These data provide the 2022 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

Time-coincident load, wind, and solar data including actual and probabilistic forecast datasets at 5-min resolution for ERCOT, MISO, NYISO, and SPP. Wind and solar profiles are supplied for existing sites as well as planned sites based on interconnection queue projects as of 2021. For ERCOT actuals are provided for 2017 and 2018 and forecasts for 2018, and for the remaining ISOs actuals are provided for 2018 and 2019 and forecasts for 2019. There datasets were produced by NREL as part of the ARPA-E PERFORM project, an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. For more information on the datasets and methods used to generate them see https://github.com/PERFORM-Forecasts/documentation.

Global Horizontal Irradiance on Africa in kWh/m2/year provided by IRENA's Global Atlas http://irena.masdar.ac.ae/

GIS data for Bhutan's direct normal irradiance (DNI), global horizontal irradiance (GHI), and latitude tilt irradiance. Researchers from NREL and the Atmospheric Sciences Research Center (ASRC) at the State University of New York (SUNY) at Albany developed the estimates of Bhutans solar resource. SUNY researchers generated the estimates of GHI and DNI using images collected at hourly intervals between December 2002 and January 2007 from the European Meteosat 5 and 7 geostationary satellites. NREL used the GHI data to generate estimates of the resource potential at latitude tilt, and to create the solar resource maps. This submission includes GIS resources of the results for this study. This data can be used to help with energy production and infrastructure planning in Bhutan.

This innovative interactive e-book is to provide a quick introduction to how Technology and Innovation are being applied in the Blue Economy. An interactive e-book is more than a pdf or static electronic document. This e-book includes text and a wide range of interactive elements (e.g., hyperlinks, photo galleries, video galleries, interactive graphs, interactive maps, interactive data/knowledge filters, etc.) drawing upon multiple online resources.

This dataset shows an assessment of the suitability of building roofs for the installation of solar panels, within Bristol. This includes a general indication of the suitability and an estimate of the amount of power that could be generated from the roof area.

Stratigraphic reservoirs with high permeability and temperature at economically accessible depths are attractive for power generation because of their large areal extent (> 100 km2) compared to the fault controlled hydrothermal reservoirs (< 10 km2) found throughout much of the western US. A preliminary screening of the geothermal power potential of sedimentary basins in the U.S. assuming present day drilling costs, a levelized cost of electricity over 30 years of $10/Wh, and realistic reservoir permeabilities, indicates that basins with heat flows of more than about 80 mW/m2, reservoir temperatures of more than 175 degrees C, and a reservoir depth of less than 4 km are required. This puts the focus for future geothermal power generation on high heat flow regions of California (e.g. the Imperial Valley and regions adjacent to The Geysers), the Rio Grande rift system of New Mexico and Colorado (especially the Denver Basin), the Great Basin of the western U.S., and high heat flow parts of Hawaii and the Alaska volcanic arc. This submission includes a Stage Gate Report on "Novel Geothermal Development of Deep Sedimentary Systems in the United States" in addition to the following resources compiled into a single PDF: Fluid-Mineral and Reactional Path Calculations (Simmons, S.F. 2012) Summary of Coupled Fluid Geochemistry with Depth Analyses in the Great Basin and Adjoining Regions (Kirby, S.M. 2012) Summary of Compiled Permeability with Depth Measurements for Basin Fill, Igneous, Carbonate, and Siliciclastic Rocks in the Great Basin and Adjoining Regions (Kirby, S.M. 2012) Review of Permeability Characteristics in Drilled, Sediment-Hosted, Geothermal Systems (Anderson, T.C. 2012) Structural Geology of the Eastern Basin and Range; Structural Cross Sections Across Western Utah and Northeastern Nevada (Schelling, D.D. 2012) Stratigraphic Reservoirs in the Great Basin-The Bridge to Development of Enhanced Geothermal Systems in the U.S. (Allis et al. 2012) Presentation: Stratigraphic Reservoirs in the Great Basin-the Bridge to Development of Enhanced Geothermal Systems in the U.S. (Allis et al. 2012) Presentation: Novel Geothermal Development of Deep Sedimentary Systems in the United States (Moore, J. and R. Allis, 2012) The Potential for Basin-Centered Geothermal Resources in the Great Basin (Allis et al. 2011) Presentation: The Potential for Basin-Centered Geothermal Resources in the Great Basin (Allis et al. 2011) Geothermal Resources in Southwestern Utah: Gravity and Magnetotelluric Investigations (Hardwick, C. 2012) Geophysical Delineation of the Crater Bench, Utah, Geothermal System (Hardwick C.L. and D.S. Chapman, 2011) Geothermal Resources in the Black Rock Desert, Utah: MT and Gravity Surveys (Hardwick, C.L and D.S. Chapman, 2012) Simulation of Heat Exchange Processes and Thermal Evolution of Deep Sedimentary Resevoirs (2012) Performance of Air-Cooled Binary Power Plants: An Analysis using Pacificorp's Blundell plant near Milford, Utah (Allis, R. and G. Larsen, 2012) Chapter 4: Reservoir Implications of CO2 in Produced Fluids and as Co-Injected Fluid (2012) Developing Geothermal Resources beneath Hot Basins (stratigraphic reservoirs) Economic Constraints - draft notes for report (Spencer, T. and R. Allis 2012) Using Hydrogeologic Data to Evaluate Geothermal Potential in the Eastern Great Basin, Western U.S. (Heilweil et al. 2012) Subsidence in Sedimentary Basins due to Groundwater Withdrawal for Geothermal Energy Development (Lowe, M. 2012) Induced Seismicity [associated with deep sedimentary basin EGS development] (McPherson, B. 2012)

