Model Selection for Great Basin Climate Projections

We selected an ensemble of future climate projections (Table 3) from the World Climate Research Programme's Coupled Model Intercomparison Project phase 5 (CMIP5). CMIP5 data for the continental US (Taylor et al, 2012) were downscaled to 12-km using the Bias Corrected Statistically Downscaled (BCSD) (Wood et al., 2004) methodology. These projections align with those adopted in the Intergovernmental Panel on Climate Change 5th assessment report (AR5; IPCC, 2014). The AR5 projections include representative concentration pathways (RCPs) that describe four potential 21st century pathways of greenhouse gas emissions and resultant atmospheric concentrations as well as air pollutant emissions and land-use changes. The RCPs include a stringent mitigation scenario (RCP2.6) that aims to constrain global warming at or below 2°C greater than pre-industrial air temperatures. There are two intermediate scenarios (RCP4.5 and RCP6.0) and one scenario with high greenhouse gas emissions (RCP8.5). RCP6.0 and RCP8.5 scenarios model conditions resulting from failure to adopt stringent efforts to constrain emissions.

Table 3. Global climate models considered in the selection process for input to the Basin Characterization Model of the Great Basin. Note that all models include representative concentration pathways (RCP) 4.5 and 8.5, but RCP2.6 and RCP6.0 are only available for a subset of models.

A subset of the CMIP5 climate futures, defined as a combination of a GCM and a representative concentration pathway emissions scenario, listed in Table 3 was selected using a statistical cluster analysis to represent the full range of potential climate projections, from warm and wet to hot and dry (details below). These climate futures were then further downscaled to 270 m and used as input into the BCM to generate a suite of climate and hydrologic response variables that have direct relevance for conservation and management of the Great Basin's natural resources and adaptation strategies. We chose to select futures to provide scenarios and models that represented wet and dry conditions for a range of temperature increases across the entire southwest, including the completed California model, and the upper Colorado, and the lower Colorado, both of which are in progress (fig. 1). As a result, all the models, once completed, will reflect consistent methodologies and applied futures to generate a contiguous set of results across the entire region. We are therefore including all 4 model domains in the methods description for model selection.

We did not select a "most likely" future climate trajectory; such a goal is fundamentally impossible given the uncertainties in emissions, model performance, and the inherent unpredictability of short-term climatic variability. The goal of having a wide-ranging ensemble of climate futures is to "expose" particular hydrological and ecological systems to varying degrees of climate change, quantify their modeled responses, and understand when and where the limits of acceptable system performance are exceeded in a "scenario-neutral" approach (Prudhomme and others, 2010; Brown, 2012). Given the known climate sensitivities of arid, semi-arid, and seasonally dry landscapes in the Great Basin this approach permits a sensitivity analysis of the landscape that can identify areas or watersheds that will be most impacted, and those that may be least affected across a range of climate projections.

A cluster analysis used effectively reduced the number of futures needed to capture the full range of variability among CMIP5 futures. Seasonal and maximum annual air temperature (T_max), minimum annual air temperature (T_min), and annual and seasonal precipitation (PPT) were averaged into three 30-year periods (2010-2039, 2040-2069, and 2070-2099). The 30-year periods were defined as climate "normals," consistent with standard climatology. Seasonal summaries included fall (September-October-November, SON), winter (December-January-February, DJF), spring (March-April-May, MAM) and summer (June-July-August, JJA).

Spatial averages of these seasonal and annual 30-year averages of Tmax and PPT were calculated for four regions: California, Great Basin, Upper Colorado, and Lower Colorado. The cluster analysis used only end of-century climate (2070-2099). All of the annual and seasonal temperature variables were highly correlated (r>0.9) which justified using only annual T_max in the cluster analysis. Seasonal PPT was used to identify the relative inputs of winter Pacific storms and summer monsoonal moisture. Some of the remaining variability among T_min and T_max at the seasonal scale is associated with varying precipitation, so screening on PPT captures this aspect of variability. For example, there is generally a positive correlation between seasonal T_min and precipitation, reflecting warmer nights under cloudy conditions. The cluster analysis to group climate futures was completed (JMP 10.0, SAS Institute), using Wards agglomerative method. The number of clusters chosen was based on the inflection point of cumulative variance explained.

