Open AccessForecasting potential evapotranspiration for Raichur district using seasonal ARIMA model
Author(s) -
Rahul Patil,
D. M. Nagaraj,
B. S. Polisgowdar,
Santosha Rathod
Publication year - 2022
Publication title -
mausam
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.243
H-Index - 12
ISSN - 0252-9416
DOI - 10.54302/mausam.v73i2.5488
Subject(s) - autoregressive integrated moving average , akaike information criterion , evapotranspiration , bayesian information criterion , partial autocorrelation function , autocorrelation , statistics , mathematics , environmental science , time series , ecology , biology
The prediction of potential evapotranspiration (PET) is quite important task for reliable management of irrigation systems. This article is generally based on the models which try to mimic the actual occurrence of the Potential evapotranspiration in the future days for a Raichur district. In this study the potential evapotranspiration was estimated with the help of max and min temperature (°C) data using a Thornthwaite method and the prediction was carried out using the seasonal Autoregressive moving average method (SARIMA). The models were developed based upon autocorrelation function (ACF) and partial autocorrelation function (PACF). Furthermore, the model with the least Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values were selected. The models selected for different stations were ARIMA(2,0,2)(1,1,2)12, ARIMA(1,0,1)(2,1,0)12, ARIMA(1,0,1)(1,1,2)12 and ARIMA(1,0,1) (2,1,0)12 for Riachur, Manvi, Sindhanuru and Lingasuguru respectively. Furthermore, the results showed that the models developed for Manvi and Sindhanuru were found to be quite promising compared to the other two stations. All four models were found to be producing better results up to a lead time forecast of one month. The models provided significant potential in improving the decision making in irrigation planning and command area management practices for better management of water resources.