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Global Waterpack - open surface water time series for the assessment of climate and human impacts on a global scale

Klein, Igor and Gessner, Ursula and Dietz, Andreas and Hirner, Andreas and Dech, Stefan and Künzer, Claudia (2018) Global Waterpack - open surface water time series for the assessment of climate and human impacts on a global scale. DLR Conference on Climate Change 2018, 17-19. Apr. 2018, Cologne, Germany.

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Official URL: https://ccc2018.besl-eventservice.de/front/index.php

Abstract

The seasonal and long-term dynamics of waterbodies, particularly in (semi-)arid and cold regions of the world are strongly dependent on precipitation amounts and temperatures respectively. Even though human influences can also strongly affect the dynamics of such waterbodies, seasonal variabilities and shifts in rainfall or temperature patterns are a key driver of desiccation/filling and freezing/thawing cycles of many waterbodies around the world. Thus, the temporal dynamics of water body extents and seasonal freezing/thawing cycles of lake ice can serve as an indicator for climatic shifts and variability and can reveal respective climate impacts on the global water availability. The Global WaterPack (GWP) time series which is being processed at the German Remote Sensing Data Center (DFD) of DLR quantifies the daily dynamics of global inland waterbodies at a daily temporal resolution. The 15-year time series is based on daily optical MODIS satellite data at a spatial resolution of 250m. The dataset and its generation have been described in detail by Klein et al. (2017). The daily temporal resolution and global coverage of the GWP has high potential to reveal insights into the impact of climate change, meteorological variability and human activities on surface water dynamics. The dataset shows not only the seasonal or long-term desiccation and filling of waterbodies but also freezing and thawing cycles of lakes. The study will present selected examples of waterbodies in (semi-arid) regions with strong seasonal and multi-annual areal dynamics, and will show long- and short-term relationships to precipitation variability and shifts. In addition, we will show the temporal behavior of selected waterbodies with seasonal freezing and thawing cycles. It will be illustrated how the spatio-temporal patterns of freezing/thawing can be quantified using the Global WaterPack time series, and how observed patterns are related to temperatures. The results emphasize the added value of the high temporal (daily) resolution of the GWP which is not available in other waterbody products. This high temporal resolution is of relevance particularly in the context of impact analyses of climate change and variability where not only obvious but also subtle shifts in timing and duration of water availability are to be considered.

Item URL in elib:https://elib.dlr.de/119945/
Document Type:Conference or Workshop Item (Poster)
Title:Global Waterpack - open surface water time series for the assessment of climate and human impacts on a global scale
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Klein, Igorigor.klein (at) dlr.deUNSPECIFIED
Gessner, Ursulaursula.gessner (at) dlr.deUNSPECIFIED
Dietz, AndreasAndreas.Dietz (at) dlr.deUNSPECIFIED
Hirner, AndreasAndreas.Hirner (at) dlr.deUNSPECIFIED
Dech, Stefanstefan.dech (at) dlr.deUNSPECIFIED
Künzer, ClaudiaClaudia.Kuenzer (at) dlr.deUNSPECIFIED
Date:April 2018
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:p. 1
Status:Published
Keywords:water body dynamics, environmental change, high temporal resolution
Event Title:DLR Conference on Climate Change 2018
Event Location:Cologne, Germany
Event Type:international Conference
Event Dates:17-19. Apr. 2018
Organizer:DLR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Klein, Igor
Deposited On:28 May 2018 09:09
Last Modified:31 Jul 2019 20:17

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