The cluster diagram indicated as a dendrogram on the left shows a 6 cluster solution, with cluster size ranging from 1 to 21 futures. Clusters are color-coded. The 6 cluster choice corresponds to the inflection point in the cumulative variance plot below the dendrogram, and suggests that 6 clusters are sufficient to capture ~ 50% of the variability and that additional clusters have a low marginal value. At least one future from each cluster is necessary to capture the range of variability. The final choices were informed by other criteria, such as consistency in emissions scenario referred to as representative concentration pathways (RCP), model performance, expert opinions, and use by other climate change assessments.

The identities of the futures by cluster is shown on the right, along with some ancillary information such as North American (NA) skill (Watterson and others, 2014), choice by experts in CA for California's 4th climate assessment (Pierce and others, 2015), and ENSO spectra rank. The final set of 12 futures is highlighted by cluster color. The set is necessarily a compromise. Where possible, futures considered by Pierce and others (2015) for California were chosen, to allow for comparability across larger spatial domains. The two wetter models (CanESM2 and CNRM_CM5) both had high NA skill. While MirocESM had poorer NA skill, the distinctive response and segregation into its own cluster merited its inclusion in the ensemble. MirocESM was the driest model overall, especially the RCP8.5 run, and represents an extreme scenario worth investigating for adaptation limits.

We plot T_max and PPT for three 30-year normals in the original 54 futures (Figure 4), and the 12 selected futures are highlighted in black. The selected futures span the full range of PPT and T_max projected by all 54 futures over the next century. The climatological differences among the regions are clearly seen.

The summer monsoon (represented by JJA PPT) in the Great Basin is a critical climatological feature that was reproduced in the selected futures (Figure 5). Most futures remain within a range of 40-55 mm, close to the historical average. We did not detect large projected trends with the exception of Canesm2, which greatly increases JJA precipitation, especially in rcp85. ACCESS1_0-rcp45 projects the driest monsoon seasons, less than 40 mm.

Monthly Climate and Hydrology for Historical (1951-2013), and 12 Projected Futures (2011-2099), for the Great Basin Region

The Basin Characterization Model (BCM) is a simple grid-based model that calculates the water balance for any time step or spatial scale by using climate inputs, precipitation, minimum and maximum air temperature. Potential evapotranspiration is calculated from solar radiation with topographic shading and cloudiness, snow is accumulated, sublimated, and melted (sublimation, snowfall, snowpack, snowmelt), and excess water moves through the soil profile, changing the soil water storage. Changes in soil water are used to calculate actual evapotranspiration, and when subtracted from potential evapotranspiration calculates climatic water deficit. Depending on soil properties and the permeability of underlying bedrock, water may become recharge or runoff. Routing is done via post-processing to estimate baseflow, streamflow, and groundwater recharge (see Flint and others, 2013). Monthly downscaled climate inputs and hydrologic output variables (Table 1) can be examined for any size polygon representing regions or watersheds, or the distribution across the landscape.

Detailed schematics, inputs and outputs can be found at https://ca.water.usgs.gov/projects/reg_hydro/projects/dataset.html. All methods and datasets follow those described in Flint and others (2013) with the exception of revised soil properties. Revisions were made to increase the soil water holding capacity to improve matches of BCM recharge estimates for the Great Basin to local estimates of recharge and measured data.

The BCM uses soil data sets from both the Soil Survey Geographical Database (SSURGO) and State Soil Geographic Database (STATSGO) (Miller and White, 1998; Wieczorek, 2013; Wieczorek, 2014). STATSGO is a coarse national dataset and is only used to fill in gaps that exist in the finer resolution SSURGO dataset. The data needed for the BCM are soil depth, soil porosity, and water content at field capacity and wilting point. Available water capacity is the water content at field capacity minus the water content at wilting point times the soil depth. Both SSURGO and STATSGO provide water content at field capacity as water content at a matric potential of -0.33 bars (-0.033MPa) and wilting point as water content at a matric potential of -15.0 bars (-1.5MPa). Although these numbers have been used to date by the BCM, current research has shown that the BCM has a tendency to overestimate recharge (RCH) by under estimating actual evapotranspiration (AET). Several methods were evaluated to develop better estimates of RCH and AET. Both SSURGO and STATSGO limit soil depth to 2.01 m, however plant rooting depths can penetrate below this arbitrary maximum soil depth and extract additional water thus increasing AET and reducing RCH. Adding additional soil depth helped in some cases, however in many cases increasing soil depth by 10 m was not deep enough to match AET or RCH where we had good estimates of both. In these cases water content at field capacity and wilting point were within a couple of percent of each other so even with the additional depth the soil could not hold enough water to match AET. Many plants, especially desert plants, can remove water to wilting points of <-60 bars (-6.0 MPa) and many natural field soils (non-agricultural) have field capacities more on the order of -0.10 bars (-0.01 MPa) (Flint and Childs, 1984a,b). Changing field capacity and wilting point to water content at these water potentials can increase the water holding capacity in most locations.

