This feature class is updated every business day using Python scripts and the WellNet database. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates. The NDWR's water monitoring database contains information related to sites for groundwater measurements. These data are used by NDWR to assess the condition of the groundwater and surface water systems over time and are available to the public on NDWR’s website. Groundwater measurement sites are chosen based on physical location and access considerations, permit terms, and to maximize the distribution of measurement points in a given basin.Groundwater monitoring sites are typically chosen based on spatial location, access, and period of record considerations. When possible NDWR tries to have a distribution of monitoring locations within a given hydrographic area. The entity who does the monitoring depends on the site – for example, some mines have well fields where they collect data and submit those data to NDWR as a condition of their monitoring plan – and some sites are monitored by NDWR staff annually or more frequently. While people can volunteer to have their well monitored, more often the NDWR staff who measure water levels recommend an additional site or staff in the office recommend alternate sites. The Chief of the Hydrology Section will review the recommendations and make a final decision on adding/changing a site. This dataset is updated every business day from a non-spatial SQL Server database using lat/long coordinates to display location. This feature class participates in a relationship class with a groundwater measure table joined using the sitename field. This dataset contains both active and inactive sites. Measurement data is provided by reporting agencies and by regular site visits from NDWR staff. For website access, please see the Water Levels site at water.nv.gov/WaterLevelData.aspx

River basins or hydrologic units are often the spatial unit used for aggregating and analyzing components of the water cycle such as precipitation, runoff, riverine discharge, etc. The hydroSHEDS dataset, derived from the Shuttle Radar Topography Mission, are the most commonly used global hydrologic unit for these analyses. But when planning water use or gaps, political boundaries need to be considered. Water provinces (Straatsma et al 2020) provide a much more realistic hydrologic unit for such purposes.Esri’s World Administration Divisions (2011) defines 3,300 subnational units. Areas less than 150,000 sq km were aggregated into 1,099 regions. The water provinces were then calculated by overlaying these regions with the major basins from hydroSHEDS. After sliver polygons were removed, the result was 1,604 unique units based on river basins but constrained by political boundaries. These water provinces provide a suitable unit for longterm water use planning, especially at local scales.A more detailed description can be accessed here.

The retrospective database is a compilation of historical water-quality and ancillary data collected before NAWQA Study Units initiated sampling in 1993. This coverage contains the point locations of monitoring locations where historical water-quality data was collected. Water-quality data were obtained by study-unit personnel from the U.S. Geological Survey (USGS) National Water Information System (NWIS), from records of State water-resource agencies, and from STORET, the U.S. Environmental Protection Agency national database. Ancillary data describing characteristics of sampled sites were compiled by NAWQA Study Units or obtained from national-scale digital maps.

Mueller and others (1995) used this data to determine preexisting water-quality conditions in the first 20 NAWQA Study Units that began in 1991. Also, Nolan and Ruddy (1996) used the data to describe areas of the United States at risk of nitrate contamination of ground water.

Supplemental_Information:

The retrospective database includes over 22,000 surface-water samples. The surface-water data are for samples collected during 1980-90 at sites that had a minimum of 25 monthly samples. Year of sampling is included in the retrospective database because it was reported most often by the various Study Units. Year of sampling also is convenient because some Study Units reported median constituent concentrations. If sampling date ranges for median values fell within a single year, then year of sampling was retained in the national data set for that sample.

Because sampling, preservation, and analytical techniques associated with these historical data changed during the period of record and are different for different agencies, reported nutrient concentrations were aggregated into the following groups: (1) ammonia as N, (2) nitrate as N, (3) total nitrogen, (4) orthophosphate as P, and (5) total phosphorus. For example, ammonia includes both ammonium ions and un-ionized ammonia. More information on methods used to aggregate constituent data is available in the report by Mueller and others (1995).

Much of the ancillary data, such as well and aquifer descriptions and land-use classification for surface-water drainage basins, were provided by NAWQA Study Units. Data evaluated at the national scale include land use, soil hydrologic group, nitrogen input to the land surface, and the ratios of pasture or woodland to cropland.

Land-use classification of surface-water sites is based on Anderson Level I categories (Anderson and others, 1976). Land use at surface-water sites was classified by NAWQA Study Unit personnel based on the Anderson Level I categories. Many surface-water sites were affected by mixed land uses, such as Forest and Agricultural, or Agricultural and Urban. Surface-water sites with very large drainage areas (greater than 10,000 square miles) were considered to be affected by multiple land uses, and were designated as Integrated land use. More detailed descriptions of the land-use categories in the retrospective database are given by Mueller and others (1995).

Soil hydrologic group was determined from digital maps compiled by the U.S. Soil Conservation Service (1993). The categorical values (A, B, C, and D) from the digital maps were converted to numbers to permit aggregation (Mueller and others, 1995). Surface-water sites were assigned the area-weighted mean for soil mapping units in the upstream drainage basin. Many surface-water sites did not have digitized basin boundaries available, so hydrologic group could not be evaluated.

Fertilizer and manure applications were estimated from national databases of fertilizer sales (U.S. Environmental Protection Agency, 1990) and animal populations (U.S. Bureau of the Census, 1989). Nitrogen input by atmospheric deposition was derived from data provided by the National Atmospheric Deposition Program/National Trends Network (1992).

Population data were obtained from the U.S. Bureau of the Census (1991). Total population in the upstream drainage was compiled for the surface-water data set.