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.

Abstract: Presentation of EDF Renewables' installed solar capacity, with two visions: Raw capacities (total capacities of the solar farms in which EDF Renewables has a stake). Net capacities (capacities corresponding to the stake held by EDF Renewables).

This page contains links to all available GIS elevation datasets, services, and related applications.

The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on characteristics of almost all electric power generated in the United States. This data includes capacity; heat input; net generation; associated air emissions of nitrogen oxides, sulfur dioxide, carbon dioxide, methane, nitrous oxide and mercury; emissions rates; resource mix (i.e., generation by fuel type); nonbaseload calculations; line losses (a.k.a., grid gross loss); and many other attributes. The data is provided at the unit and generator levels, as well as, aggregated to the plant, state, balancing authority, eGRID subregion, NERC region, and US levels. As of January 2023, the available editions of eGRID contain data for years 2021, 2020, 2019, 2018, 2016, 2014, 2012, 2010, 2009, 2007, 2005, 2004, and 1996 through 2000.

Office for National Statistics Dataset of generation of heat from renewable sources shows the UK's energy use from renewable sources used to generate heat (active solar heating, heat pumps, geothermal aquifers), 1990 to 2020

Data of high resolution (10kmx10km) Global Horizontal Irradiance (GHI) for Ghana for the years 2000, 2001 and 2002. The data are available for monthly and annual sums stored in a ESRI-Shapefile. The data are helpful for the assessment of the solar potential of the country and can give project developer a first impression of the solar resource of the country. Citation: DLR & Negawatt challenge. A curated list of datasets for the World Bank Negawatt Challenge competition in Accra and Nairobi cities: https://datahub.io/organization/negawatt-challenge

The primary aim of this Global Solar Atlas is to provide quick and easy access to solar resource and photovoltaic power potential data globally, at a click of a mouse. GIS layers and poster maps showing global, regional, and country resource potential can be found in the Download section. Further description of the data provided, the methodology for estimating solar resource potential, and guidance on how to use it, can be found in the Knowledge Base section.

The Solar API provides access to the solar potential of hundreds of millions of buildings.

This API provides international data on solar capacity and generation. Data organized by country. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The website of the LASP Interactive Solar IRradiance Data Center (LISIRD) includes a comprehensive set of solar spectral irradiance measurements from the soft X-ray (XUV) at 0.1 nm up to the near infrared (NIR) at 2700 nm, as well as state-of-the-art measurements of Total Solar Irradiance (TSI). Additionally, the website also provides an extensive set of solar irradiance models and historical solar irradiance reconstructions generated by LASP researchers.

The LASP Interactive Solar IRradiance Data Center (LISIRD): Composite Solar Lyman-alpha website includes a comprehensive set of solar spectral irradiance measurements from the soft X-ray (XUV) at 0.1 nm up to the near infrared (NIR) at 2700 nm, as well as state-of-the-art measurements of Total Solar Irradiance (TSI). Additionally, the website provides an extensive set of solar irradiance models and historical solar irradiance reconstructions generated by LASP researchers.

Life Cycle Analysis (LCA) is a comprehensive form of analysis that utilizes the principles of Life Cycle Assessment, Life Cycle Cost Analysis, and various other methods to evaluate the environmental, economic, and social attributes of energy systems ranging from the extraction of raw materials from the ground to the use of the energy carrier to perform work (commonly referred to as the “life cycle” of a product). Results are used to inform research at NETL and evaluate energy options from a National perspective.

