0.972 0.947
20200509 RNS -3.681 -1.988 -4.469 20200629 RNS 0.600 0.610 0.618
RCC 0.163 0.312 0.166 RCC 0.879 0.872 0.867
20200524 RNS 0.608 0.712 0.893
RCC 0.977 0.963 0.989
Tab.4  Evaluation of hydrological simulation accuracy of GPM,RF1 and RF2 in Yiyang Station
Fig.7  Comparison of simulation results and measured results
Fig.8  Comparison of simulation results and measured results
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