On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments

Thanh Duc Dang,Stefano Galelli,A F M Kamal Chowdhury

doi:10.5194/hess-24-397-2020

Abstract

Abstract. During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human–water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated with potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a multi-objective evolutionary algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing model that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC – with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996–2005), a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration, and baseflow. To further demonstrate the risks associated with the use of such a model, we carry out a climate change impact assessment (for the period 2050–2060), for which we use precipitation and temperature data retrieved from five global circulation models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human–water interactions in large-scale hydrological models.

Highlights

Hydrological systems consist of multiple physical, chemical, and biological processes, most of which are profoundly altered by anthropogenic interventions (Nazemi and Wheater, 2015a, b)
Can the estimation of some hydrological parameters be flawed by an inaccurate representation of water reservoir storage? What are the implications for the downstream applications of a flawed model? To answer these questions, we take the upper Mekong river basin as a case study, for which we develop a computational framework based on the Variable Infiltration Capacity (VIC) model (Liang et al, 1994) and a multi-objective evolutionary algorithm (MOEA) tasked with the problem of calibrating
To prove our hypothesis that the calibration process may somehow compensate for a deficiency in the model structure – the absence of reservoirs, in our case – we begin by analyzing the values of the goodnessof-fit statistics, namely Nash–Sutcliffe efficiency (NSE) and transformed root mean square error (TRMSE)

Summary

Introduction

Hydrological systems consist of multiple physical, chemical, and biological processes, most of which are profoundly altered by anthropogenic interventions (Nazemi and Wheater, 2015a, b). Land cover modifications or hydraulic infrastructures, for instance, affect both surface and subsurface hydrological processes by redistributing water over time and space (Haddeland et al, 2006; Bierkens, 2015). Such alterations are expected to amplify in the near future, owing to the increase in water and energy consumption (Abbaspour et al, 2015). In this context, hydrological models play a key role, as they help in the planning of the use of water resources in a sustainable way, so as to avoid adverse impacts on ecosystems and livelihoods (Bunn and Arthington, 2002; Yassin et al, 2019). The vast majority of the models currently available was initially con-

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