TECHNICAL METHODOLOGY

Transcription

1 States at Risk: America s Preparedness Report Card Technical Methodology Table of Contents Introduction... 2 Climate Threat Analysis... 2 Data Sources... 2 Conterminous United States... 2 Alaska and Hawaii... 3 Coastal Flooding Analysis... 5 Climate Threat Scores... 6 Climate Threat Indicators... 6 Alaska and Hawaii... 8 Spatial Aggregation: From Grid Cell to State... 8 Note: Using Multiple Global Climate Models... 9 Note: Considerable and Significant Climate Threat... 9 Exceptions Statistical Test Limitations of the Climate Threat Analysis Other Sources of Uncertainties References Credits And Acknowledgements Climate Preparedness Analysis The Climate Preparedness Concept Assessing Climate Preparedness Climate Preparedness Scoring Limitations of the Climate Preparedness Analysis Aggregation and Grading Scores Standardization Grading Climate Threat Grade Overall State Grade Alaska and Hawaii Limitations of the Grading Scheme Grading Example Appendix I: Detailed Preparedness Scoring Approach

2 Introduction The States at Risk: America s Preparedness Report Card project (hereafter, Report Card) focuses on five threats related to climate change extreme heat, drought, wildfires, inland flooding, and coastal flooding. It then assesses to what extent states have taken a core set of climate actions to protect people and infrastructure from the risks associated with the future changes in the five climate threats. There is no doubt that climate change could have differential and considerable impacts on natural systems as well as human systems. The scope of this climate preparedness analysis focused on the potential climate change impacts on each state and covered five major sectors: transportation, energy, water, human health and communities. Each state s climate preparedness was evaluated only for those threats determined to be of considerable magnitude and were projected to get worse in the future due to climate change. Grades were then assigned to each of the 50 states based on both the magnitude of the current and future changes in their climate threat and the action states have taken to prepare for them, as well as how these compare to other states. Climate Threat Analysis In this Report Card, the characteristics of five climate threats and their changes between the baseline and future periods were assessed across all 50 states. The characteristics of extreme heat, wildfires and inland flooding were quantified for the baseline period of year 2000 (the mean value of ) and the future period of year 2050 (the mean value of ). Since drought is cumulative period of dryness, a 50- year mean was used for both the baseline period ( ) and the future period ( ). For coastal flooding, sea levels in the years 2000 and 2050 (projected) were used. Data Sources Conterminous United States The climate threat analysis for the conterminous United States was based on "Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections" archive at dcp.ucllnl.org/downscaled_cmip_projections/. The bias- corrected statistically- downscaled (BCSD) climate projections (Reclamation, 2013) and hydrology projections (Reclamation, 2014) used are derived from the global climate model (GCM) runs in the Coupled Model Intercomparison Project 5 (CMIP5) experiment (Table 1), referenced in the IPCC Fifth Assessment Report (AR5). Climate threat analysis was performed for outputs based on 29 GCMs available in the archive under the RCP 8.5 emissions scenario. These climate and hydrology projections have a spatial resolution of ⅛ (about 140 2

3 square kilometers per grid cell), and cover the conterminous United States and portions of Canada and Mexico. Alaska and Hawaii The climate threat analysis for Alaska and Hawaii was based on the NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP) dataset, available at (Thrasher et al., 2012). The dataset is comprised of BCSD climate projections at 0.25 x 0.25 (approximately 25 km x 25 km), also derived from the CMIP5 experiment (Table 1); no downscaled hydrology projections are available in this dataset. Climate threat analysis was performed for outputs based on 21 GCMs available in the dataset under RCP 8.5. Before using the temperature projections in the climate threat analysis, a post- processing step was applied to: Exchange the values when Tmax < Tmin. Table 1. List of CMIP5 global climate models used. Model Name Modeling Center (or Group) Dataset ACCESS1-0 BCC- CSM M Commonwealth Scientific and Industrial Research Organization (CSIRO)/Bureau of Meteorology (BOM), Australia Beijing Climate Center, China Meteorological Administration, China Beijing Climate Center, China Meteorological Administration, China College of Global Change and Earth System Science, Beijing Normal University Canadian Centre for Climate Modelling and Analysis, Canada National Center for Atmospheric Research, University Corporation for Atmospheric Research, USA USBR BCC- CSM- 1-1 BNU- ESM CANESM2 CCSM4 CESM1- BGC Community Earth System Model Contributors, USA CESM1- CAM5 Community Earth System Model Contributors, USA USBR CMCC- CM Euro- Mediterranean Center on Climate Change, Italy USBR CNRM- CM5 National Centre for Meteorological Research, France CSIRO- MK Commonwealth Scientific and Industrial Research Organization/Queensland Climate Change Center of Excellence, Australia FGOALS- G2 Laboratory of Numerical Modelling for Atmospheric USBR 3

