Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Dyer, Jamie L.

Committee Member

Mercer, Andrew E.

Committee Member

Dash, Padmanava

Committee Member

Tian, Wenmeng

Date of Degree

8-9-2022

Document Type

Graduate Thesis - Open Access

Major

Earth and Atmospheric Sciences

Degree Name

Doctor of Philosophy (Ph.D)

College

College of Arts and Sciences

Department

Department of Geosciences

Abstract

Land surfaces have changed as a result of human and natural processes, such as
deforestation, urbanization, desertification and natural disasters like wildfires. Land use and land
cover change impacts local and regional climates through various bio geophysical processes across
many time scales. More realistic representation of land surface parameters within the land surface
models are essential to for climate models to accurately simulate the effects of past, current and
future land surface processes. In this study, we evaluated the sensitivity and accuracy of the
Weather Research and Forecasting (WRF) model though the default MODIS land cover data and
annually updated land cover data over southeast of United States. Findings of this study indicated
that the land surface fluxes, and moisture simulations are more sensitive to the surface
characteristics over the southeast US. Consequently, we evaluated the WRF temperature and
precipitation simulations with more accurate observations of land surface parameters over the
study area. We evaluate the model performance for the default and updated land cover simulations
against observational datasets. Results of the study showed that updating land cover resulted in
substantial variations in surface heat fluxes and moisture balances. Despite updated land use and
land cover data provided more representative land surface characteristics, the WRF simulated 2-

m temperature and precipitation did not improved due to use of updated land cover data. Further,
we conducted machine learning experiments to post-process the Noah-MP land surface model
simulations to determine if post processing the model outputs can improve the land surface
parameters. The results indicate that the Noah-MP simulations using machine learning remarkably
improved simulation accuracy and gradient boosting, and random forest model had smaller mean
error bias values and larger coefficient of determination over the majority of stations. Moreover,
the findings of the current study showed that the accuracy of surface heat flux simulations by
Noah-MP are influenced by land cover and vegetation type.

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