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113 Contributors to Non-Stationarity Climate science is still evolving, which makes planning for and incorporating climate change into adaptation projects difficult for planners and designers. One of the challenges in predicting future conditions is non-stationarity; the past can no longer be used as a basis for predicting the future. There are two primary contributors to non-stationarityâgreenhouse gas emissions and land use changes. Impacts from Emissions The 2014 National Climate Assessment (USGCRP, 2014) reports that the majority of atmospheric warming at the global scale is attributable to human-related causes, a large portion of which are the emissions that result from burning fossil fuels (e.g., coal, oil, and natural gas). These emissions include gases capable of trapping and storing heat within the Earthâs atmosphere (e.g., water vapor, CO2, CH4, N2O) and particles, such as soot or black carbon, that have an overall warming effect. As part of the Earthâs natural greenhouse effect, these heat-trapping gases are always present to a certain degree and, while they do not absorb short-wave energy that originates from the sun, they do absorb the long-wave energy that is re-radiated from the Earthâs surface, thus ensuring that the planet remains warmer than it would be otherwise and that it is sufficiently warm to sustain life. Human-related activities have increased the concentrations of these gases and particles so that the amount of heat re-radiated to the surface has increased substantially, while less heat is allowed to escape into space, causing a gradual increase in average global surface temperatures. According to the 2014 National Climate Assessment, this effect of emissions on the Earthâs heat budget is the primary cause of the global warming observed in recent decades. Impacts from Changes in Land Use and Cover Changes in land use and cover have also been found to have a significant effect on climate, in addition to climate-related risks to water resources. Land use refers to any human- related activity that takes place on land, which includes urbanization, agricultural activities, and deforestation. Land cover refers to the physical characteristics of the land, which are affected by land use, including vegetative cover (e.g., crops and trees) and impervious surfaces. One example of the effect of land use and cover on regional climate is the âurban heat islandâ effect. The high percentage of land area covered by pavement, buildings, and other types of impervious surfaces has a substantial effect on the exchange of heat and water between the ground and the atmosphere. Over the past few decades, the most significant changes in land use in the United States have been related to the amount and variety of forest cover being reduced by substantial A P P E N D I X C Climate Information, Design Guidelines, and Data Sources
114 Incorporating the Costs and Benefits of Adaptation Measures in Preparation for Extreme Weather Events and Climate ChangeâGuidebook urban development in the Northeast and Southwest, as well as to logging practices in the Southeast and Northwest. Options for mitigating against the detrimental effects of land use and cover on climate include an expansion in the size and diversity of forests; modifications to urban development to reduce energy, transportation, and water demands (e.g., rainwater capture and reuse); and shifting agricultural practices to encourage soil carbon storage. Resistance to such practices takes into account that decisions related to land use are also affected by economic, cultural, and legal considerations. Other reasons for resistance include the difficulties inherent in the implementation of many climate-friendly modifications to current land use patterns and the fact that in the majority of cases individual land owners and their communities do not realize any direct benefits from such modifications. Climate Models General Circulation Models Climate scientists have developed several quantitative models to simulate the transfer of energy and materials through the climate system (NOAA, 2017). These models allow scientists to test theories and evaluate how changes in variables could affect future conditions. General circulation models (GCMs) are mathematical models that simulate the changes in the atmosphere as a result of slow changes in some boundary conditions (such as the solar constant) or physical parameters (such as greenhouse gas concentration) (Geerts and Linacre, 1998). GCMs are developed to simulate physical processes in the atmosphere, ocean, cryosphere, and land surface, in a three-dimensional space. CMIP5 includes 39 GCMs. GCMs generally have low resolution because of the global coverage of the models, which can make them insufficient for use for some processes that occur at a smaller scale but that can help control climate, such as topography, vegetation, and hydrology. An example of a smaller-scale phenomenon not typically captured by a GCM is a tropical cyclone (i.e., tropical depressions, tropical storms, and hurricanes); a downscaled regional climate model is typically used for this purpose (e.g., Caron, Jones, and Winger, 2011). GCMs have other sources of uncertainty in their models, such as how