Optimizing land surface model for improved soil moisture estimation : bridging the gap between simulation and satellite observations | |
| Author | Bhandari, Ashok |
| Call Number | AIT Thesis no.WM-25-04 |
| Subject(s) | Soil moisture Environmental engineering |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management |
| Publisher | Asian Institute of Technology |
| Abstract | Soil moisture is a crucial hydrologic state required for water resource management in the country like Thailand, yet continuous and physically consistent estimates remain limited. Land surface models offer solution for this, but optimization of these models has been challenging due to model complexity and uncertainty. This study addresses the problem through scheme-based optimization, including updating soil texture parameter to achieve a balanced model. Full-factorial experiments governs the natural selection process through switching of six distinct land surface parametrization categories in Noah-MP v 3.6, each comprising 2-4 schemes totaling 576 experiments. Simulations were run-in high-performance computer (HPC) to identify the optimal schemes that yields the highest mean spatial skill score (e.g, KGE = 0.58, correlation = 0.74). Results revealed the spatial variability in model performance under different physics options and hence the tradeoff was considered for selection process. While the best performing scheme for soil moisture achieved the highest skill, it has led to decreased performance in evapotranspiration (ET) and terrestrial water storage anomaly (TWSA). Results from multi-variate ensemble optimization experiments (SM+TWSA+ET) demonstrated a balanced improvement, particularly in ET (correlation increased to 0.714) and TWSA (0.813), revealing impact across the physics options inherent in single-variable optimization. This variation is reflected through the under estimation of leaf area index (LAI) on wet basins and hence, is helpful for better understanding for classification of physical parameterizations on basin scale. The optimal model obtained through several experiments was useful for constructing historical databases for long term trend and drought analysis. The trend analysis showed the decline of precipitation on central and northeast basin on rainy and summer season with average of 6.35mm /wet season. However, this seasonal trend did not align with soil moisture and total water storages suggesting seasonal shifts. The probability distribution for the drought obtained from Soil moisture anomaly index (SMAI) with the long-term average from 1990 to 2023 is negatively skewed, with skewness of -0.47 with long tail on left side demonstrating the regular droughts especially on northeastern basin. These indicators were used for regional agriculture decision support especially on land suitability of economic crops like rice and spatial irrigation planning. |
| Year | 2025 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Water Engineering and Management (WM) |
| Chairperson(s) | Natthachet Tangdamrongsub; |
| Examination Committee(s) | Shanmugam, Mohana Sundaram;Shrestha, Sangam; |
| Scholarship Donor(s) | ADB Japan Scholarship Program (ADB-JSP); |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2025 |