Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.

Preparation of GIS Maps for land use and Forest cover assessment of PFI Information Year 2021

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

The Division of Forestry Geographic Information Systems home page provides information on GIS information, Spatial Data, GIS Web Applications depicting current wild land fire information and forest resource information for the entire state of Alaska.

GIS data is available on the Forest’s FTP site in the form of “shape files” or layers and is available free for downloading. To utilize these data layers you will need a program that uses the Geographic Information System (GIS) such as ESRI’s ArcMap, ArcView or the free map reading program ArcGIS Explorer. ArcGIS Explorer has tools that let you zoom in/out, print the map, and query data. It also has map tips to identify features, and a help menu. ArcGIS Explorer is available as a free download from the ESRI website. Included is a list of GIS data files available for the Shawnee National Forest. These GIS data files are updated on a continuing basis. It should be noted that this data may have been developed from sources of differing accuracy, accurate only at certain scales, based on modeling or interpretation, or incomplete while being created or revised. Overall accuracy, completeness and timeliness may vary. The following geospatial information/data was prepared by the Shawnee National Forests (US Forest Service). The Forest Service reserves the right to correct, update, modify or replace GIS data without notification. Resources in this dataset:Resource Title: Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/main/shawnee/landmanagement/gis Information about the geospatial data and a ftp link to download Forest GIS Data Shapefiles.

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

This data support the paper "A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures " by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo 2021 in the journal of "Remote Sensing" by MDPI. It includes the "Search_Effort.csv" list with the keywords and number of studies selected for further examination, the "Potential_Studies.csv" with the post-filtering of suitability and notes related to each study, the "Metadata.csv" with the information collected for each metadata variable per study, and the "ExtractedData.csv" with the information collected for each extracted dta variable per study. More information about the data collection and procedures can be found in the respective manuscript.

National Forest Inventory Continental Database is a database of forest resource attributes covering all land tenures for Australia and Territories. Forest is defined as woody vegetation in excess of 5 metres in height, with a projective foliage cover of >30%. The NFI is also collecting information outside this definition. The data is collected by aerial photo interpretation, field measurements, field Specimens, field notes, maps, and remote sensing data from satellite. The database is made up of separate State wide databases that have been normalised and collated into a single database. Scales and levels of completeness vary between state and within states. These gaps are being addressed by NFI funded regional and local scale projects.

The data base includes gf (Growth form of the vegetation), g1/s1 (the most abundant or physically predominant species in the tallest stratum), g2/s2 (another species that is always present and conspicuous in the tallest stratum), g3/s3 (species selected from any stratum, usually a lower stratum as an indicator species or to destinguish between associations), minh (minimum height in metres), maxh (maximum height in metres), medh (median height derived through consultation with the suppliers of the data), h_class (height class as per Walker and Hopkins (1990)), minpfc (minimum projective foliage cover), maxpfc (maximum projective foliage cover), medpfc (median projective foliage cover), mincc (minimum crown cover), maxcc (maximum crown cover), minc (minimum crown separation ratio), maxc (maximum crown separation ratio), c_class (cover classes as per Walker and Hopkins (1990)), plant_code (equivalent to frq_code for plantations), and description (description of the type of plantation). The data is available in ArcInfo EXPORT format (the interchange format for this Geographic Information System). The data set is about 500 megabytes.

The Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and depletion of timber on the Nation's forest land. Before 1999, all inventories were conducted on a periodic basis. The passage of the 1998 Farm Bill requires FIA to collect data annually on plots within each State. This kind of up-to-date information is essential to frame realistic forest policies and programs. Summary reports for individual States are published but the Forest Service also provides data collected in each inventory to those interested in further analysis. Data is distributed via the FIA DataMart in a standard format. This standard format, referred to as the Forest Inventory and Analysis Database (FIADB) structure, was developed to provide users with as much data as possible in a consistent manner among States. A number of inventories conducted prior to the implementation of the annual inventory are available in the FIADB. However, various data attributes may be empty or the items may have been collected or computed differently. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Links to field collection manuals and the FIADB user's manual are provided in the FIA DataMart.