the various feedback mechanisms are modeled from one GCM to another. Such mechanisms include water vapor and latent warming, clouds and long-wave radiation, effects of ocean circulation, and the reflection of short-wave radiation caused by ice and snow albedo (reflectivity). Regional Climate Models and Downscaling So that some of the physical factors that contribute to regional and local climates, such as meteorological and earth boundary conditions that occur at smaller scales, can be taken into consideration, higher-resolution nested regional climate models (RCMs) are developed from the lower resolution GCMs or from analyses of observational data through a process known as downscaling. Downscaling methods relate large-scale climate variables to regional and local variables. Statistical downscaling is the most common method employed and is based on the premise that regional climate is conditioned by the large-scale state of the climate and local physiographic features incapable of being resolved within the GCM. Large-scale climate variables are input into a downscaling statistical model to estimate higher-resolution local climate characteristics. Statistical downscaling methods (e.g., regressions, neural networks) are useful in regions where sufficient data exist for model calibration. Statistical downscaling can be used to be provide local information for a wide array of climate change impact applications. Disadvantages include the
Climate Information, Design Guidelines, and Data Sources 115 underlying assumption that the statistical relationships developed for the present day will hold into the future under the various possible forcing conditions. Data availability and quality are also key. Regions containing complex topography will likely have limited data available by which statistical relationships can be developed. The other broad category of downscaling methods is dynamical downscaling. This method uses high-resolution regional simulations to dynamically extrapolate the effects of large-scale climate processes to regional or local scales of interest (NOAA, 2017). Dynamical downscaling can be done globally or at a regional level using an RCM (Evans, 2011). Scenarios In addition to modeling the response of the global (and regional) climate system to a change in the concentrations of greenhouse gases in the atmosphere, it is important to focus on the driving forces behind anthropogenic (human caused) climate change and on human response through technology, economics, lifestyle, and policy. Climate scenarios have been developed for these purposes. Scenarios describe potential trajectories of different aspects of the future by representing not only the processes but also the impacts and potential responses related to anthropogenic climate change. Scenarios are used to transfer information from one research area to another (e.g., emissions to climate modeling) and to explore the implication of climate change on policy and decision making. It is important to note that the objective in the development of various scenarios is not to provide a method by which to predict the future, but to better understand uncertainty in various alternative futures under a changing climate and to determine how robust various decisions will be under a range of possible futures. In other words, scenarios were not developed to predict what is going to occur in the future; instead, they facilitate obtaining results from climate models and determining the effects of various decisions under a wide range of potential future conditions. Four scenarios were developed and chosen in conjunction with the release of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in 2014; each considers an alternative future in global greenhouse gas and aerosol concentrations as their initial conditions in order to allow a determination of their impact on the climate system and on socioeconomic conditions. The scenarios are referred to as representative concentration pathways (RCPs) and represent the total radiative forcing pathway and level (in watts per square meter, W/m2) that will occur in the year 2100 from cumulative human emissions of greenhouse gases from all sources. The four RCPs are RCP 8.5, RCP 6, RCP 4.5, and RCP 2.6. â¢ RCP 8.5 represents rising radiative forcing leading to 8.5 W/m2 in the year 2100. â¢ RCP 6 and RCP 4.5 represent a stabilization in radiative forcing (without overshoot) of 6.0 and 4.5 W/m2 after the year 2100. â¢ RCP 2.6 represents a peak in radiative forcing of about 3 W/m2 before the year 2100 and declining afterwards. RCP 8.5 considers the most pessimistic future while RCP 2.6 represents the most optimistic scenario. The use of each scenario in various climate models then gives an estimate of the range of potential future climate conditions that can be expected by the year 2100. Several entities have developed guidance for selecting scenarios for risk-based transportation planning that consider extreme weather and climate change. These guidance documents are summarized in Table C-1. Understanding climate risks to the transportation system is also important to determining when and how to incorporate climate adaptation into project planning. Table C-2 summarizes resources that provide guidance on understanding these risks.
