| Abstract |
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One of the important factors in water resources management is the determination of design flood associated with determining the size, capacity and age of the water resources structures to be built. Determination of design flood can be done in various ways, one of which is very popular to date is discharge prediction using synthetic unit hydrograph (SUH) approach. The use of unit hydrograph models has been widely applied in various parts of the world, especially in Indonesia, some of which are Snyder, Nakayasu, GAMA I and ITB-1. These methods are considered to have a good performance because it has to accommodate the characteristics of watersheds in a model parameter that greatly contributed to the process of rainfall-runoff transformation. However, in some cases it also provides a sizeable deviation, especially in Indonesia, considering that watersheds in Indonesia have different characteristics with watersheds in the United States where Snyder Unit Hydrograph developed. To overcome these problems, the unit hydrograph performance must be improved so that it can be used in various watersheds to obtain the smallest deviations. This research was conducted in 8 watersheds located in Central Sulawesi Province, Indonesia. This study aimed to improve the performance of Snyder Unit Hydrograph Model, covering Snyder, Nakayasu, SCS, GAMA I, ABG and ITB-1. The improvement of model performance was conducted by adjusting model parameters, in this case using Solver Tool on Microsoft Excel. Evaluation was done by the error indicator such as coefficient of Nash-Sutcliffe model efficiency (E). The study result showed that model parameter adjustment could decrease a deviation of SUH model parameter for peak discharge and average peak time up to 30% and could increase Nash– Sutcliffe Model Efficiency Coefficient (E) up to over 80%. The decrease of a deviation of SUH model parameter and the increase of E coefficient revealed that optimization using solver facility was effectively undertaken. However, not all deviations decreased but even increased significantly after optimization. It happened because the process of parameter optimization occurred simultaneously, and it was only based on a purpose function by maximizing Nash–Sutcliffe Model Efficiency Coefficient (E). The adjustment in this coefficient caused the increase or decrease of a parameter deviation of SUH Model depending on E value achieved on the optimization process. Overall, it could be declared that the decrease of a parameter deviation of SUH model was accompanied by the increase of Nash–Sutcliffe Model Efficiency Coefficient (E). |
