Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature. Issue 4 (19th April 2018)

• Record Type: Journal Article
• Authors: Zink, Matthias; Mai, Juliane; Cuntz, Matthias; Samaniego, Luis
• Is Part Of: Water resources research. Volume 54:Issue 4(2018)
• Page Start: 2976
• Page End: 2998
• Publication Date: 2018-04-19
• Subjects: mesoscale hydrologic model mHM -- satellite‐derived land surface temperature -- parameter estimation uncertainty -- pattern matching -- prediction in ungauged basins; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2017WR021346
• Languages: English
• ISSNs: 0043-1397

Abstract

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. Key Points: Parameter estimation framework using spatially distributed land surface temperature Assessment of performance of streamflow by calibration with land surface temperature only Improved parameter constraint with calibration using streamflow and land surface temperature