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ASSESSMENTS OF MULTISCALE PRECIPITATION DATA FUSION AND SOIL MOISTURE DATA ASSIMILATION AND THEIR ROLES IN HYDROLOGICAL FORECASTS

Wang, Shugong (2012) ASSESSMENTS OF MULTISCALE PRECIPITATION DATA FUSION AND SOIL MOISTURE DATA ASSIMILATION AND THEIR ROLES IN HYDROLOGICAL FORECASTS. Doctoral Dissertation, University of Pittsburgh.

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    Abstract

    Precipitation is the most important input for hydrological simulations and soil moisture contents (SMCs) are the most important state variables of hydrological system. We can improve hydrological simulations by improving the quality of precipitation data and assimilating satellite-measured SMC data into land surface simulation. Multiscale data fusion is an effective approach to derive precipitation data due to the multiscale characteristics of precipitation measurements. Multiscale data assimilation is the exact approach to assimilate satellite-measured SMC data into land surface simulations when measurements and model simulations are not at the same spatial resolution. To date, no systematic assessments of these approaches have been conducted in hydrological simulations. For the purpose of improving hydrological forecast, this study assesses influences of precipitation data fusion and soil moisture data assimilation on the simulations of streamflow, SMCs and evapotranspiration over 14 watersheds selected from the Ohio River Basin. As the technical basis of this study, a large-scale flow routing scheme and a parameter calibration scheme with multiple precipitation inputs are developed for Noah LSM. A multiscale data fusion algorithm, namely Multiscale Kalman Smoother (MKS) based framework, which plays an important role in multiscale precipitation data fusion and multiscale soil moisture data assimilation, is assessed in a large experimental site with 2246 precipitation events in 2003. Three precipitation data products are derived by fusing NLDAS-2 precipitation data product and NEXRAD MPE precipitation data product with the MKS-based framework. For the assessment over the 14 watersheds in three individual years, essential improvements of hydrological simulation have been found for a half number of cases. Findings of this assessment show that precipitation data fusion is a statistically effective approach to improve hydrological simulations. To assess the influences of soil moisture data assimilation on hydrological simulation, AMSR-E SMC data are assimilated into land surface simulation by Noah LSM. Results show that soil moisture data assimilation has not improved hydrological simulations for most of cases because AMSR-E data underestimate SMC compared with model simulations. However, for those cases in which precipitation data overestimate real precipitation, the soil moisture data assimilation has been proved as an effective approach to improve hydrological simulations.


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    Item Type: University of Pittsburgh ETD
    Creators/Authors:
    CreatorsEmailORCID
    Wang, Shugongwangsgcn@gmail.com
    ETD Committee:
    ETD Committee TypeCommittee MemberEmailORCID
    Committee ChairLiang, Xuxuliang@pitt.edu
    Committee MemberBain, Daniel J.dbain@pitt.edu
    Committee MemberAbad, Jorge D.jabad@pitt.edu
    Committee MemberMao, Zhi-Hongzhm4@pitt.edu
    Title: ASSESSMENTS OF MULTISCALE PRECIPITATION DATA FUSION AND SOIL MOISTURE DATA ASSIMILATION AND THEIR ROLES IN HYDROLOGICAL FORECASTS
    Status: Published
    Abstract: Precipitation is the most important input for hydrological simulations and soil moisture contents (SMCs) are the most important state variables of hydrological system. We can improve hydrological simulations by improving the quality of precipitation data and assimilating satellite-measured SMC data into land surface simulation. Multiscale data fusion is an effective approach to derive precipitation data due to the multiscale characteristics of precipitation measurements. Multiscale data assimilation is the exact approach to assimilate satellite-measured SMC data into land surface simulations when measurements and model simulations are not at the same spatial resolution. To date, no systematic assessments of these approaches have been conducted in hydrological simulations. For the purpose of improving hydrological forecast, this study assesses influences of precipitation data fusion and soil moisture data assimilation on the simulations of streamflow, SMCs and evapotranspiration over 14 watersheds selected from the Ohio River Basin. As the technical basis of this study, a large-scale flow routing scheme and a parameter calibration scheme with multiple precipitation inputs are developed for Noah LSM. A multiscale data fusion algorithm, namely Multiscale Kalman Smoother (MKS) based framework, which plays an important role in multiscale precipitation data fusion and multiscale soil moisture data assimilation, is assessed in a large experimental site with 2246 precipitation events in 2003. Three precipitation data products are derived by fusing NLDAS-2 precipitation data product and NEXRAD MPE precipitation data product with the MKS-based framework. For the assessment over the 14 watersheds in three individual years, essential improvements of hydrological simulation have been found for a half number of cases. Findings of this assessment show that precipitation data fusion is a statistically effective approach to improve hydrological simulations. To assess the influences of soil moisture data assimilation on hydrological simulation, AMSR-E SMC data are assimilated into land surface simulation by Noah LSM. Results show that soil moisture data assimilation has not improved hydrological simulations for most of cases because AMSR-E data underestimate SMC compared with model simulations. However, for those cases in which precipitation data overestimate real precipitation, the soil moisture data assimilation has been proved as an effective approach to improve hydrological simulations.
    Date: 02 February 2012
    Date Type: Publication
    Defense Date: 05 August 2011
    Approval Date: 02 February 2012
    Submission Date: 11 November 2011
    Release Date: 02 February 2012
    Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
    Patent pending: No
    Number of Pages: 324
    Institution: University of Pittsburgh
    Thesis Type: Doctoral Dissertation
    Refereed: Yes
    Degree: PhD - Doctor of Philosophy
    Uncontrolled Keywords: Assessments, multiscale, precipitation data fusion, soil moisture data assimilation, hydrological forecast
    Schools and Programs: Swanson School of Engineering > Civil and Environmental Engineering
    Date Deposited: 02 Feb 2012 11:25
    Last Modified: 16 Jul 2014 17:03

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