Open AccessStreamflow Simulation of Progo River by Using SWAT Model
Author(s) -
Pradipta Nandi Wardhana,
Rafizal Afif
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1051/1/012067
Subject(s) - watershed , soil and water assessment tool , hydrology (agriculture) , swat model , environmental science , drainage basin , weir , watershed management , streamflow , watershed area , geology , geography , computer science , cartography , geotechnical engineering , machine learning
Watershed is an area formed by the ridges of the mountains that limit a region. The natural boundary of the watershed was the result of geomorphology and hydrology. Watershed as water catchment area is very potential to provide water in a territory. Hence, the watershed management needs to be applied as well as possible. The Progo River watershed is located along the Central Java and Special Region of Yogyakarta. Progo River watershed have several creeks that ends at Trisik beach located on the south side of Java land headed to Indian Ocean. Simulation analysed the water discharge in Progo River with Sapon Weir outlet using Soil Water Assessment Tools (SWAT) with time step period for 3 years, between 2013 until 2015. Data of watershed condition used as input data were soil characteristic data, land use data, land slope data, climate data, and water discharge measurement data. SWAT simulation process was carried out through four stages, they were watershed delineation, formation of a hydrological response unit, data processing, and model simulation with simulation testing result based on R 2 statistic parameter, NS model efficiency, and PBIAS parameter. From the result of the simulation, it was known that the comparison of the result between water discharge simulation and field observation shows various quantitative result. Each of evaluation parameter gave different performance classification. R 2 statistic parameter evaluation gave good-very good classification, while NS model efficiency and PBIAS parameter gave not satisfactory performance classification.