116 Incorporating the Costs and Benefits of Adaptation Measures in Preparation for Extreme Weather Events and Climate ChangeâGuidebook Additional Considerations Intensity, Duration, and Frequency When analyzing changes in precipitation patterns as part of the planning, design, and opera- tion of a particular water resources project, the relationship between rainfall intensity, duration, and frequency, referred to as IDF curves, is important. IDF curves are a common tool used by engineers to determine the amount of rain expected to fall within a given amount of time for a desired annual exceedance probability or its reciprocal, the return period. IDF curves are often used to derive depth-duration-frequency relationships, which allow the estimation of the total rainfall amount corresponding to a return period or, conversely, the return period associated with an observed rainfall event. In order to estimate the IDF curves for a desired region, observed and computed rainfall data are required at a range of temporal resolutions. For example, dura- tions used in NOAA Atlas 14 (https://hdsc.nws.noaa.gov/hdsc/pfds/) range from 5 minutes up to 60 days. Return periods for rainfall totals and intensities are determined using a standard rain- fall distribution function (e.g., Gumbel, Log Pearson Type III, and Generalized Extreme Value, among others) and, in the case of NOAA Atlas 14, cover a range of 1 to 1,000 years. For example, if a location has a 100-year, 24-hour rainfall total of 7.43 inches, the site can expect to exceed a total of 7.43 inches of rainfall in any 24-hour period annually only once in every 100 years, which corresponds to an annual exceedance probability of 1 percent for such an event. Gradual Change versus Extreme Events One of the primary questions to consider when planning water resources projects in the face of a changing climate is whether to focus on the effects of gradual trends related to, for exam- ple, frequent rainfall or river discharge events, or to more drastic changes in the intensity of extreme events. To answer this question, it needs to be understood that climate change will likely Resource Title Author/Organization Region Criteria for Selecting Climate Scenarios (2013) Intergovernmental Panel on Climate Change International Scenarios for Climate Assessment and Adaptation (2015) U.S. Global Change Research Program Climate Model Comparison Tool The Infrastructure and Climate Network Northeast A Framework for Considering Climate Change in Transportation and Land Use Scenario Planning (2012) The Interagency Transportation, Land Use, and Climate Change Pilot Program U.S. DOT Volpe National Transportation System Center Pilot project on Cape Cod Central New Mexico Climate Change Scenario Planning Project (2015) The Interagency Transportation, Land Use, and Climate Change Pilot Program U.S. DOT Volpe National Transportation System Center Scenario planning projectâCentral NM FHWA Climate Change Vulnerability Assessment Pilot Project: Hampton Roads (2014) FHWA Virginia Department of Transportation Climate change vulnerability assessment modelâ Hampton Roads, VA National Table C-1. Frameworks/guidelines for selecting scenarios for risk-based transportation planning that considers extreme weather and changing climate.
Climate Information, Design Guidelines, and Data Sources 117 Resource Title Author/Organization Links Potential Impacts of Climate Change on U.S. Transportation (2008) Committee on Climate Change and U.S. Transportation TRB https://www.nap.edu/catalog/12179/pot ential-impacts-of-climate-change-on-us- transportation-special-report First International Conference on Surface Transportation System Resilience to Climate Change and Extreme Weather Events (2015) TRB http://www.google.com/url?sa=t&rct=j&q =&esrc=s&source=web&cd=4&ved=2ahUK EwjEku278sHcAhUId98KHSKiDe0QFjADeg QIAxAC&url=http%3A%2F%2Fonlinepubs.t rb.org%2Fonlinepubs%2Fconferences%2F 2015%2FClimateChange%2FProgram.pdf& usg=AOvVaw00k-6Uv1XIWZKgMrUT34g7 National Climate Assessment, Chapter 5: Transportation (2014) U.S. Global Change Research Program http://nca2014.globalchange.gov/report/s ectors/transportation Building Climate Resilient Transportation (2016) FHWA https://www.fhwa.dot.gov/environment/ climate_change/adaptation/publications/ bcrt_brochure.cfm Integrating Extreme Weather Risk into Transportation Asset Management (2012) AASHTO http://climatechange.transportation.org/p df/extrweathertamwhitepaper_final.pdf Virtual Framework for Vulnerability Assessment (2016) FHWA http://www.fhwa.dot.gov/environment/cl imate_change/adaptation/adaptation_fra mework/ Flooded Bus Barns and Buckled Rails: Public Transportation and Climate Change Adaptation (2011) FTA https://www.transit.dot.gov/sites/fta.dot. gov/files/FTA_0001_- _Flooded_Bus_Barns_and_Buckled_Rails. pdf Planning for Systems Management & Operations as Part of Climate Change Adaptation (2013) FHWA http://www.ops.fhwa.dot.gov/publication s/fhwahop13030/ Challenges and Opportunities for Integrating Climate Adaptation Efforts across State, Regional and Local Transportation Agencies (2015) National Center for Sustainable Transportation https://escholarship.org/uc/item/5t88h66 m Vulnerability Assessment Scoring Tool (2015) FHWA https://toolkit.climate.gov/tool/vulnerabil ity-assessment-scoring-tool-vast Risk-Based Transportation Asset Management: Building Resilience into Transportation Assets Report 5âManaging External Threats Through Risk-Based Asset Management (2013a) FHWA https://www.fhwa.dot.gov/asset/pubs/hif 13018.pdf Table C-2. Sources of guidance for understanding climate risk to the transportation system.
