Estimating annual runoff in response to forest change: A statistical method based on random forest
文献类型：期刊论文
| 作者 | Li, Ming1; Zhang, Yongqiang2; Wallace, Jeremy1; Campbell, Eddy1 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 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. |
| WOS关键词 | WATER YIELD ; HYDROLOGICAL RESPONSE ; VEGETATION CHANGES ; JARRAH FOREST ; CATCHMENT ; STREAMFLOW ; BALANCE ; EVAPOTRANSPIRATION ; IMPACT ; ISSUES |
| 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研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000568830400035 |
| 出版者 | ELSEVIER |
| 资助机构 | Western Australia Water Foundation ; CSIRO Water for a Healthy Country Flagship ; CAS Talents Program ; National Natural Science Foundation of China |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。