Regional hydrologic response to climate change in the conterminous United States using high-resolution hydroclimate simulations☆
Introduction
In the conterminous United States (CONUS), several studies based on modeling and observations show that climate change is resulting in the intensification of extreme precipitation and temperature (Diffenbaugh and Ashfaq, 2010), earlier snowmelt (Ashfaq et al., 2013, Abatzoglou, 2011, Mote, 2006), increases in the frequency and intensity of floods and droughts (Mahoney et al., 2012, Strzepek et al., 2010, Narisma et al., 2007, Frumhoff et al., 2007, Knox, 1993), and changes in the timing and magnitude of streamflow (Stewart et al., 2005, Milly et al., 2005). Such changes in hydrological conditions will have an immediate impact on local and regional communities and could have severe consequences for agriculture, property and human losses, energy production, and ecosystems. However, climate change impacts vary from region to region because of differences in geographical characteristics and local climate; thus hydrological response to climate change will be region-specific, depending on the dominant physical processes of a particular region (Hay et al., 2011). Therefore, it is necessary to understand the effects of projected climate change on regional hydrological cycles to support policy makers for more informed adoption and mitigation decisions. Additionally, understanding the spatial distribution of temporal variations of runoff is also important for water resource managers, because finer-scale modeling results can be used to infer practical water resource management decisions such as water allocation and reservoir operation.
Although a number of previous studies have investigated the impacts of climate change on water availability in the US, many of the studies focused on the western US (e.g., Rasmussen et al., 2014, Tohver et al., 2014, Hamlet et al., 2013, Ficklin et al., 2013, Barnett et al., 2004, Christensen et al., 2004, Payne et al., 2004, Stewart et al., 2005, Hamlet and Lettenmaier, 1999, Lettenmaier et al., 1999, Nash and Gleick, 1991, Milly et al., 2005, Seager et al., 2007, Seager and Vecchi, 2010, Mote et al., 2003). Comparatively few studies evaluated the future climate change impacts on the CONUS hydrology (e.g., Wolock and McCabe, 1999, Rosenberg et al., 2003, Thomson et al., 2005, Hay et al., 2011, Hagemann et al., 2013). Many of these past studies relied on hydrological outputs from Global Climate Models (GCMs) to drive one-way coupled hydrologic simulations. However, because of the coarser resolution of GCM grid cells, typically on the order of 150–200 km, hydrologic projections based on raw GCM outputs cannot be used directly for regional-scale water resource management studies. Thus, downscaling and bias-correction procedures are required to bring global climate change signals into watershed-scale hydrologic projections to support resource evaluation.
Different downscaling methods, such as bias-correction spatial disaggregation (BCSD; Wood et al., 2004), bias-correction constructed analogs (BCCA; Maurer et al., 2010), multivariate adaptive constructed analogs (MACA; Abatzoglou and Brown, 2012), and dynamical downscaling (e.g., North American Regional Climate Change Assessment Program; Mearns et al., 2012, Mearns et al., 2013) have been used to support hydroclimate impact assessment in the CONUS (Hamlet et al., 2013, Christensen et al., 2004, Johnson et al., 2012, Glotter et al., 2014, Qiao et al., 2014, Takle et al., 2010, Elguindi and Grundstein, 2013, Bürger et al., 2011). In general, these methods either relied on statistical techniques that can be used to downscale temperature and precipitation from a large number of GCMs, or used computationally intensive regional climate models (RCMs) to downscale all hydroclimate variables in sub-daily time steps through physical relationships. Nevertheless, it should be noted that the effects of different downscaling methods on future hydroclimate projections have not been fully understood, and a consensus on the most suitable downscaling approach for future hydroclimate studies has yet to be reached (e.g., Chen et al., 2013).