118 Incorporating the Costs and Benefits of Adaptation Measures in Preparation for Extreme Weather Events and Climate ChangeâGuidebook result in gradual changes in the mean of many of the variables described in the previous section. Levels at which an event is classified as extreme will also shift. The result is not that there will definitely be a higher number of extreme events but that the extreme events that will occur will be more intense compared with a scenario in which climate change does not exist. For example, the current extreme 100-year event may be classified as a less-extreme 50-year event in the future even though the intensity remains unchanged. The number of events, whether they are more or less frequent, is related to climate variability, which consists of mechanisms and global teleconnections that can cause oscillations in such variables as rainfall and discharge, resulting in certain regions being wetter or drier depending on the phase and the strength of the mechanism or teleconnections. Climate change superimposed on top of climate variability results in an exacerbation or, in some cases, a suppression of these wetter and drier conditions, causing a shift in the event magnitude associated with a specific return period. Estimating the magnitude of this shift is the focus of myriad studies related to any one of the variables already discussed and comes with much uncertainty. Any adaptive measures that are incorporated into a future climate adap- tation project needs to consider both the magnitude and uncertainty of climate change impacts if any analysis of the potential benefits of such measures is to be made. Some resources for evaluat- ing projected climate change and extreme weather impacts are summarized in Table C-3. Type Source Data Data Publishing Date Geographic Coverage Atmospheric Data Historical Atmospheric Expert Team on Climate Change Detection and Indices (ETCCDI) Observation-based gridded data of extreme climate indices 2013 United States (land-only) Historical Atmospheric NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) Model reanalysis using observed historical conditions 2008â present Global Non- Downscaled Atmospheric and Sea Level Rise Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5) CMIP5*, hosted at Lawrence Livermore National Laboratory Data Portal 2013 Global Non- Downscaled Atmospheric and Sea Level Rise U.S. Global Change Research Programâs 2014 National Climate Assessment Predominantly SRES A2 and B1 from CMIP3 2007 United States Downscaled Atmospheric and Hydrology Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections CMIP5 and CMIP3 (Western U.S. hydrology) 2007â2014 United States Sea Level Rise Data Local Sea Level Rise Global Sea Level Rise Scenarios for the United States National Climate Assessment â¢ Linear extrapolation of historical data (low) â¢ IPCC AR4 (low intermediate) â¢ Various (high intermediate and high) 2007â2012 United States Table C-3. Authoritative sources of projections of future climate and sea level. The entities responsible for producing this information will provide updates over time.
