Climate change has become an integral part of conducting hydrologic and ecological studies in the twenty-first century. In general, the best scientific evidence suggests that global warming has been occurring and will continue to occur during this century no matter what management approaches to ameliorate climate change are implemented (California Department of Water Resources 2008). Were we to eliminate all anthropogenic greenhouse gas emissions today, about half of the anthropogenic CO2 would be removed from the atmosphere within 30 years, but the remaining atmospheric CO2 would remain for centuries (IPCC 2007). To assess the impacts of climate change, many global socio-economic scenarios are being developed by the Intergovernmental Panel on Climate Change [IPCC] to provide climate scenarios that take into account estimates of possible magnitudes of greenhouse gas emissions that are responsible for much of the climate change. These scenarios are used as boundary conditions for global climate models [GCMs] that provide us with insight into how human behavior in the future may influence changes in climate. These GCMs lack orographic detail, having a coarse spatial resolution with a grid-cell size on the order of 2.5° × 2.5° (approximately 275 × 275 km2), which is far too coarse for landscape or basin-scale models that investigate hydrologic or ecological implications of climate change. The meso-scale (1 to 100 km) climate surfaces provided by most GCM outputs are also too coarse to provide correlations of ecological processes and vegetation distribution needed for understanding threats to biodiversity, and for conservation planning.
Physical and hydrologic processes such as springtime snowmelt, aquifer recharge, forest die-off, or vegetation distributions occur at a myriad of spatial scales. Oak woodlands may be dominant on north-facing slopes in one basin, while another has no aspect bias. Snow melting in the high-elevation Sierra Nevada Mountains under warming climatic conditions may be delayed by weeks in some subbasins in comparison to others (Lundquist and Flint 2006), providing uncertainty for biological and water-resource processes. Conditions driving the processes may be far more relevant at the hillslope scale for some investigations, such as rare plant species distribution, runoff and overland flow as ungauged streamflow distributions of evapotranspiration for agricultural and native vegetation, etc.; the subbasin scale may be appropriate for springtime runoff for fisheries, and the regional scale may be the necessary tool to evaluate water resources in the southwest. The majority of climate change studies are using readily available climate projections at scales greater than 1 km.
The need for fine-scale investigations of ecological processes for species distribution models is related to the differences in model results between meso-scale (coarse) and topo-scale (fine; 0.01 to 1 km) environments, whereby fine-scale models that capture fine-scale environments show markedly different range loss and extinction estimates than coarse-scale models for some species. Results from the western US suggest that fine-scale models may predict vegetation to persist where coarse-scale models show no suitable future climate (Guisan and Thuiller 2005; Dobrowski 2010). Fine-scale spatial heterogeneity should provide greater opportunity for migration and reassembly of communities (Ackerly et al. 2010). This is related to the topographic variation in climate at the topo-scale environment that can exert strong influences on establishment patterns (Callaway and Davis 1998; Keyes et al. 2009). At a finer scale (well below the spatial resolution available in commonly used gridded climate products such as the Parameter-elevation Regressions on Independent Slopes Model [PRISM] at 4 km and 800 m, and WorldClim at 1 km), topoclimate diversity may provide significant spatial buffering that will modulate the local impacts of climate change. Several researchers are currently linking simple fine-scale (25 to 50 m) climatologies to correlational species distribution models (Randin et al. 2009; Trivedi et al. 2008).
Climatic data are normally available at a spatial scale of 1,000 to 10,000 km2, while plant growth is normally measured at a much smaller scale of 100 m2 to 10 km2. Thus, a plant may actually 'experience' a local climate that is quite different from the larger scale climatic data used to quantify climate-growth relationships (Peterson et al. 1998). The scale of topoclimates (0.5 km to 10 m) is the spatial scale at which topography can be used to describe the climate near the ground (Geiger et al. 2003), thus more closely approximating the experience of the organism. The discrete influence of complex environments on habitats and species incorporates topographic shading that influences solar radiation and evapotranspiration, frost pockets or cold-air pooling, and differences in soils, all of which can be described on the basis of topoclimates.
