Estimating annual runoff in response to forest change: A statistical method based on random forest

doi:10.1016/j.jhydrol.2020.125168

	Estimating annual runoff in response to forest change: A statistical method based on random forest
	Li, Ming 1; Zhang, Yongqiang 2; Wallace, Jeremy 1; Campbell, Eddy 1
刊名	JOURNAL OF HYDROLOGY
	2020-10-01
卷号	589 页码:14
关键词	Annual runoff Forest thinning Predictive model Machine learning Remote sensing Forest index
ISSN号	0022-1694
DOI	10.1016/j.jhydrol.2020.125168
通讯作者	Li, Ming(Ming.Li@csiro.au)
英文摘要	Population growth and climate change have put pressure on policy makers in southwest Western Australia to increase water supply to urban areas. A potential contribution to solving this problem is thinning of forested catchments to increase runoff. This study uses a machine learning approach, random forest, to relate catchment annual runoff to a range of predictors including climate variables and catchment attributes, and to estimate runoff increases from forest thinning. This approach identifies important predictors and enables prediction. The most important predictor is 'ForestIndex' calculated from calibrated satellite imagery and providing a consistent surrogate measure of forest density. This approach estimates annual runoff and carefully assesses potential model predictability by three modes of cross-validation. Our approach leads to more accurate annual runoff predictions than linear regression and the Fu's model (e.g. reducing RMSE by 41% and 63% respectively). We provide an example to predict the change in annual runoff in response to forest reduction under certain rainfall scenarios. The predicted runoff increase varies greatly amongst catchments from zero to 60 mm per 5 unit ForestIndex reduction.
资助项目	Western Australia Water Foundation ; CSIRO Water for a Healthy Country Flagship ; CAS Talents Program ; National Natural Science Foundation of China[41971032]
WOS关键词	WATER YIELD ; HYDROLOGICAL RESPONSE ; VEGETATION CHANGES ; JARRAH FOREST ; CATCHMENT ; STREAMFLOW ; BALANCE ; EVAPOTRANSPIRATION ; IMPACT ; ISSUES
WOS研究方向	Engineering ; Geology ; Water Resources
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000568830400035
资助机构	Western Australia Water Foundation ; CSIRO Water for a Healthy Country Flagship ; CAS Talents Program ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156933]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Ming
作者单位	1.CSIRO Data61, 26 Dick Perry Ave, Kensington, NSW 6151, Australia 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Li, Ming,Zhang, Yongqiang,Wallace, Jeremy,et al. Estimating annual runoff in response to forest change: A statistical method based on random forest[J]. JOURNAL OF HYDROLOGY,2020,589:14.
APA	Li, Ming,Zhang, Yongqiang,Wallace, Jeremy,&Campbell, Eddy.(2020).Estimating annual runoff in response to forest change: A statistical method based on random forest.JOURNAL OF HYDROLOGY,589,14.
MLA	Li, Ming,et al."Estimating annual runoff in response to forest change: A statistical method based on random forest".JOURNAL OF HYDROLOGY 589(2020):14.