Uncertainties in climate change projections and regional downscaling in the tropical Andes: implications for water resources management

Abstract

Abstract. Climate change is expected to have a large impact on water resources worldwide. A major problem in assessing the potential impact of a changing climate on these resources is the difference in spatial scale between available climate change projections and water resources management. Regional climate models (RCMs) are often used for the spatial disaggregation of the outputs of global circulation models. However, RCMs are time-intensive to run and typically only a small number of model runs is available for a certain region of interest. This paper investigates the value of the improved representation of local climate processes by a regional climate model for water resources management in the tropical Andes of Ecuador. This region has a complex hydrology and its water resources are under pressure. Compared to the IPCC AR4 model ensemble, the regional climate model PRECIS does indeed capture local gradients better than global models, but locally the model is prone to large discrepancies between observed and modelled precipitation. It is concluded that a further increase in resolution is necessary to represent local gradients properly. Furthermore, to assess the uncertainty in downscaling, an ensemble of regional climate models should be implemented. Finally, translating the climate variables to streamflow using a hydrological model constitutes a smaller but not negligible source of uncertainty.

Climate change in Central America and Mexico: regional climate model validation and climate change projections

“…As a result, the dry season precipitation bias is reduced in the RCM in these regions (regions 5 and 6; see Table 3). Underestimation of precipitation along the leeward slopes (region 5) and overestimation along the windward slopes (region 6) is also detected in a PRECIS study over South America (Urrutia and Vuille 2009;Buytaert et al 2010) and other RCM studies as well (Caldwell et al 2009;da Rocha et al 2009). …”

Assessing uncertainty of climate change impacts on long-term hydropower generation using the CMIP5 ensemble—the case of Ecuador

“…First, the use of the delta method to estimate the percentage changes of climate variables compared to a historic baseline entails assumptions about the nature of the changes, including a lack of change in the variability and spatial patters of climate (NORDEN 2010). The lack of meteorological data and high variability of the climate system in the Tropical Andes region complicate the use of more complex downscaling methods (Buytaert et al 2010) and using downscaled information can be no more reliable than the climate model simulation that underlies it; more detail does not automatically imply better information (Taylor et al 2012). Reliance on climate data from KNMI and downscaling from 0.5°grids may also result in incorrect inflows for regions with complex topography where there are sharp changes in rainfall and runoff over short distances.…”

“…The magnitude of climate change impacts on hydropower generation is usually assessed by running a baseline calibrated hydrological model driven by various climate projections as input forcing data, followed by an electricity generation model (Hay et al 2002). To assess uncertainty related to climate change, studies use a combination of emission or concentration scenarios to derive a range of probable results but use only data from a limited number of GCMs, often only the mean value of GCM results is used (Buytaert et al 2010). For instance, the studies by CEPAL (2012) and De Lucena et al (2010) assessed vulnerability of hydropower to future climate projections for IPCC's SRES A2 and B2 scenarios 2 and one GCM (HadCM3) for Chile and Brazil, respectively.…”

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

“…Therefore, recent studies tend to use the higher-resolution regional climate models (RCMs) to preserve the physical coherence between atmospheric and land surface variables (Bergstrom et al, 2001;Anderson et al, 2011). As such, when evaluating the impact of climate change on water resources on a watershed scale, the use of RCMs instead of GCMs is preferable since RCMs have proven to provide more reliable results for impact study of climate change on regional water resources than GCM models (Buytaert et al, 2010;Elguindi et al, 2011). However, the raw RCM simulations may be still biased especially in the mountainous regions (Murphy, 1999;Fowler et al, 2007), which makes the use of RCM outputs as direct input for hydrological model challenging.…”