Modeling daily evapotranspiration time series based on Non-Linear Autoregressive Exogenous (NARX) method and climate variables for a data-deficient region.

Abstract

For flood-prone, developing nations where hydrological data is scarce, an innovative methodological approach is essential. This study aims to explore the potentiality of modelling daily evapotranspiration time series by checking causal relationship among the available climate variables in a flood-prone, data-deficient region like Samar in the Philippines. First, to verify if the available variables (rainfall, air pressure and the four (4) Niño Sea Surface Temperature (SST) Indices) have direct effects to evapotranspiration, a causality test called Convergent Cross-Mapping (CCM) was used. Interestingly, only the Niño SST indices and air pressure were found to have direct effects. Results showed that air pressure and the four (4) Niño SST Indices when combined with Non-Linear Autoregressive Exogenous (NARX) method, can effectively model evapotranspiration. This study raises a significant advancement in evapotranspiration modelling as it is the first to model and pinpoint the potentiality of causal relationship of air pressure and the four (4) Niño SST Indices to daily evapotranspiration time series. This method is found to be potentially suitable for disaster-prone regions where hydrological data is limited.