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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)
Version 1
: Received: 5 December 2020 / Approved: 7 December 2020 / Online: 7 December 2020 (14:36:02 CET)
How to cite:
Umar, F.; Diyana, Z.; Yusuf, B.; Nurhidayu, S. 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). Preprints2020, 2020120163. https://doi.org/10.20944/preprints202012.0163.v1
Umar, F.; Diyana, Z.; Yusuf, B.; Nurhidayu, S. 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). Preprints 2020, 2020120163. https://doi.org/10.20944/preprints202012.0163.v1
Umar, F.; Diyana, Z.; Yusuf, B.; Nurhidayu, S. 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). Preprints2020, 2020120163. https://doi.org/10.20944/preprints202012.0163.v1
APA Style
Umar, F., Diyana, Z., Yusuf, B., & Nurhidayu, S. (2020). 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). Preprints. https://doi.org/10.20944/preprints202012.0163.v1
Chicago/Turabian Style
Umar, F., Badronnisa Yusuf and Siti Nurhidayu. 2020 "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)" Preprints. https://doi.org/10.20944/preprints202012.0163.v1
Abstract
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. 32 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 scatterplot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modelling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
