A surface water body dataset with daily temporal resolution: Selected examples and application potential of the Global WaterPack

Kurzfassung

Information on the distribution of inland waters bodies and their seasonal variability is important for supporting water and land management and informed decision making. Furthermore, such data can be crucial basis for scientific analyses in the field of regional and global environmental research. Key functions and services of wetlands for example are closely related to the temporal variations of surface water availability and inundation cycles. Over the past years the mapping of temporal water dynamics from earth observation data has received increasing attention. Open data policies as well as the progress in computing power and processing techniques have played a major role for this development. In the past decades there has been several optical instruments available collecting data on global scale with very high temporal resolution, such as MODIS or SPOT-VGT/Proba-V. With the launch of Sentinel-3 and Suomi-NPP such observations at global scale and high temporal resolution has been secured for the near future. Recently, many studies already underlined the relevance of mapping water with high temporal resolution presenting essential results and interesting findings. In our research we are presenting DLRs Global WaterPack, a MODIS-based 250m time series dataset of surface water dynamics with daily temporal resolution. Using examples of lakes and reservoirs from around the Earth, the potential of the Global WaterPack to capture relevant parameters such as inundation frequency and duration, timing of flooding and water retreat, as well as freezing and thawing cycles, is presented. Results are compared with high resolution spatial reference data and in-situ measurements. The application potential of the Global WaterPack time series for monitoring and assessing changes is discussed. Furthermore, we discuss this potential based on selected examples which underline the added value of water surface detection at high temporal resolution in the context of climate and environmental change. Short and long-term dynamics of lakes and reservoirs and the underlying climatic or anthropogenic processes often cannot be determined in detail in regards to time by assessing interrupted time series or multi-temporal watermask snapshot observations. The entire MODIS time series from 06/2002-today is currently being processed and the dataset will be tested as input for global hydrological models. Additionally, quantification of uncertainty is being planned and will be part of future development.