Climate Information, Design Guidelines, and Data Sources 119 Type Source Data Data Publishing Date Geographic Coverage Local Sea Level Rise U.S. Army Corps of Engineers Sea Level Rise Change Curve Calculator â¢ Linear extrapolation of historical data (low) â¢ Intermediate and high 2015 United States Local Sea Level Rise NOAAâs Global Sea Level Rise Scenarios for the U.S. National Climate Assessment Linear extrapolation of historical data (low) â¢ â¢ Intermediate-low: considers risk primarily from expansion caused by ocean warming â¢ Intermediate-high: same as intermediate- low with the addition of limited ice sheet loss â¢ Highest: complete ice sheet loss 2012 United States *CMIP data refers to the Coupled Model Intercomparison Project, a product of the IPCC. CMIP5 results correspond to the IPCCâs Fifth Assessment Report and CMIP3 corresponds to the Fourth Assessment Report. from IPCC and National Research Council Table C-3. (Continued). Emerging Climate Design Guidance While currently there are no set rules for incorporating the impacts of climate change into design of infrastructure, several agencies have developed guidelines that can be considered and incorporated into the design process. These guidance documents are summarized in Table C-4. In addition to these guidance documents, several sources of climate-related data are available that can be applied to existing models. Sources of climate data are included in Table C-5. Some additional tools available to help estimate flooding risks from climate change are sum- marized in Table C-6.
120 Incorporating the Costs and Benefits of Adaptation Measures in Preparation for Extreme Weather Events and Climate ChangeâGuidebook No. Agency/ Author Publication/ Software Date Expiration Summary 1 U.S. Army Corps of Engineer s (USACE) ECB-2016-25 9/16/2016 9/16/2018 Guidance for Incorporating Climate Change Impacts to Inland Hydrology in Civil Works Studies, Design, and Projects Engineering Construction Bulletin 2016-25 recommends that a qualitative analysis be conducted to determine observed trends reflected in gauge records, as well as consult the potential future trends projected by global climate models. For this purpose, USACE has developed a Nonstationarity Detection Tool. 2 ETL 1100-2-3 4/28/2017 4/27/2021 Guidance for Detection of Nonstationarities in Annual Maximum Discharges Engineering Technical Letter 1100-2-3 provides guidance for abrupt or slowly varying changes (non-stationarities) in analyses of annual maximum discharges. This guidance does not detect the potential presence of long- term persistence in the discharge time series. This ETL discusses a total of 12 non-stationarity detection statistical tests that can be applied to the annual maximum stream gauge record and is supported by a non-stationarity detection tool. 3 USACE USACE USACE Nonstationarity Detection Tool 4/28/2017 4/27/2021 This ETL is supported by a web-based tool, which applies statistical tests capable of detecting abrupt non- stationarity (change points) in gauge records and allows the user to identify continuous periods of statistically homogeneous (stationary) peak stream flow records. The tool is supported by a Userâs Manual, but, for access, the general public is required to install a DOD Certificate. Annual maximum flow estimates of all USGS stream gauges that had over 30 years of record (as of 2014) are pre-loaded and can be accessed through the tool for each HUC-4 watershed. A total of 12 non- stationarity detection tests are available for this purpose. Subsequently, a trend analysis can be conducted on the resulting subset of stationary flow records identified using another feature of the tool. 4 Climate Hydrology Assessment tool 9/16/2016 9/16/2018 This is part of the non-stationarity detection tool. At the pour point of each HUC-4 watershed, this tool plots annual monthly maximum flows projected through 2100 by 93 different climate model simulations. Arithmetic average of the 93 projected results is also generated for each HUC-4 basin. The statistically downscaled climate model data (CMIP5) are pre-loaded in the tool. Table C-4. Agencies are developing guidance regarding how to consider and incorporate climate change into the design process.
