The primary objective of this app is to quantify daily rainfall and snowfall amounts from total precipitation and visualize the time series of both quantities at daily and user-defined seasonal timescales into the future under different climate scenarios for a point location within the Contiguous United States (CONUS). This app also provides an observed historical time series of snowfall and rainfall data, based on gridMET, which is used in the development of the MACAv2-METDATA downscaled climate projections data considered in this application. This application allows a user to quantify, visualize and download snowfall and rainfall daily data as well as seasonal data for a user-selected season ranging from previous year's January through current December with water year as default (previous October through current September) from (i) observations (1980-2020) and (ii) future climate projections (1950-2099) available from 40 downscaled climate projections from MACAv2-METDATA datasets which include both RCP 4.5 and RCP 8.5 emission scenarios.

Vapour-pressure deficit (VPD) is the difference between the amount of actual moisture in the air and the amount of moisture the air can hold when it is saturated. VPD is a measure of the atmospheric evaporative demand (i.e., atmospheric thirst). Anomalously high VPD often corresponds with increased water stress in the plants and wildfire risk. The goal of this app is to quantify and visualize the extremes in VPD at different timescales (weeks to months) that have occurred in the recent historical period (1981-2020) and projected in future under various climate scenarios for any location within the Contiguous United States (CONUS). This application allows a user to:   Quickly acquire VPD data for a single point location as an nc file Quantify, visualize and ability to download mean VPD for a user selected timescale as number of days Quantify, visualize and download number of extreme events per year for a user selected threshold and timescale

Standardized Precipitation Evapotranspiration Index (SPEI) quantifies standardized departures in the difference between precipitation and potential evapotranspiration (PET) at different timescales. For more information on SPEI go to https://spei.csic.es/home.html. The primary objective of this app is to quantify and visualize the time series of SPEI at different timescales (1-month to 12-month) projected into the future under different climate scenarios for a point location within the Contiguous United States (CONUS). This app also provides observed historical time series of SPEI based on the training data, gridMET, which is used in the development of the MACAv2-METDATA downscaling climate projections data that is considered in this application. This application allows a user to quantify, visualize and download SPEI for a user-selected month and timescale (1 month - 12 month) for the (i) observed period (1979-2020) and (ii) future climate scenario (1950-2099) available from 40 downscaled projections from MACAv2-METDATA datasets that considers both RCP 4.5 and RCP 8.5 emission scenarios.

Forest Stress Drought Index (FDSI) measures dryness or drought stress across the water year by integrating anomalies in cold season (November - March) precipitation and warm season vapor pressure deficit (August - October of previous year and May - July of current year). The formulation of FDSI incorporates calibration using tree ring data in US west (Williams et al., 2013; https://www.nature.com/articles/nclimate1693 ). This index is particularly appropriate for the western US ecosystems but could also be relevant for other ecosystems where the cold season provides a large majority of annual precipitation. FDSI is found to be a sensitive metric at identifying extreme drought years such as 2002, 2012 and 2018 across Colorado for example. FDSI could also be forward projected in time, using projections of climate drivers, to assess changing severity of yearly droughts and to quantify reoccurrence frequency of an extreme historical drought in a future period. The primary objective of this app is to quantify and visualize the time series of FDSI projected into the future under different climate scenarios for a point location within the Contiguous United States (CONUS). This app also provides observed historical time series of FDSI based on the training data, gridMET, which is used in the development of the MACAv2-METDATA downscaling climate projections data that is considered in this application. This application allows a user to quantify, visualize and download FDSI for the (i) observed period (1980-2020) and (ii) future climate scenario (1951-2099) available from 40 downscaled projections from MACAv2-METDATA datasets that considers both RCP 4.5 and RCP 8.5 emission scenarios.

This application performs a systematic space-time analysis of Integrated Vapor Transport (IVT) over the entire world for the user-selected period and season. This user-friendly application allows to examine the different and complex patterns of moisture transport globally. Navigate through various tabs to explore composites, trends, and examine anomalies and climatologies of IVT. With the capability to customize parameters such as the selected season, period of the record, and significance levels, this app allows users to gain valuable insights into the trends and dynamics of climatic phenomena.

This application identifies spatially-cont iguous precipitation clusters (where each cluster represents a region that has a congruent precipitation characteristic) for a user-defined season and examines the nature of large-scale drivers (atmos pheric circulation and moisture transport; Ocean Sea Surface Temperatures) that influences seasonal precipitation for that cluster. It performs a systematic space-time analysis of user-selected seasonal precipitation over the Contiguous United States (CONUS) for the user-selected historical time-period by employing Partition Around Medoid (PAM) clustering technique proposed by Bracken et al. 2015 to identify clusters that are contiguous in space. This user-friendly application allows for examining the relationships between seasonal rainfall, sea surface temperature (SST), and Integrated Vapor Transport (IVT). The app allows for navigating through various options to explore the clustering pattern, correlate SST with precipitation clusters, and examine anomalies and climatologies of IVT. With the capability to customize parameters such as seasonality, period of the record, and significance levels, this app allows users to gain valuable insights into large-scale ocean-atmospheric processes influencing regional precipitation in CONUS.

