A stochastic simulation strategy designed to study the future of extreme low flows in the context of electricity generation

In a work published in 2022 (Parey and Gailhard 2022, [1]) a methodology designed to estimate extreme low flow, and based on stochastic modeling has been described and tested. This methodology was suited for a single watershed and involved a single site multivariate stochastic generator of consistent temperature and rainfall timeseries. Since then, methodological issues were raised, linked on the one hand to the hydrological modeling in a cascading basins context and on the other hand to the need of being able to produce and handle an ensemble of climate projections in a reasonable computing time. The first point refers to spatial added to multivariate consistency needed in the sub-basins to obtain coherent streamflow simulations, the second to the computational efficiency of the stochastic weather generator fitting and use.

Further investigations have shown that the multivariate stochastic generation was detrimental for the performance of the extreme events reproduction, especially for long heat waves such as the 2003 event in France. Furthermore, adding spatial consistency, in addition to the multivariate one, in the generator was not straightforward. Therefore, another weather generation strategy has been proposed and tested. It consists in using single variable generators, simple for precipitation and more sophisticated in the case of temperature for the purpose of heat wave projection, used independently and synchronized a posteriori through an empirical copula coupling approach linked with bootstrapping.

After a detailed description of the proposed approach to generate a large number of spatially and mutually consistent temperature and rainfall timeseries, its application to project future low flows in a French watershed of interest for electricity generation will be demonstrated with an example.

 

 

Reference:

[1] Parey, S.; Gailhard, J.: Extreme Low Flow Estimation under Climate Change. Atmosphere 2022, 13, 164. https://doi.org/10.3390/atmos13020164