A suite of investigations has detected the improvement in developing species-environment associations using information to account for topographic complexity. Lookingbill and Urban (2003,2005) determined that spatial variations in temperature have a large influence on the distribution of vegetation and are therefore, a vital component of species distribution models (Ashcroft et al. 2008). Topographic variability of a steep alpine terrain creates a multitude of fine-scale thermal habitats that is mirrored in plant species distribution, warning against projections of the responses of alpine plant species to climate warming that adopt a broad-scale isotherm approach (Scherrer and Korner 2010). Topographic complexity and the associated fine-scale heterogeneity of climate dictate the velocity with which current temperature isoclimates are projected to move under climate change scenarios, and this spatial heterogeneity in climate represents an important spatial buffer in response to climate change (Loarie et al. 2009; Ackerly et al. 2010). Wiens (1989) notes that choice of spatial scale is critical in analyzing species-environment associations, and Guisan and Thuiller (2005) describe it as a central problem in bioclimate modeling. The 1-km (or greater) scale was shown to be less effective for species distribution modeling when multiple biophysical attributes, climate, geology, and soils were being used for correlation analyses in a study of forest composition and sudden oak death in the Big Sur region (Davis et al. 2010). In this study, it was determined that the 90-m resolution climate data proved especially important in resolving the strongly contrasting and locally inverted temperature regimes associated with the marine boundary layer near the coast and for approximating the sampling scale of the field sites. A similar conclusion was reached in a California-wide study of valley oak genetic adaptation to rapid climate change, where 90-m climate data provided excellent correlations with the geographic patterns of multivariate genetic variation associated with climatic conditions (Sork et al. 2010).
An example of increases in variability with decreases in scale is illustrated in Ackerly et al. (2010). In this example, the PRISM mesoclimate gradient exhibits a range of just 3°C in January minimum temperatures on the landscape of the San Francisco Peninsula. However, topoclimatic effects modeled at a 30-m scale add a local variability of 8°C nested within the mesoclimate. They conclude that the effects of topoclimatic gradients on the distribution and abundance of organisms can be profound in the Bay Area grasslands, where fine-scale topography provides resilience in the face of year-to-year climate variation, influencing the emergence time of Bay Checkerspot butterflies in relation to the phenology of its host plants (Weiss and Weiss 1998; Hellman et al. 2004). Although downscaling at a regional level to 30 m can be prohibitive due to large file sizes and model runtimes, a fine scale of 270 m captures the topographic variability and corresponding ranges in air temperature, providing for information and enhanced interpretation for conservation planning.
Downscaling is the process of transferring the climate information from a climate model with coarse spatial and fine temporal scales to the fine scale required by models that address effects of climate. Although dynamical downscaling can be achieved using a regional climate model, it is computationally expensive and currently is not practical for processing multi-decadal and/or multimodel simulations from GCMs. A viable alternative that is adequate for many applications is to use statistical downscaling, which has the advantage of requiring considerably less computational resources. In addition, GCM outputs are biased (warmer, colder, wetter, or drier than current conditions) and need to be corrected (transformed) to properly represent modern climate. To convert the results of these coarse scale and biased GCM outputs for input into local scale models, there needs to be a reasonable and systematic process of downscaling and bias correction to produce new data sets that correctly represent the implications of the GCMs but at a scale applicable to local studies. In this paper, we provide an additional example to illustrate the relevance of fine-scale applications at the 270-m scale.
This paper provides a novel approach to address the complex impacts of climate change on the landscape as a result of changes in precipitation and air temperature and the resultant hydrologic response. The approach combines downscaling of global climate projections at 2° spatial resolution to a fine scale of 270-m spatial resolution, verified for accuracy with measured data, and applies the results to a hydrologic model to illustrate the potential application for analyses of impacts of climate change to ecological processes at the landscape, basin, and hillslope scales.
This discussion describes the method used to downscale and bias-correct national monthly GCM outputs and provides new internally consistent data sets for hydrologic and ecological-scale modeling for the US at 4 km, the southwest including California at 270 m, and California at 90 m. These datasets are currently being used in multiple state and region-wide investigations at 270 m and 90 m, and the procedure descriptions will address the 270-m fine-scale resolution. For illustrative purposes, fine-scale applications of these downscaled datasets of ecological and hydrologic correlations to variation in climate are provided using a relatively dry model with business-as-usual emissions.