Drought Index Portal (DrIP) was developed to display, compare, and extract time series for various indicators of drought in the contiguous United States. The Host Team collaborated with CIRES IT to host the tool under the colorado.edu domain with updates and maintenance carried out by CIRES IT.   

The Experimental Winter Precipitation Forecast provides a forecast of winter precipitation (December-March) for the western United States and is determined using both Pacific and Atlantic Ocean temperatures. Forecasts are available November - February and use the most recent 2-3 months of observed Pacific Ocean temperatures (El Niño Southern Oscillation or ENSO temperature index) and observed Atlantic Ocean temperatures (Atlantic Quadpole Mode or AQM temperature index). The Documentation tab at the top right of the page provides more detailed information on the climate and atmospheric science used to develop the forecast. Use the Validation tab see how well the Experimental Winter Precipitation Forecast performs using historical winter precipitation (1892-2024). The Experimental Winter Precipitation Forecast Tool and the underlying research was developed by scientists at the University of Utah in close collaboration with Salt Lake City Department of Public Utilities and Western Water Assessment, part of the Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder.  

In response to mounting wildfire risks, land managers across the country will need to dramatically increase proactive wildfire management (e.g. fuel and forest health treatments). While human communities vary widely in their vulnerability to the impacts of fire, these discrepancies have rarely informed prioritizations for wildfire mitigation treatments. The ecological values and ecosystem services provided by forests have also typically been secondary considerations. To identify locations across the conterminous US where proactive wildfire management is likely to be effective at reducing wildfire severity and to yield co-benefits for vulnerable communities and ecological values, we developed a set of spatial models that estimated wildfire mitigation potential (based on wildfire hazard and biophysical forest conditions) and either included or excluded information on vulnerable human communities, ecological values and ecosystem services. We then compared areas with high wildfire mitigation potential alone to refined ‘focal areas’ that overlaid social and ecological considerations to quantify the potential benefits of targeted wildfire mitigation treatments. Inclusion of social and ecological considerations substantially increased representation of vulnerable communities and ecological values in focal areas relative to the model that considered wildfire alone. For instance, restoration in these refined focal areas would cover 28% greater imperilled species richness, 45% greater water importance and 26% more families falling below the poverty line. By examining overlap between our refined focal areas and U.S. Forest Service top ranked firesheds (a prominent existing wildfire prioritization scheme), we show that our analysis can help to target wildfire mitigation efforts within firesheds to areas with particularly high social vulnerability and/or ecological value, providing an important compliment to a prioritization scheme based largely on risk to structures. Our results highlight the importance of considering ecological and social factors when implementing wildfire mitigation treatments and provide actionable guidance for integrating these considerations into existing prioritizations.

Core sagebrush areas (CSAs), patches of high sagebrush ecological integrity, continue to decline despite significant conservation and restoration investments across the sagebrush biome. Historically, conservation decisions in the biome have been driven by wildlife species-specific demands, but increasing recognition of the scale of threats and the pace of ecosystem degradation has compelled a shift towards threat-based ecosystem management. Therefore, there is a need to evaluate the scale of conservation implementation relative to the rate of degradation or loss from specific threats to the biome to assess whether a conservation deficit exists. To this end, we: 1) quantified and compared the average hectares of conservation practices implemented annually relative to the hectares of CSA loss attributed to each threat; 2) evaluated the relative amount of conservation actions in core sagebrush areas, growth opportunity areas, and other rangeland areas; and 3) assessed how much additional conservation may be needed to stop CSA declines. We then quantified how better spatial targeting and enhanced coordination might reduce the total additional amount of future conservation needed, and evaluated how an influx of resources can close the conservation gap, or the deficit between the conservation needed to offset annual loss and degradation and the capacity for conservation implementation. We found that current rates of conservation (e.g., hectares treated annually) are markedly lower than rates of CSA loss (∼10% of average annual loss). Furthermore, most conservation actions, ∼90% for some treatment types, occurred outside of CSAs likely reducing the efficacy of these conservation actions at retaining and restoring intact sagebrush rangelands. Additionally, we found that conservation efforts will need to increase by more than an order of magnitude (at least 10x) annually to halt CSA declines. However, through better spatial targeting of conservation actions, the increase in conservation needed to stop CSA loss could be reduced by 70% or more. This analysis demonstrates the divergent futures that may await the sagebrush biome pending key decisions regarding conservation targeting, stakeholder cooperation, and the strategic addition of resources.