Analyse der raumzeitlichen Dynamik der Wasservolumina in großen Feuchtgebieten und Seen durch die Kombination von Fernerkundung mit makroskaliger hydrologischer Modellierung
Zusammenfassung der Projektergebnisse
The overall goal of the project WLDYN was to quantify the water storage dynamics of large lakes and wetlands around the globe by combining satellite information on water level (elevation) and open water area (extent) with global-scale hydrological modelling of the stateof-the-art model WaterGAP Global Hydrology Model (WGHM). An improved retracking algorithm lead to an update of the DAHITI data base of inland water levels from altimeter data (http://dahiti.dgfi.tum.de/en/). Surface water area was estimated mainly from optical Landsat data, and in case of the Pantanal wetland from ALOS-PALSAR radar data. The area estimates were compared to the Global Inundation Extent from Multi-Satellites (GIEMS) data set. For six large lakes and wetlands that were focus of WLDYN, water level (elevation) – area – storage relationships were derived. Consistent time series of elevation, area and storage for the four lakes under consideration were published at PANGAEA. For the Pantanal wetland, it was found that a strong hysteresis effect occurs between water level and area (and thus, storage). The representation of wetland extent within WGHM by the Global Lakes and Wetlands Database (GLWD) is spatially more contiguous in contrast to remote sensing, apparently due to cartographic generalization. Wetland areas represented in both data sources typically include those around the big river flow lines. As a result, no feasible remote sensing product for comparing the “true” extent of wetlands with GLWD wetland extent was found. Representation of storage dynamics by standard WGHM could be improved for Lake Constance using slight adjustment of parameters. The representation of reservoir storage dynamics was improved by including the specific year of reservoir operation start. For additional 74 large lakes (by type: 46 lakes, 21 reservoirs and 7 regulated lakes), an in-depth analysis using various remote sensing sources for water level and area was made in order to estimate the parameters for the WGHM area-storage function, namely the exponent p and the active storage depth. In 13 cases, the active storage depth was changed from 5 to 10 m, including Lake Turkana (p = 3.32) and Lake Tonle Sap (p = 2.2). We tried to derive functions to estimate both parameters for all large lakes from physiogeographic characteristics of the lakes. However, a regression analysis of p with mean topographic slope around the lakes, even if distinguishing size or type did not yield a general dependency. Large area changes related to water level (elevation) changes only occur in case of relatively flat terrains, with exponent p values below the standard 3.32, and mostly an area between 1000 – 5000 km2. On the other hand, also for many lakes in flat terrains no area variation was observed, possibly due to the short available observational remote sensing record for lake areas. It was not possible to determine a globally valid relationship between topographic slope around lakes and the exponent p. WGHM areastorage functions could be compared to Hydroweb water level – area – storage functions for 65 lakes of restricted usability because of unknown water level – area functions. A flexible calibration application has been developed for WGHM using the state-ofthe-art multi-objective evolutionary algorithm Borg-MOEA and a Pareto-based optimization scheme. The algorithms allow calibration against large-scale GRACE gravity–based Total Water Storage Variations (TWSV), but also to include contributions of individual water compartments, such as Evapotranspiration (ET), and Surface Water Storage (SWS). SWS seemed to be critical for model calibration, i.e., a reasonable fit for SWS was achieved only when SWS was explicitly used in the calibration. The triplet of ET, SWS and TWSV produced the best solutions among all triplets. A considerable improvement of the simulation results of the calibrated WGHM model was obtained when all four observables (i.e. including streamflow Q) were used to constrain the model.
Projektbezogene Publikationen (Auswahl)
- (2015): DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry.- Hydrology and Earth System Sciences 19 (10): 4345-4364
Schwatke C., Dettmering D., Bosch W., Seitz F.
(Siehe online unter https://doi.org/10.5194/hess-19-4345-2015) - (2015): Potential of SARAL/AltiKa for inland water applications.- Marine Geodesy 38 (Supplement 1): 626-643
Schwatke C., Dettmering D., Boergens E., Bosch W.
(Siehe online unter https://doi.org/10.1080/01490419.2015.1008710) - (2015): Remote sensing of storage fluctuations of poorly gauged reservoirs and state space model (SSM)-based estimation. Remote Sensing 7(12): 17113-17134
Singh A., Kumar U., Seitz F.
(Siehe online unter https://doi.org/10.3390/rs71215872) - (2016): Lake level estimation based on CryoSat-2 SAR altimetry and multi-looked waveform classification.- Remote Sensing 8 (11), 885
Göttl F., Dettmering D., Müller F.L., Schwatke C.
(Siehe online unter https://doi.org/10.3390/rs8110885) - (2016): Potential of ENVISAT radar altimetry for water level monitoring in the Pantanal wetland.- Remote Sensing 8 (7), 596
Dettmering D., Schwatke C., Boergens E., Seitz F.
(Siehe online unter https://doi.org/10.3390/rs8070596) - (2016): Satellite altimetry and SAR remote sensing for monitoring inundation in the Pantanal wetland. In: Ouwehand L. (Ed.) Proceedings of the Living Planet Symposium 2016, Prague, Czech Republic, ESA SP-740
Dettmering D., Strehl F., Schwatke C., Seitz F.
- (2016): Treating the hooking effect in satellite altimetry data: a case study along the Mekong River and its tributaries.- Remote Sensing 8 (2), 91
Boergens E., Dettmering D., Schwatke C., Seitz F.
(Siehe online unter https://doi.org/10.3390/rs8020091) - (2017): Fifteen Years (1993–2007) of Surface Freshwater Storage Variability in the Ganges- Brahmaputra River Basin Using Multi-Satellite Observations.- Water 9 (4), 245
Salameh, E., Frappart, F., Papa, F., Güntner, A., Venugopal, V., Getirana, A., Prigent, C., Aires, F., Labat, D., Laignel, B.
(Siehe online unter https://doi.org/10.3390/w9040245) - (2017): River Levels Derived with CryoSat-2 SAR Data Classification-A Case Study in the Mekong River Basin.- Remote Sensing 9 (12)
Boergens E., Nielsen K., Andersen O., Dettmering D., Seitz F.
(Siehe online unter https://doi.org/10.3390/rs9121238)
