Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models

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Abstract

Changes in global climate will have significant impact on local and regional hydrological regimes, which will in turn affect ecological, social and economical systems. However, climate-change impact studies on hydrologic regime have been relatively rare until recently, mainly because Global Circulation Models, which are widely used to simulate future climate scenarios, do not provide hourly or daily rainfall reliable enough for hydrological modeling. Nevertheless, more reliable rainfall series corresponding to future climate scenarios can be derived from GCM outputs using the so called ‘downscaling techniques’. This study applies two types of statistical (a stochastic and a regression based) downscaling techniques to generate the possible future values of local meteorological variables such as precipitation and temperature in the Chute-du-Diable sub-basin of the Saguenay watershed in northern Québec, Canada. The downscaled data is used as input to two different hydrologic models to simulate the corresponding future flow regime in the catchment. In addition to assessing the relative potential of the downscaling methods, the paper also provides comparative study results of the possible impact of climate change on river flow and total reservoir inflow in the Chute-du-Diable basin. Although the two downscaling techniques do not provide identical results, the time series generated by both methods indicates a general increasing trend in the mean daily temperature values. While the regression based downscaling technique resulted in an increasing trend in the mean and variability of daily precipitation values, such a trend is not obvious in the case of precipitation time series downscaled with the stochastic weather generator. Moreover, the hydrologic impact analysis made with the downscaled precipitation and temperature time series as input to the two hydrological models suggest an overall increasing trend in mean annual river flow and reservoir inflow as well as earlier spring peak flows in the basin.

Introduction

Human activities, primarily the burning of fossil fuels and changes in land cover and use, are nowadays believed to be increasing the atmospheric concentrations of greenhouse gases. This alters energy balances and tends to warm the atmosphere which will result in climate change. Some reports indicate that mean annual global surface temperature has increased by about 0.3–0.6 °C since the late 19th century and it is anticipated to further increase by 1–3.5 °C over the next 100 years (IPCC, 1995). These changes in global climate appear to most severely affect the mid and high latitudes of the Northern Hemisphere, where temperatures have been noticeably getting warmer since 1970s (IPCC, 2001). Such changes in climate will also have significant impact on local and regional hydrological regimes, which will in turn affect ecological, social and economical systems. Nevertheless, substantial differences are observed in regional changes in climate compared to the global mean change.

The Global Climate Models (GCMs) generally used to simulate the present climate and project future climate with forcing by greenhouse gases and aerosols typically divide the atmosphere and ocean into a horizontal grid with a horizontal resolution of 2 to 4° latitude and longitude, with 10 to 20 layers in the vertical. In general, most GCMs simulate global and continental scale processes in detail and provide a reasonably accurate representation of the average planetary climate. Over the past decade, the sophistication of such models has increased and their ability to simulate present and past global and continental-scale climates has substantially improved. Nevertheless, while GCMs demonstrated significant skill at the continental and hemispherical scales and incorporate a large proportion of the complexity of the global system, they are inherently unable to represent local sub-grid-scale features and dynamics (Wigley et al., 1990, Carter et al., 1994). Moreover, GCMs were not designed for climate change impact studies and do not provide a direct estimation of hydrological responses to climate change. Therefore, in climate change impact studies, hydrological models are needed to simulate sub-grid scale phenomenon. However, such hydrological models require input data (such as precipitation) at similar sub-grid scale, which has to be provided by converting the GCM outputs into at least a reliable daily rainfall series at the selected watershed scale. The methods used to convert GCM outputs into local meteorological variables required for reliable hydrological modeling are usually referred to as ‘downscaling’ techniques.

There are various downscaling techniques available, but it is not clear which one provides the most reliable estimates of daily rainfall time series. This study aims to investigate and evaluate two of the more promising statistical downscaling techniques, and provide an inter-comparison study using a sub-basin of the Saguenay–Lac-Saint-Jean watershed in northern Québec as an experimental site. In addition to assessing the relative potential of the two downscaling methods, the paper also provides comparative analysis of conceptual and empirical hydrologic models as applied to hydrologic impact study.

Section snippets

Statistical downscaling methods

Spatial ‘downscaling’ approaches have recently emerged as a mean of relating the large scale atmospheric predictor variables to local or station-scale meteorological series which could be used as input to hydrological models. Formally, the concept of regional climate being conditioned by the large-scale state may be written as: R=F(L) where R represents the predictand (a local climate variable), L is the predictor (a set of large-scale climate variables), and F a deterministic/stochastic

Study area and data used

The study area selected in this research for the application and evaluation of downscaling methods is the Chute-du-Diable basin located in the Saguenay–Lac-Saint Jean watershed (Fig. 1). Saguenay is a well-known flood prone region as many Canadians still remember the year-1996 flood of this river. There are a large number of reservoirs and dams in the Saguenay watershed and most of the large reservoirs are managed by the Aluminum Company of Canada (ALCAN) for hydroelectric power production.

Downscaling daily rainfall and temperature time series

Recently, two well recognized statistical downscaling tools are made available to the broader climate change impact study community via the Canadian Climate Impacts and Scenarios project (http://www.cics.uvic.ca/scenarios/). The first one implements a regression based method and is referred to as Statistical Down-Scaling Model (SDSM) (Wilby et al., 2002) while the second is a stochastic weather generator called Long Ashton Research Station Weather Generator (LARS-WG) (Semenov and Barrow, 1997,

Hydrologic impact of climate change

Two sets of hydrologic impacts corresponding to the climate change scenario described in Section 3 are considered. The first impact considered is on the total daily inflows into the Chute-du-Diable reservoir located on the Peribonka River while the second impact concerns the daily stream flow of the Serpent River located at the north-western part of the Chute-du-Diable watershed. Chute-du-Diable is one of the large reservoirs in the region operated by ALCAN for hydroelectric production. In

Conclusions

The objective of this study is downscaling of large scale atmospheric variables from GCM outputs to climate variables at regional and local scale in order to investigate the hydrological impact of future climate change scenario. Downscaling is necessary since the hydrological models normally used for impact studies require local meteorological time series, which are compatible with the size of the watershed. Two sets of precipitation and temperature time series from the Chute-du-Diable

Acknowledgements

This work was made possible through a grant from the Canadian Climate Change Action Fund, Environment Canada, and a grant from the Natural Sciences and Engineering Research Council of Canada to the second author. The authors gratefully acknowledge the contributions of Bruno Larouche (P.Eng., ALCAN Company) and Xiaogang Shi (MSc, McMaster University) in the modeling of the Chute-du-Diable watershed with CEQUEAU and HBV-96 hydrologic models, respectively.

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