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2017. The NSRDB provides foundational information to support U.S. Department of Energy programs, research, and the general public. The NSRDB provides time-series data at 30 minute resolution of resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms developed by the University of Wisconsin. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate solar irradiance (GHI, DNI, and DHI). The Global Horizontal Irradiance (GHI) is computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, FARMS is used to compute GHI. The Direct Normal Irradiance (DNI) for cloud scenes is then computed using NREL's DISC model (uses empirical relationships between the global and direct clearness indices to estimate the direct beam component of irradiance). The PATMOS-X model uses half-hourly radiance images in visible and infrared channels from the Geostationary Operational Environmental Satellite (GOES) series of geostationary weather satellites. Ancillary variables needed to run REST2 and FARMS (e.g., aerosol optical depth, precipitable water vapor, and albedo) are derived from NASA's Modern Era-Retrospective Analysis (MERRA-2) dataset. Temperature and wind speed data are also derived from MERRA-2 and provided for use in NREL's System Advisor Model (SAM) to compute PV generation.

The National Renewable Energy Laboratory's (NREL) Photovoltaic (PV) Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The PVRDB data are organized by city and year of lidar collection. Five geospatial layers are available for each city and year: 1) the raster extent of the lidar collection, 2) buildings identified from the lidar data, 3) suitable developable planes for each building, 4) aspect values of the developable planes, and 5) the technical potential estimates of the developable planes.

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.

Task-3 Tail-Ended-Grid Tail Ended Grid Station IESCO with Regards To-Maximum Load, Length of Feeders, KVA Connected, Category-wise consumers with load in (KW), Percentage Losses and specific category consumers

Data repository for measurements from 9 automated solar stations in Pakistan. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 10 minute average values for solar radiation levels, temperature, air pressure and wind speed. From 2018 onward, the measurement stations are being operated by the Government of Pakistan, together with NREL and USAID. For more information and additional outputs, please visit: https://esmap.org/node/3058. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan-Solar Radiation Measurement Data, 2017,

The NREL PVDAQ is a large-scale time-series database containing system metadata and performance data from a variety of experimental PV sites and commercial public PV sites. The datasets are used to perform on-going performance and degradation analysis. Some of the sets can exhibit common elements that effect PV performance (e.g. soiling). The dataset consists of a series of files devoted to each of the systems and an associated set of metadata information that explains details about the system hardware and the site geo-location. Some system datasets also include environmental sensors that cover irradiance, temperatures, wind speeds, and precipitation at the site.

The Renewable Electricity Procurement Options Data (RE-POD) is an aggregated dataset meant to help local jurisdictions and utility customers within those jurisdictions understand the options that may be available to them to procure renewable electricity or renewable energy credits to meet energy goals. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Renewables.ninja allows users to run simulations of the hourly power output from wind and solar power plants located anywhere in the world. The tool helps make scientific-quality weather and energy data available to a wider community.

The SMART-DS datasets (Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios) are realistic large-scale U.S. electrical distribution models for testing advanced grid algorithms and technology analysis. This document provides a user guide for the datasets. This dataset contains synthetic detailed electrical distribution network models, and connected timeseries loads for the greater San Francisco (SFO), Greensboro, and Austin areas. It is intended to provide researchers with very realistic and complete models that can be used for extensive powerflow simulations under a variety of scenarios. The data is synthetic, but has been validated against thousands of utility feeders to ensure statistical and operational similarity to electrical distribution networks in the US. The OpenDSS data is partitioned into several regions (each zipped separately). After unzipping these files, each region has a folder for each substation, and subsequent folders for each feeder within the substation. This allows users to simulate smaller sections of the full dataset. Each of these folders (region, substation and feeder) has a folder titled "analysis" which contains CSV files listing voltages and overloads throughout the network for the peak loading time in the year. It also contains .png files showing the loading of residential and commercial loads on the network for every day of the year, and daily breakdowns of loads for commercial building categories. Time series data is provided in the "profiles" folder including real and reactive power at 15 minute resolution along with parquet files in the "endues" folder with breakdowns of building end-uses.

The PV_Live web API (Application Programming Interface) provides access to near-real-time and historical estimates of PV generation on the GB transmission network. It returns a computer-friendly (JSON) response for use in software, websites, apps etc. The Sheffield Solar research group is formed in the Physics & Astronomy department, at the University of Sheffield, as part of the Grantham Centre for Sustainable Futures. It works to bridge the gap between the research lab and how solar photovoltaic (PV) technology is used in the real world and to understand its performance and impact.

