基于CLDAS融合降水的中国降雨侵蚀力研究

doi:10.12118/j.issn.1000-6060.2022.019

Abstract

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.

Key words: rainfall erosivity, EI₆₀ model, CLDAS fusion precipitation

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.

Figures/Tables 10

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省份（地区）	年降雨侵蚀力/MJ·mm·hm^-2·h^-1·a^-1		省份（地区）	年降雨侵蚀力/MJ·mm·hm^-2·h^-1·a^-1
省份（地区）	范围	平均值	省份（地区）	范围	平均值
北京市	334~2777	1552	湖南省	1529~6013	3405
天津市	1517~3232	2042	广东省	2573~15716	7009
河北省	140~3100	1153	广西省	2003~16295	5331
山西省	211~1805	620	海南省	2947~12717	8710
内蒙古	1~1642	348	重庆市	1424~4811	2616
辽宁省	383~4121	1615	四川省	73~5678	1488
吉林省	391~2071	859	贵州省	678~5935	27908
黑龙江省	223~2198	834	云南省	159~6715	1674
上海市	2423~4399	3371	西藏	2~53718	583
江苏省	2085~5533	3490	陕西省	229~3599	878
浙江省	1642~7449	4129	甘肃省	0~2314	228
安徽省	1802~8002	3549	青海省	0~16551	309
福建省	2597~8529	4713	宁夏	60~1016	251
江西省	2728~7664	4587	新疆	0~10163	63
山东省	1307~4077	2382	台湾	3207~14078	7966
河南省	494~4030	1840	香港	2928~9190	5771
湖北省	719~5702	2763	澳门	6364~12119	8747

省份（地区）	年均降雨量 /mm	年均侵蚀密度 /MJ·hm^-2·h^-1·a^-1	年均暴雨量 /mm	省份（地区）	年均降雨量 /mm	年均侵蚀密度 /MJ·hm^-2·h^-1·a^-1	年均暴雨量 /mm
北京市	553.58	2.75	75.19	湖南省	1452.26	2.33	233.48
天津市	556.38	3.67	93.98	广东省	1889.15	3.66	503.05
河北省	496.50	2.20	52.62	广西省	1613.90	3.26	361.97
山西省	498.18	1.22	29.05	海南省	1910.84	4.55	619.89
内蒙古	294.58	1.06	11.88	重庆市	1197.47	2.18	179.20
辽宁省	653.05	2.41	107.45	四川省	900.14	1.47	88.83
吉林省	600.07	1.46	43.84	贵州省	1169.93	2.33	158.69
黑龙江省	550.93	1.50	34.57	云南省	1092.31	1.47	81.70
上海市	1305.19	2.58	231.82	西藏	388.74	1.08	29.14
江苏省	1112.86	3.18	237.27	陕西省	651.67	1.32	53.28
浙江省	1686.28	2.44	297.65	甘肃省	287.51	0.63	7.61
安徽省	1266.17	2.82	261.37	青海省	329.23	0.84	8.62
福建省	1764.18	2.68	352.82	宁夏	291.60	0.86	8.48
江西省	1734.80	2.63	327.61	新疆	140.19	0.47	1.83
山东省	704.64	3.37	147.78	台湾	1709.28	4.62	711.58
河南省	764.97	2.34	123.28	香港	1624.13	3.51	468.18
湖北省	1155.33	2.34	191.13	澳门	1943.85	4.36	538.17

Rainfall erosivity in China based on CLDAS fusion precipitation

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[1]	WANG Kai, CHEN Lu, MA Jinhui, LIU Fei. Calculation of rainfall erosivity in China with TRMM Data [J]. , 2015, 38(5): 948-959.
[2]	HU Lin，SANG Yong-zhi，SU Jing，ZHANG Wen-jing，WANG Qi. Spatial and temporal characteristics of rainfall erosivity in Shaanxi Province [J]. , 2014, 37(6): 1101-1107.