Spatiotemporal Analysis on the Teleconnection of ENSO and IOD to the Stream Flow Regimes in Java, Indonesia

Adam Rus Nugroho,Ichiro Tamagawa,Morihiro Harada

doi:10.3390/w14020168

Abstract

While many studies on the relationship between climate modes and rainfall in Indonesia already exist, studies targeting climate modes’ relationship to streamflow remain rare. This study applied multiple regression (MR) models with polynomial functions to show the teleconnection from the two prominent climate modes—El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)—to streamflow regimes in eight rivers in Java, Indonesia. Our MR models using data from 1970 to 2018 successfully show that the September–November (SON) season provides the best predictability of the streamflow regimes. It is also found that the predictability in 1970–1989 was better than that in 1999–2018. This suggests that the relationships between the climate modes and streamflow in Java were changed over periods, which is suspected due to the river basin development. Hence, we found no clear spatial distribution patterns of the predictability, suggesting that the effect of ENSO and IOD are similar for the eight rivers. Additionally, the predictability of the high flow index has been found higher than the low flow index. Having elucidated the flow regimes’ predictability by spatiotemporal analysis, this study gives new insight into the teleconnection of ENSO and IOD to the Indonesian streamflow.

Highlights

The perspective of streamflow management has been suggested to be shifted toward climate and ecological dynamics [1]; the need for predicting streamflow regimes will increase
We applied the multiple regression (MR) analyses with the second- and third-order polynomial functions to find the best temporal variation set for regressing the indices of El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) to the flow regime indices
This study developed the relationship between river flow regimes in Java and climate indices of ENSO and IOD by multiple regression (MR) models in ten different temporal data sets

Summary

Introduction

The perspective of streamflow management has been suggested to be shifted toward climate and ecological dynamics [1]; the need for predicting streamflow regimes will increase. Many studies have developed physical hydrological models to predict streamflow. Finding a good streamflow prediction by statistical models is not easy, due to various climate phenomena affecting hydrological processes. Fewer studies related to statistical hydrological models are found for predicting streamflow, especially in Indonesia. The rainfall in Indonesia is affected by at least two global climate phenomena: the. El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD). The ENSO is a periodical variation in sea surface temperatures over the Pacific Ocean, which consists of El Niño (associated with drought in Indonesia) and La Niña phases (associated with the flood in Indonesia) [2,3]. The IOD is a seasonal oscillation of sea surface temperatures in the Indian Ocean. The high activity of the IOD has been recognized to cause droughts in Indonesia [6]

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