In order to develop a range of water contents at different matric potentials using soil texture and organic matter from SSURGO and STATSGO, a variety of soil physics based equations were investigated (Campbell, 1985; Gupta and Larson, 1979; Rawls, and others, 2003; Saxton and Rawls, 2006). These equations, often based on the same laboratory data sets, are in general agreement with each other but some provide more utility than others. Organic matter is an important contributor to available water capacity (Gupta and Larson, 1979; Hudson, 1994; Huntington, 2006; Rawls and others, 2003; Saxton and Rawls, 2006). Saxton and Rawls (2006) incorporated the relation between organic matter and available water capacity and provided enough information that we could develop water retention equations for each soil type using a simple power function (Brooks and Corey, 1964). These new equations were used with soil texture (percent sand, silt, and clay) and percent organic matter to develop water retention curves for each location and we used water content at -0.1 bars for field capacity and -30 bars for wilting point to run the BCM for the Great Basin.

Monthly Climate and Hydrology for Historical (1951-2013), and 12 Projected Futures (2011-2099), for the Great Basin Region

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Flint, L.E. and Flint, A.L., 2014, California Basin Characterization Model: A Dataset of Historical and Future Hydrologic Response to Climate Change, U.S. Geological Survey Data Release, doi:10.5066/F76T0JPB

Flint, L.E., Flint, A.L., Thorne, J.H., and Boynton, R., 2013, Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance: Ecol. Processes 2:25.

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Thorne, J.H., Boynton, R., Flint, L.E., Flint, A.L., Le, T.N., 2012, Development and application of downscaled hydroclimatic predictor variables for use in climate vulnerability and assessment studies: CEC-500-2012-010. California Energy Commission, Sacramento.

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Publications

Basin Characterization Model Development

Flint, L.E. and Flint, A.L., 2014, California Basin Characterization Model: A Dataset of Historical and Future Hydrologic Response to Climate Change, U.S. Geological Survey Data Release, doi:10.5066/F76T0JPB

Flint, L.E., Flint, A.L., and Thorne, J.H., 2014, Evaluating Climate Change Using the Basin Characterization Model: USGS Fact Sheet 2014-3098

Flint, L.E., Flint, A.L., Thorne, J.H., and Boynton, R., 2013, Fine-scale hydrological modeling for climate change applications; using watershed calibrations to assess model performance for landscape projections; Ecological Processes 2:25

Thorne, J.H., Boynton, R., Flint, L.E., Flint, A.L., and Le, T-N., 2012, Development and application of downscaled hydroclimatic predictor variables for use in climate vulnerability and assessment studies. California Energy Commission. Publication number: CEC-500-2012-010.

Flint, L.E., Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes 2012 1:1. doi:10.1186/2192-1709-1-2

Flint, L.E., and Flint, A.L., 2012, Estimation of Stream Temperature in Support of Fish Production Modeling under Future Climates in the Klamath River Basin: USGS Scientific Investigations Report 2011-5171, vi, 31 p.

Flint, L.E., and Flint, A.L., 2008, Regional analysis of ground-water recharge, in Stonestrom, D.A., Constantz, J., Ferré, T.P.A., and Leake, S.A., eds., Ground-water recharge in the arid and semiarid southwestern United States: U.S. Geological Survey Professional Paper 1703, p. 29-59.

Flint, A.L., and Flint, L.E., 2008, Integration of regional hydrologic modeling using FORTRAN and ArcGIS, Water Resources IMPACT 10(1) p. 31-35.

