期刊
HYDROLOGICAL PROCESSES
卷 27, 期 7, 页码 1033-1045出版社
WILEY
DOI: 10.1002/hyp.9215
关键词
streamflow seasonality; water resources; climate change; uncertainty; South Korea
资金
- Construction Technology Innovation Program (CTIP)
- Ministry of Land, Transportation, and Maritime Affairs (MLTM) of Korean government
- National Research Foundation of Korea (NRF)
- Korea government (MEST) [2011-0030839]
- National Research Foundation of Korea [2011-0030839] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Seasonality in hydrology is closely related to regional water management and planning. There is a strong consensus that global warming will likely increase streamflow seasonality in snow-dominated regions due to decreasing snowfall and earlier snowmelt, resulting in wetter winters and drier summers. However, impacts to seasonality remain unclear in rain-dominated regions with extreme seasonality in streamflow, including South Korea. This study investigated potential changes in seasonal streamflow due to climate change and associated uncertainties based on multi-model projections. Seasonal flow changes were projected using the combination of 13 atmosphereocean general circulation model simulations and three semi-distributed hydrologic models under three different future greenhouse gas emission scenarios for two future periods (2020s and 2080s). Our results show that streamflow seasonality is likely to be aggravated due to increases in wet season flow (July through September) and decreases in dry season flow (October through March). In South Korea, dry season flow supports water supply and ecosystem services, and wet season flow is related to flood risk. Therefore, these potential changes in streamflow seasonality could bring water management challenges to the Korean water resources system, especially decreases in water availability and increases in flood risk. Copyright (c) 2012 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据