Selected GIS data that encompass Carson National Forest are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Universal Transverse Mercator Zone: 13 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Resources in this dataset:Resource Title: Carson National Forest GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprdb5202766

ABSTRACT This paper presents a GIS methodological approach for mapping forest landscape multifunctionality. The aims of the present study were: (1) to integrate and prioritize production and protection functions by multicriteria spatial analysis using the Analytic Hierarchy Process (AHP); and (2) to produce a multifunctionality map (e.g., production, protection, conservation and recreation) for a forest management unit. For this, a study area in inner Portugal occupied by forest and with an important protection area was selected. Based on maps for functions identified in the study area, it was possible to improve the scenic value and the biodiversity of the landscape to mitigate fire hazard and to diversify goods and services. The developed methodology is a key tool for producing maps for decision making support in integrated landscape planning and forest management.

Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004. Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004.

Selected GIS data that encompass Kaibab National Forest are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Universal Transverse Mercator Zone: 12 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Resources in this dataset:Resource Title: Kaibab National Forest GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprdb5209305

Some of the finest mountain scenery in the Southwest is found in the 1.6-million-acre Santa Fe National Forest. Here, you can find the headwaters of Pecos, Jemez, and Gallinas Rivers; mountain streams; lakes; and trout fishing. Travel into Pecos, San Pedro Parks, Chama, and Dome Wildernesses via wilderness pack trips, saddle, or on 1,000 miles of hiking trails. Try whitewater rafting on the Rio Chama or Rio Grande from May to September. Consider turkey, elk, deer, and bear hunting, or visit one of many nearby Indian pueblos, Spanish missions, and Indian ruins. Golden aspen grace the high country from September to October and snow blankets Santa Fe Ski Basin in winter. The Santa Fe National Forest GIS data available for download includes Santa Fe National Forest Geospatial (GIS) Datasets, Motor Vehicle Use Map (MVUM) Travel Aids - digital maps and data of the SFNF to upload to GPS units or Smart Phones, 7.5 Minute Topographic Maps (PDF and GeoTIFF) - US Forest Service topo maps only, USFS Geospatial Clearinghouse - includes GIS data of vegetation treatments, administrative boundaries, inventoried roadless areas, FSTopo datasets, USGS Map Locator and Downloader - download current and historic topo maps, Hardcopy Maps with information on how to purchase hard copy visitor, wilderness, or topographic maps. Resources in this dataset:Resource Title: Santa Fe National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/main/santafe/landmanagement/gis

Selected GIS data that encompass Lincoln National Forest are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Universal Transverse Mercator Zone: 13 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Resources in this dataset:Resource Title: Lincoln National Forest GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprdb5203236

Dateset contains extensive GIS layer territorial descriptions for four selected territories in ecologically and socially heterogeneous seasonally-dry forests of South America, subjected to rapid change mainly due to global climatic, ecological and social drivers, from descriptive layer like topography to land use change. Main objective is to identify the proximate and underlying causes of land-use change and deforestation in each of the four sites within the framework of the project “Socio-ecological resilience in the face of global environmental change in heterogeneous landscapes – building a common platform for understanding and action”. The underlying causes of land use changes were identified through a review of literature, news articles and any other relevant sources of information. The project propose the construction of a shared platform to understand socio-ecological resilience and adaptive capacity in the face of rapid large-scale environmental change. This will include the co-production, between scientists and a wide range of other stakeholders, of a shared framework, a set of research questions and a detailed path for their implementation in empirical research, practice and policy.