Within the database, concentrations less than detection are reported as negative values of the detection limit. Missing values are indicated by a decimal point. (During processing of the tabular data, these decimal points were replaced will NULL values; See Data_Quality_Information section.

Historical data can be of limited use in national assessments because of inconsistencies between and within agencies in database structure and format and in sample collection, preservation, and analytical procedures. For example, changes in sample collection and analytical procedures can cause shifts in constituent concentrations that are unrelated to possible changes in environmental factors. See Mueller and others (1995) for assumptions and limitations associated with the retrospective database.

[Summary provided by the EPA.]

The Natural Communities Commonly Associated with Groundwater (NCCAG) dataset is a compilation of phreatophytic vegetation, regularly flooded natural wetlands and riverine areas, and springs and seeps extracted from 48 publicly available state and federal agency datasets. Two habitat classes are included in the dataset: wetland features commonly associated with the surface expression of groundwater under natural, unmodified conditions; and vegetation types commonly associated with the sub-surface presence of groundwater (phreatophytes). The NCCAG dataset began as an amalgamation of vegetation and wetland datasets with different scales, resolutions, attribute details, and classifications. A working group comprised of DWR, California Department of Fish and Wildlife (CDFW) and The Nature Conservancy (TNC) further reviewed the vegetation and wetland datasets and conducted a screening process to identify the vegetation and wetland types considered to be commonly associated with groundwater (Klausmeyer et al., 2018). The NCCAG dataset can be used as a starting point to investigate and identify groundwater dependent ecosystems (GDEs) within a groundwater basin. Identifying GDEs requires detailed understanding of the land use, groundwater levels, hydrology, and geology of a location. This comprehensive understanding of geology, hydrology, and biology is not available at the statewide scale. Further investigation and verification of the connection and dependence between groundwater and mapped vegetation and wetlands at a local scale may be needed for water managers in sustainable groundwater management planning.

Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: - CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m - CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management. The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices. Processing steps: To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value. File naming: Until December 2020: c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif e.g.: c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif From January 2021 on: c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif e.g.: c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif The date within the filename is year and month of aggregated timestamp. NOTE: data for 2023-04 are missing, since they are not available from CLMS Pixel values: 0: Sea 70: Water 255: No water Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 27:17:30N south: 14:43:30N west: 17:04:30W east: 04:48:00W Temporal extent: January 2019 - December 2023 (except: April 2023) Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original dataset license: Generated using European Union's Copernicus Land Monitoring Service information Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, info@mundialis.de Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

The Natural Communities Commonly Associated with Groundwater (NCCAG) dataset is a compilation of phreatophytic vegetation, regularly flooded natural wetlands and riverine areas, and springs and seeps extracted from 48 publicly available state and federal agency datasets. Two habitat classes are included in the dataset: wetland features commonly associated with the surface expression of groundwater under natural, unmodified conditions; and vegetation types commonly associated with the sub-surface presence of groundwater (phreatophytes). The NCCAG dataset began as an amalgamation of vegetation and wetland datasets with different scales, resolutions, attribute details, and classifications. A working group comprised of DWR, California Department of Fish and Wildlife (CDFW) and The Nature Conservancy (TNC) further reviewed the vegetation and wetland datasets and conducted a screening process to identify the vegetation and wetland types considered to be commonly associated with groundwater (Klausmeyer et al., 2018). The NCCAG dataset can be used as a starting point to investigate and identify groundwater dependent ecosystems (GDEs) within a groundwater basin. Identifying GDEs requires detailed understanding of the land use, groundwater levels, hydrology, and geology of a location. This comprehensive understanding of geology, hydrology, and biology is not available at the statewide scale. Further investigation and verification of the connection and dependence between groundwater and mapped vegetation and wetlands at a local scale may be needed for water managers in sustainable groundwater management planning.

This layer is a snapshot of stream gages from the fall of 2020. It is the product of an attempt to compile a comprehensive, geospatial list of long-term stream gages whose data is publicly available. Initially, the layer will consist of USGS and CDEC gages. Over time, local (county, municipal, etc.) gages will be added. This layer is not claimed to be authoritative. In cases where this layer and the data maintained by the source entity differ, this layer always defers to the source entity. For analysis purposes, the gage point locations have been altered by SWRCB to coincide with the corresponding line features in the National Hydrography Dataset (NHD) Medium Resolution. The original point locations can be found "x" and "y" fields of the layer's attribute table.For questions, contact the SWRCB Division of Water Rights: DWR@waterboards.ca.gov.Data dictionary: Field Name Description Data Type

SiteID Site ID Text

SiteName Site Name Text

Operator Agency or entity which operates the gage Text

DataSource The agency or entity which publishes the data online (source not exclusive) Text

SiteStatus Is the site, in general, active or inactive? Text - Active or Inactive

Stage_YN Did the gage report stage at any time? Text - Y or N or U

Stage_POR Stage period of record in days (if a site had multiple stage sensors or duration codes, then the max POR was used) Integer

Stage_Status Status of stage reporting (active/inactive) Text

Stage_RealTime Is/was stage reported hourly or more frequently? Text - Y or N

Flow_YN Did the gage report flow at any time? Text - Y or N or U

Flow_POR Flow period of record in days (if a site had multiple flow sensors or duration codes, then the max POR was used) Integer

