Modern non-contact methods for data acquisition are becoming widely used for monitoring soil erosion and for assessing soil degradation after rainfall events. Photogrammetric methods are especially favored to obtain a detailed and precise digital surface model (DSM) of the surveyed area. This paper introduces the algorithm and its Python implementation as a tool for ArcGIS software, which makes efficient automatic calculations of the volume of erosion rills or gullies. The input parameters are a DSM, and the rill edge polygon. The method was tested on an artificially created rill, where the result acquired using presented method was compared to the real volume. The comparison showed that the algorithm may underestimate the volume by 10–15%. In addition, the influence of the position of the rill edge polygon was tested on two DSMs of erosion rills. The resulting volumes of the rills, calculated on the basis of eight different edge polygons, varied by 5%. The algorithm also automates interpolation of the surface prior to erosion, which simplifies its usage in firstly monitored regions. The algorithm can also be used for volumetric analyses in other research areas and it is made available as a supplement of the publication.

Vegetation classification in the Lower Kuskokwim area utilizes Spot 2015 and ESRI basemap imagery and has been interpreted by the State of Alaska, Department of Natural Resources, Division of Forestry, Northern Region. Vegetation layer includes attributes for volume calculations of timbered polygons. Sample plot layer includes individual sample tree attributes. Sample stand layer includes volume calculations for sample stands. Selected stands were sampled for volume in 2004.

The database includes effluent volumes, contaminant mass emissions (ME), average constituent concentrations, and toxicity. Constituent concentration data were standardized to monthly time steps. For constituents analyzed more than once per month, the arithmetic mean of all results in a given month was calculated. Where the frequency of constituent analysis was less than monthly or data for a given month were not available, the arithmetic mean of available data within the given year was calculated and used to populate months for which no data existed. The monthly flow and concentration data were then used to calculate annual discharge volumes and constituent mass emissions for each facility. Annual average flow-weighted concentrations (FWC) were calculated by dividing the annual ME for a given constituent by the total annual effluent volume (V).

The database includes effluent volumes, contaminant mass emissions (ME), average constituent concentrations, and toxicity. Constituent concentration data were standardized to monthly time steps. For constituents analyzed more than once per month, the arithmetic mean of all results in a given month was calculated. Where the frequency of constituent analysis was less than monthly or data for a given month were not available, the arithmetic mean of available data within the given year was calculated and used to populate months for which no data existed. The monthly flow and concentration data were then used to calculate annual discharge volumes and constituent mass emissions for each facility. Annual average flow-weighted concentrations (FWC) were calculated by dividing the annual ME for a given constituent by the total annual effluent volume (V).

Operational level forest inventory data was acquired in 2019 and provided the basis for mapping, quantifying and assessing area-wide forest and commercial timber resources and for establishing the AAC for SSE. Forest inventory data from 2019 and the analysis in 2020 provides the following forest management benefits: Updated Timber Type data layer (map) contained in the State’s GIS for SSE Data acquired and analyzed through the forest inventory project was entered into the State’s GIS to create an updated timber type layer (map) of the commercial forest timber base in SSE containing individual timber stands. Updated timber type descriptors for each individual stand include stand species composition, stand density and per acre timber volume. SSE Forest Inventory Report July 17, 2020 4 Using the GIS to analyze the relationships between the commercial timber resource and other forest resources (transportation network, fish and wildlife habitat, cultural resources, etc.) allows the DOF to undertake and complete complex forest planning documents such as the Five-Year Schedules of Timber Sales (FYSTS), and Forest Land Use Plans (FLUPs) used to guide both broad scale and site-specific forest management activities. The GIS also allows DOF to track changes to the commercial timber base resulting from management activities including timber harvest, stand regeneration/reforestation, and timber stand improvement projects such as precommercial tree thinning. Updated Annual Allowable Cut for SSE The GIS timber type map for SSE, updated with the 2019 forest inventory data, formed the basis for area (acreage) and timber volume (board feet) figures necessary to calculate an updated AAC. The new GIS timber type map and associated data files along with newly available LiDAR data provided the raw data necessary to perform the growth and yield modelling to estimate timber volume and characteristics in the developing young growth stands over the course of the rotation.

This data set contains the supplemental information documents for Estimating Volumes of Coastal Shell Midden Sites Using Geometric Solids: An Example from Tseshaht Territory, Western Vancouver Island, British Columbia, Canada. Included documents include python code used for midden volume calculations, plots of coefficient of variation change, and per site midden volume estimates.

