Forecast and uncertainty analysis of extreme precipitation in China from ensemble of multiple climate models

Peng Deng,Jianting Zhu

doi:10.1007/s00704-021-03660-7

Abstract

Global climate change is expected to have a major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control, and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various global climate models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, generalized extreme value (GEV) distribution, and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian model averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period, and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

Highlights

Global climate change has had a major impact on the hydrological cycle in the past few decades, leading to large-scale fluctuations in the water resources system
We present a Bayesian Model Averaging (BMA) approach in ensemble prediction of extreme precipitation in locations of China based on a variety of CMIP5 climate models
The BMA is developed for the Generalized Extreme Value (GEV) distribution of historical measured data, which can predict the extreme precipitation in future climate scenarios and the uncertainty of the predicted results

Summary

Introduction

Global climate change has had a major impact on the hydrological cycle in the past few decades, leading to large-scale fluctuations in the water resources system. Relationships based solely on historical data may not reflect future hydrological conditions (Yang et al 2019), and new approaches are needed to incorporate expected changes and uncertainties into assessment, planning, and design. Many studies have assessed the uncertainty of climate change impact analysis due to climate model selection (e.g., Qi et al 2017; Graham et al 2007; Minville et al 2008). Chen et al (2017) studied the impacts of weighting climate models for hydro-meteorological climate change, and showed that uncertainty due to hydrological modeling is significantly smaller than that related to the choice of a climate model. Yuan et al (2018) coupled the climate models with the hydrologic model to evaluate the impact of climate change on future extreme flood changes. Chen et al (2017) studied the impacts of weighting climate models for hydro-meteorological climate change, and showed that uncertainty due to hydrological modeling is significantly smaller than that related to the choice of a climate model. Maraun and Widman (2018) simulated summer mean precipitation at two locations in Norway by GCMs, and corrected the residual bias in terms of the observed and simulated climate change signals

Methods

Results

Conclusion