Flint, A.L., Flint, L.E., Hevesi, J.A., and Blainey, J.M., 2004, Fundamental concepts of recharge in the Desert Southwest: a regional modeling perspective, in Groundwater Recharge in a Desert Environment: The Southwestern United States, edited by J.F. Hogan, F.M. Phillips, and B.R. Scanlon, Water Science and Applications Series, vol. 9, AGU, Washington, D.C., 159-184.

Flint, A.L., 2004, Recharge mapping at Yucca Mountain, Nevada — the scale effect; Box 4-2 in Groundwater Fluxes Across Interfaces, National Research Council, National Academies Press, Washington, D.C., p. 50-51.

Hevesi, J.A., Flint, A.L., and Flint, L.E., 2003, Simulation of net infiltration and potential recharge using the distributed-parameter watershed model, INFILv3, of the Death Valley Regional Flow System, Nevada and California: U.S. Geological Survey Water Resources Investigation Report 03-4090.

Basin Characterization Model Applications

Watershed Modeling and Climate Change Threats

Esralew, R., Flint, L.E., Thorne, J.H., Boynton, R., and Flint, A.L., 2015, A framework for effective use of hydroclimate models in climate-change adaptation planning for managed habitats with limited hydrologic response data, Environmental Management DOI 10.1007/s00267-015-0569-y

Byrd, K.B., Flint, L.E., Alvarez, P., Casey, C.F., Sleeter, B.M., Soulard, C.E., Flint, A.L., Sohl, T.L., (2015), Integrated climate and land use change scenarios for California rangeland ecosystem services: wildlife habitat, soil carbon, and water supply. Landscape Ecology, 30(4): 729-750. 10.1007/s10980-015-0159-7

Thorne, J.H., Flint, L.E., Flint, A.L., Boynton, R., 2015, The magnitude and spatial patterns of historical and future hydrologic change in California's watersheds. Ecosphere 6(2):24. http://dx.doi.org/10.1890/ES14-00300.1

Kershner, J.M., editor. 2014. A Climate Change Vulnerability Assessment for Focal Resources of the Sierra Nevada. Version 1.0. EcoAdapt, Bainbridge Island, WA.

Flint, L.E., and Flint, A.L., 2012, Simulation of climate change in San Francisco Bay Basins, California: Case studies in the Russian River Valley and Santa Cruz Mountains: U.S. Geological Survey Scientific Investigations Report 2012-5132, 55 p.

Micheli, L., Flint, L.E., Flint, A.L., Weiss, S.B., and Kennedy, M., 2012, Downscaling future climate scenarios to the watersed scale: a North San Francisco Bay Estuary case study, San Francisco Estuary and Watershed Science, 10(4).

Flint, L.E., Flint, A.L., Curtis, J.A., and Buesch, D.C., 2011, A Preliminary Water Balance Model for the Tigris and Euphrates River System: USGS Open-File Report.

Recharge Modeling

Faunt, C.C., Stamos, C.L., Flint, L.E., Wright, M.T., Burgess, M.K., Sneed, Michelle, Brandt, Justin, Martin, Peter, and Coes, A.L., 2015, Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the Borrego Valley, San Diego County, California: U.S. Geological Survey Scientific Investigations Report 2015–5150, 135 p., http://dx.doi.org/10.3133/sir20155150.

Siade, A.J., Nishikawa, R., Martin, P. 2015, Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA), Hydrogeology Journal (23) 1267-1291.

Phillips, S.P., Rewis, D.L., and Traum, J.A., 2015, Hydrologic model of the Modesto Region, California, 1960-2004, U.S. Geological Survey Scientific Investigations Report 2015-5045.

Hanson, R.T., Flint, L.E., Faunt, C.C., Gibbs, D.R., and Schmid W., 2014, Hydrologic models and analysis of water availability in Cuyama Valley, California, U.S. Geological Survey Scientific Investigations Report 2014-5150.

Hanson, R.T., Schmid, W., Faunt, C.C., Lear, J., and Lockwood, B. 2014, Integrated hydrologic model of Pajaro Valley, Santa Cruz, and Monterey Counties, California, U.S. Geological Survey Scientific Investigations Report 2014-5111.

