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

摘要/Abstract

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。

关键词: 降雨侵蚀力, EI₆₀模型, CLDAS多源融合降水

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

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.

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.

图/表 10

图1

图2

图3

图4

表1

图5

图6

图7

表2

图8

参考文献 30

[1]	Zhang G H, Nearing M A, Liu B Y. Potential effects of climate change on rainfall erosivity in the Yellow River Basin of China[J]. Transactions of the Asae, 2005, 48(2): 511-517. doi: 10.13031/2013.18325
[2]	孙从建, 王佳瑞, 郑振婧, 等. 黄土高原塬面保护区降雨侵蚀力时空分布特征及其影响因素研究[J]. 干旱区地理, 2020, 43(3): 568-576.
	[Sun Congjian, Wang Jiarui, Zheng Zhenjing, et al. Temporal and spatial distribution characteristics of rainfall erosivity and its influencing factors in the protected area of the Loess Plateau[J]. Arid Land Geography, 2020, 43(3): 568-576. ]
[3]	胡琳, 苏静, 桑永枝, 等. 陕西省降雨侵蚀力时空分布特征[J]. 干旱区地理, 2014, 37(6): 1101-1107.
	[Hu Lin, Su Jing, Sang Yongzhi, et al. Spatial and temporal characteristics of rainfall erosivity in Shaanxi Province[J]. Arid Land Geography, 2014, 37(6): 1101-1107. ]
[4]	殷水清, 薛筱婵, 岳天雨, 等. 中国降雨侵蚀力的时空分布及重现期研究[J]. 农业工程学报, 2019, 35(9): 105-113.
	[Yin Shuiqing, Xue Xiaochan, Yue Tianyu, et al. Spatiotemporal distribution and return period of rainfall erosivity in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(9): 105-113. ]
[5]	伊力哈木·伊马木. 新疆维吾尔自治区1981—2018年降雨侵蚀力的空间变化特征[J]. 水土保持通报, 2020, 40(4): 1-9.
	[Yimamu Yilihamu. Spatial variation of rainfall erosivity in Xinjiang Uygur Autonomous Region from 1981 to 2018[J]. Bulletin of Soil and Water Conservation, 2020, 40(4): 1-9. ]
[6]	Pandey A, Mishra S K, Gautam A K. Soil erosion modeling using satellite rainfall estimates[J]. Journal of Water Resource and Hydraulic Engineering, 2015, 4(4): 325-318.
[7]	Vrieling A, Hoedjes J C B, Velde M. Towards large-scale monitoring of soil erosion in Africa: Accounting for the dynamics of rainfall erosivity[J]. Global and Planetary Change, 2014, 115(5): 33-43. doi: 10.1016/j.gloplacha.2014.01.009
[8]	王凯, 陈璐, 马金辉, 等. TRMM数据在中国降雨侵蚀力计算中的应用[J]. 干旱区地理, 2015, 38(5): 948-959.
	[Wang Kai, Chen Lu, Ma Jinhui, et al. Calculation of rainfall erosivity in China with TRMM Data[J]. Arid Land Geography, 2015, 38(5): 948-959. ]
[9]	黄进平. 基于GPM数据的肃南地区降雨侵蚀力计算[J]. 陕西水利, 2020(11): 36-38.
	[Huang Jinping. Calculation of rainfall erosivity in Sunan Area based on GPM data[J]. Shaanxi Water Resources, 2020(11): 36-38. ]
[10]	章文波, 谢云, 刘宝元. 利用日雨量计算降雨侵蚀力的方法研究[J]. 地理科学, 2002, 22(6): 705-711. doi: 10.13249/j.cnki.sgs.2002.06.705
	[Zhang Wenbo, Xie Yun, Liu Baoyuan. Rainfall erosivity estimation using daily rainfall amounts[J]. Scientia Geographica Sinica, 2002, 22(6): 705-711. ] doi: 10.13249/j.cnki.sgs.2002.06.705
[11]	Kim J, Han H, Kim B, et al. Use of a high-resolution-satellite-based precipitation product in mapping continental-scale rainfall erosivity: A case study of the United States[J]. Catena, 2020, 193: 104602, doi: 10.1016/j.catena.2020.104602. doi: 10.1016/j.catena.2020.104602
[12]	徐静, 史明昌, 王晓云, 等. 辽宁省细河流域土壤侵蚀监测方法对比研究[J]. 水土保持通报, 2011, 31(2): 103-107, 251.
	[Xu Jing, Shi Mingchang, Wang Xiaoyun, et al. Comparison of soil erosion monitoring methods in Xihe River Basin in Liaoning Province[J]. Water and Soil Conservation Bulletin, 2011, 31(2): 103-107, 251. ]
[13]	熊立华, 刘成凯, 陈石磊, 等. 遥感降水资料后处理研究综述[J]. 水科学进展, 2021, 32(4): 627-637.
	[Xiong Lihua, Liu Chengkai, Chen Shilei, et al. Review of post-processing research for remote-sensing precipitation products[J]. Advances in Water Science, 2021, 32(4): 627-637. ]
[14]	Sun S, Shi C, Pan Y, et al. Applicability assessment of the 1998—2018 CLDAS multi-source precipitation fusion dataset over China[J]. Journal of Meteorological Research, 2020, 34(4): 879-892. doi: 10.1007/s13351-020-9101-2