Flow_Status Status of flow reporting (active/inactive) Text

Flow_RealTime Is/was flow reported hourly or more frequently? Text - Y or N

WatQual_YN Did the gage report one or more water quality parameters at any time? Includes parameters such as water chemistry, dissolved oxygen, and turbidity, but not temperature Text - Y or N or U

WatQual_POR Water quality period of record in days (if a site had multiple water quality sensors or duration codes, then the max POR was used) Integer

WatQual_Status Status of water quality reporting (active/inactive) Text

WatQual_RealTime Is/was water quality reported hourly or more frequently? Text - Y or N

Temp_YN Did the gage report water temperature at any time? Text - Y or N or U

Temp_POR Temperature period of record in days (if a site had multiple temperature sensors or duration codes, then the max POR was used) Integer

Temp_Status Status of temperature reporting (active/inactive) Text

Temp_RealTime Is/was temperature reported hourly or more frequently? Text - Y or N

EcosysMgmt Primary purpose or benefit of gage is ecosystem management (flow and water quality) Y - water manager survey, B - prioritization analysis high raw score

PubSafety Primary purpose or benefit of gage is flood or public safety Y - water manager survey, F - flood water manager survey, B - prioritization analysis high raw score

WtrSupply Primary purpose of gage or benefit is water supply (municipal or agricultural) Y - water manager survey, G - groundwater water manager survey, B - prioritization analysis high raw score

WtrQuality Primary purpose or benefit of gage is water quality B - prioritization analysis high raw score

refpotential Reference gage or potential reference gage with Action Y or N

FloodMgmt Primary purpose or benefit of gage is flood management Y - water manager survey, F - flood survey answer, B - prioritization analysis high raw score

GrdwtrMgmt Primary purpose of gage is groundwater management Text - Y or N

Ref_GagesII Is the gage site considered a reference site in Gages II dataset? Text - Y or N

StrmOrder Strahler stream order Integer

UCDStrmClass UCD eFlows stream classification Text

StreamType Type of water conveyance the gage is measuring (e.g. Stream/River, Canal/Ditch, Artificial Path, etc.) Text

TotDASqKM Total drainage area in square kilometers Double

TotDASqMi Total drainage area in square miles Double

GNISID_MedRes GNIS (Geographic Names Information System) identification number of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

RchCd_MedRes Reach Code identification number of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

COMID_MedRes COM ID (common identifier) of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

Assessment Assessment categories indicating use cautions (generated by SWRCB staff) Text

WtrRtNotes Notes concerning water rights that may impact gage measurements (generated by SWRCB staff) Text

SWRCB_Note Notes to inform use of gage data (generated by SWRCB staff) Text

WebLink Web address to access each gage's data Text

x_orig X coordinate as provided by source entity (NAD83 CA Teale Albers meters) Double

y_orig Y coordinate as provided by source entity (NAD83 CA Teale Albers meters) Double

WtrshdNm_HUC8 Name of containing HUC8 watershed Text

HUC8 Containing HUC 8 (Hydrologic Unit Code 8) identifier Text

WtrshdNm_HUC10 Name of containing HUC10 watershed Text

HUC10 Containing HUC 10 (Hydrologic Unit Code 10) identifier Text

WtrshdNm_HUC12 Name of containing HUC12 watershed Text

HUC12 Containing HUC 12 (Hydrologic Unit Code 12) identifier Text

GageGap_Status Status of Gage for Gage Gap Analysis (e.g. Well-Gaged, AWG = Almost Well-Gaged, or Exclude) Text

Infrastructure Gage is suspected of being located on infrastructure Text - Y or N or YC (yes but connected)

ReactivateSF Gage is a candidate for reactivation Text - Y or N

Gage_History Reactivation gage history priority based on gage metadata alone (e.g. period_of_record, parameter status, end-date and other factors, but not including based on gage gap or management criteria). 1 is the top score. Long

AddFlow_2Stage Upgrade candidate: gage is actively reporting stage, potential upgrade to flow and stage Text - Y or N

AddFlow_2WQ Upgrade candidate: gage is actively reporting water quality or temperature data, but not flow and/or stage. Text - Y or N

AddTelemetry Upgrade candidate: gage is actively reporting stage and/or flow, but not in real-time Text - Y or N

AddTemp_2Flow Upgrade candidate: gage is actively reporting stage and/or flow, but not water temperature Text - Y or N

Gage Status Indicated whether gage is Active - High Quality, Active - Limited Use, Inactive, Underwater, or Not a stream Gage Text

waterbody Name of waterbody that may cover gage Text

reference gage Gage is considered an active reference quality gage Text - Y or N

Tier Indicates priority level for upgrades and reactivation, with 1 the highest Numeric

Primary Benefit Primary benefit of gage for existing gages, reactivation, and upgrade gages Text

SB19 Action Recommended Recommendation for gage improvement, if any Text

CNRFC Gage is a California Nevada River Forecast Center Gage Text = Forecast or Model

Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser. Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

This feature class is updated every business day using Python scripts and the WellNet database. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates.The NDWR's water monitoring database contains information related to sites for surface water measurements. These data are used by NDWR to assess the condition of the groundwater and surface water systems over time and are available to the public on NDWR’s website. Surface water measurement sites are chosen based on physical location and access considerations, permit terms, and to maximize the distribution of measurement points in a given basin.Surface water monitoring sites are typically chosen based on spatial location, access, and period of record considerations. When possible NDWR tries to have a distribution of monitoring locations within a given hydrographic area. The entity that does the monitoring depends on the site – for example, some mines have well fields where they collect data and submit those data to NDWR as a condition of their monitoring plan – and some sites are monitored by NDWR staff annually or more frequently. While people can volunteer to have their site monitored, more often the NDWR staff who measure flow rates recommend an additional site or staff in the office recommend alternate sites. The Chief of the Hydrology Section will review the recommendations and make a final decision on adding/changing a site. This dataset is updated every business day from a non-spatial SQL Server database using lat/long coordinates to display location. This feature class participates in a relationship class with a surface water measure table joined using the sitename field. This dataset contains both active and inactive sites. Measurement data is provided by reporting agencies and by regular site visits from NDWR staff. For website access, please see the Spring and Stream Flow site at water.nv.gov/SpringAndStreamFlow.aspx.