Drexler, J.Z., Knifong, D., Tuil, J, Flint, L.E., and Flint, A.L., 2013, Fens as whole-ecosystem gauges of groundwater recharge under climate change. J. Hydrol. http://dx.doi.org/10.1016/j.jhydrol.2012.11.056

Flint, L.E., Flint, A.L., Stolp, B.J., and Danskin, W.R., 2012, A basin-scale approach for assessing water resources in a semiarid environment: San Diego region, California and Mexico, Hydrology and Earth System Sciences, 16, 1-17.

Hanson, R.T., Flint, L.E., Flint, A.L., Dettinger, M.D., Faunt, C.C., Cayan, D., and Schmid, W., 2012, A method for physically based model analysis of conjunctive use in response to potential climate changes, Water Resources Research, vol. 48, W00L08,

Flint, A.L. and Flint, L.E., 2012, Recharge, in Martin, P., and Flint, L.E., (eds), Geohydrology of Big Bear Valley, California: Phase 1—Geologic Framework, Recharge, and Preliminary Assessment of the Source and Age of Groundwater, USGS Scientific Investigations Report 2012-5100, 112p.

Flint, A.L., Flint, L.E., and Masbruch, M.D., 2011, Appendix 3: Input, Calibration, Uncertainty, and limitation of the basin characterization model, in Conceptual model of the Great Basin Carbonate and alluvial aquifer system, U.S. Geological Survey Scientific Investigation Report 2010-5193, 20 p.

Flint, A.L., and Flint, L.E., 2007, Application of the basin characterization model to estimate in-place recharge and runoff potential in the Basin and Range carbonate-rock aquifer system, White Pine County, Nevada, and adjacent areas in Nevada and Utah: USGS Scientific Investigations Report 2007-5099, 20 p.

Heilweil, V.M., Brooks, L.E., editors, 2010, Conceptual model of the Great Basin carbonate and alluvial aquifer system, U.S. Geological Survey Scientific Investigations Report 2010-5193, 191 p.

Laczniak, R.J., Flint, A.L., Moreo, M.T., Knochenmus, L.A., Lundmark, K.W., Pohll, G., Carroll, R.W.H., Smith, J.L., Welborn, T.L., Heilweil, V.M., Pavelko, M.T., Hershey, R.L., Thomas, J.M., Earman, S., and Lyles, B.F., 2007, Ground-water budgets in Welch, A.H., Bright, D.J., and Knochenmus, L.A. eds., 2007, Water resources of the Basin and Range carbonate-rock aquifer system, White Pine County, Nevada, and adjacent areas in Nevada and Utah—A Report to Congress: U.S. Geological Survey Scientific Investigations Report 2007-5261, p. 43-82.

Blasch, K., Hoffmann, J.P., Bryson, J., Graser, L., Leon, E., and Flint, A., 2006, Hydrogeology of the upper and middle Verde River watersheds, central Arizona: U.S. Geological Survey Scientific Investigations Report 2005-5198.

Wildfire and Forest Health

Mann, M.L., Batllori, E., Moritz, M.A., Waller, E.K., Berck, P., Flint, A.L., Flint, L.E., and Dolfi, E., 2016, Incorporating anthropogenic influences into fire probability models: effects of human activity and climate change on fire activity in California, PLoS ONE 11(4): e0153589. Doi:10.1371/journal.pone.0153589

Millar C.I., Westfall, R.D., Delany, D.L., Flint, A.L.,, and Flint, L.E., 2015, Recruitment patterns and growth of high-elevation pines in response to climatic variability (1883-2013), western Great Basin, USA. Canadian J Forest Res, DOI: 10.1139/cjfr-2015-0025

Anderegg, W.R., Flint, A.L., Huang, C-Y., Flint, L.E., Berry, J.A., Davis, F., Field, C.B., 2015, Tree mortality predicted from drought-induced vascular damage. Nature Geoscience, 8(5), 367-371., DOI: 10.1038/ngeo2400.

Millar C.I., Westfall, R.D., Delany, D.L., Flint, A.L.,, and Flint, L.E., 2015, Recruitment patterns and growth of high-elevation pines in response to climatic variability (1883-2013), western Great Basin, USA. Proceedings of PACLIM 2015.