The Trails Layer is designed to provide information about National Forest System trail locations and characteristics to the public. When fully realized, it will describe trail locations, basic characteristics of the trail, and where and when various trail uses are prohibited, allowed and encouraged. Because the data readiness varies between Forests, each Forest will approve which level of attribute subset are published for that forest. Forests can provide no information or one of three attribute subsets describing trails. The attribute subsets include TrailNFS_Centerline which includes the location and trail name and number; TrailNFS_Basic which adds information about basic trail characteristics; and TrailNFS_Mgmt which adds information about where and when users are prohibited, allowed, and encouraged. When a Forest chooses to provide the highest attribute subset, TrailNFS_Mgmt, these attributes must be consistent with the Forest's published Motorized Vehicle Use Map (MVUM). Metadata for the individual Forest feature classes used to compile this feature class are available at data.fs.usda.gov/geodata/edw/dir_trails.php. Metadata

Global Forestry Software Market is segmented by Application (Forest Management_ Timber Tracking_ GIS Mapping_ Resource Planning), Type (Cloud-based Solutions_ Desktop Software_ Mobile Applications), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Forests to Faucets 2.0 builds upon the national Forests to Faucets(2011) by updating base data and adding new threats including wildfire, invasive pests, and future stresses such as climate-induced changes in land use and water quantity. The purpose of this project is to quantify, rank, and illustrate the geographic connection between forests and other natural cover (private and public), surface drinking water supplies, and the populations that depend on them–the ecosystem service of water supply. The project assesses subwatersheds across the US to identify those important to downstream surface drinking water supplies as well as evaluate a subwatersheds natural ability to produce clean water based on its biophysical characteristics: percent natural cover, percent agricultural land, percent impervious, percent riparian natural cover, and mean annual water yield. Using data from a variety of existing sources and maps generated through GIS analyses, the project uses maps and statistics to describe the relative importance of private forests and National Forest System lands to surface drinking water supplies across the United States. The data produced by this assessment provides information needed to identify opportunities for water market approaches or schemes based upon payments for environmental services (PES).September 2023 Update: Water yields (Q_YLD_MM; PER_Q40_45; PER_Q90_45; PER_Q40_85; PER_Q90_85) were updated to tie back to WASSI source data. All Forests to Faucets models and indices were recalculated. HUCs that did not have a corresponding water yield from WASSI were recalculated to the nearest HUC. AttributeDescriptionSourcesAcresHUC 12 AcresCalculated using ArcGISSTATESStatesWBD 2019HUC1212-digit Hydrologic Unit CodeWBD 2019NAME12-digit Hydrologic Unit NameWBD 2019HUTYPEHUC TypeWBD 2019From_HUC 12 From for routingWBD 2019ToHUC 12 To for routingWBD 2019 (edited by USDA FS)LevelHUC levelCalculated HUC level from outlet (1) to headwater (351)NLCDAcres of NLCDNLCDPER_NLCDPercent of HUC with NLCD DataCalculated using ArcGISFOREST_ACAcres of all forestNLCD Forest = 41,42,43, 90NLCDPER_FORPercent ForestCalculated using ArcGISAG_ACAcres of agricultural landNLCD = 81,82NLCDPER_AGPercent agricultural landCalculated using ArcGISIMPV_ACAcres of ImperviousNLCDPER_IMPVPercent ImperviousCalculated using ArcGISNATCOVER_ACAcres of Natural CoverNLCD = 11,12,41,42,43,51,52,71,90,95NLCDPER_NATCOVPercent Natural CoverCalculated using ArcGISRIPNAT_ACAcres of riparian natural coverSinan AboodPER_RIPNATPercent riparian natural coverCalculated using ArcGISQ_YLD_MM Mean Annual Water Yield in mm (Q) based on the historical time period (1961 to 2015). Baseline water yield for 2010.WASSI , Updated September 2023R_NATCOVNatural Cover score for APCWCalculated using ArcGISR_AGAgricultural land score for APCWCalculated using ArcGISR_IMPVImpervious Surface score for APCWCalculated using ArcGISR_RIPRiparian Natural