4 Model Name Modeling Center (or Group) Dataset Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, China FIO- ESM The First Institute of Oceanography, SOA, China GFDL- CM3 GFDL- ESM2G GFDL- ESM2M National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA GISS- E2- R National Aeronautics and Space Administration Goddard USBR Institute for Space Studies, USA HADGEM2- AO Met Office Hadley Centre, UK USBR HADGEM2- CC Met Office Hadley Centre, UK USBR HADGEM2- ES Met Office Hadley Centre, UK USBR Institute for Numerical Mathematics, Russian Academy INMCM4 of Sciences, Russia Dynamical Meteorology Laboratory at the Pierre- Simon IPSL- CM5A- LR Laplace Institute, France Dynamical Meteorology Laboratory at the Pierre- Simon IPSL- CM5B- MR Laplace Institute, France Atmosphere and Ocean Research Institute, National MIROC- ESM Institute for Environmental Studies/Japan Agency for Marine- Earth Science and Technology, Japan Atmosphere and Ocean Research Institute, National MIROC- ESM- CHEM Institute for Environmental Studies/Japan Agency for Marine- Earth Science and Technology, Japan Atmosphere and Ocean Research Institute, National MIROC5 Institute for Environmental Studies/Japan Agency for Marine- Earth Science and Technology, Japan MPI- ESM- LR MPI- ESM- MR MRI- CGCM3 NORESM1- M Max Planck Institute for Meteorology, Germany Max Planck Institute for Meteorology, Germany Meteorological Research Institute, Japan Meteorological Agency, Japan Norwegian Climate Center, Norway 4

5 Coastal Flooding Analysis The sea level rise and coastal flood analysis aimed to delineate zones with 1% annual flood risk (100- year coastal floodplains), given baseline and projected sea levels in the years 2000 and 2050, and to tabulate the current population residing within them. Median sea level rise (SLR) projections from Kopp et al. (2014) are for 2050 under RCP 8.5 at 69 tide gauges along the U.S. coast. The projections take vertical land motion into account. The baseline 100- year coastal floodplains used are unmodified from FEMA floodplain maps and coastal area classification (Crowell, 2013). To generate new coastal floodplains following sea level rise, it is first necessary to find elevations along the margins encapsulating baseline floodplains. To do so, this analysis first constructed a list of points along each edge that are no further than 1 arcsec apart. Employing NOAA s high- accuracy Coastal LIDAR Digital Elevation Model (DEM), the analysis then sampled land elevation at each of these points, referenced to local mean higher high water (MHHW). All samples with land elevation less than or equal to zero meters (already below high tide line) or greater than five meters (empirically found to be very strong outliers) were removed from this list. The remaining samples provided approximate elevations, above MHHW, along all margins of FEMA s 100- year coastal flood layers. To reduce the impact of noise due to errors in the DEM and particularly in FEMA s flood maps, which were derived using older and less accurate elevation data, each state was then divided into a 0.2 x 0.2 degree grid. Each grid cell was assigned to the county within which its centerpoint resides; and within each grid cell, the median elevation of samples was computed. Any cells with zero samples were ignored. To further prune outliers, sample points within all cells with median values two or more standard deviations away from the mean of all cell medians in the same county, were removed. For each pixel in the DEM, inverse distance weighted interpolation was then performed using the nearest five grid cells, to compute the estimated 100- year flood elevation (referenced to MHHW) at that point. By subtracting the flood elevation from land elevation, a new land elevation map referenced to flood height was created. Finally, this new flood- referenced elevation was thresholded against the projected local SLR at each point to produce estimated 100- year coastal floodplain maps for the year Local SLR projections were based on nearest- neighbor interpolation from the 69 tide gauges. Before using these maps to assess flood exposure risk, they were further refined by removing all low- lying areas isolated by topography or levees from the ocean. Data from the Mid- term Levee Inventory (FEMA/USACE) was used for levees, which was assume to be high enough and strong enough to protect against any flood. Connected components analysis was used to remove all areas that were protected by levees and the natural topography of the land. The US Census provides place boundaries, block boundaries and block populations ( data/data/tiger- line.html), which were used to compute population and land exposed under each of our flood maps, before tabulation up to state level. 5

