Advancing flood forecasting for Northern Thailand using RRI model and data assimilation (Record no. 2561)

MARC details
000 -LEADER
fixed length control field 03942nas a2200373 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260817161747.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260114s20259999th u ms t 000 eng d
035 ## - SYSTEM CONTROL NUMBER
System control number .b12470545
099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number AIT Thesis no.WM-25-10
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Pandey, Bikram
245 10 - TITLE STATEMENT
Title Advancing flood forecasting for Northern Thailand using RRI model and data assimilation
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Pathum Thani, Thailand :
Name of publisher, distributor, etc. Asian Institute of Technology,
Date of publication, distribution, etc. 2025
300 ## - PHYSICAL DESCRIPTION
Extent 148 leaves :
Other physical details ill.+
Accompanying material 1 online
490 1# - SERIES STATEMENT
Series statement Thesis;
Volume/sequential designation no. WM-25-10
500 ## - GENERAL NOTE
General note A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M. Eng.) - Asian Institute of Technology, 2025
520 ## - SUMMARY, ETC.
Summary, etc. Flooding 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Flood forecasting
Geographic subdivision Thailand, Northern
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Rain and rainfall
General subdivision Mathematical models
Geographic subdivision Thailand, Northern
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Natthachet Tangdamrongsub,
Relator term Chairperson
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Shrestha, Sangam,
Relator term Examination Committee
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Shanmugam, Mohana Sundaram,
Relator term Examination Committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SET DEAN Scholarships,
Relator term Scholarship Donor
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element AIT Scholarship,
Relator term Scholarship Donor
810 2# - SERIES ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Asian Institute of Technology.
Title of a work Thesis;
Volume/sequential designation no. WM-25-10
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Full-Text
Uniform Resource Identifier <a href="http://203.159.5.9/ait-thesis/detail.php?q=B23299">http://203.159.5.9/ait-thesis/detail.php?q=B23299</a>
907 ## - LOCAL DATA ELEMENT G, LDG (RLIN)
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b mnarc
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902 ## - LOCAL DATA ELEMENT B, LDB (RLIN)
a 260115
998 ## - LOCAL CONTROL INFORMATION (RLIN)
Operator's initials, OID (RLIN) 0
Cataloger's initials, CIN (RLIN) 260115
First date, FD (RLIN) m
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945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnarc
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 67-Electronic Resource
909 ## - LOCAL ITEMS USED
Barcode Barcode : -
CREATED CREATED : 2026-01-14
RECORD Id RECORD # : i13566465
LPATRON LPATRON : 0
LCHKIN LCHKIN : -
RENEWALS # RENEWALS : 0
-- # OVERDUE : 0
-- IUSE3 : 0
-- TOT CHKOUT : 0
-- TOT RENEW : 0
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Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Total checkouts Full call number Date last seen Copy number Price effective from Koha item type
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library Archives 17/08/2026   AIT Thesis no.WM-25-10 17/08/2026 1 17/08/2026 67-Electronic Resource
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