Staff View: Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal dri...

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Main Authors:	Fadhliani, Zulkafli, Zed Diyana, Yusuf, Badronnisa, Abu Bakar, Siti Nurhidayu
Format:	Article
Published:	Multidisciplinary Digital Publishing Institute 2021
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id	oai:psasir.upm.edu.my:96092
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spelling	oai:psasir.upm.edu.my:96092 http://psasir.upm.edu.my/id/eprint/96092/ Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE) Fadhliani Zulkafli, Zed Diyana Yusuf, Badronnisa Abu Bakar, Siti Nurhidayu Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1—considering the flexibility, modularity, and portability—and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e., the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. Thirty-two years’ continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatter plot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modeling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed Fadhliani and Zulkafli, Zed Diyana and Yusuf, Badronnisa and Abu Bakar, Siti Nurhidayu (2021) Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE). Water, 13 (3). art. no. 317. pp. 1-16. ISSN 2073-4441 https://www.mdpi.com/2073-4441/13/3/317 10.3390/w13030317
institution	UPM IR
collection	UPM IR
description	Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1—considering the flexibility, modularity, and portability—and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e., the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. Thirty-two years’ continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatter plot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modeling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.
format	Article
author	Fadhliani Zulkafli, Zed Diyana Yusuf, Badronnisa Abu Bakar, Siti Nurhidayu
spellingShingle	Fadhliani Zulkafli, Zed Diyana Yusuf, Badronnisa Abu Bakar, Siti Nurhidayu Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)
author_facet	Fadhliani Zulkafli, Zed Diyana Yusuf, Badronnisa Abu Bakar, Siti Nurhidayu
author_sort	Fadhliani
title	Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)
title_short	Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)
title_full	Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)
title_fullStr	Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)
title_full_unstemmed	Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)
title_sort	assessment of streamflow simulation for a tropical forested catchment using dynamic topmodel—dynamic fluxes and connectivity for predictions of hydrology (decipher) framework and generalized likelihood uncertainty estimation (glue)
publisher	Multidisciplinary Digital Publishing Institute
publishDate	2021
_version_	1782761503498174464
score	12.935284

Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)

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