6 This analysis assumes uniform density Census blocks, except for zero density over wetland areas, as described in more detail in (Strauss et al., 2012). Climate Threat Scores The calculation of a state climate threat score consists of 4 basic steps: (1) Climate threat indicators: Compute climate indicator values at each of the grid cells covering the United States, for the baseline period and future period separately. (2) Spatial aggregation: For both the baseline period and future period, aggregate the gridded climate indicators values to the state level, using two weighting methods: a. The absolute measure, which weights intermediate geographies (counties and watersheds) by their total vulnerable population. b. The relative measure, which weights intermediate geographies (counties and watersheds) by their vulnerable population as a percentage of state population. (3) State climate threat level: Calculate a climate threat level for both time periods by combining the absolute and relative measures. (4) State climate threat score: Calculate a climate threat score by combining two elements: the climate threat level for the baseline period and the change in the climate threat level between the baseline period and the future period. Each of these steps is detailed below. Climate Threat Indicators To quantify the level of climate threat, an indicator was selected to represent each of the five climate threats (Table 2). Each indicator has its own strengths and weaknesses, and there is no single best indicator to represent a particular climate threat. The indicators were chosen on the basis that they could be used to reflect the general condition for their respective climate threats and allow spatial comparison across the U.S. The scope of this Report Card focuses on state level climate actions intended for human systems, and climate change is likely to have differential effects on different demographics. Hence, the climate threat analysis put more emphasis on a state s vulnerable population. Table 2 shows the groups of population considered to be especially vulnerable in relation to extreme heat, wildfire, and inland flooding, which were used as weighting factors during the spatial aggregation process up to the state level (described below). In all cases, 2010 Census data was used and populations were assumed to remain unchanged in the future therefore, changes in climate threat level were due to climate change. 6

7 For drought, it was difficult to determine a meaningful vulnerable population, as drought impacts on population tend to be less direct and less location- dependent. For coastal flooding, a weighting factor was not used as the indicator already measured population at risk. Table 2. Indicators and Weighting Factors for Each Climate Threat. Climate Threat Indicator Extreme Heat Average annual number of heatwave days Average number of days each year with daily maximum temperature exceeding the 95th percentile of daily maximum temperature in the baseline ( ) period for at least three consecutive days. Drought Severity of widespread summer drought Sum of soil moisture deficit (standard score) in the summer months for model grid cells where z- score < - 1, based on the mean and standard deviation, when at least 30% of grid cells in a state meet this criterion. Wildfire Average annual number of days with high wildfire potential Average number of days each year with Keetch- Byram Drought Index exceeding 600. Inland Flooding Average annual severity of high flow events Sum of runoff volume that exceeds the 95th percentile of daily total runoff in the baseline ( ) period. Weighting Factor County- level population under the age of 5 and aged 65 or over living below the poverty line. N/A County- level population living in the wildland- urban interface (WUI) 1. HUC4- level population living on FEMA 100- year riverine floodplain. Coastal Flooding Number of people at risk of a 100- year coastal flood N/A 1 The wildland- urban interface is the area where structures and other human development meet or intermingle with undeveloped wildland (Radeloff et al., 2005). 7

8 Alaska and Hawaii Since the NASA dataset contains only BCSD climate projections and no BCSD hydrology projections, hydrology- related indicators for drought and inland flooding were not computed for Alaska and Hawaii. Spatial Aggregation: From Grid Cell to State The climate models divide the United States into a grid. With the exception of coastal flooding, the values for each of the climate threat indicators (Table 2) were calculated separately at each grid cell, before aggregating to a state- level value as described below. For drought, the gridded standard score anomalies were totaled. This captures both the magnitude and spatial extent of the drought events. Absolute and Relative Measures With the exception of drought, two state values an absolute and a relative measure were derived for each climate indicator. For extreme heat, wildfire and inland flooding, the state absolute and relative measures were calculated by multiplying the sub- state climate indicator value by its vulnerable population and vulnerable population as a percentage of total state population, respectively, before summing across the state. It is necessary to account for both of these situations: for instance, vulnerable population may represent 1% of State A s population and 8% of State B s population; however, this could mean that State A has 45,000 vulnerable people and State B has 5,000 vulnerable people. For extreme heat and wildfire, the county- mean indicator values were weighted according to the county- level distribution of the state s vulnerable population. For inland flooding, the HUC4- mean results were weighted by the HUC4- level distribution of the state s vulnerable population. A spatial unit larger than a county or watershed implies averaging of dissimilar results due to the topographic heterogeneity across a state. For coastal flooding, the state absolute and relative measures were the number of people and the percentage of state population living on land within the baseline or projected 100- year coastal floodplain, respectively. State Climate Threat To combine the absolute and relative measures, a data standardization process was performed for the baseline and future periods, and for each climate indicator separately. 8

