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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.  </idPurp><idAbs>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 is specifically for the 1971-2000 normal period and summarizes the HL classification for clusters of assessment units for the continental U.S.  Note that the assessment units were clustered by the 5-class HL code to minimize the file size.</idAbs><idCredit>Chas E. Jones Jr.*, Scott G. Leibowitz, Keith A. Sawicz, Randy L. Comeleo, Laurel E. Stratton, Phillip E. Morefield, Chris P. Weaver

as cited in: Using hydrologic landscape classification and climatic time series to assess hydrologic vulnerability of the Western U.S. to climate (Submitted to HESS)
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(b) Seasonality of precipitation surplus, (c). Surface permeability, (d) Climate, and (e) Terrain]. Wigington et al. (2013) developed their HL classification based on climatic and physical characteristics of the physical watershed. They defined five indices to characterize the major drivers that control the magnitude and timing of water movement through the landscape and into the ground or stream network: (1) climate, which describes the overall availability of water on the landscape, (2) seasonality of water surplus, which is the season when the maximum excess of water is available to infiltrate into the soil column or flow as surficial runoff, (3) subsurface permeability, (4) terrain, and (5) surface permeability. Note that Wigington et al. (2013) referred to subsurface and surface permeability as aquifer and soil permeability, respectively. The five HL indices, described in more detail below (Sections 1.2.1  through 1.2.5), are typically concatenated into a 5-character HL code (e.g., WsLMH, SwHTH, or DfHfL) that characterizes an AU.</attrdef><attrdefs>USEPA</attrdefs><attrdomv><udom>Wigington et al. (2013) developed their HL classification based on climatic and physical characteristics of the physical watershed. They defined five indices to characterize the major drivers that control the magnitude and timing of water movement through the landscape and into the ground or stream network: (1) climate, which describes the overall availability of water on the landscape, (2) seasonality of water surplus, which is the season when the maximum excess of water is available to infiltrate into the soil column or flow as surficial runoff, (3) subsurface permeability, (4) terrain, and (5) surface permeability. Note that Wigington et al. (2013) referred to subsurface and surface permeability as aquifer and soil permeability, respectively. The five HL indices are typically concatenated into a 5-character HL code (e.g., WsLMH, SwHTH, or DfHfL) that characterizes an AU.</udom></attrdomv></attr><attr xmlns=""><attrlabl Sync="TRUE">Terrain</attrlabl><attalias Sync="TRUE">Terrain</attalias><attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">254</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale><attrdef>To classify terrain, we used the same approach as Wigington et al. (2013). We analyzed a 30 m Digital Elevation Model to classify the landscape based upon the topographic characteristics of each AU. “Mountainous” (M) areas had AUs with &lt;10 % of the area identified as flat (&lt; 1 % slope) and greater than 300 m of total relief. AUs with more than 50 % area having &lt; 1 % slope were classified as “flat” (F).  All other AUs were identified as “transitional” (T).</attrdef><attrdefs>USEPA</attrdefs><attrdomv><udom>“Mountainous” (M) areas had AUs with &lt;10 % of the area identified as flat (&lt; 1 % slope) and greater than 300 m of total relief. AUs with more than 50 % area having &lt; 1 % slope were classified as “flat” (F).  All other AUs were identified as “transitional” (T).</udom></attrdomv></attr><attr xmlns=""><attrlabl Sync="TRUE">Climate</attrlabl><attalias Sync="TRUE">Climate</attalias><attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">254</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale><attrdef>The Wigington et al. (2013) approach derived the climate index from the FMI (Feddema, 2005), 
where FMI values range from -1.0 (arid) to 1.0 (very wet). P is the mean precipitation (mm) over a 30-year normal, which is derived from climate data described in Section 0, and PET is the potential evapotranspiration (mm) calculated using the Hamon (1961) method, that utilizes mean daily temperature, daytime length (calculated based on latitude), and a calibration coefficient. The range of FMI values was the basis for a climate index consisting of six classes: arid (A; -1.0 ≤ FMI &lt; -0.66), semiarid (S; -0.66 ≤ FMI &lt; -0.33), dry (D; -0.33 ≤ FMI &lt; 0.0), moist (M; 0.0 ≤ FMI &lt; 0.33), wet (W; 0.33 ≤ FMI &lt; 0.66), and very wet (V; 0.66 ≤ FMI &lt; 1.0) (Wigington et al., 2013). FMI was calculated from regional precipitation rasters (described in Section 0) for each period of interest. The FMI value was then averaged over each AU.
