The Dependable Flow Analysis for Irrigation Intake using Satellite-based Global Precipitation Measurement (GPM) in the Jratun Seluna River Basin Khafidzotun Nimah (a), Hanggar Ganara Mawandha (a*), Yekti Anggun E.D (b) , L.M Kesuma (c), Chandra Setyawan (a), Ngadisih Ngadisih (a)
a) Department of Agricultural Engineering and Biosystem, Gadjah Mada University, Yogyakarta, 55281, Indonesia
* hanggar.g.m[at]ugm.ac.id
b) Department of Civil Engineering, Janabadra University, Yogyakarta, 55231, Indonesia
c) Engineer Professional Study Program, Gadjah Mada University, Yogyakarta, 55281, Indonesia
Abstract
The availability of water in the river basin possesses essential benefits for human life, habitats, energy, and farming. As the river basin collects and stores inland water run-off as a result of rainfall, it keeps a maintainable river discharge or so-called a dependable flow. As a large irrigation system takes place in central java, the Jratunseluna river basin contributes to providing water for 257 thousand hectares of irrigation area with the river basin^s area 9.896 km2. In this case, the dependable flow used for irrigation intake^s design needs to be conducted simultaneously. As a ground-based instrument is used, the accuracy may depend on the number of instruments deployed on the ground due to it is a point-based measurement. On the other hand, the Global Precipitation Measurement (GPM) collects large spatial data at near-real-time in one observation cycle. Here, the use of satellite data looks the prominent challenge dealing with a large-scale area target.
This study aims to analyze the accuracy of satellite-based data related to rainfall amount used as an input to calculate the dependable flow compared to the analyses generated from the ground-based data. The analysis uses the Mock model with the calibrated parameter obtained from the observed flow data for 10 years from 2010 to 2020. The model was developed under 3 scenarios- first, the rain gauge-based models, second, the GPM data without gauge calibration, and third, the GPM data with gauge calibration in advance. The results show that the estimated dependable flows under three conditions are comparable to the observed data in terms of the correlation coefficient, RMSE, and NSE scores. The average of the statistical test for the correlation coefficient, RMSE, and NSE obtains 0.75, 3.6, and 0.71, respectively. Through this study, the use of satellite-based GPM data is expected to be more extensive to support modern agricultural management activities.
