| 基于CLDAS融合降水的中国降雨侵蚀力研究 |
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| 引用本文： | 梁宇靖,沈润平,师春香,邢雅洁,孙帅.基于CLDAS融合降水的中国降雨侵蚀力研究[J].干旱区地理,2022,45(5):1333-1346. |
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| 作者姓名： | 梁宇靖 沈润平 师春香 邢雅洁 孙帅 |
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| 作者单位： | 1.南京信息工程大学地理科学学院,江苏 南京 2100442.国家气象信息中心,北京 100081 |
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| 基金项目： | 国家重点研发计划项目(2018YFC1506602);国家自然科学基金重点项目(42161054);国家气象信息中心结余资金项目(NMICJY202106) |
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| 摘 要： | 气象站和卫星降雨资料估算降雨侵蚀力时存在无法反映空间异质性且精度差的问题,基于CLDAS多源融合降水,利用EI60模型从不同的时空尺度对中国的降雨侵蚀力进行评估,并结合降雨量、侵蚀性降雨次数、侵蚀密度等指标,探讨降雨对土壤侵蚀的潜在作用。结果表明：（1） CLDAS降雨侵蚀力与地面实测数据在不同的时间尺度均有良好的回归关系,相关系数达到0.8以上,与CMORPH降雨侵蚀力相比,其相对误差显著降低,可以准确反映全国范围的降雨侵蚀力季节性变异。（2） 在2001—2020年,不同雨量区的降雨侵蚀力、降雨量和侵蚀性降雨次数的变化趋势基本一致,高雨量区的年际变化波动剧烈,侵蚀性降雨次数和暴雨过程协同影响降雨侵蚀力的大小。（3） 空间上,中国的降雨侵蚀力值的特点为东南沿海地区高、西北内陆地区低。时间上,侵蚀性降雨集中在5—8月,夏、秋两季对土壤造成的侵蚀影响更大。（4） 通过对年降雨量、年侵蚀密度和年暴雨量进行分区定量分析,结果表明暴雨量与侵蚀密度成正相关关系,即年降雨量一定,暴雨事件越多,降雨侵蚀密度越大。
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| 关 键 词： | 降雨侵蚀力 EI60模型 CLDAS多源融合降水 |
| 收稿时间： | 2022-01-12 |
Rainfall erosivity in China based on CLDAS fusion precipitation |
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| LIANG Yujing,SHEN Runping,SHI Chunxiang,XING Yajie,SUN Shuai.Rainfall erosivity in China based on CLDAS fusion precipitation[J].Arid Land Geography,2022,45(5):1333-1346. |
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| Authors: | LIANG Yujing SHEN Runping SHI Chunxiang XING Yajie SUN Shuai |
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| Affiliation: | 1. College of Geosciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China2. National Meteorological Information Centre, Beijing 100081, China |
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| Abstract: | Rain gauge data is still the most popular data for estimating rainfall erosivity. However, although this precipitation data is highly accurate, it is not suitable for estimating rainfall erosivity on a large, regional scale. It can only reflect the situation of rainfall erosivity in the area surrounding the weather station. Although satellite imagery can reflect the spatial and temporal distribution of precipitation, its application in various professional fields is limited by its poor accuracy. Fusion precipitation data is produced by fusing precipitation data from different sources, such as station precipitation, satellite precipitation, and radar precipitation, all on the same spatial and temporal scales. The advantage of combining multiple sources is that it produces data that more closely represents the true spatial distribution of precipitation. With the high temporal resolution CMA land data assimilation system (CLDAS) fusion precipitation, this study uses the EI60 model to assess rainfall erosion in China on different spatial and temporal scales and to determine the potential role of rainfall on soil erosion by precipitation, erosive rainfall, and precipitation erosion density. The article draws the following conclusions: (1) CLDAS rainfall erosivity is slightly lower than the rain gauge results but presents a very good regression relationship with rain gauge erosivity, with high correlation coefficients at different time scales, and a lower margin of error than the rainfall erosivity from the Climate Prediction Center Morphing (CMORPH) technique, which can accurately reflect the rainfall erosivity variation on a national scale. (2) In different rainfall zones, trends in rainfall erosivity, rainfall volume, and the number of erosive rainfall events from 2001 to 2020 are generally consistent, with sharp inter-annual fluctuations in high rainfall zones. (3) Spatially, Chinese rainfall erosivity is characterized by high values in the southeast coastal areas and low values in the northwest inland areas. Temporally, erosive rainfall is concentrated from May to August. Rainfall in summer and autumn results in greater erosive impacts on the soil. (4) The quantitative analysis of annual rainfall, annual erosion density, and annual storms shows that storm and rainfall erosion density are positively correlated. In other words, the more rainstorm events there are, the higher the rainfall erosion density and rainfall erosivity. This study can serve as a scientific reference for theoretical research on soil erosion and soil conservation practices in China. |
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| Keywords: | rainfall erosivity EI60 model CLDAS fusion precipitation |
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