Climate Information, Design Guidelines, and Data Sources 121 No. Agency/ Author Publication/ Software Date Expiration Summary 5 EPA Storm Water Management Model Climate Adjustment Tool (SWMM-CAT) 9/2014 N/A SWMM-CAT is a stand-alone utility program to EPAâs Storm Water Management Model (SWMM). SWMM-CAT generates location-specific adjustments for monthly evaporation, monthly rainfall, and 24-hour design rainfall depths derived using downscaled global climate model projections (CMIP3). Design rainfall adjustment factors to be applied with National Weather Service recommended values are estimated for 5-, 10-, 15-, 30, 50-, and 100-year return periods. Adjustment factors are computed for near term (2020â2049) and far term (2045â2074) for three potential future climate scenarios: hot/dry, median change, warm/wet. 6 EPA Climate Resilience Evaluation and Awareness Tool Version 3.0 (CREAT) 5/2016 N/A CREAT is a web-based informational tool to assist drinking water, wastewater, and stormwater utility owners and operators in addressing climate change risks. Access to CREAT appears to be limited to EPA employees and consultants. For projected climate conditions, CREAT uses CMIP5 projections for RCP 8.5. Total storm precipitations for future periods is one of the parameters estimated by CREAT for the purpose of estimating future threats to the water industries. 7 FEMA Climate Regression Equations 3/2016 FEMA has developed climate regression equations for 21 HUC-2 watersheds covering the mainland United States to estimate 10- and 100-year peak flow discharges through 2060. These equations are unpublished. 8 The White House Environmental Review and Permitting Process for Infrastructure 8/15/2017 N/A Presidential Executive Order (EO) on Establishing Discipline and Accountability in the Environmental Review and Permitting Process for Infrastructure rescinds Executive Order 13690 establishing Federal Flood Risk Management Standards (FFRMS). FFRMS recommended three approaches to account for climate changeâbest available that incorporates future changes in flooding based on climate science, applying a freeboard to the 100-year flood elevation, or using 500- year flood elevation. Table C-4. (Continued). (continued on next page)
122 Incorporating the Costs and Benefits of Adaptation Measures in Preparation for Extreme Weather Events and Climate ChangeâGuidebook No. Agency/ Author Publication/ Software Date Expiration Summary 9 USGS, NY Future Flow Explorer, Version 1.5 2015 N/A The rural regression equations published by USGS assume climate stationarity; these equations are widely used for water resources computations. The New York State USGS has used CMIP5 projections to update the climate parameters in the 2006 regression equation. These future projections for frequency discharges are offered through a web-based application titled Application of Flood Regressions and Climate Change Scenarios. The web tool computed peak discharges for 1.25-, 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-year frequency events for three time periods, 2025â2049, 2050â2074, and 2075â2099. RCP 4.5 and 8.5 simulations of five of the CMIP5 models that best reproduced the past precipitation were used in the future peak flow estimation. 10 HEC-17 8/1/2016 N/A HEC-17, âHighways in the River Environment: Floodplains, Extreme Events, Risk, and Resilienceâ recommends methodologies to evaluate the stationarity or non-stationarity of past climate at a location of interest by examining rainfall and stream flow records. If a trend is detected, HEC-17 proposes methodologies to account for that change in design parameters (rainfall and discharge). 11 North- east Regional Climate Center New Yorkâ Specific Intensity- Duration- Frequency (IDF) Curves 2015 Intensity-duration-frequency (IDF) curves published for New York consider two emissions scenarios (RCP 4.5 and RCP 8.5) and cover three time periods: through 2039, 2040â2069, and 2070â2099. IDFs for 2-, 5-, 10-, 25-, 50-, and 100-year return periods are considered. FHWA Table C-4. (Continued).