Available water supply (AWS) is the total volume of water (in centimeters) that should be available to plants when the soil, inclusive of rock fragments, is at field capacity. It is commonly estimated as the amount of water held between field capacity and the wilting point, with corrections for salinity, rock fragments, and rooting depth. AWS is reported as a single value (in centimeters) of water for the specified depth of the soil. AWS is calculated as the available water capacity times the thickness of each soil horizon to a specified depth. For each soil layer, available water capacity, used in the computation of AWS, is recorded as three separate values in the database. A low value and a high value indicate the range of this attribute for the soil component. A "representative" value indicates the expected value of this attribute for the component. For the derivation of AWS, only the representative value for available water capacity is used. The available water supply for each map unit component is computed as described above and then aggregated to a single value for the map unit by the process described below. A map unit typically consists of one or more "components." A component is either some type of soil or some nonsoil entity, e.g., rock outcrop. For the attribute being aggregated (e.g., available water supply), the first step of the aggregation process is to derive one attribute value for each of a map unit's components. From this set of component attributes, the next step of the process is to derive a single value that represents the map unit as a whole. Once a single value for each map unit is derived, a thematic map for the map units can be generated. Aggregation is needed because map units rather than components are delineated on the soil maps. The composition of each component in a map unit is recorded as a percentage. A composition of 60 indicates that the component typically makes up approximately 60 percent of the map unit. For the available water supply, when a weighted average of all component values is computed, percent composition is the weighting factor. SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey. The most common use of these data is communication of soil conditions to contractors working in the park. Additional uses of these data include analysis by park partners and researchers of the physical and chemical properties of soils, including their effect and influence on the management of natural habitats, ecosystem health, and natural resource inventory. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a 7.5 minute quadrangle format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is required. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the Map Unit Record relational database, which gives the proportionate extent of the component soils and their properties. These data represent a specific interpretation of the SSURGO soils data produced by the NRCS, using the NRCS Soil Data Viewer version 6.0. Building site development interpretations are designed to be used as tools for evaluating soil suitability and identifying soil limitations for various construction purposes. As part of the interpretation process, the rating applies to each soil in its described condition and does not consider present land use. Example interpretations can include corrosion of concrete and steel, shallow excavations, dwellings with and without basements, small commercial buildings, local roads and streets, and lawns and landscaping. This is a hybrid data product produced using NRCS SSURGO soils data. These data should not be considered SSURGO-compliant, as data used in this product is the result of merging data from several separate SSURGO databases. The NRCS does not endorse or support this hybrid product.The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park Available Water Supply.

The Integrated Report is a biennial publication on the quality of Michigan’s water resources. The Clean Water Act (CWA) requires Michigan to prepare a biennial report on the quality of its water resources as the principal means of conveying water quality protection/monitoring information to the United States Environmental Protection Agency (USEPA) and the United States Congress. The Integrated Report satisfies the listing requirements of Section 303(d) and the reporting requirements of Section 305(b) and 314 of the CWA. This 2024 draft point locations layer includes only beach locations and provides access to the preliminary dataset during the public comment period. All attainment values for specified designated use attainment categories include 2024 integrated report values.Further information, including a comprehensive 303(d) list, can be found on EGLE’s Integrated Report webpage.For questions about this data and EGLE's response, contact Molly Rippke, at RippkeM@michigan.gov.

Field Name

Description

AUID

Assessment Unit Identification number includes the corresponding HUC12 of the hydrographic feature, followed by a unique numeric identifier. This field is used to identify assessment units and submit water quality information to EPA. It should be used to reference assessments described in EGLE’s biannual integrated report and EPA’s How’s My Waterway information system.

WaterbodyName

Waterbody name for the corresponding hydrographic feature. Derived from authoritative datasets and/or local knowledge

Description

A basic location description of the point location, can include county, city, town, approximate area, or location type

Latitude

Latitude measurement using decimal degrees notation

Longitude

Longitude measurement using decimal degrees notation

HowsMyWaterwayLink

Link to how’s my waterway, an EPA data hub that displays additional information about AUIDs

EPAIRCategory

Environmental Protection Agency Integrated Report Category for an individual AUID. These categories indicate whether a waterbody is supporting designated uses or not. More information can be found here.

PartialBodyContactAttainment

This field indicates the attainment status of AUIDs in respect to the Partial Body Contact designated use. This refers to the use of a surface water that may cause the human body to come into direct contact with the water, but normally not to the point of complete submergence, such as wading or boating. Water bodies are evaluated for the Total Body Contact (TBC) and Partial Body Contact (PBC) recreation using E. coli bacteria as an indicator for other harmful pathogens.

