000			03942nas a2200373 a 4500
005			20260817161747.0
008			260114s20259999th u ms t 000 eng d
035			_a.b12470545
099		9	_aAIT Thesis no.WM-25-10
100	1		_aPandey, Bikram
245	1	0	_aAdvancing flood forecasting for Northern Thailand using RRI model and data assimilation
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2025
300			_a148 leaves : _bill.+ _e1 online
490	1		_aThesis; _vno. WM-25-10
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2025
520			_aFlooding in Northern Thailand{u2019}s Nan River Basin poses significant risks during the monsoon season due to its mountainous terrain and exposure to the monsoon trough. This study applies the physically based Rainfall Runoff Inundation (RRI) model and Particle Filter Data Assimilation (PF-DA) technique to enhance flood forecasting in the Upper Nan River Basin. The study begins by calibrating and validating the RRI model using interpolated HII rain gauge data and observed discharge at stations N1 and N64. The model performed reliably in both the 2024 calibration and the 2022 validation period, producing realistic runoff responses with positive efficiency scores, low RMSE, and acceptable bias. A grid-wise, monthly Quantile Mapping bias correction was applied to the 0.25° daily GFS rainfall to reduce systematic biases. It effectively lowered the raw GFS overestimation and improved simulated discharge, but also dampened extremes, causing underestimated flood peaks.This study applied a daily RRI-Particle Filter data assimilation (PF-DA) system using 16 particles and Gaussian system noise, with rainfall boundary coordinates as state variables and an RMSE-based likelihood. Applying PF-DA during the 2022 and 2024 monsoon seasons yielded consistent gains across a 1-10 day lead time. Daily assimilation of observed water levels refined the model states, reduced false peaks from raw GFS, and softened the strong underestimation produced by bias-corrected GFS, improving hydrograph timing and stabilizing both flooding and recession periods. Short lead gains were notable for raw GFS, where KGE exceeded 0.5 for lead days 1-3 instead of only 1-2 without assimilation. Overall improvements were substantial, as KGE rose from 0.392 to 0.494 (~ 11%) and bias dropped from +0.543 m to +0.129 m in 2022. In 2024, KGE increased from 0.331 to 0.421 (~28%) with bias reduced from +0.568 m to +0.218 m. While PF-DA significantly improved short and medium-range forecasts, long-lead performance still depends on better rainfall forcing and maintaining sufficient ensemble spread. While QM improves overall stability, it also reduces extreme rainfall, leading to underestimated peak flows, showing the need for improved bias correction methods that better preserve extremes.
650		0	_aFlood forecasting _zThailand, Northern
650		0	_aRain and rainfall _xMathematical models _zThailand, Northern
700	0		_aNatthachet Tangdamrongsub, _eChairperson
700	1		_aShrestha, Sangam, _eExamination Committee
700	1		_aShanmugam, Mohana Sundaram, _eExamination Committee
710	2		_aSET DEAN Scholarships, _eScholarship Donor
710	2		_aAIT Scholarship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis; _vno. WM-25-10
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B23299
907			_a.b12470545 _bmnarc _ca
902			_a260115
998			_b0 _c260115 _dm _eh _fa _g0
945			_lmnarc
942			_c67
909			_aBarcode : - _bCREATED : 2026-01-14 _cRECORD # : i13566465 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c2561 _d2561