The PV_Forecast service supplies forecasts of nationally- and regionally-aggregated electricity generation from solar Photovoltaics (PV) connected to the GB transmission network. Sheffield Solar currently run three tiers of PV_Forecast: Nationally-aggregated, regionally-aggregated by DNO License Area (a.k.a PES or GSP Group) and regionally-aggregated by Grid Supply Point (GSP). The Sheffield Solar research group is formed in the Physics & Astronomy department, at the University of Sheffield, as part of the Grantham Centre for Sustainable Futures. It works to bridge the gap between the research lab and how solar photovoltaic (PV) technology is used in the real world and to understand its performance and impact.

This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Spatial extent: * 10-km includes lower 48 states and Hawaii * 40-km includes lower 48, AK and HI. Includes shapefiles, .kmz, metadata. Data is called "Dynamic maps, GIS data & Analysis tools" in Archivers app (as of 2/25/2017). Internet Archive URL: https://web.archive.org/web/2019*/http://www.nrel.gov/gis/data_solar.html

The EDP Open Data hub shares operational data from EDP assets

This dataset contains number of solar installations and capacity (in kW) since 2001 and per locality. The data is organised by size of solar installation. It is provided by the Australian Photovoltaic Institute. 

This dataset contains number of solar installations and capacity (in kW) since 2001 and per locality. The data is organised by size of solar installation. It is provided by the Australian Photovoltaic Institute. 

Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission. A newer version of the data exists and can be found linked in the resources of this submission under "Solar-to-Grid Public Data File Updated 2021".

Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.

We provide data from two models of different manufactures (A and B).The modules are located in Faro and each model has three different orientations: vertical, optimal and horizontal. The granularity of the data is one minute and the timezone is UTC (Coordinated Universal Time). All files have daylight saving time correction.

The EDP Open Data hub shares operational data from EDP assets

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial dataset "sup3rcc_conus_mriesm20_ssp585_r1i1p1f1" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical average climate, not the actual historical weather that we experienced. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the *possible* future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Repository for the measurements from the meteorological station located in Dar es Salaam (Tanzania). Data will be upload monthly. The measurements started on May 21st, 2015. For more information, please visit: http://esmap.org/re_mapping_tnz For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Tanzania-Solar Radiation Measurement Data, 2017,

Terence Eden solar data are sourced directly from his solar inverter based off his rooftop solar panels using the Fronius API. it is assumed that the data from there are broadly accurate. This is a personal rooftop yield dataset These data are licensed to you as CC BY-SA. If you use them in an academic paper, you are expected to publish as open access.

The MCS Data Dashboard is designed to provide near-real-time updates using MCS Installations Database (MID) data, to track the adoption of small-scale renewable installations in the UK. By producing data visualisations, the MCS Data Dashboard paints a dynamic picture of the uptake and distribution of small-scale renewable installations in the UK. It also uncovers insights into the MCS contractor community. Data files for each visualisation are available to download.

Berkeley Lab's Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed through year-end 2017, with preliminary trends for the first half of 2018. As in years past, the primary emphasis is on describing changes in installed prices over time and variation in pricing across projects based on location, project ownership, system design, and other attributes. New to this year, however, is an expanded discussion of other project characteristics in the large underlying data sample. Future editions will include more of such material, beyond the reports traditional focus on installed pricing. The trends described in this report derive primarily from project-level data reported to state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data were collected and cleaned for more than 1.3 million individual PV systems, representing 81% of U.S. residential and non-residential PV systems installed through 2017. The analysis of installed pricing trends is based on a subset of roughly 770,000 systems with available installed price data.

A machine readable collection of documented solar siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled based on a locality-by-locality review zoning ordinances after completing an initial review of scholarly legal articles. The citations for each ordinance are included in the spreadsheet.

Berkeley Labs "Utility-Scale Solar", 2022 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2021, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

(Link to Metadata) 2022 update. The only statewide solar potential layer available used in the Act 174 effort comes from the 2010 Renewable Energy Atlas of Vermont (REAVT) effort (EnvironOther_Solar). This layer and the Renewable Energy Atlas are now hosted by the Energy Action Network (eanvt.org). This ground mount only resource was further processed to meet the requirements of Act 174 by removing areas with “known constraints” and to identify areas with “possible constraints” as outlined in the “Act 174 Energy Planning Standards” (see Act 174 municipal guidance link above). All constraint layers represent 2022 conditions.

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.