TotalBodyContactAttainment

This field indicates the attainment status of AUIDs in respect to the Total Body Contact designated use. This refers to the use of a surface water for swimming or other recreational activity that causes the human body to come into direct contact with the water to the point of complete submergence. Water bodies are evaluated for the Total Body Contact (TBC) and Partial Body Contact (PBC) recreation using E. coli bacteria as an indicator for other harmful pathogens.

ColdWaterFisheryAttainment

This field indicates the attainment status of AUIDs in respect to the Cold Water Fishery designated use. This use includes the protection of waters where the dominant species under natural conditions would be temperature intolerant indigenous species. Examples include members of the following families: Salmon, Trout, Cod, Whitefish

WarmWaterFisheryAttainment

This field indicates the attainment status of AUIDs in respect to the Warm Water Fishery designated use. This use includes the protection of waters where the dominant species under natural conditions would be temperature tolerant indigenous non- salmonid species. Examples include members of the following families: Pearch, Panfish, Bowfin, Bass, Catfish, Pike

OtherIndigenousAquaticLifeAttai

This field indicates the attainment status of AUIDs in respect to the Other Indigenous Aquatic Life designated use. This use includes the protection of waters for macroinvertebrate and aquatic plant communities. Macroinvertebrate examples include mayflies, stoneflies, and caddisflies.

FishConsumptionAttainment

This field indicates the attainment status of AUIDs in respect to the Fish Consumption designated use. This use includes the protection of aquatic communities and human health related to. consumption of fish and shellfish. In other words, this use means that not only can fish and shellfish thrive in a waterbody, but when caught, can also be safely eaten by humans.

PublicWaterSupplyAttainment

This field indicates the attainment status of AUIDs in respect to the Public Water Supply designated use. This use includes waters that are the source for drinking water supplies and often includes waters for food processing. Waters for drinking water may require treatment prior to distribution in public water systems.

EPA303dImpairment

This field indicates whether an AUID is listed as impaired, or not supporting a designated use, in the corresponding integrated report. 1 = Impaired, 0 = not Impaired

BeachGuardLink

This field provides a link to the beach guard information system managed by EGLE. This system can be used to retrieve more beach-specific information related to AUIDs.

Available water supply (AWS) is the total volume of water (in centimeters) that should be available to plants when the soil, inclusive of rock fragments, is at field capacity. It is commonly estimated as the amount of water held between field capacity and the wilting point, with corrections for salinity, rock fragments, and rooting depth. AWS is reported as a single value (in centimeters) of water for the specified depth of the soil. AWS is calculated as the available water capacity times the thickness of each soil horizon to a specified depth. For each soil layer, available water capacity, used in the computation of AWS, is recorded as three separate values in the database. A low value and a high value indicate the range of this attribute for the soil component. A "representative" value indicates the expected value of this attribute for the component. For the derivation of AWS, only the representative value for available water capacity is used. The available water supply for each map unit component is computed as described above and then aggregated to a single value for the map unit by the process described below. A map unit typically consists of one or more "components." A component is either some type of soil or some nonsoil entity, e.g., rock outcrop. For the attribute being aggregated (e.g., available water supply), the first step of the aggregation process is to derive one attribute value for each of a map unit's components. From this set of component attributes, the next step of the process is to derive a single value that represents the map unit as a whole. Once a single value for each map unit is derived, a thematic map for the map units can be generated. Aggregation is needed because map units rather than components are delineated on the soil maps. The composition of each component in a map unit is recorded as a percentage. A composition of 60 indicates that the component typically makes up approximately 60 percent of the map unit. For the available water supply, when a weighted average of all component values is computed, percent composition is the weighting factor. SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey. The most common use of these data is communication of soil conditions to contractors working in the park. Additional uses of these data include analysis by park partners and researchers of the physical and chemical properties of soils, including their effect and influence on the management of natural habitats, ecosystem health, and natural resource inventory. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a 7.5 minute quadrangle format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is required. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the Map Unit Record relational database, which gives the proportionate extent of the component soils and their properties. These data represent a specific interpretation of the SSURGO soils data produced by the NRCS, using the NRCS Soil Data Viewer version 6.0. Building site development interpretations are designed to be used as tools for evaluating soil suitability and identifying soil limitations for various construction purposes. As part of the interpretation process, the rating applies to each soil in its described condition and does not consider present land use. Example interpretations can include corrosion of concrete and steel, shallow excavations, dwellings with and without basements, small commercial buildings, local roads and streets, and lawns and landscaping. This is a hybrid data product produced using NRCS SSURGO soils data. These data should not be considered SSURGO-compliant, as data used in this product is the result of merging data from several separate SSURGO databases. The NRCS does not endorse or support this hybrid product.The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park Available Water Supply.