Climate Information, Design Guidelines, and Data Sources 123 Data Type Source URL Coastal levels (observed) NOAA Tides and Currents https://tidesandcurrents.noaa.gov/products.html Drought indices (satellite) NOAA NCEI https://www1.ncdc.noaa.gov/pub/data/cirs/climdiv/ Drought indices (satellite) NOAA CPC http://www.cpc.ncep.noaa.gov/products/analysis_moni toring/cdus/palmer_drought/ Elevation (satellite) USGS EE https://earthexplorer.usgs.gov/ Evaporation (observed) NOAA NCEI https://www.ncdc.noaa.gov/cdo-web/datasets Evaporation (observed) NOAA NCEI http://climate.ncsu.edu/cronos Evaporation (satellite) NASA MODIS https://modis.gsfc.nasa.gov/data/dataprod/index.php Groundwater levels (observed) USGS NWIS http://waterdata.usgs.gov/nc/nwis/current/?type=flow Groundwater levels (observed) NC DWR http://www.ncwater.org/?page=343 Land cover (satellite) NC DWR https://earthexplorer.usgs.gov/ Land cover (satellite) MRLC NLCD https://www.mrlc.gov/ Precipitation index NOAA NCEI https://www.ncdc.noaa.gov/temp-and- precip/drought/nadm/indices Rain (observed) NOAA NCEI https://www.ncdc.noaa.gov/cdo-web/datasets Rain (observed) NC CRONOS https://climate.ncsu.edu/cronos Rain (radar) NOAA NCEI https://www.ncdc.noaa.gov/nexradinv/index.jsp Rain (satellite) NOAA NCEI https://data.nodc.noaa.gov/cgi- bin/iso?id=gov.noaa.ncdc:C00979 Rain (satellite) NOAA NCEI https://www.ncei.noaa.gov/data/precipitation- persiann/access/ Rain (satellite) NASA GPM https://pps.gsfc.nasa.gov/ppsindex.html Rain (satellite) NASA MODIS https://modis.gsfc.nasa.gov/data/dataprod/index.php Reservoir inflow/levels (observed) USACE http://epec.saw.usace.army.mil/ Reservoir inflow/levels (observed) Duke Energy https://www.duke-energy.com/community/lakes Sea level trends (observed) NOAA https://tidesandcurrents.noaa.gov/sltrends/sltrends.ht ml Snow/ice (observed) NOAA https://www.ncdc.noaa.gov/snow-and-ice/daily-snow/ Snow/ice (satellite) NASA MODIS https://modis.gsfc.nasa.gov/data/dataprod/index.php Soil characteristics (observed) USDA https://sdmdataaccess.nrcs.usda.gov/ Soil moisture (observed) NOAA CPC http://www.cpc.ncep.noaa.gov/products/Soilmst_Monit oring/US/Soilmst/Soilmst.shtml Soil moisture (observed) NC CRONOS http://climate.ncsu.edu/cronos Soil moisture (satellite) NASA MODIS https://modis.gsfc.nasa.gov/data/dataprod/index.php Streamflow (observed) USGS NWIS http://waterdata.usgs.gov/nc/nwis/current/?type=flow Surface water levels (observed) USGS NWIS http://waterdata.usgs.gov/nc/nwis/current/?type=flow Table C-5. Sources of climate data. (continued on next page)
124 Incorporating the Costs and Benefits of Adaptation Measures in Preparation for Extreme Weather Events and Climate ChangeâGuidebook Data Type Source URL Temperature (observed) NOAA NCEI https://www.ncdc.noaa.gov/cdo-web/datasets Temperature (observed) NC CRONOS http://www.nc-climate.ncsu.edu/cronos Vegetative health (satellite) USGS EE http://earthexplorer.usgs.gov Vegetative health (satellite) NASA NEO http://neo.sci.gsfc.nasa.gov Vegetative health (satellite) USGS LP DAAC https://lpdaac.usgs.gov/dataset_discovery/modis/modis _products_table Wind speed/direction (observed) NOAA NCEI https://www.ncdc.noaa.gov/cdo-web/datasets Wind speed/direction (observed) NC CRONOS http://www.nc-climate.ncsu.edu/cronos Table C-5. (Continued). Tool Description URL SimCLIM SimCLIM is a software tool designed to facilitate the assessment of risks from climate change. It uses CMPI5 climate data and presents results in map, graph, and chart formats. http://www.climsystems.com/simcli m/ EPAâs Storm Water Management Model SWMM-CAT (SWMM) is used to plan, analyze, and design for stormwater runoff, combined sanitary sewers, and drainage systems in urban areas. The CAT add-on allows climate change projections to be https://www.epa.gov/water- research/storm-water-management- model-swmm#add-in incorporated into the SWMM analysis. SWMM-CAT provides a set of location- specific adjustments that were derived from CMIP data. SLAMM: Sea Level Affecting Marshes Model This tool helps to illustrate the long- term impacts of sea level rise on marshes. It has been expanded to evaluate the inundation frequency of road infrastructure under future sea level rise and storm-surge conditions. http://www.warrenpinnacle.com/pro f/SLAMM/index.html SLOSH (Sea, Lake, and Overland Surges from Hurricanes) Model SLOSH is used as a storm-prediction model to predict storm-surge heights and wind intensity of hurricanes. It can be used to evaluate hurricane scenarios and predict storm-surge intensity. https://slosh.nws.noaa.gov/slosh/ Table C-6. Tools available to help estimate risks from climate change on flooding.