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.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.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

This stormwater forecast script tool was developed by the Natural Resources Department at the Atlanta Regional Commission.WHAT IS THE STORMWATER FORECAST?In 2022, the District developed a novel water quantity-based indicator, the Stormwater Forecast, to support watershed managers with ongoing challenges related to water quality, streambank erosion, and nuisance flooding.The Stormwater Forecast is a planning-level estimate of the total potential storage volume required by Stormwater Control Measures to manage runoff from development at a basin scale under both current and future conditions. Based on current development patterns, the results of the Stormwater Forecast show the 15-county Metro Atlanta region should be managing up to 27 billion cubic feet of runoff volume with Stormwater Control Measures, and if regulations remain the same the total volumes are estimated to increase by up to 100 percent by 2040. STORMWATER FORECAST USER GUIDEThe Stormwater Forecast User Guide outlines steps for calculating stormwater runoff volumes for an area of interest using the Stormwater Forecast and performing a Stormwater Forecast Gap Analysis using the custom stormwater runoff volume results.STORMWATER FORECAST GEOPROCESSING PACKAGEThe Stormwater Forecast Geoprocessing Package contains the Stormwater Forecast Script Tool and a geodatabase with the following four parameters needed to execute the tool. AreaofInterestStormwaterForecastDevelopedAreaNLCD_Imperviousness_2019.tifThe Stormwater Forecast Script Tool provides users with an automated calculation method for calculating custom stormwater runoff volumes within an area of interest using the Stormwater Forecast.FIELD ABBREVIATIONS AND DESCRIPTIONS FOR STORMWATER FORECAST RESULTSUnique_ID = Unique Identification Characters for Stormwater Forecast SubcatchmentNHD_Sub_ID = National Hydrography Dataset Subcatchment Identification Numbers HUC_12 = Hydrologic Unit Code-12 Identification Numbers County = County Name HUC_8 = Hydrologic Unit Code-8 Identification Numbers MRB = HUC-8 Major River Basin Name Area_Dev_a = 2019 Developed Area, in acresImpv_Area = 2019 Total Impervious Area within Developed Area, in acresAOI_19_WQ = 2019 Water Quality Volume for Area of Interest, in cubic feet AOI_19_CP = 2019 Channel Protection Volume for Area of Interest, in cubic feetAOI_19_OF = 2019 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_30_WQ = 2030 Water Quality Volume for Area of Interest, in cubic feet AOI_30_CP = 2030 Channel Protection Volume for Area of Interest, in cubic feetAOI_30_OF = 2030 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_40_WQ = 2040 Water Quality Volume for Area of Interest, in cubic feet AOI_40_CP = 2040 Channel Protection Volume for Area of Interest, in cubic feetAOI_40_OF = 2040 Overbank Flood Protection Volume for Area of Interest, in cubic feetRequired Software: Esri’s ArcGIS Pro and Esri’s Spatial Analyst and Image Analyst Extensions

When precipitation falls on the surface of the Earth, much of it is captured in storage (e.g. lakes, aquifers, soil moisture, snowpack, and vegetation). Precipitation that exceeds the storage capacity of the landscape becomes runoff, which flows into river systems. Overland flow is the most visible form of runoff, causing erosion and flash floods, but subsurface flow is the larger contributor in many watersheds. Subsurface flow can emerge on the surface through springs, or more commonly, seep into rivers and lakes through their banks. In urban areas, impervious land cover drastically increases the amount of surface runoff generated, which sweeps trash and urban debris into waterways and increases the likelihood and severity of flash floods. In agricultural areas, surface or subsurface runoff can carry excess salts and nutrients, especially nitrogen and phosphorus. This map contains a historical record showing the amount of runoff generated each month from March 200 to present. It is reported in millimeters, so multiply by a surface area to calculate the total volume of runoff.Dataset SummaryThe GLDAS Runoff layer is a time-enabled image service that shows average monthly runoff from 2000 to the present measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: RunoffUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. t is useful for scientific modeling, but only at global scales.By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean. Mean runoff for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. If you do this, make sure to also select a specific variable. The minimum time extent is one month, and the maximum is 8 years. Variables: This layer has three variables: total runoff, surface flow and subsurface flow. By default total is shown, but you can select a different variable using the multidimensional filter, or by applying the relevant raster function. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