9 The absolute measure for each state was standardized by dividing the value of each absolute measure, for each indicator, by its maximum value in the baseline period across all the states analyzed. The same standardization process was also applied to the relative measures. For each climate indicator, the standardized values of the state absolute and relative measures were then summed with equal weights to give the state climate threat level for each time period. State Climate Threat Score For each indicator, the state climate threat score was mean of the baseline threat level and the change in threat level between the baseline and future periods. Note: Using Multiple Global Climate Models State climate threat for each indicator was computed separately for each GCM; only the central tendency (median) values were reported. This climate threat analysis adopted a multi- model approach because results from a single GCM only suggest a single trajectory of how future climate might unfold, which overlooks the full range of possible future conditions and could therefore be misleading. Since there is no way to scientifically determine the single most accurate projection of future conditions, results presented here are based on multiple GCMs known as a multi- model ensemble The GCM results were not weighted in any way; each model was considered equally credible, with each projection equally plausible (note: but not equally likely). This is because: A model that does well reproducing the past climate does not necessarily mean it would do well for the future, especially when long- term climate projections cannot be validated directly through observations; and, Whether or not a GCM does well at reproducing the past climate can vary with the aspect of past climate one is evaluating its performance on. Therefore, model rankings can vary with the metrics/criteria used for evaluation. Even for analyses that rank and weight models, there is no universally agreed metric for separating good from bad models. Note: Considerable and Significant Climate Threat It is particularly important for states to prepare for climate threats that have a certain level of seriousness and are projected to get worse in a changing climate. The emphasis of this Report Card was on climate threats determined to be of considerable magnitude and significantly increasing in the future due to climate change, thus the level of climate preparedness was only assessed for the qualified state climate threats. The assumption was that states should already be acting on those climate threats with a considerable magnitude in the baseline period 9

10 though not projected to get worse with climate change. In the case of coastal flooding, all the coastal states were projected to experience sea level rise, thus were all assessed for their state climate preparedness. For the other climate threats, the number of states assessed for their climate preparedness are: Extreme Heat: 48 states Drought: 36 states Wildfire: 24 states Inland Flooding: 32 states Either one of the following conditions would qualify a state climate threat for the climate preparedness analysis: A state with a climate threat that exceeded the threshold in the baseline period in either absolute measure (relative to other states) or relative measure (relative to the state itself); and, the threat level is (statistically) significantly increasing in the future; OR A state with a climate threat that was below the threshold in the baseline period in either absolute or relative measure but was projected to exceed the baseline threshold in the future. The threshold of an indicator, in both the absolute and relative measures, is the median value, in the baseline period, of the state climate threat the lower 48 states. For instance, this included the top 24 (of the 48) states that have the highest number of vulnerable people, or the highest percentage of the state's population, in the baseline period. Exceptions There were two cases where the change in climate threat in the 2050 period were not statistically significant but were included in the climate preparedness assessment; these are inland flooding in California and drought in Nevada. This was because both of them have statistically significant increases in the 2030 period ( ), which implies that states should also consider preparing for the associated near- term climate risks. Statistical Test For each state, the significance of change for each climate indicator (except for coastal flooding) between the baseline and future periods was determined by the Wilcoxon Signed- Rank test (two sample, paired). Only states with an increase in their threat level at p < 0.05 were considered to be statistically significant. Limitations of the Climate Threat Analysis This climate threat analysis has a number of limitations and assumptions, some of which include: 10

11 Results in this climate threat analysis are subject to the uncertainties associated with the global climate models and hydrological model, as well as the downscaling approach used. Results are conditional upon the selected climate threat indicators and weighting factors, as well as datasets (e.g., wildland- urban interface and FEMA flood maps) used to construct them, which may be more suitable for some regions than others. The use of 2010 Census information in the coastal flooding analysis and weighting factors for other climate threats assumed uniform density Census blocks. Population living within the FEMA 100- year riverine floodplain was estimated using available digital data. This dataset covers more than 70% of state population in 30 of the 32 states analyzed for inland flooding, with 100% coverage in 7 states. For states without 100% coverage, areas already digitized and those that were not yet digitized were assumed to have the same proportion of population in the FEMA floodplain. The coastal flooding analysis method assumed that storm risks will remain constant; and that small changes in sea level height will not affect local storm surge dynamics. Other Sources of Uncertainties While this climate threat analysis was based on an ensemble of global climate models and one hydrological model, the effects of other sources of uncertainties have not been accounted for. Some examples include uncertainties associated with: Downscaling: The hydro- climatic datasets used are only based on one statistical downscaling approach Bias- Correction Spatial Disaggregation. Uncertainties associated with other statistical (e.g., Bias- Correction Constructed Analogues, BCCA), and dynamical downscaling approaches, have not been explored. Emission scenario: Results are only based on RCP8.5. Hydrological model: The hydrology dataset used are solely based on outputs from the Variable Infiltration Capacity hydrologic model (VIC). Parameter: The hydro- climatic datasets used are only based on one model variant of each global climate model and VIC. Parameter uncertainties have not been explored by using variants of a given model with different plausible ranges of parameter values, known as a perturbed physics ensemble (PPE). Natural climate variability: This is difficult to estimate due to the short instrumental records and difficulties in reconstructing past climate conditions using paleoclimatology proxies, which imply an incomplete description of variability on decadal and longer time scales. 11

