Extreme Low Flow Estimation under Climate Change

Sylvie Parey,Joël Gailhard

doi:10.3390/atmos13020164

Abstract

Climate change’s impact on water availability has been widely studied, including its impact on very rare values quantified by return levels using the statistical extreme value theory. However, the application of this theory to estimate extreme low flows is barely justified due to a large temporal dependency and a physically highly bounded lower tail. One possible way of overcoming this difficulty is to simulate a very large sample of river flow time series consistent with the observations or the climate projections in order to enable empirical rare percentile estimations. In this paper, such an approach based on simulation is developed and tested for a small mountainous watershed in the French Alps. A bivariate generator of daily temperature and rainfall, developed in collaboration with Paris-Saclay University and based on hidden Markov models, is used to produce a large number of temperature and rainfall time series, further provided as input to a hydrological model to produce a similarly large sample of river flow time series. This sample is statistically analyzed in terms of low flow occurrence and intensity. This framework is adapted to the analysis of both current climate conditions and projected future climate. To study historical low flow situations, the bivariate temperature and rainfall model is fitted to the observed time series while bias-adjusted climate model outputs are used to calibrate the generator for the projections. The approach seems promising and could be further improved for use in more specific studies dedicated to the climate change impact on local low flow situations.

Highlights

The adaptation to climate change of hydropower or thermal power plants necessitates the identification and characterization of high impact hazards
Starting from the observed precipitation and temperature time series for the Souloise at Infernet watershed covering the period 1970–2013, the bivariate stochastic model has been calibrated and used to simulate 1000 equivalent 44-year time series of joint precipitation and temperature evolutions. These time series have been used as inputs for the hydrological MORDOR model to produce 1000 44-year river flow time series
A bivariate stochastic model based on a non-homogenous hidden Markov model is used to produce a very large sample of temperature and precipitation time series, which are in turn transformed into river flow thanks to a rainfall runoff model

Summary

Introduction

The adaptation to climate change of hydropower or thermal power plants necessitates the identification and characterization of high impact hazards. The application of the theory necessitates the best possible balance between the need for selecting the highest values among a large enough number of events so that the convergence to the limit distributions can be assumed, and selecting a large enough number of values so that the fitting of the limit distribution can be reliable Such a balance is difficult to achieve when dealing with low river flows, because these situations are long lasting, which means, on the one hand, that temporal dependence is high and, on the other hand, that only a low number of such events generally occur each year. As in [7], the computation of a large sample of time series with similar characteristics is made with a stochastic weather generator

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