Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1002/2017WR021346
Titel (primär) Conditioning a hydrologic model using patterns of remotely sensed land surface temperature
Autor Zink, M.; Mai, J.; Cuntz, M.; Samaniego, L. ORCID logo
Quelle Water Resources Research
Erscheinungsjahr 2018
Department CHS
Band/Volume 54
Heft 4
Seite von 2976
Seite bis 2998
Sprache englisch
UFZ Querschnittsthemen RU5;
Abstract Hydrologic models are usually calibrated using observed river runoff at catchment outlets. Streamflow, however, represents an integral response of the entire catchment and is observed at a few locations worldwide. Parameter estimation based on streamflow has the disadvantage that it does not consider the spatiotemporal variability of hydrologic states and fluxes such as evapotranspiration. Remotely sensed data, in contrast, include these variabilities and are broadly available. In this study, we assess the predictive skill of satellite‐derived land surface temperature (Ts) with respect to river runoff (Q). We developed a bias insensitive pattern‐matching criterion to focus the parameter optimization on spatial patterns of Ts. The proposed method is extensively tested in six distinct large German river basins and cross‐validated in 222 additional basins in Germany. We conclude that land surface temperature calibration outperforms random drawn parameter sets, which could be meaningful for calibrating hydrologic models in ungauged locations. A combined calibration with Q and Ts reduces the root mean squared error in the predicted evapotranspiration by 8% compared to flux tower observations but reduces the NSEs of the streamflow predictions by 6% on average for the six large basins. Our results show that patterns of Ts better constrain model parameters when considered in a calibration next to Q, which finally reduces parametric uncertainty.
dauerhafte UFZ-Verlinkung
Zink, M., Mai, J., Cuntz, M., Samaniego, L. (2018):
Conditioning a hydrologic model using patterns of remotely sensed land surface temperature
Water Resour. Res. 54 (4), 2976 - 2998 10.1002/2017WR021346