12 References Crowell, M., J. Westcott, S. Phelps, T. Mahoney, K. Coulton, and D. Bellomo, 2013: Estimating the United States Population at Risk from Coastal Flood-Related Hazards. In Coastal Hazards, edited by Charles W Finkl, Springer. doi: / Kopp, R.E., R.M. Horton, C.M. Little, J.X. Mitrovica, M. Oppenheimer, D.J. Rasmussen, B.H. Strauss, and Cl. Tebaldi, 2014: Probabilistic 21st and 22nd Century Sea-Level Projections at a Global Network of Tide-Gauge Sites, Earth s Future, 2(8), doi: /2014ef Radeloff, V.C., R.B. Hammer, S.I Stewart, J.S. Fried, S.S. Holcomb, and J.F. McKeefry. 2005: The Wildland Urban Interface in the United States, Ecological Applications, 15, Reclamation, 2013: Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with preceding Information, and Summary of User Needs, prepared by the U.S. Department of the Interior, Bureau of Reclamation, Technical Services Center, Denver, Colorado. 47pp. Reclamation, 2014: Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Hydrology Projections, Comparison with preceding Information, and Summary of User Needs, prepared by the U.S. Department of the Interior, Bureau of Reclamation, Technical Services Center, Denver, Colorado. 110 pp. Strauss, B.H, R. Ziemlinski, J.L. Weiss, and J.T. Overpeck, 2012: Tidally Adjusted Estimates of Topographic Vulnerability to Sea Level Rise and Flooding for the Contiguous United States, Environmental Research Letters, 7(1). IOP Publishing: doi: / /7/1/ Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrology and Earth System Sciences, 16(9), Credits And Acknowledgements We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this analysis) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Climate scenarios used were from the dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). We are grateful to Eitan Frachtenberg and Brian Rumsey for assistance in data management and analysis for this climate threat analysis. 12

13 Climate Preparedness Analysis The Climate Preparedness Concept This Report Card quantifies the states climate preparedness in terms of the actions they have taken that aim to prepare them for potential climate change impacts and risks. Embedded in our criteria for preparedness assessment are the following questions: 1. Is the state taking action to address its current risks from the climate threat? 2. Has the state undertaken activities to understand its future changes in vulnerabilities and risks from each climate threat? 3. Has the state planned for adaptation to the future changes in risks from each climate threat? 4. Is the state implementing specific actions to address future changes in risks to each climate threat? In order to provide quantifiable and comparable results, the concept of climate preparedness in this Report Card focuses on whether states have taken actions to prepare for the projected changes in the climate threats assessed, rather than whether those actions are sufficient to address their changing climate risks. Despite advances in climate science and climate models, it is almost impossible to predict the exact changes in future climate a state will face, what the precise climate impacts on the community will be, nor whether a state s actions are sufficient to address those impacts. For each of the climate threats assessed, states were evaluated for their climate preparedness to that threat across five major sectors: Transportation, Energy, Water, Human Health, and Communities (which includes non- transportation and non- energy infrastructure), as well as the state as a whole. Each sector was selected based on the critical role it plays in modern society. In some cases, certain climate threats were assumed to have a relatively insignificant impact on a given sector, it would be difficult to assess whether a sector has prepared for that impact, or the impact was already covered under another sector. In such cases, the sector was not assessed for those climate threats. These not applicable sector- threat intersections are: Transportation & drought: Although drought can affect transportation infrastructure such as through effects on right- of- way vegetation, soil integrity underlying infrastructure, and inland waterway navigation the impacts are less significant and uniformly applicable nationwide than those of other climate threats. In addition, transportation agencies generally do not have specific policies or programs related to drought; necessary maintenance and repairs are covered by general programs within the agencies, so it is difficult to tease out whether states are preparing for this threat. Water & inland flooding: Research for the water sector revolved around sufficient availability of water for the states needs. While inland flooding can affect water quality, those effects are covered under the human health sector. Also, inland flooding 13

