Work

Time-Varying Bivariate Modeling for Predicting Hydrometeorological Trends in Jakarta Using Rainfall and Air Temperature Data

Year: 2025

Type: article

Abstract: Changes in rainfall patterns and irregular air temperature have become essential issues in analyzing hydrometeorological trends in Jakarta. This study aims to select the best copula of the stationary and non-stationary copula models and visualize and explore the relationship between rainfall and air temperature to predict hydrometeorological trends. The methods used include combining univariate Lognormal and Generalized Extreme Value (GEV) distributions with Clayton, Gumbel, and Frank copulas, as well as parameter estimation using the fminsearch algorithm, Markov Chain Monte Carlo (MCMC) simulation, and a combination of both. The results show that the best model is the non-stationary Clayton copula estimated using MCMC simulation, which has the lowest Akaike Information Criterion (AIC) value. This model effectively captures extreme dependence in the lower tail of the distribution, indicating a potential increase in extreme low events such as cold droughts. Visualization of the best model through contour plots shows a shifting center of the distribution over time. This study contributes to developing dynamic hydrometeorological models for adaptation planning of changing hydrometeorological trends in Indonesia.

Source: Hydrology

DOI: 10.3390/hydrology12100252

Authors: Suci Nur Setyawati, Sri Nurdiati, I Wayan Mangku, Ionel Haidu, Mohamad Khoirun Najib

Institutions: IPB University, Babeș-Bolyai University, Centre de Recherche en Géographie, Université de Lorraine

Language: English

Topic: Hydrology and Watershed Management Studies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,172