Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins
Introduction
The Mediterranean basin is a quasi-closed sea with a marked orography on its periphery and a high urbanization of its coastline. Its climate is characterized by mild winters and hot and dry summers. The marked orography often triggers intense events that may cause flash floods and the hot and dry weather in summer causes low flows to be long and severe. In this context, for planning purposes, it is important to evaluate the possible impacts of climate change on water resources in such a region.
Global climate models (GCM) are the main tool used to study the future climate. According to Giorgi and Lionello (2008), the study of several GCM simulations shows “a robust and consistent picture of climate change over the Mediterranean emerges, consisting of a pronounced decrease in precipitation, especially in the warm season, except for the northern Mediterranean areas (e.g. the Alps) in winter”. It is also expected that the variability increases. In fact, according to Giorgi (2006) the Mediterranean basin is one of the planet’s hot-spots of climate change.
However, GCMs do not have enough resolution to study the regional and local scales. Their current resolution of 300 km (Solomon et al., 2007) misses most of the important relief surrounding the Mediterranean basin. Furthermore, at this scale, they are often biased. This obliges us to downscale the outputs of these models.
The usual strategy in impact studies has a top to bottom structure. Global socio-economic assumptions are made (Nakicenovic et al., 2000), which are then used to force GCMs, which are then downscaled and unbiased. This downscaling can be dynamical (computationally expensive) or statistical (less expensive) (Mearns et al., 1999). If the chosen method is dynamical, a limited area atmospheric model, which can simulate in more detail the climate on a smaller area, is forced at the edges of the domain by the outputs of a GCM (Hewitson and Crane, 1996). These models are known as regional climate models (RCM) and have a typical resolution of 50 km or 25 km. Often, dynamical and statistical downscaling methods are presented as mutually exclusive, but, in fact, as it will be seen in further sections, they can be used together.
The resolution of a RCM is not enough for most hydrological models, thus they need to be further downscaled and bias-corrected (Christensen et al., 2008) to produce atmospheric forcings at the adequate resolution (10 km) (Wood et al., 2004). Thus it is necessary to further downscale the output of these models and to develop methods to reconstruct the regional climate in relation to climate on a larger scale.
In these studies, the emission scenario and the GCM are the main sources of uncertainty (Boé, 2007, Maurer and Hidalgo, 2008). But, unfortunately, each step of the downscaling procedure also has associated uncertainty. All these uncertainties add up and constitute a cascade of uncertainty that must be taken into account. Thus, a complete impact study must look at all kinds of uncertainty. Many studies, have focused on the uncertainty related to the GCM (Christensen and Lettenmaier, 2007, Hamlet and Lettenmaier, 1999, Maurer and Duffy, 2005, Minville et al., 2008, Wilby et al., 2006) but fewer studies have focused on uncertainties related to downscaling to the resolution of the impact model (Boé et al., 2007, Dibike et al., 2005, Khan et al., 2006), which might also be important and is often neglected.
Within this study we look at the impacts of climate change on the French Mediterranean basins. Our goal is to force the hydrological model SIM with three atmospheric forcings representing the climate of the future. These forcings are build from the same RCM simulation using three different methods of downscaling and bias-correction. This should enable us to estimate the hydrological response to climate change, and to estimate the uncertainties related to the last step of downscaling and bias-correction of the climate simulation.
Section snippets
The french Mediterranean context
This article is focused on the French Mediterranean region. Fig. 1 shows the French Mediterranean basin, plus some rivers that do not reach the Mediterranean sea but are Mediterranean in climatological terms. These are situated on the Massif Central.
The largest French Mediterranean basin is the Rhône. Two of the main tributaries of the Rhône are alpine and have a very important nival component. These tributaries are also heavily influenced by hydropower production. But, in our context, we are
Methodology
In this study, three different methods are used to downscale and bias-correct the outputs of one single RCM simulation, using a gridded database of observations. In the next sections, the gridded database, the RCM and the downscaling methods are described.
Description of the hydrological model
In this study, a recent version (Quintana Seguí et al., 2009) of the SAFRAN-ISBA-MODCOU (SIM) model (Habets et al., 2008) is used. This model is the result of combining the SAFRAN meteorological analysis, the ISBA surface scheme and the MODCOU hydrogeological model. Only the main features of the model are described in this paper.
ISBA (Boone et al., 1999, Noilhan and Planton, 1989) is a soil–vegetation-atmosphere transfer (SVAT) scheme. It is used to simulate the exchanges in heat, mass and
Results
Two periods of 30 years were selected to compare present and future climate. For present climate, it was chosen to study the period August 1970–July 2000. The period selected for the future is: August 2035–July 2065.
The significance of the anomalies is evaluated using an adaptation of the Student test that does not require the assumption of the equality of the variances of the compared samples. This adaptation is often referred to as the Welch’s test (Welch, 1947).
Discussion and conclusions
There are many sources of uncertainty in impact studies. The main source is related to the GCM simulation (Boé, 2007), which is often taken into account, but many studies do not take into account the uncertainties related to the final step of downscaling and to the bias-correction of GCM or RCM simulations. In this study, the uncertainties related to this last step were assessed.
Relating precipitation, it was shown that the methods produce similar long term annual averages, but there are
Acknowledgements
This work was partly supported by the program ACI-FNS “Aléas et Changements Globaux” of the French research ministry under the project CYPRIM and the French Agence nationale de la recherche under the project MEDUP. The authors are grateful to Joël Noilhan for his continuous support during this research and his previous work, which made this study possible. The authors are also very grateful to the anonymous reviewers whose comments and suggestions helped to improve the clarity of the text.
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