Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Water

Kristina M. McNyset²  Carol J. Volk²  Chris E. Jordan¹ 

[1] Northwest Fisheries Science Center, NOAA Fisheries 2725 Montlake Blvd E., Seattle, WA 98112, USA;South Fork Research, Inc., 44842 SE 145th St. North Bend, WA 98045, USA;

关键词: land surface temperature;    MODIS;    stream temperature models;   

DOI  :  10.3390/w7126660

来源: mdpi

【 摘 要 】

Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r² = 0.95, Root Mean Squared Error (RMSE) = 1.25 °C; subwatershed r² = 0.88, RMSE = 2.02 °C; and basin-wide r² = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r² = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.