Riparian corridors are important areas that maintain connectivity throughout the state of California. The riparian corridors complement the northern Sierra Nevada foothills wildlife connectivity project linkages to further achieve connectivity in the study area. We identified 280 riparian corridors represented by 232 named creeks, 43 named rivers, and 5 sloughs, forks or runs. The major corridors are the Sacramento, San Joaquin, Pit, Tuolumne, Merced, Feather and Stanislaus rivers. The 280 riparian corridors connect 201 landscape blocks. The riparian corridors complement the focal species linkages by providing many east-west corridors while the majority of linkages have a north-south orientation. Also by following the entire passage of the riparian area, these corridors run through many of the landscape blocks across the study area, helping to provide connectivity outside of habitat patch areas.We identified riparian corridors by selected streams, rivers and creeks from the NHD (National Hydrography Dataset) for state of California. From the NHD dataset, features named ‘StreamRiver’ were extracted from the ‘NHDFlowline’ vector dataset. A code 46006 was then used to extract perennial rivers and streams from the ‘StreamRiver’ dataset. However, this step resulted in a stream and river layer with many small segments. In order to reduce the number of segments and identify complete stream/river lines, we intersected the perennial rivers and streams layer with the CDFW statewide streams layer (‘CA_Streams_Statewide’) using the ‘Select by Location’ tool in ArcMap (‘CA_Streams_Statewide’ layer as target layer and the streams and rivers layer we extracted from NHD as a target layer). Second, we extracted features named ‘ArtificialPath’ from the ‘NHDFlowline’ vector dataset. Artificial paths represent the flow of water into, through, and out of features delineated using area; for example, rivers wide enough to be delineated as a polygon are represented by an artificial path flowline at their center line. Therefore, large rivers are often coded as “artificial path” in the NHD dataset. We then selected only those artificial paths with Geographic Names Information System (GNIS) names, with the assumption that artificial path features without names are “very minor streams, only of use to hydrologist” (http://nhd.usgs.gov). Next we used the same method we implemented for streams and rivers in order to remove small segments and have complete lines. The artificial path dataset is not coded to discriminate between perennial and intermittent ones similar to stream and river features. As a result, artificial paths that intersected with perennial streams and rivers were selected to represent permanent waterways. Then, the perennial stream and river layer and the artificial paths layer were merged into one dataset. After the merge we added a 500 m buffer to each side of the riparian area.We compared this merged stream/river layer with riparian vegetation classification data as a cross check. The riparian vegetation classification data are from the 2011 Northern Sierra Nevada Foothills and 2013 Eastern Central Valley fine-scale vegetation maps developed by the Vegetation Classification and Mapping Program (VegCamp) at the California Department of Fish and Wildlife. For areas outside the foothills and eastern central valley we used land cover data compiled by California Department of Forestry and Fire Protection (CDF) Fire and Resource Assessment Program (FRAP) in 2006, representing data for the period between 1997 and 2002. The resulting perennial dataset was then merged with the wetland and riparian datasets to represent perennial water sources in California. For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at https://goto.arcgisonline.com/landscape11/USA_Soils_Drainage_Class.Soils vary widely in their ability to retain or drain water. The rate at which water drains into the soil has a direct effect on the amount and timing of runoff, what crops can be grown, and where wetlands form. In soils with low drainage rates water will pond on the soil's surface. Poorly drained soils are desirable when growing crops like rice where the fields are flooded for cultivation but other crops need better drained soils.Dataset SummaryPhenomenon Mapped: Drainage Class of SoilsUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: July 2020ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for drainage class is derived from the gSSURGO map unit aggregated attribute table field Drainage Class - Dominant Condition (drclassdcd).The layer has an attribute field for Drainage Class and a description field for use in pop-ups. The eight values of drainage class with corresponding attribute table index value are defined by the NRCS Soil Survey Manual as:0. Excessively drained: Water is removed very rapidly. The occurrence of internal free water commonly is very rare or very deep. The soils are commonly coarse-textured and have very high hydraulic conductivity or are very shallow.1. Somewhat excessively drained: Water is removed from the soil rapidly. Internal free water occurrence commonly is very rare or very deep. The soils are commonly coarse-textured and have high saturated hydraulic conductivity or are very shallow.2. Well drained: Water is removed from the soil readily but not rapidly. Internal free water occurrence commonly is deep or very deep; annual duration is not specified. Water is available to plants throughout most of the growing season in humid regions. Wetness does not inhibit growth of roots for significant periods during most growing seasons. The soils are mainly free of the deep to redoximorphic features that are related to wetness.3. Moderately well drained: Water is removed from the soil somewhat slowly during some periods of the year. Internal free water occurrence commonly is moderately deep and transitory through permanent. The soils are wet for only a short time within the rooting depth during the growing season, but long enough that most mesophytic crops are affected. They commonly have a moderately low or lower saturated hydraulic conductivity in a layer within the upper 1 m, periodically receive high rainfall, or both.4. Somewhat poorly drained: Water is removed slowly so that the soil is wet at a shallow depth for significant periods during the growing season. The occurrence of internal free water commonly is shallow to moderately deep and transitory to permanent. Wetness markedly restricts the growth of mesophytic crops, unless artificial drainage is provided. The soils commonly have one or more of the following characteristics: low or very low saturated hydraulic conductivity, a high water table, additional water from seepage, or nearly continuous rainfall.5. Poorly drained: Water is removed so slowly that the soil is wet at shallow depths periodically during the growing season or remains wet for long periods. The occurrence of internal free water is shallow or very shallow and common or persistent. Free water is commonly at or near the surface long enough during the growing season so that most mesophytic crops cannot be grown, unless the soil is artificially drained. The soil, however, is not continuously wet directly below plow-depth. Free water at shallow depth is usually present. This water table is commonly the result of low or very low saturated hydraulic conductivity of nearly continuous rainfall, or of a combination of these.6. Very poorly drained: Water is removed from the soil so slowly that free water remains at or very near the ground surface during much of the growing season. The occurrence of internal free water is very shallow and persistent or permanent. Unless the soil is artificially drained, most mesophytic crops cannot be grown. The soils are commonly level or depressed and frequently ponded. If rainfall is high or nearly continuous, slope gradients may be greater.7. Subaqueous Soils: These soils are under the surface of a body of water. (There are only a few of these in the entire dataset.)What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "drainage class" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "drainage class" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This layer is a snapshot of stream gages from the fall of 2020. It is the product of an attempt to compile a comprehensive, geospatial list of long-term stream gages whose data is publicly available. Initially, the layer will consist of USGS and CDEC gages. Over time, local (county, municipal, etc.) gages will be added. This layer is not claimed to be authoritative. In cases where this layer and the data maintained by the source entity differ, this layer always defers to the source entity. For analysis purposes, the gage point locations have been altered by SWRCB to coincide with the corresponding line features in the National Hydrography Dataset (NHD) Medium Resolution. The original point locations can be found "x" and "y" fields of the layer's attribute table.For questions, contact the SWRCB Division of Water Rights: DWR@waterboards.ca.gov.Data dictionary:

