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Hydrologic Landscape Vulnerability Assessment Data
JonesEtAl_2021_Study_Locations.shp: This shapefile includes the areas of interest used in our analysis for the analyses published in Jones et al. 2021. Each area of interest is divided into numerous polygonal assessment units as described in the accompanying research article. Each polygon has attributes that include FID, Name, Number (used in our published map figure and tables), and shape area (in meters squared).
US_HLScenarioMerge.shp: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. This paper illustrates how the HL approach can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and climate vulnerability analyses, we provide a planning approach that could allow resource managers to consider how future climate conditions may impact important economic and conservation resources. The data in this data set provides the Feddema Moisture Index, classified climate class, and classified season class for each time decade and 30 yr normal period from 1900-2010 and the 10 analyzed climate model projections described in the manuscript.
Complete Metadata
| bureauCode |
[ "020:00" ] |
|---|---|
| describedBy | https://pasteur.epa.gov/uploads/10.23719/1522399/documents/Data%20Dictionay.csv |
| describedByType | text/csv |
| identifier | https://doi.org/10.23719/1522399 |
| programCode |
[ "020:000" ] |
| references | null |
| rights | null |