Chapter 1 Introduction

1.1 The IN-WOP project

Current practice in integrated water management predominantly use multi-objective optimization approaches with aggregated objectives. This biases results towards the status quo and against innovative solutions, can foster stakeholder resistance when they do not recognize their values and objectives in the optimization formulation, while also raising ethical concerns related to the inclusion of undesirable and/or hidden trade-offs. In contrast, many-objectives optimization approaches can consider many non-aggregated objectives, which has the potential to enrich the solution space with alternative courses of action that better reflect the diverging perspectives of stakeholders, and align better with ethical concerns. From the viewpoint of ethics, disaggregated assessment criteria are preferred as these may avoid undesirable and hidden trade-offs.

The overarching aim of this project is to investigate the contribution of many-objective optimization approaches to IWRM. For this, we use three water management cases in a comparative evaluation of many-objective approaches in diverse hydrological and cultural setting. In Italy our focus is on the Lake Como Basin located in the Italian Southern Alps, serving irrigated agriculture and competing demands from navigation, fishery, energy production environmental and flood protection. In the Seine River, France, we focus on the coordinated regulation of the Seine river discharge to reduce both floods and droughts. In Tunisia we address the anthropogenic impact on the management of water resources in the Merguellil Basin.

1.2 Work package 3: The Seine River Basin study case

Our approach of many-objective optimization consists in performing many single-objective optimizations and to derived management rules from prioritization of these objectives.

To do so, we first evaluate the risk of non-achievement of each objective independently for a given climatology and a given state of the system. Then, we derive management rules by prioritizing the riskiest objectives in the daily decision making.

The main tasks performed for the work package 3 are summarized in Figure

First, catchment uninfluenced flows are modeled at a daily time step with a semi-distributed GR4J model based on the airGRiwrm R package (Dorchies, Delaigue, and Thirel 2022) and forced by 11 GCM/RCM scenarios for both RCP4.5 and RCP8.5 between 1950 and 2100.

Then, these flows are used to assess the risk of non-achievement of each objective taking into account the current reservoir volume, the day of the year and a selection of climate scenarios and periods. This assessment is derived from the statistical distribution of the minimum (resp. maximum) volume required in the reservoirs for a given drought (resp. flood) objective calculated by a single objective dynamic programming optimisation.

The result of this assessment is available to the public through an interactive Shiny website (http://irmara.g-eau.fr) that allows to experiment management scenarios in real time.

Finally, “risk-based” management rules are derived by prioritising the riskiest objectives and balancing proactive and reactive decisions taking into account a hedging policy.

At the same time, we have modeled the current management rules over historical and future periods and we use these outputs to compare this management over the “risk-based” management rules. Both modelling results are specifically analysed for their distributive justice.

1.3 Abstract

This report presents the results of the calibration of the semi-distributed hydrological model of the Seine River Basin up to Vernon, using a set of 156 hydrometric stations. It also present the simulation of the uninfluenced flow forced by 11 climate projections under RCP 4.5 and 8.5. and the impact of the climate change over the XIXth century on the basin hydrology.

This modelling is carried out using the airGRiwrm extension of the airGR model (Dorchies, Delaigue, and Thirel 2022) at daily time step.

The calibration is performed using observed influenced flows and known influence of the reservoirs. We use a collection of hydrological indicators characterizing all regime conditions (including droughts and floods) in order to assess this calibration.

Removing the reservoirs from the model allows one to simulate the flow without the influence of the reservoirs (Terrier et al. 2020). We use then the climate projections to simulate these “uninfluenced” flows under climate change.

The simulated “uninfluenced” flows under climate change will be use afterward for assessing the reservoir management risks and for computing the derived “risk based” rules.

In order to assess the climate change impact on water resources, we compute the evolution of hydrological indicators for the near future (2021-2050), the middle (2041-2070), and the end of the century (2071-2100) compared to the current situation (1976-2005).

References

Dorchies, David, Olivier Delaigue, and Guillaume Thirel. 2022. airGRiwrm: Modeling of Integrated Water Resources Management Based on airGR. R Package Version 0.6.1.” Dorchies, David; Delaigue, Olivier; Thirel, Guillaume, 2022, "airGRiwrm: Modeling of Integrated Water Resources Management based on airGR. R Package version 0.6.1", https://doi.org/10.15454/3CVD1I, Recherche Data Gouv, V1. https://doi.org/10.15454/3CVD1I.
Terrier, Morgane, Charles Perrin, Alban de Lavenne, Vazken Andréassian, Julien Lerat, and Jai Vaze. 2020. “Streamflow Naturalization Methods: A Review.” Hydrological Sciences Journal, November, 1–25. https://doi.org/10.1080/02626667.2020.1839080.