Field Name Description Data Type

SiteID Site ID Text

SiteName Site Name Text

Operator Agency or entity which operates the gage Text

DataSource The agency or entity which publishes the data online (source not exclusive) Text

SiteStatus Is the site, in general, active or inactive? Text - Active or Inactive

Stage_YN Did the gage report stage at any time? Text - Y or N or U

Stage_POR Stage period of record in days (if a site had multiple stage sensors or duration codes, then the max POR was used) Integer

Stage_Status Status of stage reporting (active/inactive) Text

Stage_RealTime Is/was stage reported hourly or more frequently? Text - Y or N

Flow_YN Did the gage report flow at any time? Text - Y or N or U

Flow_POR Flow period of record in days (if a site had multiple flow sensors or duration codes, then the max POR was used) Integer

Flow_Status Status of flow reporting (active/inactive) Text

Flow_RealTime Is/was flow reported hourly or more frequently? Text - Y or N

WatQual_YN Did the gage report one or more water quality parameters at any time? Includes parameters such as water chemistry, dissolved oxygen, and turbidity, but not temperature Text - Y or N or U

WatQual_POR Water quality period of record in days (if a site had multiple water quality sensors or duration codes, then the max POR was used) Integer

WatQual_Status Status of water quality reporting (active/inactive) Text

WatQual_RealTime Is/was water quality reported hourly or more frequently? Text - Y or N

Temp_YN Did the gage report water temperature at any time? Text - Y or N or U

Temp_POR Temperature period of record in days (if a site had multiple temperature sensors or duration codes, then the max POR was used) Integer

Temp_Status Status of temperature reporting (active/inactive) Text

Temp_RealTime Is/was temperature reported hourly or more frequently? Text - Y or N

FloodMgmt Primary purpose of gage is flood management Text - Y or N

EcosysMgmt Primary purpose of gage is ecosystem management (flow and water quality) Text - Y or N

GrdwtrMgmt Primary purpose of gage is groundwater management Text - Y or N

PubSafety Primary purpose of gage is public safety Text - Y or N

WtrSupply Primary purpose of gage is water supply (municipal or agricultural) Text - Y or N

Ref_GagesII Is the gage site considered a reference site in Gages II dataset? Text - Y or N

StrmOrder Strahler stream order Integer

UCDStrmClass UCD eFlows stream classification Text

StreamType Type of water conveyance the gage is measuring (e.g. Stream/River, Canal/Ditch, Artificial Path, etc.) Text

TotDASqKM Total drainage area in square kilometers Double

TotDASqMi Total drainage area in square miles Double

GNISID_MedRes GNIS (Geographic Names Information System) identification number of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

RchCd_MedRes Reach Code identification number of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

COMID_MedRes COM ID (common identifier) of the NHD line segment the gage is on (from the NHD Medium Resolution dataset) Text

WebLink Web address to access each gage's data Text

x_orig X coordinate as provided by source entity (NAD83 CA Teale Albers meters) Double

y_orig Y coordinate as provided by source entity (NAD83 CA Teale Albers meters) Double

WtrshdNm_HUC8 Name of containing HUC8 watershed Text

HUC8 Containing HUC 8 (Hydrologic Unit Code 8) identifier Text

WtrshdNm_HUC10 Name of containing HUC10 watershed Text

HUC10 Containing HUC 10 (Hydrologic Unit Code 10) identifier Text

WtrshdNm_HUC12 Name of containing HUC12 watershed Text

HUC12 Containing HUC 12 (Hydrologic Unit Code 12) identifier Text

GageGap_Status Status of Gage for Gage Gap Analysis (e.g. Well-Gaged, AWG = Almost Well-Gaged, or Exclude) Text

Infrastructure Gage is suspected of being located on infrastructure Text - Y or N

ReactivateSF Gage is a candidate for reactivation Text - Y or N

Priority_Reactivate Reactivation priority based on gage metadata alone (e.g. period_of_record, parameter status, end-date and other factors, but not including based on gage gap or management criteria) Text

AddFlow_2Stage Upgrade candidate: gage is actively reporting stage, potential upgrade to flow and stage Text - Y or N

AddFlow_2WQ Upgrade candidate: gage is actively reporting water quality or temperature data, but not flow and/or stage. Text - Y or N

AddTelemetry Upgrade candidate: gage is actively reporting stage and/or flow, but not in real-time Text - Y or N

