气候变化领域集成服务门户(CCPortal): Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability

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DOI	10.1016/j.rse.2020.112207
	Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability
	Klein I.; Mayr S.; Gessner U.; Hirner A.; Kuenzer C.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Remote sensing time series offer the possibility to monitor surface water at dense temporal intervals. Open data archives as well as developments in cloud computing are the main drivers towards and increased availability of raw data allowing for the extraction of detailed information on water bodies such as natural lakes and artificial reservoirs. At the same time, changes in precipitation patterns, increasing frequency and intensity of droughts, intensification of human water use, and regulatory upstream measurements affect water resources around the world today. With regard to water availability and supply-demand balance, artificial water reservoirs have become most important elements e.g. for hydropower, irrigated agriculture, flood control, as well as for domestic and industrial water use. Nevertheless, publicly accessible information on reservoirs is still not harmonized and available at global scale. Therefore, it is more essential than ever to acquire detailed knowledge about spatio-temporal variability of water resources - especially reservoirs - and the drivers of their dynamics. In this study, we analyze daily water extent time series of the 1267 largest reservoirs worldwide based on the existing DLR-DFD Global WaterPack product derived from MODIS data (Klein et al., 2017). The study aims to present an experimental way of spatio-temporal variability analysis by implementing the TIMESAT software which is usually used for vegetation analyses. In our experimental approach we derive information on the timing when the open surface water areas of reservoirs increase and decrease by identifying start date, end date and duration of such reservoir cycles as well as timing of maximum surface water extent (hydro-metrics). For four selected reservoirs, these hydro-metrics derived from surface water extent are compared with hydro-metrics derived from in-situ water level measurements or altimetry datasets and are discussed in more detail. Based on the presented examples we demonstrate the potential of high temporal resolution surface water extent data and spatio-temporal variability analyses with TIMESAT for future applications supporting the understanding of reservoir variability as a result of water management and hydroclimatic variability. © 2020 Elsevier Inc.
英文关键词	Daily temporal resolution; Intra-annual variability; MODIS; Reservoirs seasonality; Surface water area; TIMESAT
语种	英语
scopus关键词	Agricultural robots; Flood control; Hydroelectric power; Hydroelectric power plants; Open Data; Radiometers; Remote sensing; Reservoir management; Surface waters; Time series; Water levels; Water management; Artificial reservoirs; Experimental approaches; High temporal resolution; Hydroclimatic variability; Irrigated agriculture; Precipitation patterns; Spatiotemporal variability; Supply-demand balances; Reservoirs (water); flood control; hydroelectric power plant; MODIS; satellite altimetry; seasonal variation; seasonality; software; temporal variation; time series analysis; water availability; Varanidae
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179014
作者单位	German Aerospace Center, German Remote Sensing Data Center, Oberpfaffenhofen, Germany; Institute of Geology and Geography, Chair of Remote Sensing, University of Wuerzburg, Germany
推荐引用方式 GB/T 7714	Klein I.,Mayr S.,Gessner U.,et al. Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability[J],2021,253.
APA	Klein I.,Mayr S.,Gessner U.,Hirner A.,&Kuenzer C..(2021).Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability.Remote Sensing of Environment,253.
MLA	Klein I.,et al."Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability".Remote Sensing of Environment 253(2021).