Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
2 results
Search Results
Item Estimation of Terrestrial Water Storage in the Western United States Using Space-based Gravimetry, Ground-based Sensors, and Model-based Hydrologic Loading(2020) Yin, Gaohong; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Accurate estimation of terrestrial water storage (TWS) is critically important for the global hydrologic cycle and the Earth's climate system. The space-based Gravity Recovery and Climate Experiment (GRACE) mission and land surface models (LSMs) have provided valuable information in monitoring TWS changes. In recent years, geodetic measurements from the ground-based Global Positioning System (GPS) network have been increasingly used in hydrologic studies based on the elastic response of the Earth's surface to mass redistribution. All of these techniques have their own strengths and weaknesses in detecting TWS changes due to their unique uncertainties, error characteristics, and spatio-temporal resolutions. This dissertation investigated the potential of improving our knowledge in TWS changes via merging the information provided by ground-based GPS, GRACE, and LSMs. First, the vertical displacements derived from ground-based GPS, GRACE, and NASA Catchment Land Surface Model (Catchment) were compared to analyze the behavior and error characteristics of each data set. Afterwards, the ground-based GPS observations were merged into Catchment using a data assimilation (DA) framework in order to improve the accuracy of TWS estimates and mitigate hydrologic state uncertainty. To the best of our knowledge, this study is the first attempt to assimilate ground-based GPS observations into an advanced land surface model for the purpose of improving TWS estimates. TWS estimates provided by GPS DA were evaluated against GRACE TWS retrievals. GPS DA performance in estimating TWS constituent components (i.e., snow water equivalent and soil moisture) and hydrologic fluxes (i.e., runoff) were also examined using ground-based in situ measurements. GPS DA yielded encouraging results in terms of improving TWS estimates, especially during drought periods. Additionally, the findings suggest a multi-variate assimilation approach to merge both GRACE and ground-based GPS into the LSMs to further improve modeled TWS and its constituent components should be pursued as a new and novel research project.Item VARIATIONAL DATA ASSIMILATION OF SOIL MOISTURE INFORMATION(2005-04-20) Grunmann, Pablo Javier; Kalnay, Eugenia E; Mitchell, Kenneth E; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research examines the feasibility of using observations of land surface temperatures (in principle available from satellite observations) to initialize soil moisture (which is not available on a continental scale). This problem is important because it is known that wrong soil moisture initial conditions can negatively affect the skill of numerical weather prediction models. Since this problem requires the availability of a good soil model, considerable effort was devoted to the improvement of several aspects of the NCEP Noah land surface model and its numerical properties (reliability, efficiency, updates and differentiability). When tested against the experimental station data at Champaign, IL collected by Dr. Tilden Meyers of NOAA/ARL, where the surface fluxes, precipitation, and surface temperature were available, the Noah model forced with observed downward radiative surface fluxes and near-surface meteorology, including precipitation, was able to reproduce the observations quite well. A method for data assimilation was developed and tested, in a manner similar to 4-dimensional variational assimilation (4D-Var) in the sense of applying the temporal behavior of the observed variable but with a single spatial dimension (land surface models are typically “column models”, as they do not usually compute horizontal derivatives). The results show that it is indeed possible to assimilate land surface temperature and use it to correct soil moisture initial conditions, which may manifest significant errors if, for example, the precipitation forcing the model is significantly biased. This is true, however, only if the surface forcings besides precipitation are essentially correct. When surface forcing come from the North American Land Data Assimilation System (NLDAS) as they would be available for operational use over the US, the results are not satisfactory. This is because the assimilation changes the soil moisture to correct for problems in the simulated land surface temperature that are at least partially due to other sources of errors, such as the surface radiative fluxes. We suggest that in order to succeed in the soil moisture initialization, more (and more accurate) observations are needed in order to constrain the dependence of the observation part of the cost function solely on soil moisture.