[15]	Xie Y, Koch S, Mcginley J, et al. A space-time multiscale analysis system: A sequential variational analysis approach[J]. Monthly Weather Review, 2011, 139(4): 1224-1240. doi: 10.1175/2010MWR3338.1
[16]	Wischmeier W H, Smith D D. Rainfall energy and its relationship to soil loss[J]. Eos, Transactions American Geophysical Union, 1958, 39(2): 285-291. doi: 10.1029/TR039i002p00285
[17]	Yue T, Yin S, Xie Y, et al. Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records[J]. Earth System Science Data, 2022, 14(2): 665-682. doi: 10.5194/essd-14-665-2022
[18]	Brown L C, Foster G R. Storm erosivity using idealized intensity distributions[J]. Transactions of the Asae, 1987, 30(2): 379-386. doi: 10.13031/2013.31957
[19]	Panagos P, Ballabio C, Borrelli P, et al. Spatio-temporal analysis of rainfall erosivity and erosivity density in Greece[J]. Catena, 2016, 137: 161-172. doi: 10.1016/j.catena.2015.09.015
[20]	从辰宇, 韩剑桥, 焦菊英, 等. 台风“利奇马”暴雨引发的土壤侵蚀调查研究——以山东省临朐县为例[J]. 水土保持通报, 2019, 39(5): 337-344.
	[Cong Chenyu, Han Jianqiao, Jiao Juying, et al. Investigation on soil erosion from typhoon Lekima rainstorm: A case study in Linqu County, Shandong Province[J]. Bulletin of Soil and Water Conservation, 2019, 39(5): 337-344. ]
[21]	Renard K G, Foster G R, Weesies G A, et al. Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation (RUSLE)[M]. Texas:USDA, Agticutural Research Service, 1996: 23.
[22]	邱德勋, 穆兴民, 尹殿胜, 等. 大理河流域干旱变化特征及其与极端降水的关系[J]. 干旱区地理, 2021, 44(5): 1240-1249.
	[Qiu Dexun, Mu Xingmin, Yin Diansheng, et al. Variation characteristics of drought and its relationship with the extreme precipitation in Dali River Basin[J]. Arid Land Geography, 2021, 44(5): 1240-1249. ]
[23]	孟现勇, 王浩, 刘志辉, 等. 基于CLDAS强迫CLM3.5模式的新疆区域土壤温度陆面过程模拟及验证[J]. 生态学报, 2017, 37(3): 979-995.
	[Meng Xianyong, Wang Hao, Liu Zhihui, et al. Simulation and verification of land surface soil temperatures in the Xinjiang Region by the CLM3.5 model forced by CLDAS[J]. Acta Ecologica Sinica, 2017, 37(3): 979-995. ]
[24]	刘松楠, 汪君, 王会军. 多源降水在门头沟山洪模拟中的应用及比较[J]. 气象, 2021, 47(7): 817-829.
	[Liu Songnan, Wang Jun, Wang Huijun. Application and comparison of multi-source rainfall data in the simulation of flash flood in Mentougou of Beijing[J]. Meteorological Monthly, 2021, 47(7): 817-829. ]
[25]	孙小龙, 宋海清, 李平, 等. 基于CLDAS资料的内蒙古干旱监测分析[J]. 气象, 2015, 41(10): 1245-1252.
	[Sun Xiaolong, Song Haiqing, Li Ping. Analysis of drought monitoring in Inner Mongolia based on CLDAS data[J]. Meteorological Monthly, 2015, 41(10): 1245-1252. ]
[26]	杨轩. 降雨侵蚀力模型研究进展[J]. 现代农业科技, 2019(14): 199-202, 206.
	[Yang Xuan. Computation method and application of rainfall erosivity[J]. Modern Agricultural Science and Technology, 2019(14): 199-202, 206. ]
[27]	王万中, 焦菊英, 郝小品, 等. 中国降雨侵蚀力R值的计算与分布(I)[J]. 水土保持学报, 1995, 9(4): 7-18.
	[Wang Wanzhong, Jiao Juying, Hao Xiaopin, et al. Study on rainfall erosivity in China[J]. Journal of Soil and Water Conservation, 1995, 9(4): 7-18. ]
[28]	Xie Y, Yin S, Liu B, et al. Models for estimating daily rainfall erosivity in China[J]. Journal of Hydrology, 2016, 535: 547-558. doi: 10.1016/j.jhydrol.2016.02.020
[29]	张黎明, 林金石, 于东升, 等. 我国南方地区降雨侵蚀力指标R 的建立研究——以江西鹰潭地区为例[J]. 水土保持研究, 2011, 18(5): 1-4.
	[Zhang Liming, Lin Jinshi, Yu Dongsheng. et al. Establishment of index R in rainfall erosivity in south China: Taking the area of Yingtan as an example[J]. Research of Soil and Water Conservation, 2011, 18(5): 1-4. ]
[30]	师春香, 谢正辉. 基于静止气象卫星观测的降水时间降尺度研究[J]. 地理科学进展, 2008, 27(4): 15-22. doi: 10.11820/dlkxjz.2008.04.003
	[Shi Chunxiang, Xie Zhenghui. A time downscaling scheme of precipitation by using geostationary meteorological satellite data[J]. Progress in Geography, 2008, 27(4): 15-22. ] doi: 10.11820/dlkxjz.2008.04.003

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