Linking Global Climate Models to an Integrated Hydrologic Model: Using an Individual Station Downscaling Approach

摘要

Scientists integrate models to assess the impacts of climate change and climate variability on water resources and ecosystems. In this context, integration occurs between atmospheric and hydrologic models by downscaling and bias correcting Global Circulation Models (GCM)climate projections (e.g., precipitation and temperature data sets) and feeding these data as boundary conditions into a hydrologic model. Integrated models can help water managers and policymakers make more informed decisions when planning for future climate conditions, such as managing for changes in the timing and quantity of available water (Hay et al. 2002; Salathé 2004; Hanson and Dettinger 2005; Maurer and Hidalgo 2008). Downscaling is an important step in integrated modeling because the spatial resolution of GCM simulated temperature and precipitation (～100 – 250 km) is too coarse for watershed-scale hydrologic modeling. As such, downscaling approaches combine GCM climate output with regional climate models (RCMs) and/or sub-regional climate information to alleviate the scale problem (Hay et al. 2002; Fowler et al. 2007; Maurer and Hidalgo 2008; Huntington and Niswonger 2011).In mountainous environments, it is especially important to incorporate fine-scale information into coarse GCM projections for hydrologic modeling because of the altitude-dependence of precipitation and temperature that is not reflected at the GCM resolution (Seager and Vecchi 2010). Downscaling GCM data is necessary to properly simulate hydrologic processes that are sensitive to altitude (e.g., snow pack development, snowmelt runoff, soil moisture, and evapotranspiration). Many methods have been used for downscaling GCM output to a finer spatial resolution, and most result in temperature and precipitation data sets at spatial resolutions around 10 km (Lettenmaier et al. 1999; Wood et al. 2002; Salathé 2003; Wood et al. 2004; Maurer and Hidalgo 2008), making further downscaling necessary for watershed-scale hydrologic modeling, which is typically at a scale of <1 km.Downscaling methods generally fall into three categories, statistical, dynamical, and hybrid statistical-dynamical downscaling. Statistical methods are the most commonly used (Wilby et al. 2004); however, dynamical and hybrid methods are becoming more popular due to their ability to incorporate non-stationary, regional-scale atmospheric processes into downscaled results (Leung et al. 2003). Statistical downscaling refers to modifying GCM output such that the statistical characteristics of the output (e.g., probability of occurrence) are consistent with the statistical characteristics of fine-scale climate data, typically taken from climate stations. Dynamical downscaling refers to the use of RCMs to produce climate data through regional-scale atmospheric simulations at a spatial scale smaller than the GCMs scale. Typically, RCMs simulate climate at scales between 10 and 50 km using initial and boundary conditions provided by GCMs, and they require large computational resources relative to statistical approaches. However, RCMs provide a more realistic representation of regional climate processes as compared to statistical downscaling, yet the accuracy depends largely, as is the case with statistical downscaling, on the large-scale results provided by GCMs (Piani et al. 2010).Statistical downscaling approaches are computationally efficient and are easily reproducible, but they rely on relationships that are invariant in a changing climate between large-scale GCM and regional scale climate variables (Sharma et al. 2007). The efficiency of statistical downscaling facilitates the use of many different GCM model outputs and scenarios to generate a wide range of climate projections, which is useful considering model uncertainty (Tebaldi and Knutti 2007; Pielke et al. 2009). Primary drawbacks associated with statistical downscaling include: (1) the stationary assumption of the regression relationships may not hold for climate co