McIntyre, P.J., Thorne, J.H., Dolanc, C.R., Flint, A.L., Flint, L.E., Kelly, M., and Ackerly, D.D., 2015, 20th century shifts in forest structure in California: denser forests, smaller trees, and increased dominance of oaks, Proc. Nat. Acad. Sciences, doi/10.1073/pnas.1410186112.

van Mantgem, P.J., Nesmith, J.C.B., Keifer, M.B., Knapp, E.E., Flint, A.L., and Flint, L.E., 2013, Climatic stress increases forest fire severity across the western United States, Ecology Letters (doi:10.1 111/ele. 12151)

Das, A., Stephenson, N., Das, T., van Mantgem, P., and Flint, A.L., 2013 Forecasting climate-related tree mortality in energy- versus water- limited forests, PLoS ONE 8(7): e69917. doi:10.1371/journal.pone.0069917

Millar, C.I., Westfall, R.D., Delany, D.L., Bokach, M.J., Flint, A.L., Flint L.E., 2012, Forest mortality in high-elevation whitebark pine (Pinus albicaulis) forests of eastern California, USA: influence of environmental context, bark beetles, climatic water deficit, and warming. Can. J. For. Res. 42: 749–765.

Biodiversity and Species Distribution Modeling

Serra-Diaz, J.M., Franklin, J., Dillon, W.W., Syphard, A.D., Davis, F.W., and Meenteneyer, R., 2016, California forests show early indications of both range shifts and local persistence under climate change, Global Ecology and Biogeography 25, 164-175, DOI: 10.1111/geb.12396

Khosravi, R., Hemami, M., Malekian, M., Flint, A.L., and Flint, L.E., 2016, Maxent modeling for predicting potential distribution of goitered gazelle in central Iran: the effect of extent and grain size on performance of the model, Turk J Zool 40:574-585, doi:10.3906/zoo-1505-38

Serra-Diaz, J.M., Franklin, J., Sweet, L.D., McCullough, I.M., Syphard, A.D., Regan, H.M., Flint, L.E., Flint, A.L., Dingman, J.R., Moritz, M., Redmond, K., Hannah, L., Davis, F.W. Averaged 30 year climate change projections mask opportunities for species establishment: Ecography 10/2015; DOI:10.1111/ecog.02074

McCullough, I.M., Davis, F.W., Dingman, J.R., Flint, L.E., Flint, A.L., Serra-Diaz, J.M., Syphard, A.D., Moritz, M.A., Hannah, L., and Franklin, J., 2015, High and dry: high elevations disproportionately exposed to regional climate change in Mediterranean-climate landscapes: Landscape Ecology, DOI:10.1007/s10980-015-0318-x

Hannah, L., Flint, L., Syphard, A., Moritz, M., Buckley, L., and McCullough, I., Place and process in conservation planning for climate change: A reply to Keppel and Wardell-Johnson: Trends in Ecology and Evolution 30(5) (DOI:10.1016/j.tree.2015.03.008)

Hannah, L., Flint, L., Syphard, A.D., Moritz, M.A., Buckley, L.B., and McCullough, I.M., 2014, Fine-grain modeling of species' response to climate change: holdouts, stepping-stones, and microrefugia, Trends in Ecology and Evolution, vol. 29, no. 7. dx.doi.org/10.1016/j.tree.2014.04.006

Chang, M.Y., Carlino, Jennifer, Barnes, Christopher, Blodgett, D.L., Bock, Andy, Everette, A.L., Fernette, G.L., Flint, L.E., Gordon, Janice, Govoni, D.L., Hay, L.E., Henkel, H.S., Hines, M.K., Holl, S.L., Homer, Collin, Hutchison, V.B., Ignizio, D.A., Kern, Tim, Lightsom, F.L., Markstrom, S.L., O'Donnell, Michael, Schei, J.L., Schmid, L.A., Schoephoester, K.M., Schweitzer, P.N., Skagen, S.K., Sullivan, D.J., Talbert, Colin, and Warren, M.P., 2015, Community for data integration 2013 annual report: U.S. Geological Survey Open-File Report 2015–1005, 36 p., http://dx.doi.org/10.3133/ofr20151005.