14 could affect the infrastructure related to water supply and distribution, but non- transportation infrastructure is covered by the communities sector. Communities & extreme heat: The communities sector covers non- transportation, and non- energy, infrastructure. Most infrastructure is not greatly affected by extreme heat. Communities & drought: The communities sector covers non- transportation and non- energy infrastructure. Most infrastructure is not greatly affected by drought, except in very specific geographic areas with high plasticity soils. While services to communities can be affected, those services are covered in other sectors. A state s climate preparedness is derived from a multitude of factors. One cannot focus on a single action of a state and declare it prepared. Rather, climate preparedness comes from a combination of general emergency preparedness and hazard mitigation planning, understanding and planning for state- specific climate risks, and implementation of and financing of coordinated climate preparedness actions. There is certainly no one size fits all way to prepare for the potential impacts associated with future changes in climate. There is also no one (or several) action(s) that will guarantee that a state is prepared. Assessing Climate Preparedness Sixteen indicators were identified for each climate threat and sector that represented different actions a state might take to increase its climate preparedness. These indicators each fell under one of the following categories: 1. Category 1: Is the state taking action to address its current risks from the climate threat? A state cannot be prepared for future climate threats if it is not prepared for its current ones. This category considers actions that the state has already taken to address the climate threats, for example, through the state hazard mitigation planning process (extreme heat is sometimes excluded), whether the state has strong communications systems to alert its communities about extreme weather events (including disadvantaged populations), and other activities aimed to reduce current climate risks within the state. 2. Category 2: Has the state undertaken activities to understand its future changes in vulnerability and risks from each climate threat? It is difficult for a state to prepare for the potential changes in climate risks when it does not know what they are. Therefore, this analysis examined whether there was evidence that the state was making an effort to understand any future changes in its climate risks. For example, whether a state has published information on how the state s climate may change, or conducted a climate change vulnerability assessment. 3. Category 3: Has the state planned for adaptation to the future changes in risks from each climate threat? Planning involves developing a plan to adapt to the state s changing climate threats that covers the sectors examined. This analysis considered whether there was a climate action plan in progress or already completed, whether there was evidence that relevant agencies were involved in developing the plan (rather than a plan developed by one stakeholder group that made recommendations for other 14

15 groups that did not have the opportunity to weigh in), whether the plan evaluated relative effectiveness or feasibility of different climate change adaptation measures, and whether the plan was endorsed by the State and included a clear implementation timeline and responsibilities. 4. Category 4: Is the state implementing specific actions to address future changes in risks to each climate threat? Implementation goes beyond the actual plan, and considers whether a State is taking actions. The analysis investigated whether there was evidence that climate change adaptation or resiliency measures were eligible projects for state- administered funding programs, whether there were (optional) state policies or technical guidance for how to incorporate climate change projections in agency programs or planning activities, whether there were (mandatory) state regulations that require agencies to considers climate change projections in programs, investments, or other activities, and whether there was evidence that climate change has already been accounted for in state planning activities or investments. Climate Preparedness Scoring Category 1 covers the actions that address climate risks in the present- day, and Categories 2 to 4 focus on those intended to address changes in risks under future climates. Each of the four categories is comprised of up to six indicators. Each of these indicators, in turn, were broken into up to 25 sub- indicators specific to each climate threat and sector combination. Performance against each of these indicators is assigned a score of 1-4, based on specific criteria designed to reflect the seriousness and rigor with which a state is addressing each of the categories for each of the climate threats and sectors. Each state received a score for each of the four categories that represented the percentage of possible points earned. A state climate preparedness score for each threat was the mean of Category 1 score (preparedness for current climate risks) and the mean score for Categories 2-4 (future climate risks). This scoring methodology was developed in consultation with an Expert Panel of five leading experts in the areas of climate change indicator development, assessment techniques, and state approaches to preparedness. This group reviewed and refined the methodology prior to launch of the study. Expert Panel members include: Dr. Virginia Burkett (U.S. Geological Survey), Dr. Melissa Kenney (University of Maryland), Dr. Thomas Wilbanks (Oak Ridge National Labs), Dr. LaDon Swann (Mississippi- Alabama Sea Grant Consortium), and Ms. Susan Love (Delaware Department of Natural Resources). The climate preparedness scores were entirely evidence- based. Each indicator was first evaluated through extensive web- based research, including reviewing publicly- available documents and state- published web content describing state actions that fall under the four categories. To augment findings from the review of publicly- available documents, representatives from state agencies responsible for the sectors were then interviewed. These interviews filled any gaps in the online research and confirmed findings. 15

