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自然资源遥感  2025, Vol. 37 Issue (2): 66-79    DOI: 10.6046/zrzyyg.2023352
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于随机森林的降水产品降尺度及其水文适用性评估
陈多妍(), 史岚()
南京信息工程大学地理科学学院,南京 210044
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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摘要 全球卫星降水计划(global precipitation measurement mission,GDM)等降水产品以连续分布、空间范围广等优势被广泛应用于流域研究,但存在精度不足、空间分辨率低等问题。该文基于随机森林(random forests,RF)降尺度模型融合多源影响因子生成2种高空间分辨率的日降水产品RF1和RF2,并输入HEC-HMS(the hydrologic engineering centers-hydrologic modeling system)模型中模拟信江流域日径流量变化,评价RF1和RF2对GPM水文适用性的改进。结果表明: RF1和RF2均能改进GPM的数据精度与分布细节,RF2相关性更高、误差更小,探测降水事件的命中率方面RF1更优; RF1与GPM模拟的径流曲线相似且有较大改善,RF2修正了GPM的部分高估且在部分时段更接近真实流量曲线峰值,但因站点分布不均影响了对地形复杂区的预测,模拟精度受限。总体上看,RF1和RF2都能够有效反映信江流域的日降水变化情况,不同程度改进了GPM在水文应用能力。
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陈多妍
史岚
关键词 ： 降尺度,  GPM,  随机森林,  径流模拟,  信江流域    
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.
Key words： downscaling    GPM    random forest    runoff simulation    Xinjiang River basin
收稿日期: 2023-11-14      出版日期: 2025-05-09
ZTFLH:  P412.27  
基金资助:江苏省研究生科研与实践创新项目“鄱阳湖流域卫星降水产品降尺度研究与径流模拟”(KYCX22-1130)
通讯作者: 史 岚(1978-),女,副教授,主要从事3S技术与气象应用研究。Email: sl_nim@163.com。
作者简介: 陈多妍(1999-),女,硕士研究生,主要从事气象与GIS应用研究。Email: dotz910@163.com。
引用本文:   
陈多妍, 史岚. 基于随机森林的降水产品降尺度及其水文适用性评估[J]. 自然资源遥感, 2025, 37(2): 66-79.
CHEN Duoyan, SHI Lan. Downscaling of precipitation products based on the random forest and assessment of their hydrologic applicability. Remote Sensing for Natural Resources, 2025, 37(2): 66-79.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023352      或      https://www.gtzyyg.com/CN/Y2025/V37/I2/66
Fig.1  信江流域概况
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模型计算方法选择
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  各地形因子的相关系数
Fig.2  GPM,RF1及RF2汛期各月平均降水量空间分布
Fig.3  不同雨量等级下降水预测值分布散点图
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  不同产品日降水精度与降水事件探测能力评价
Fig.4  日降水产品分月精度比较
Fig.5  GPM,RF1和RF2各评价指标空间分布
Fig.6  子流域划分与气象站控制范围示意图
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  弋阳站GPM,RF1,RF2径流模拟精度评估
Fig.7  模拟结果与实测结果对比(RF2较GPM模拟较好场次)
Fig.8  模拟结果与实测结果对比(RF2较GPM模拟较差场次)
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