TY - JOUR
T1 - Rainfall-runoff-inundation (RRI) model for Kalu River, Sri Lanka
AU - Herath, Ruchiru D.
AU - Pawar, Uttam
AU - Aththanayake, Dushyantha M.
AU - Siriwardhana, Kushan D.
AU - Jayaneththi, Dimantha I.
AU - Palliyaguru, Chatura
AU - Gunathilake, Miyuru B.
AU - Rathnayake, Upaka
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.
PY - 2024/4
Y1 - 2024/4
N2 - Climate change, urbanization, and many anthropogenic activities have intensified the floods in today’s world. However, poor attention was given to mitigation strategies for floods in the developing world due to funding and technical limitations. Developing flood inundation maps from historical flood records would be an important task in mitigating any future flood damages. Therefore, this study presents the predictive capability of the Rainfall-Runoff-Inundation (RRI) model, a 2D coupled hydrology-inundation model, and to build flood inundation maps utilizing available ground observation and satellite remote sensing data for Kalu River, Sri Lanka. Despite the lack of studies in predicting flood levels, Kalu River is an annually flooded river basin in Sri Lanka. The comparative results between ground-based rainfall (GBR) measurement and satellite rainfall products (SRPs) from the IMERG satellite have shown that SRPs underestimate peak discharges compared to GBR data. The accuracy and the reliability of the model were assessed using ground-measured discharges with a high coefficient of determination (R2 = 0.89) and Nash–Sutcliffe model efficiency coefficient (NSE = 0.86). Therefore, the developed RRI model can successfully be used to simulate the inundation of flood events in the KRB. The findings can directly be applied to the stakeholders.
AB - Climate change, urbanization, and many anthropogenic activities have intensified the floods in today’s world. However, poor attention was given to mitigation strategies for floods in the developing world due to funding and technical limitations. Developing flood inundation maps from historical flood records would be an important task in mitigating any future flood damages. Therefore, this study presents the predictive capability of the Rainfall-Runoff-Inundation (RRI) model, a 2D coupled hydrology-inundation model, and to build flood inundation maps utilizing available ground observation and satellite remote sensing data for Kalu River, Sri Lanka. Despite the lack of studies in predicting flood levels, Kalu River is an annually flooded river basin in Sri Lanka. The comparative results between ground-based rainfall (GBR) measurement and satellite rainfall products (SRPs) from the IMERG satellite have shown that SRPs underestimate peak discharges compared to GBR data. The accuracy and the reliability of the model were assessed using ground-measured discharges with a high coefficient of determination (R2 = 0.89) and Nash–Sutcliffe model efficiency coefficient (NSE = 0.86). Therefore, the developed RRI model can successfully be used to simulate the inundation of flood events in the KRB. The findings can directly be applied to the stakeholders.
KW - Kalu River, Floods
KW - Rainfall-runoff-inundation (RRI) model
KW - Satellite rainfall products (SRPs)
UR - http://www.scopus.com/inward/record.url?scp=85173444113&partnerID=8YFLogxK
U2 - 10.1007/s40808-023-01877-1
DO - 10.1007/s40808-023-01877-1
M3 - Article
AN - SCOPUS:85173444113
SN - 2363-6203
VL - 10
SP - 1825
EP - 1839
JO - Modeling Earth Systems and Environment
JF - Modeling Earth Systems and Environment
IS - 2
ER -
