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Estimating snow water resources from space: a passive microwave remote sensing data assimilation study in the Sierra Nevada, USA

Li, Dongyue
http://rave.ohiolink.edu/etdc/view?acc_num=osu1468028060

Abstract Details

2016, Doctor of Philosophy, Ohio State University, Geodetic Science and Surveying.
Improving the estimation of snow water equivalent (SWE) in the Sierra Nevada is critical for the water resources management in California. In this study, we carried out an experiment to estimate SWE in the Upper Kern Basin, Sierra Nevada, by assimilating AMSR-E observed brightness temperatures (Tb) into a coupled hydrology and radiative transfer model using an ensemble Kalman batch reanalysis. The data assimilation framework merges the complementary SWE information from modeling and observations to improve SWE estimates. The novelty of this assimilation study is that both the modeling and the radiance data processing were specifically improved to provide more information about SWE. With the enhanced SWE signals in both simulations and observations, the batch reanalysis stands a better chance of successfully improving the SWE estimates. The modeling was at a very high resolution (90m) and spanned a range of mountain environmental factors to better characterize the effects of the mountain environment on snow distribution and radiance emission. We have developed a dynamic snow grain size module to improve the radiance modeling during the intense snowfall events. The AMSR-E 37GHz V-pol observed Tb was processed at its native footprint resolution at ~100 square km. In the batch assimilation, the model predicted the prior SWE and Tb; the prior estimate of an entire year was then updated by the dry-season observations at one time. One advantage of this is that the prior SWE of a certain period is updated using the observations both before and after this period, which takes advantage of the temporally continuous signal of the seasonal snow accumulation in the observations. We found the posterior SWE estimates showed improved accuracy and robustness. During the study period of 2003 to 2008, at point-scale, the average bias of the six-year April 1st SWE was reduced from -0.17 m to -0.02m, the average temporal SWE RMSE of the snow accumulation season decreased by 51.2%. The basin-scale results showed that the April 1st SWE bias reduced from -0.17m to -0.11m, and the temporal SWE RMSE of the accumulation season decreased by 23.6%.
Michael Durand (Advisor)
Ian Howat (Committee Member)
C.K. Shum (Committee Member)
202 p.
Earth; Environmental Engineering; Hydrology; Remote Sensing
Data assimilation, remote sensing, snow hydrology

Recommended Citations

Citations

  • Li, D. (2016). Estimating snow water resources from space: a passive microwave remote sensing data assimilation study in the Sierra Nevada, USA [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1468028060

    APA Style (7th edition)

  • Li, Dongyue. Estimating snow water resources from space: a passive microwave remote sensing data assimilation study in the Sierra Nevada, USA. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1468028060.

    MLA Style (8th edition)

  • Li, Dongyue. "Estimating snow water resources from space: a passive microwave remote sensing data assimilation study in the Sierra Nevada, USA." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1468028060

    Chicago Manual of Style (17th edition)

osu1468028060
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This open access ETD is published by The Ohio State University and OhioLINK.