Cover score for APCWCalculated using ArcGISR_QMean Annual Water Yield score for APCWCalculated using ArcGISAPCWAbility to Produce Clean Water (APCW)= (R_NATCOV+R_AG+R_IMPV+R_RIP) * R_QCalculated using ArcGISAPCW_RAPCW Score (0-100 Quantiles)Calculated using RGWNumber of groundwater water intakesSDWISSWNumber of surface water intakes (includes GU, groundwater under the influence of surface water)SDWISGW_POPNumber of groundwater water consumersSDWISSW_POPNumber of surface water consumers (includes GU, groundwater under the influence of surface water)SDWISGL_IntakesNumber of surface water intakes in the Great LakesSDWISGL_POPNumber of surface water consumers in the Great LakesSDWISSUM_POPP, Total number of Surface water consumers SW_POP+ GL_POPSDWISPRDrinking water protection model PRn = ∑(Wi Pi)Calculated using RPOP_DSSum of surface drinking water population downstream of HUC 12 ∑(SUM_POPn) **Cannot be summed across multiple HUC12 due to double counting down stream populations. Only accurate for an individual HUC12.Calculated using RIMPRaw Important Areas for Surface Drinking Water (IMP) Value. As developed in Forests to Faucets (USFS 2011), the Important Areas for Surface Drinking Water (IMP) model can be broken down into two parts: IMPn = (PRn) * (Qn)Calculated using R, Updated September 2023IMP_RIMP, Important Areas for Surface Drinking Water (0-100 Quantiles)Calculated using R, Updated September 2023NON_FORESTAcres of non-forestPADUS and NLCDPRIVATE_FORESTAcres of private forestPADUS and NLCDPROTECTED_FORESTAcres of protected forest (State, Local, NGO, Permanent Easement)PADUS, NCED, and NLCDNFS_FORESTAcres of National Forest System (NFS) forestPADUS and NLCDFEDERAL_FORESTAcres of Other Federal forest (Non-NFS Federal)PADUS and NLCDPER_FORPRIPercent Private ForestCalculated using ArcGISPER_FORNFSPercent NFS ForestCalculated using ArcGISPER_FORPROPercent Protected (Other State, Local, NGO, Permanent Easement, NFS, and Federal) ForestCalculated using ArcGISWFP_HI_ACAcres with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_WFPPercent of HU 12 with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_IDRISKPercent of HU 12 that is at risk for mortality - 25% of standing live basal area greater than one inch in diameter will die over a 15- year time frame (2013 to 2027) due to insects and diseases.Krist, et Al,. 2014PERDEV_1040_45% Landuse Change 2010-2040 (low)ICLUSPERDEV_1090_45% Landuse Change 2010-2090 (low)ICLUSPERDEV_1040_85% Landuse Change 2010-2040 (high)ICLUSPERDEV_1090_85% Landuse Change 2010-2090 (high)ICLUSPER_Q40_45% Water Yield Change 2010-2040 (low) WASSI , Updated September 2023PER_Q90_45% Water Yield Change 2010-2090 (low) WASSI , Updated September 2023PER_Q40_85% Water Yield Change 2010-2040 (high) WASSI , Updated September 2023PER_Q90_85% Water Yield Change 2010-2090 (high) WASSI , Updated September 2023WFP(APCW_R * IMP_R * PER_WFP )/ 10,000Wildfire Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023IDRISK(APCW_R * IMP_R * PER_IDRISK )/ 10,000Insect & Disease Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023DEV1040_45(APCW_R * IMP_R * PERDEV_1040_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_45(APCW_R * IMP_R * PERDEV_1090_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023DEV1040_85(APCW_R * IMP_R * PERDEV_1040_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_85(APCW_R * IMP_R * PERDEV_1090_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_45-1 * (APCW_R * IMP_R * PER_Q40_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_45-1 * (APCW_R * IMP_R * PER_Q90_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_85-1 * (APCW_R * IMP_R * PER_Q40_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_85-1 * (APCW_R * IMP_R * PER_Q90_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023WFP_IMP_RWildfire Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023IDRISK_RInsect & Disease Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023RegionUS Forest Service Region numberUSFSRegionnameUS Forest Service Region nameUSFSHUC_Num_DiffThis field compares the value in column HUC12(circa 2019 wbd) with the value in HUC_12 (circa 2009 wassi)-1 = No equivalent WASSI HUC. Water yield (Q_YLD_MM) was estimating using the nearest HUC.USFS, Updated September 2023HUC_12_WASSIWASSI HUC numberWASSI, Updated September 2023