Graduation Semester and Year
Fall 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Department
Civil Engineering
First Advisor
Yu Zhang
Abstract
Soil moisture is a critical component of the terrestrial water and energy cycles, influencing key hydrological and atmospheric processes such as infiltration, runoff generation, evapotranspiration, and atmospheric boundary layer dynamics. Accurate representation of soil moisture is essential for reliable hydrological modeling, drought monitoring, and seasonal to sub-seasonal (S2S) weather forecasting. Vegetation significantly affects soil moisture dynamics through processes like transpiration, canopy interception, and root water uptake, especially in transitional climates where conditions fluctuate between arid and humid states. However, errors in vegetation phenology representation can lead to substantial biases in soil moisture estimates within land surface models (LSMs) and satellite retrieval algorithms.
This dissertation investigates the roles of vegetation on soil moisture dynamics in transitional climates, emphasizing the importance of improving phenology representation in both LSMs and retrieval algorithms. In the first study, we enhance the Noah-MP LSM by integrating an alternative, more accurate Leaf Area Index (LAI) dataset derived from satellite observations, replacing the default climatological LAI that often exhibits phase errors compared to observed vegetation cycles. The improved LAI strengthens the vertical coupling between surface and root-zone soil moisture, leading to more accurate simulations, particularly in water-limited regions. This enhancement also increases the efficacy of surface soil moisture data assimilation, allowing better propagation of surface information to deeper soil layers.
In the second study, we address limitations in the SMAP Dual-Channel Algorithm (DCA), which relies on climatological Vegetation Optical Depth (VOD) and prescribed surface temperatures, introducing biases in soil moisture estimates. We propose a revised algorithm that integrates real-time VOD derived from MODIS Normalized Difference Vegetation Index (NDVI) data and simultaneously retrieves surface temperature and soil moisture through dynamic optimization. Application of the revised algorithm across the Southwestern United States demonstrates significant improvements in soil moisture retrievals, with reduced root mean square errors and biases. By capturing dynamic vegetation conditions and adjusting surface temperature, the algorithm provides more accurate soil moisture estimates.
In the third study, we evaluate the effects of improving precipitation forcing and vegetation phenology on the Noah-MP model's representation of soil moisture climatology and anomalies over Texas. By integrating bias-corrected high-resolution precipitation data and improved LAI, we assess their individual and combined impacts on drought depiction. Our findings reveal that while improving precipitation inputs has a more substantial effect on soil moisture climatology and drought assessment, enhancing vegetation phenology further refines model outputs. The combined improvements result in the most accurate representation of soil moisture and drought conditions, underscoring the importance of accurate inputs for reliable drought monitoring.
Collectively, this research underscores the critical roles of vegetation phenology and accurate precipitation inputs in soil moisture dynamics within transitional climates. By improving phenology representation in both LSMs and retrieval algorithms and addressing uncertainties in precipitation forcing, we enhance the accuracy of soil moisture estimation. These improvements have significant implications for S2S weather forecasting, as accurate soil moisture and vegetation representations are essential for capturing land–atmosphere interactions that influence boundary layer development, convection initiation, and precipitation patterns. The insights gained support the development of more effective tools and strategies for drought monitoring, water resource management, and weather prediction in the context of climate variability and change.
Keywords
Soil moisture dynamics, Vegetation phenology, Land surface models, Transitional climates, Phenology representation, Leaf area index, Surface soil moisture data assimilation, Vegetation optical depth, SMAP, Surface temperature retrieval, Precipitation forcing, Drought monitoring, Seasonal to sub-seasonal forecasting, Land–atmosphere interactions, Convection initiation, Water resource management, Climate variability and change
Disciplines
Civil Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Hassani, Farhad, "Roles of vegetation on soil moisture dynamics in transitional climates- lessons from improving phenology representation in land surface model and retrieval algorithm" (2024). Civil Engineering Dissertations. 505.
https://mavmatrix.uta.edu/civilengineering_dissertations/505