气候变化领域集成服务门户(CCPortal): 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

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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/157035
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).