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DOI | 10.2166/wcc.2018.130 |
Surface runoff response to climate change based on artificial neural network (ANN) models: A case study with Zagunao catchment in Upper Minjiang River; Southwest China | |
Lin Y.; Wen H.; Liu S. | |
发表日期 | 2019 |
ISSN | 20402244 |
起始页码 | 158 |
结束页码 | 166 |
卷号 | 10期号:1 |
英文摘要 | Climate change and its hydrological consequences are of great concern for water resources managers in the context of global change. This is especially true for Upper Minjiang River (UMR) basin, where surface runoff was reported to decrease following forest harvesting, as this unusual forest-water relationship is perhaps attributed to climate change. To quantify the hydrological impacts of climate change and to better understand the forest-water relationship, an artificial neural network (ANN)- based precipitation-runoff model was applied to Zagunao catchment, one of the typical catchments in UMR basin, by a climate scenario-based simulation approach. Two variables, seasonality and CTsm (cumulative temperature for snow melting), were devised to reflect the different flow generation mechanisms of Zagunao catchment in different seasons (rainfall-induced versus snow meltingoriented). It was found that the ANN model simulated precipitation-runoff transformation very well (R2 1/4 0.962). Results showed runoff of Zagunao catchment would increase with the increase in precipitation as well as temperature and such a response was season dependent. Zagunao catchment was more sensitive to temperature rise in the non-growing season but more sensitive to precipitation change in the growing season. Snow melting-oriented runoff reduction due to climate change is perhaps responsible for the unusual forest-water relationship in UMR basin. © 2019 The Authors. |
英文关键词 | Artificial neural networks (ANN); Climate change; Land-use and land-cover change; Upper Minjiang river |
语种 | 英语 |
scopus关键词 | Catchments; Climate models; Forestry; Land use; Melting; Neural networks; Rivers; Runoff; Snow; Artificial neural network models; Climate scenarios; Forest harvesting; Generation mechanism; Hydrological impacts; Land use and land cover change; Minjiang River; Precipitation change; Climate change; artificial neural network; climate change; land cover; land use change; runoff; water management; water resource; China; Min River [Sichuan]; Sichuan; Zagunao Basin |
来源期刊 | Journal of Water and Climate Change |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/148010 |
作者单位 | National Environmental Monitoring Center, State Oceanic Administration, Dalian, 116023, China; College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China; Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, 100091, China |
推荐引用方式 GB/T 7714 | Lin Y.,Wen H.,Liu S.. Surface runoff response to climate change based on artificial neural network (ANN) models: A case study with Zagunao catchment in Upper Minjiang River; Southwest China[J],2019,10(1). |
APA | Lin Y.,Wen H.,&Liu S..(2019).Surface runoff response to climate change based on artificial neural network (ANN) models: A case study with Zagunao catchment in Upper Minjiang River; Southwest China.Journal of Water and Climate Change,10(1). |
MLA | Lin Y.,et al."Surface runoff response to climate change based on artificial neural network (ANN) models: A case study with Zagunao catchment in Upper Minjiang River; Southwest China".Journal of Water and Climate Change 10.1(2019). |
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