In addition to the need for downscaling, the importance of fine-scale land surface modeling – particularly in topographically complex river basins, where topographic effects on hydrologic predictions are significant – has also been highlighted in a number of recent studies (Haddeland et al., 2002, Wood et al., 2011, Vano et al., 2014, Rasmussen et al., 2014). However, although these studies provide valuable watershed-scale hydroclimate information, the finer-resolution models have seldom been applied in a large study domain (e.g., regions or continents) mainly because of the data and computational limitations. In addition, given the differences in spatial and temporal resolution, model structure, and calibration approaches, the results from different finer-resolution studies cannot be inter-compared to provide a regionally coherent picture of future hydrology at a larger scale. To identify regions that are more sensitive to projected future climate changes (in terms of watershed-scale hydrologic response), a spatially and temporally consistent hydroclimate simulation framework is required.
To capture the fine-scale processes and to better understand regional and local hydrological responses to near future climate change, this study uses a hierarchal modeling framework to generate a large ensemble of computationally intensive hydroclimate projections for the evaluation of climate change impacts on regional hydrology across the entire CONUS. A hybrid dynamical and statistical downscaling is used for the refinement of GCM climate change signals for hydrologic simulation. While recent studies have demonstrated the added value of RCMs for impact assessment (Di Luca et al., 2012, Di Luca et al., 2013, Zhang et al., 2011, Chen et al., 2013, Elguindi and Grundstein, 2013), this study provides the most detailed (to date) characteristics of near-term regional and local hydroclimate projections using a high-resolution hydrologic model driven by ten dynamically downscaled and bias-corrected projections from an RCM. Here, we focus on understanding spatial and temporal hydrological change at the sub-basin scale in response to near-term future climate projections in the US. Changes in projected hydroclimate variables are further used to improve the understanding of the likely causes of changes in hydrological extremes, timing of peak runoff, and snow variables. Region-to-region variations in hydrological projection uncertainties are also examined. We present general methodology in Section 2, results in Section 3, discussion in Section 4, and conclusions in Section 5.
Section snippets
Climate projections and downscaling
Using a hybrid downscaling approach (i.e., dynamical and statistical), coarser-resolution GCM outputs are first dynamically downscaled to 18 km resolution using the International Centre for Theoretical Physics Regional Climate Model version 4 (RegCM4) (Giorgi et al., 2012). Choice of RegCM4 is based on the extensive use of its earlier versions over the U.S. for high-resolution multi-decadal climate change simulations (e.g., Diffenbaugh et al., 2005, Diffenbaugh et al., 2011, Mearns et al., 2012,
Model evaluation
To evaluate RegCM4-simulated baseline runoff against the control-run runoff, we compare the cumulative probability distribution of simulated monthly average runoff during the overlapping historical period (1981–2005) in Fig. 4. For reference, observed runoff from the WaterWatch dataset is also shown for the same period. Although, the magnitude of the monthly average runoff mostly compares well between control-run simulation and the WaterWatch-based runoff, a few regions show biases in the
Discussion
Our analysis of potential impacts of climate change on hydrological response in the CONUS, as summarized in Table 3, shows that there is a spatial and temporal heterogeneity in the runoff response resulting from a combination of changes in temperature and precipitation as discussed below.
Conclusions
This study presents the results of simulated regional hydrologic responses to projected climate changes in the CONUS. We use a high-resolution hydrologic model to project changes in key hydrologic indicators, forced by high-resolution climate forcings from a ten-member dynamical downscaled ensemble of CMIP5 GCMs through an RCM under RCP 8.5 with further statistical bias correction. The analysis in this study enables us to understand how future changes in daily temperature and precipitation
Acknowledgments
We thank the editor and anonymous reviewers for their insightful and constructive comments. This study was funded by the Regional and Global Modeling Program, Office of Science, and the Wind and Water Power Technologies Office, Office of Energy Efficiency and Renewable Energy of the US Department of Energy (DOE), and supported a DOE Report to Congress under Section 9505 of the SECURE Water Act of 2009 (Public Law 111-11). This research used resources of the Oak Ridge Leadership Computing
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