Date of Defense
14-11-2024 7:30 PM
Location
F1-1117
Document Type
Thesis Defense
Degree Name
Master of Science in Water Resources
College
College of Engineering
Department
Civil and Environmental Engineering
Abstract
Land use and land-cover (LULC) changes have profound impacts on hydrological responses, influencing both runoff dynamics and flood risks. This study focuses on two contrasting environments undergoing rapid LULC transformations: the arid Wadi Ham watershed in the UAE and the tropical Imphal watershed in India. The study addresses the need for accurate LULC data in hydrological modeling, particularly in climate-sensitive regions where changes in land use are accelerating. The primary objective of this research was to assess the impact of LULC changes on the hydrological behavior of the Wadi Ham and Imphal watersheds. It aimed to elucidate how these changes affect runoff and flood risks in arid and tropical environments through hydrological modeling. A region-specific LULC classification was conducted using high-resolution PlanetScope and Sentinel-2 data, which provide unique spatial, spectral, and temporal resolutions. Two different classification algorithms, the Maximum Likelihood Classifier and the Random Forest Classifier were employed. A change detection analysis was carried out using multi-temporal satellite data to identify LULC changes, and the GSSHA (Gridded Surface Subsurface Hydrological Analysis) model was used to simulate the hydrological impacts of these changes on runoff and flood patterns in both watersheds. The model of the tropical Imphal Watershed was further calibrated using flood reports to improve its accuracy. Results show that LULC changes significantly influence hydrological responses in both watersheds. In the arid watershed, Wadi Ham, LULC changes intensified runoff, whereas in the tropical Imphal watershed, the integration of LULC data and model calibration led to more precise flood risk assessments. Despite these adjustments, relative changes in hydrological behavior remained consistent, even when model validation and calibration were conducted. This study demonstrated the critical role of high-resolution LULC data in improving the accuracy of hydrological models, particularly in regions with limited ground truth data. PlanetScope imagery combined with the Random Forest classifier yielded the most accurate and realistic LULC maps tailored to each region’s unique environmental context. This research offered a methodology for assessing the hydrological response from LULC changes for regions where ground truth data is scarce or unavailable.
Included in
UTILITY OF NEW REMOTE SENSING DATA IN EVALUATING THE HYDROLOGICAL RESPONSE TO LAND USE AND LAND COVER CHANGES
F1-1117
Land use and land-cover (LULC) changes have profound impacts on hydrological responses, influencing both runoff dynamics and flood risks. This study focuses on two contrasting environments undergoing rapid LULC transformations: the arid Wadi Ham watershed in the UAE and the tropical Imphal watershed in India. The study addresses the need for accurate LULC data in hydrological modeling, particularly in climate-sensitive regions where changes in land use are accelerating. The primary objective of this research was to assess the impact of LULC changes on the hydrological behavior of the Wadi Ham and Imphal watersheds. It aimed to elucidate how these changes affect runoff and flood risks in arid and tropical environments through hydrological modeling. A region-specific LULC classification was conducted using high-resolution PlanetScope and Sentinel-2 data, which provide unique spatial, spectral, and temporal resolutions. Two different classification algorithms, the Maximum Likelihood Classifier and the Random Forest Classifier were employed. A change detection analysis was carried out using multi-temporal satellite data to identify LULC changes, and the GSSHA (Gridded Surface Subsurface Hydrological Analysis) model was used to simulate the hydrological impacts of these changes on runoff and flood patterns in both watersheds. The model of the tropical Imphal Watershed was further calibrated using flood reports to improve its accuracy. Results show that LULC changes significantly influence hydrological responses in both watersheds. In the arid watershed, Wadi Ham, LULC changes intensified runoff, whereas in the tropical Imphal watershed, the integration of LULC data and model calibration led to more precise flood risk assessments. Despite these adjustments, relative changes in hydrological behavior remained consistent, even when model validation and calibration were conducted. This study demonstrated the critical role of high-resolution LULC data in improving the accuracy of hydrological models, particularly in regions with limited ground truth data. PlanetScope imagery combined with the Random Forest classifier yielded the most accurate and realistic LULC maps tailored to each region’s unique environmental context. This research offered a methodology for assessing the hydrological response from LULC changes for regions where ground truth data is scarce or unavailable.