ESTIMATION OF RESERVOIR EVAPORATION USING REMOTE SENSING BY APPLYING EMPIRICAL METHOD (Case Study: Saguling Reservoir) - MAHARDITA (NIM: 12814031), DINDA
Saguling Reservoir serves as power source for Hydroelectric Power Plants (PLTA), fisheries, and fulfillment of local needs. Accurate information about reservoir evaporation that indicates potential evaporation is needed in analyzing water balance and water resource management. Evaporation also plays a role in determining the moisture content in the atmosphere. Estimation of reservoir evaporation is important because direct measurement methods using evaporation pans produce less accurate data, this problem occurs on Saguling Reservoir evaporation data. The lack of observation data from ground station is a major problem in estimating evaporation. Therefore, in this study an estimation of reservoir evaporation was conducted using empirical methods with satellite data input. Satellite data, Land Surface Temperature from Himawari and Atmospheric Profile from MODIS are used to obtain information on temperature, relative humidity, and solar radiation parameter. Evaporation estimation results using Blaney-Criddle, Kharuffa, Hargreaves, Schendel, and Multiple Linear Regression methods compared to reference evaporation are calculated using a combination method (Penman) with meteorological parameters input from observations. Observation was conducted using AWS (Automatic Weather Station) at three observation points, namely DAM Saguling, Maroko, and Bongas on 4th May to 7th June 2018. Best estimation method was selected using the Percentage Eror (PE) and Taylor Diagram. Satellite data can be used as an alternative input because it is quite able to explain the parameters of temperature, humidity, and radiation corresponding to observations.. The results showed statistically that the best evaporation estimation method for Saguling Reservoir was Calibrated Schendel followed by EQ1 generated from Multiple Linear Regression. However, in explaining spatial variations EQ1 is better than Calibrated Schendel.