16 Limitations of the Climate Preparedness Analysis As with any methodology, there are some important limitations to note. First, the analysis did not evaluate adequacy of state climate actions. For example, states were awarded points in Category 3 for having a climate change adaptation plan with certain characteristics; however, whether the proposed climate change adaptation measures would be sufficient for managing the potential climate change impacts were not assessed. Similarly, points were awarded in Category 4 for states that provide financial resources to fund climate resiliency initiatives, but it would be difficult to determine whether the levels of funding are adequate. Secondly, this analysis focused entirely on climate actions intended for human systems. State actions aimed for natural systems such as ecosystems and biodiversity were not assessed. Another limitation is that the scope of this Report Card focused specifically on climate actions at the state level. In some states, local communities are making strong efforts to prepare for climate change risks; similarly, there are cases where actions taken by federal or municipal agencies, or the private sector are making a state better prepared. Because this Report Card focused only on state actions, actions undertaken by non- state entities were not taken into account when developing the climate preparedness scores. Finally, because all scores were evidence- based, there were a few situations where full points were not awarded for some indicators even though some climate action might be underway. These situations were few, and generally arose because of two situations: (1) the state has decided to take action (such as by preparing a climate change vulnerability assessment or adaptation plan), but the initiative was in the very early stages during the research process, and draft documents were not yet available or the content of the intended plans or programs could not be confirmed with detailed information. The information contained in the Report Card is up- to- date as of October 15, 2015, so some states may have completed additional activities since that date, and their grades will not reflect those activities; and, (2) the content of key documents could not be obtained, reviewed, and confirmed. For example, Delaware is the only state that does not make its State Hazard Mitigation Plan publicly available, therefore the content of the plan could not be assessed. Aggregation and Grading For each indicator, the climate threat score and climate preparedness score were combined to give a final threat score for each threat. To achieve this, both the climate threat score and climate preparedness score were first standardized. Scores Standardization To compute the grade for each climate threat assessed for each state, both the climate threat score and climate preparedness score were converted to standard scores, which put these two 16

17 different components on the same standard scale to enable comparison. Each standard score is expressed as number of standard deviations from the mean of the distribution. Grading Climate Threat Grade In this Report Card, the grading method was developed such that any state has the potential to get an overall A (by being well prepared) or F (by being poorly prepared) grade, regardless of its climate threat level. To achieve this, the climate threat score and preparedness score were weighted by a 1 : 3.51 ratio (the minimum weighting factor to meet the above criterion). Thus, for each climate threat: Final threat score = 3.51 Preparedness standard score Threat standard score The final score was translated into a grade according to the following percentile scale: Percentile Grade 93 A 90 A B B 65 B- 55 C+ 45 C 35 C D D 10 D- Below 10 F Overall State Grade For overall state scores, the final scores for all the threats faced by a given state were added. Thus a state with five climate threats would have more opportunity to do well or poorly than a state with two climate threats. Grades were then assigned to these overall state scores by the same percentile scale. Alaska and Hawaii For Alaska and Hawaii, the grades for extreme heat and coastal flooding were derived in the same way as the lower 48 states, except that these two states were not included in the 17

18 distribution during the data standardization process. This was because a different set of BCSD climate projections was used to compute the extreme heat indicator, and only extreme heat and coastal flooding were assessed for Alaska and Hawaii due to the absence of BCSD hydrology projections. Limitations of the Grading Scheme Some of the limitations and assumptions associated with the aggregation and grading process are outlined below: Grades are based on both the magnitude of the climate threats and level of state climate action, each evaluated relative to other states, as opposed to using absolute measures. The 3.51 weighting used for climate preparedness may over- or under- emphasize it compared to the climate threat. Equal weights were applied to the climate threats analyzed for any given state, absolute/relative measures, and baseline/future components i.e., a low value in one can offset or average out a high value. This may under- represent the true level of state climate threat and preparedness (e.g., inland flooding may pose a greater risk than extreme heat in one state but not another). The sensitivity of the results to different levels of tradeoff has not been explored. Grading Example As an example of the grading process, consider the grading of Iowa. (1) The climate indicator calculations and aggregation methods described above yield final threat and preparedness scores of: Threat Preparedness Extreme Heat: Drought: Inland Flooding: Note that these scores are not directly comparable to one another. 18

19 (2) Conversion to a standard score [ (!!!), where μ is the mean and σ is the standard deviation! across state scores for each threat] allows comparison of the threat and preparedness scores on the same scale: Standard Scores Threat Preparedness Extreme Heat: Drought: Inland Flooding: (3) The final threat score is 3.51 times the threat standard score minus the preparedness standard score: Extreme Heat: 3.51 * (- 0.27) (- 0.60) = Drought: 3.51 * (- 0.07) (- 0.86) = 0.60 Inland Flooding: 3.51 * (- 0.46) (- 0.68) = (4) Grades are assigned by the percentile of each score within the distribution of scores of all states assessed for the threat: Threat Score Percentile Grade Grade range Extreme Heat: 54.8 C Drought: 62.8 C Inland Flooding: 51.0 C As can be seen, Iowa s grade for extreme heat is a high C, its grade for drought is a high C+, and its grade for inland flooding is a mid- range C. (5) To calculate the overall grade, the three final threat scores (for extreme heat, drought, and inland flooding) are added to yield an overall score: (- 0.35) (- 0.95) = (6) Overall grades are assigned by the percentile of the overall score within the distribution of all states overall scores: Iowa Overall Score Overall Score Percentile Grade Grade range C Note that the overall grade may not correspond to an average of a state s individual threat grades; the threat grades compare states performance for a given threat, the overall grades compare states performance across all threats they face. 19

