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Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability

Klein, Igor and Mayr, Stefan and Gessner, Ursula and Hirner, Andreas and Kuenzer, Claudia (2021) Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability. Remote Sensing of Environment, 253. Elsevier. doi: 10.1016/j.rse.2020.112207. ISSN 0034-4257.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0034425720305800?via%3Dihub


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.

Item URL in elib:https://elib.dlr.de/138513/
Document Type:Article
Title:Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Klein, IgorIgor.Klein (at) dlr.deUNSPECIFIED
Mayr, Stefanstefan.mayr (at) dlr.deUNSPECIFIED
Gessner, Ursulaursula.gessner (at) dlr.deUNSPECIFIED
Hirner, AndreasAndreas.Hirner (at) dlr.deUNSPECIFIED
Kuenzer, Claudiaclaudia.kuenzer (at) dlr.deUNSPECIFIED
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.rse.2020.112207
Keywords:Reservoirs seasonality; Daily temporal resolution; MODIS; TIMESAT; Surface water area; Intra-annual variability
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Klein, Igor
Deposited On:08 Dec 2020 16:10
Last Modified:08 Dec 2020 16:10

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