</attrdef><attrdefs>USEPA</attrdefs><attrdomv><udom>arid (A; -1.0 ≤ FMI &lt; -0.66), semiarid (S; -0.66 ≤ FMI &lt; -0.33), dry (D; -0.33 ≤ FMI &lt; 0.0), moist (M; 0.0 ≤ FMI &lt; 0.33), wet (W; 0.33 ≤ FMI &lt; 0.66), and very wet (V; 0.66 ≤ FMI &lt; 1.0)</udom></attrdomv></attr><attr xmlns=""><attrlabl Sync="TRUE">Season</attrlabl><attalias Sync="TRUE">Season</attalias><attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">254</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale><attrdef>We used the Leibowitz et al. (2016) approach to develop a seasonality index that identifies the season of the maximum monthly average snowpack-corrected surplus water (S’m), where S’m (Eq. (2)) is the average snowpack-corrected water surplus (mm) for month m, Sm is monthly water surplus (P - PET), and Pm and PETm are monthly precipitation and monthly PET, respectively. PACKm* is a monthly bias-corrected snowpack value (in mm of snow water equivalent, or SWE) restricted to values greater than zero, based on the Leibowitz et al. (2016) modifications to the Leibowitz et al. (2012) snowpack model. Note, however, that 〖ΔPACK〗_m^*can have negative values, which represents snow melt. S’m was calculated for the regional raster for each month, before identifying the month of maximum S’m for the majority of pixels in each AU. The month of maximum S’m was used to identify the season of maximum S’m based upon four seasonality classes: fall (f; October–December), winter (w; January–March), spring (s; April–June), and summer (u; July–September). The PNW analysis by Leibowitz et al. (2016) only included two seasonality classes; summer seasonality did not occur and fall and winter were combined into a winter class, since this represented the PNW’s wet season.  For our analysis, we kept winter and fall separate and used all four seasonality classes, because fall and winter are distinct seasons in other parts of the nation.
</attrdef><attrdefs>USEPA</attrdefs><attrdomv><udom>We used the Leibowitz et al. (2016) approach to develop a seasonality index that identifies the season of the maximum monthly average snowpack-corrected surplus water (S’m). The month of maximum S’m was used to identify the season of maximum S’m based upon four seasonality classes: fall (f; October–December), winter (w; January–March), spring (s; April–June), and summer (u; July–September). 
</udom></attrdomv></attr><attr xmlns=""><attrlabl Sync="TRUE">SoilPerm</attrlabl><attalias Sync="TRUE">SoilPerm</attalias><attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">254</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale><attrdef>For surface permeability, the Wigington et al. (2013) HL approach utilized the STATSGO soil permeability raster developed by Pennsylvania State University Center for Environmental Informatics (www.cei.psu.edu) for the top 10 cm of soil (Miller and White, 1998) in the conterminous U.S. The STATSGO soils database was selected because of its complete coverage of the conterminous U.S., despite SSURGO’s higher spatial resolution, which did not have complete spatial coverage of the U.S. They identified whether the majority of each AU had high (H; &gt;1.52 cm/hr) or low (L; ≤ 1.52 cm h-1) soil permeability. We applied the same approach to classify surface permeability of each AU into two classes throughout the region.</attrdef><attrdefs>USEPA</attrdefs><attrdomv><udom> high (H; &gt;1.52 cm/hr) or low (L; ≤ 1.52 cm h-1) soil permeability</udom></attrdomv></attr><attr xmlns=""><attrlabl Sync="TRUE">AqPerm</attrlabl><attalias Sync="TRUE">AqPerm</attalias><attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">254</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale><attrdef>Leibowitz et al. (2016) utilized the Comeleo (2014) aquifer permeability dataset.  We applied a similar approach from the Stratton et al. (2016) aquifer permeability datasets, which is herein referred to as subsurface permeability. Each of these datasets classify the subsurface permeability into high (H) and low permeability (L) classes, which are assigned with a threshold guideline of 8.5 x 10-2 m day-1 hydraulic conductivity. Using these data, we analyzed the subsurface permeability of each AU by identifying the subsurface permeability class for the majority of pixels within each AU in the three south western states. </attrdef><attrdefs>USEPA</attrdefs><attrdomv><udom>high (H) and low permeability (L) classes, which are assigned with a threshold guideline of 8.5 x 10-2 m day-1 hydraulic conductivity.</udom></attrdomv></attr><attr xmlns=""><attrlabl Sync="TRUE">area_km2</attrlabl><attalias Sync="TRUE">area_km2</attalias><attrtype Sync="TRUE">Double</attrtype><attwidth Sync="TRUE">19</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale><attrdef>Area in km2</attrdef><attrdefs>usepa</attrdefs><attrdomv><edom xmlns=""><edomv>numeric area</edomv><edomvd>length x width = area</edomvd><edomvds>usepa</edomvds></edom></attrdomv></attr></detailed></eainfo><mdDateSt Sync="FALSE">2020-03-31</mdDateSt><Binary><Thumbnail><Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAlgCWAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK
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