Assessment of Changes in Daily Rainfall Extremes in Jakarta using Climate Projections and Nonstationary Extreme Value Analysis Velautham Daksiya,1, 2 Pradeep Mandapaka,3 and Edmond Y. M. Lo3,4 1
Environmental Process Modelling Centre, Nanyang Environmental & Water Research Institute, Nanyang Technological University,
Singapore 2
Interdisciplinary Graduate School, Nanyang Technological University, Singapore
3
Institute of Catastrophe Risk Management, Nanyang Technological University, Singapore
4
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
Correspondence should be addressed to Velautham Daksiya;
[email protected]
Future projections from climate model simulations generally indicate occurrence of more intense rainfall events separated by longer dry periods. However, we have limited understanding on how the changes in rainfall extremes affect flood risk management at local scales. The climate change projections get more complex with the uncertainties involved. This work analyses the changes in daily rainfall extremes over Jakarta, Indonesia. We conducted frequency analysis of annual maximum daily rainfall using conventional (stationary) as well as nonstationary extreme value analysis. The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) with 0.25̊ resolution gridded daily precipitation projections containing 20 GCMs with RCP 4.5 and RCP 8.5 are used. The climate change trends are studied using a 35 years of moving window for the period of 2020-2100.The change factors for daily rainfall from the historical to future time period are computed and the uncertainties are quantified. The change factors thus obtained are applied to the observed rainfall data, and the hydrology of major river basins (Ciliwung and Cengkareng) in Jakarta is simulated. The discharge at the basin outlet is obtained using HEC-HMS and change factors from stationarity and nonstationary analysis. The results illustrate the sensitivity of change factors and basin discharge to stationarity and nonstationary analysis, and facilitate better flood mitigation decisions with changing climate.