20 Appendix I: Detailed Preparedness Scoring Approach This appendix provides an overview of the approach used to evaluate preparedness, as well as detailed scoring guidelines for evaluating the preparedness indicators. General Approach and Definitions This section provides and overview of the preparedness indicators, the general research process, and key guidelines for research. Scope The preparedness evaluation considered actions that states are taking to prepare for climate change within 5 sectors, with consideration of 5 climate threats. The sectors evaluated are: Transportation State- owned transportation networks and assets. This will include the state highway system for all states. Energy Provision of energy/ability of residents to get electricity this is largely related to energy infrastructure ranging from generation (power plants) through transmission and distribution infrastructure (transfer stations, power lines). Focus is on activities by the state energy department and public utilities commission (and not individual utility companies). Water Drinking water supply and demand. Includes infrastructure ranging from storage (dams and reservoirs) through distribution infrastructure (pipes, aqueducts). Focus is on the state water department and public utilities commission (and not individual utility companies). Health Public health (morbidity, mortality). Communities Buildings: homes, businesses, and related infrastructure (except for transportation, energy, and water infrastructure). The climate threats considered are: Coastal Flooding Flooding caused by sea level rise or storm surge. Can refer to permanent inundation or temporary flooding. Hurricanes fall under coastal flooding for coastal states. Inland Flooding Flooding or erosion from heavy rains. Extreme Heat Hot days, heat waves. Wildfire Wildfire (not urban fires). Drought Summer drought. 20

21 Climate Change Preparedness Indicators Overview The report card uses the following set of indicative actions (indicators) to evaluate each state s climate change preparedness. Each indicator is applied to each of the 5 threats (extreme heat, drought, wildfires, inland flooding, coastal flooding) and/or the 5 sectors (transportation, energy, water, health, communities), for a range of 5 to 25 sub- indicators. For example Indicator 1.1 Does the state have programs to mitigate [threat] impacts to [sector]? Is actually a series of 25 questions: Does the state have programs to mitigate Extreme Heat impacts to Transportation? Does the state have programs to mitigate Drought impacts to Energy? and so on. The indicators are classified under four categories to address the stages of adaptation planning and implementation. The first category identifies actions taken to address current climate risks while the remaining three categories identify actions to understand and prepare for future changes in risks due to climate change. Table 3 lists these categories and their associated indicators. For each indicator, a state could earn up to four points depending on the specific actions taken to date. Specifics on the point system are found in the Indicator- Specific Scoring Guidelines, starting on page 23. Table 3: Overview of Indicators for Each Category of Preparedness Category Indicator Number of Sub- Indicators Is the state taking action to address its current risks from the climate threat? Has the state undertaken activities to understand its future changes in vulnerabilities and risks from each Does the state have programs to mitigate [threat] impacts to [sector]? Does the state have a technical assistance program to help communities mitigate [threat] impacts? Does the state have a disaster response plan for [threat]? Does the state provide emergency communications materials to citizens for [threat]? Is there evidence the state is providing, or there is no need for the state to provide*, the following to support vulnerable populations in the event of [threat]? What is the state s bond rating? 1 Has the state published general information on future [threat] risk? Has the state agency responsible for [sector] published information on future [threat] risk/vulnerabilities? Is the state tracking [threat] impacts to [sector]?

22 climate threat? Has the state planned for adaptation to each of the future changes in risks from each climate threat? Is the state implementing specific actions to reduce future changes in risks to each climate threat? Has the state conducted public outreach regarding future [threat] risk? Does the state have a general adaptation plan covering [threat]? Does the state have an agency- level adaptation plan covering [sector]? Has the state established a dedicated source of funding for [threat] climate change adaptation in the [sector] sector? Has the state adopted a policy or issued technical guidance to incorporate climate projections for [threat] into [sector]- specific programs, investments, or activities? Does the state have regulations or policy requirements to incorporate climate projections for [threat] into [sector]- specific programs, investments, or activities? Is there evidence that state agencies have incorporated climate projections for [threat] in [sector]- specific programs, investments, or activities that will directly reduce risk? Process The research process involved a two- prong approach. First, extensive web- based research was conducted in order to review plans, policies, and other information available online for each state. Then because a lot of information may not be published in publicly- available documents on the web the research team reached out to specific agencies within each state to gather additional information. The research was conducted using the following process: 1. Conduct web research. a. Review state organizational chart to note relevant agencies. Identify the agencies responsible for each sector. b. Review centralized resources to identify major adaptation- related activities in the state. For example: Georgetown Climate Central Adaptation Clearinghouse, EPA State and Local Climate and Energy Program, Centers for Disease Control Building Resilience Against Climate Effects (BRACE) program, Federal Highway Administration Climate Resilience Pilots program. c. Review all state agency websites and search for evidence of each indicator. 22