AddTemp_2Flow Upgrade candidate: gage is actively reporting stage and/or flow, but not water temperature Text - Y or N

This layer includes a polygon shapefile with information about surficial deposits at 1:500,000 scale used in preparing the GCSM for Wisconsin. Surficial deposits are defined as the unconsolidated materials above the bedrock. Because the "soils" layer used in the GCSM includes only the material in the first 5 feet below the land surface, the surficial deposits layer is intended to account for the unconsolidated material between the soil and the top of the bedrock.The texture and permeability of the surficial deposits affect the rate at which infiltrating water will reach the water table. In Wisconsin, these surficial deposits are often the most important factor indetermining groundwater contamination susceptibility.See the usage documentation (https://www.arcgis.com/home/item.html?id=e1e89ae505594459a46407f1daf4ad5d) and the Full report (https://www.arcgis.com/home/item.html?id=fd4d0c43abc04b4ab915586d9a0e89dd) for more information.

This feature layer is part of SDGs Today. Please see sdgstoday.org.Armed conflicts arise from many sources, including border disputes, civil war, and religious and tribal clashes. Increasingly, these conflicts are originating due to poor environmental conditions, such as lack of access to water resources and arable land, drought, and famine. The Armed Conflict Location & Event Data Project (ACLED), a disaggregated data collection, analysis, and crisis mapping project, maintains a database of all forms of human conflict from over 50 developing countries.ACLED is the most widely used real-time data and analysis source on political violence and protest around the world. It collects the dates, actors, locations, fatalities, and modalities of all reported political violence and protest events across major regions, including Africa, South and Southeast Asia, Central Asia and the Caucasus, the Middle East, Latin America, the Caribbean, Southeastern and Eastern Europe, and the Balkans. ACLED uses four types of data sources for its analysis: traditional media, reports from NGOs/governments, local partner data, and social media. Each week, ACLED researchers analyze thousands of sources in multiple languages to provide the most comprehensive database on political violence and demonstrations.

This map is part of SDGs Today. Please see sdgstoday.org.Armed conflicts arise from many sources, including border disputes, civil war, and religious and tribal clashes. Increasingly, these conflicts are originating due to poor environmental conditions, such as lack of access to water resources and arable land, drought, and famine. The Armed Conflict Location & Event Data Project (ACLED), a disaggregated data collection, analysis, and crisis mapping project, maintains a database of all forms of human conflict from over 50 developing countries.ACLED is the most widely used real-time data and analysis source on political violence and protest around the world. It collects the dates, actors, locations, fatalities, and modalities of all reported political violence and protest events across major regions, including Africa, South and Southeast Asia, Central Asia and the Caucasus, the Middle East, Latin America, the Caribbean, Southeastern and Eastern Europe, and the Balkans. ACLED uses four types of data sources for its analysis: traditional media, reports from NGOs/governments, local partner data, and social media. Each week, ACLED researchers analyze thousands of sources in multiple languages to provide the most comprehensive database on political violence and demonstrations.

Many resource consents contain a condition limiting the taking of water when a river or waterway is on restriction.A residual flow applies to specific consents that take water from a tributary of a main river. A residual flow recognises that a tributary stream often has different flow characteristics from the main river stem. It is set at the point of take on a case-by-case basis, to provide for the aquatic ecosystems and natural character of the source water body. Intended Use The residual flow sites layer is intended to show the location of the monitoring sites associated with maintaining residual flows. Attribute Information Monitoring Site informationSiteID – Unique identification number for this site in Environment Canterbury database systems Waterway – name of the water feature that this site relates to for residual flow purposes Location – name for the location that the monitoring is undertaken RestrictionType – type of restriction (in this case Residual Flow restriction) that this site is used for monitoring. ReferenceSystem – Environment Canterbury data management system that the monitoring site information is being managed within.ReferenceNo – Internal ID for this site within the listed reference data management system SiteAccount – Internal account for monitoring site. GroupAccount – Monitoring group. QARCode – Quality assurance code that describes the spatial accuracy of the site information. 1 = Differential GPS (advanced) or Geodetic Land Survey (1 - 2m); 2 = Standard handheld GPS (2 - 15m); 3 = Site visit (10 - 50m); 4 = Old Grid reference ±100m, no location sketch, or location not checked (50 - 300m); 5 = Proposed Location, should be within 50m (< 50m) Altitude – Approximate altitude of the monitoring site (above mean sea level) relative to the datum listed in the AltitudeDatum field. Values where the data is missing or displays 0 represent sites where that information is not available. AltitudeDatum – The vertical datum that the listed altitude value was recorded using. See https://www.linz.govt.nz/data/geodetic-system/datums-projections-and-heights/vertical-datums for more information about the vertical datums commonly used in New Zealand.IsActive – Current status on whether a site is being used for residual flow monitoring. Records with this value set to No are not currently part of residual flow monitoring for consents. CatchmentNo – unique identification number for the hydrological catchment that the monitoring site lies within. See https://opendata.canterburymaps.govt.nz/datasets/catchment-boundaries/explore for more details. CatchmentDesc – name used for hydrological catchment that the monitoring site lies within. Typically this is the name of water body that that the catchment area represents. GIS AttributesSpatial IDs: OBJECTIDSpatial Fields: SHAPE, NZTMX & NZTMY – Approximate location of monitoring site in New Zealand Transverse Mercator coordinatesLowFlowSource – Environment Canterbury data management system that the low flow site information is being managed within.