Predictive Model for Northern Thailand Rainfall Using Niño Indexes and Sea Surface Height Anomalies in the South China Sea

Abstract

Northern Thailand rainfall (NTR) plays a crucial role in supplying surface water resources for downstream regions that millions of Thais rely on. The NTR has been reported to be adversely affected by the recent climate change making it impossible to accurately predict rainfall for better water management. In this work, we attempt to find an indicator that can be used to predict monthly NTR using an oceanic index based on sea surface height anomaly (SSHA) called the South China Sea Index (SCSI). First, we investigate the lead-lag relationships between NTR and several well-known indices. Relationships of NTR-Niño1+2 and NTR-Niño3 appear to be relatively strong. We then perform empirical orthogonal function (EOF) analysis on SSHA in the South China Sea and observe that the 2nd principal component (PC) time series and NTR strongly correlate. However, direct use of PC time series is computationally costly. Instead, we use SSHA information relating to the second EOF mode to create SCSI without performing EOF analysis. The correlation of SCSI-NTR is negatively strong. Lastly, we forecast NTR using SARIMAX models with Niño1+2, Niño3, and SCSI as inputs. The best model was SARIMAX (1, 0, 1)(0, 0, 2)12 using SCSI and Nino3 as inputs with AIC = 2368.705, RMSE = 51.167 mm per month and R2 = 0.732. Result raises capacity for effective climate change-related planning and management in the area.