%A Shugong Wang
%T ASSESSMENTS OF MULTISCALE PRECIPITATION DATA FUSION AND SOIL MOISTURE DATA ASSIMILATION AND THEIR ROLES IN HYDROLOGICAL FORECASTS
%X 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.
%D 2012
%K Assessments, multiscale, precipitation data fusion, soil moisture data assimilation, hydrological forecast
%I University of Pittsburgh
%L pittir10467