Heller, N.E., Krietler, J., Weiss, S., Ackerly, D., Racinos, A., Brancifort, R., Flint, L., Flint, A., Thrasher, B., Micheli, L. 2015, Targeting climate diversity in conservation land networks to build resilience to climate change, Ecosphere 6(4):65. http://dx.doi.org/10.1890/ES14-00313.1.

Ackerly DD, Cornwell WK, Weiss SB, Flint LE, Flint AL (2015) A Geographic Mosaic of Climate Change Impacts on Terrestrial Vegetation: Which Areas Are Most at Risk? PLoS ONE 10(6): e0130629. doi:10.1371/journal.pone.0130629

Chornesky, E.A., Ackerly, D.D., Beier, P., Davis, F.W., Flint, L.E., Lawler, J.J., Moyle, P.B., Moritz, M.A., Scoonover, M., Alvarez, P., Byrd, K., Heller, N.E., Micheli, E.R., and Weiss, S.B., 2015, Adapting California's ecosystems to a changing climate, BioScience 65, no. 3 (2015): 247-262.

Maher, S.P., Morelli, T.L., Hershey, M., Flint, A.L., Flint, L.E., Moritz, C., and Beissinger, S.T., 2015, Erosion of refugia in the Sierra Nevada meadows network with climate change, accepted by Ecological Applications.

Chardon, N.I., Cornwell, W.K., Flint, L.E., Flint, A.L., and Ackerly, D.D., 2014, Topographic, latitudinal and climatic distribution of Pinus coulteri: geographic range limits are not at the edge of the climate envelope, Ecography 37:001-012 (doi: 10.1111/ecog.00780)

Curtis, J.A., Flint, L.E., Flint, A.L., Lundquist, J.D., 2014, Incorporating cold-air pooling into downscaled climate models increases potential refugia for snow-dependent species within the Sierra Nevada Ecoregion, CA. PLoS ONE 9(9): e106984. doi:10.1371/journal.pone.0106984

Beltran B.J., Franklin, J., Syphard, A.D., Regan, H.M., Flint, L.E., Flint, A.L., 2013, Effects of Climate Change and Urban Development on the Distribution and Conservation of Vegetation in a Mediterranean Type Ecosystem, International Journal of Geographical Information Science, 28:8, 1561-1589, DOI: 10.1080/13658816.2013.846472

Conlisk, E., Syphard, A.D., Franklin, J., Flint, L., Flint, A., Regan, H. 2013, Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models, Global Change Biology (2013) 19, 858–869, doi: 10.1111/gcb.12090

Conlisk, E., Lawson, D., Syphard, A., Franklin, J., Flint, L., Flint, A., Regan, H. 2012, The roles of dispersal, fecundity, and predation in the population persistence of an oak (Quercus engelmannii) under global change, PLoS ONE 7(5): e36391. doi:10.1371/journal.pone.0036391.

Franklin, J., Davis, F., Ikegami, M., Syphard, A., Flint, L., Flint, A., Hannah, L., 2012, Modeling plant species distributions under future climates: how fine-scale do climate projections need to be? Global Change Biology doi: 10.1111/gcb.12051.

Regan, H.M., Syphard, A.D., Franklin, J., Swab, R.M., Markovchick, L., Flint, A.L., Flint, L.E., Zedler, P.H., 2011, Evaluation of assisted colonization strategies underglobal change for a rare, fire-dependent plant: Global Change Biology doi: 10.1111/j.1365-2486.2011.02586.x

Sork, V.L., Davis, F.W., Westfall, R., Flint, A., Ikegami, M., Want, H., and Grivet, D. 2010, Gene movement and genetic association with regional climate gradients in California valley oak (quercus lobate Nee) in the face of climate change, Molecular Ecology 19, 3806-3823. doi: 10.1111/j.1365-294X.2010.04726.x

Finn, S.P., Kitchell, K, Baer, L A, Bedford, D, Brooks, M.L., Flint, A.L., Flint, L.E., Matchett, J.R., Mathie, A, Miller, D.M., Pilliod, D, Torregrosa, A, and Woodward, A, 2010, Great Basin Integrated Landscape Monitoring Pilot Summary Report: U.S. Geological Survey Open-File Report 2010-1324, 50 p.