Enhanced Soil Moisture and Streamflow Estimation in Ungauged or Data-Scarce Watersheds

Watershed models are important tools for quantifying hydrologic processes and evaluating water management practices. These models simplify complex systems through parametrization and assumptions, particularly in semi-distributed frameworks like Soil and Water Assessment Tool (SWAT). Clear conceptualization is critical to realize adequate representation of internal hydrologic processes, particularly in saturated excess runoff dominated watersheds where the spatio-temporal dynamics of saturated areas influence both the distributed (soil moisture state, runoff generation, or pollutant export) and integrated (streamflow) watershed responses. While streamflow is commonly used to constrain model parameters due to its availability and integrative nature, the issue of equifinality, where multiple parameter sets yield similar streamflow, but divergent internal process estimates, can lead to significant uncertainty. This dissertation addresses these challenges by introducing new and improved techniques for representing field scale soil moisture patterns and using satellite soil moisture data for model calibration in ungauged and data-scarce watersheds, especially for watersheds where variable source area runoff mechanism dominate. Chapter 2 proposes the topographic index (TI) as a tool to represent the spatial soil moisture patterns. Using unsupervised machine learning on in-situ soil moisture data from a 4.2 ha field, three distinct soil moisture groups were identified. TI values were classified into three groups using various approaches (equal-interval, equal-area, k-means, Fisher) and digital elevation model (DEM) sources (United States Geological Survey (USGS) and drone-based LiDAR) generating topographic index classes (TIC). Performance was evaluated using Spearman's correlation and misclassification rates. Results showed that low-resolution LiDAR and USGS DEMs outperformed high-resolution LiDAR DEMs, with equal-interval classification yielding the best performance. The result showed resampling to a lower resolution improved the performance of LiDAR DEMs, while TICs derived from publicly available USGS DEMs outperformed those from LiDAR DEMs. Among classification approaches equal-interval provided the highest performance. Overall, the result showed a three classes TIC can represent field scale soil moisture pattern and the effect of DEM type, resolution, and classification approach can be substantial. Chapter 3 develops a variable source area (VSA) SWAT (SWAT-VSA) model for a 14 km² watershed, incorporating the three-class TIC to improve hydrologic response unit (HRU) definition. Downscaled and bias-corrected satellite soil moisture data, along with streamflow data, were used for single and multi-objective calibration. Calibration using soil moisture data improved field-scale moisture estimation, while multi-objective calibration enhanced overall model performance. The three-class TIC structure reduced computational cost while effectively representing VSA dynamics. Chapter 4 demonstrated the utility of downscaled and bias corrected satellite soil moisture for streamflow estimation in ungauged watersheds. Three watersheds in the northeastern US with flow monitoring stations were analyzed. Soil moisture-calibrated models, using root mean squared error (RMSE) and SPatial EFficiency (SPAEF) metrics, were compared to streamflow-calibrated and regionalization-based models. While streamflow data and regionalization technique performed better for streamflow estimation, soil moisture calibrated models incorporating spatial metrics in calibration yielded comparable streamflow predictions. Satellite-based calibration offers an objective alternative to traditional regionalization, especially when properly downscaled, bias-corrected, and scaled. This research demonstrates the potential of satellite soil moisture data to improve hydrologic model performance and reduce uncertainty in data-scarce environments, offering a scalable and objective approach to watershed modeling.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Biological Systems Engineering

Department dc:contributor.department

Biological Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Asfaw, Binyam Workeye

Chair dc:contributor.committeechair

• Easton, Zachary

Committee members dc:contributor.committeemember

• Czuba, Jonathan A.
• Hession, William Cully
• Rippy, Megan A.

• Topographic Indices; Satellite Soil Moisture; Downscaling; Bias Correction; K-means Clustering; Calibration; SWAT model; Parameters; Streamflow; Ungauged

Statement dc:rights

• Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:45315

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/140545