Download: HereThe Saskatchewan Ministry of Environment, Forest Service Branch, has developed a forest resource inventory (FRI) which meets a variety of strategic and operational planning information needs for the boreal plains. Such needs include information on the general land cover, terrain, and growing stock (height, diameter, basal area, timber volume and stem density) within the provincial forest and adjacent forest fringe. This inventory provides spatially explicit information as 10 m or 20 m raster grids and as vectors polygons for relatively homogeneous forest stands or naturally non-forested areas with a 0.5 ha minimum area and a 2.0 ha median area. Gross biological volume per hectare - softwood (GBVSWD) is an expression of in-the-tree stem softwood volume (m3) on a per-hectare basis. Calculations are made from the ground to the tip. GBVSWD is available here as a color-mapped 16-bit unsigned integer raster grid in GeoTIFF format with a 20 m pixel resolution. An ArcGIS Pro layer file (*.lyrx) is supplied for viewing GBVSWD data in the following 50 m3/ha categories.Domain: [NULL, 0…1000].RANGELABELREDGREENBLUE0 <= GBVSWD < 250NANANA25 <= GBVSWD < 7550638118175 <= GBVSWD < 12510066101160125 <= GBVSWD < 17515068121138175 <= GBVSWD < 22520071140117225 <= GBVSWD < 2752507416096275 <= GBVSWD < 3253008517879325 <= GBVSWD < 37535012319174375 <= GBVSWD < 42540016120370425 <= GBVSWD < 47545019821666475 <= GBVSWD < 52550023622961525 <= GBVSWD < 57555025522653575 <= GBVSWD < 62560025520940625 <= GBVSWD < 67565025519128675 <= GBVSWD < 72570025517416725 <= GBVSWD < 7757502551563775 <= GBVSWD < 8258002531399825 <= GBVSWD < 87585025112120875 <= GBVSWD < 92590024910331925 <= GBVSWD < 9759502468543975 <= GBVSWD <= 100010002446754For more information, see the Forest Inventory Standard of the Saskatchewan Environmental Code, Forest Inventory Chapter.

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using Seasonal and Axle (if applicable) day-of-week adjustment factors.Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan Update Cycle: AADT & CAADT volumes are created and released every year.Transportation Data Management System (TDMS) AADT Calculation HelpTraffic Monitoring Program

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using Seasonal and Axle (if applicable) day-of-week adjustment factors.Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan Update Cycle: AADT & CAADT volumes are created and released every year.Transportation Data Management System (TDMS) AADT Calculation HelpTraffic Monitoring Program

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using Seasonal and Axle (if applicable) day-of-week adjustment factors.

Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan

Update Cycle: AADT & CAADT volumes are created and released every year.

Transportation Data Management System (TDMS) AADT Calculation Help

Traffic Monitoring Program

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using Seasonal and Axle (if applicable) day-of-week adjustment factors.

Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan

Update Cycle: AADT & CAADT volumes are created and released every year.

Transportation Data Management System (TDMS) AADT Calculation Help

Traffic Monitoring Program

The database includes effluent volumes, contaminant mass emissions (ME), average constituent concentrations, and toxicity. Constituent concentration data were standardized to monthly time steps. For constituents analyzed more than once per month, the arithmetic mean of all results in a given month was calculated. Where the frequency of constituent analysis was less than monthly or data for a given month were not available, the arithmetic mean of available data within the given year was calculated and used to populate months for which no data existed. The monthly flow and concentration data were then used to calculate annual discharge volumes and constituent mass emissions for each facility. Annual average flow-weighted concentrations (FWC) were calculated by dividing the annual ME for a given constituent by the total annual effluent volume (V).

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using Seasonal and Axle (if applicable) day-of-week adjustment factors.

Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan

Update Cycle: AADT & CAADT volumes are created and released every year.

Transportation Data Management System (TDMS) AADT Calculation Help

Traffic Monitoring Program

Volumetric estimations for Fenwick, Weaver, & Isle of Wight shoals used vibracore data collected in 1992 & 1997, volumes were calculated in 2000 (Conkwright, Williams and Christiansen, 2000).Borrow Area 2 has been virtually exhausted by dredging. Between 1988-1992, ~2.9 million m^3 was dredged from Borrow Area 2, ~4.2 million m^3 dredged from Borrow Area 3, and ~3.2 million m^3 dredged from Borrow Area 9 (Wells, 1994).Revised volumentric estimations for Shoals B, C, D, Little Gull Bank & Great Gull Bank (1996) "In the 1993 sand resources study (Conkwright and Gast, 1994b) the seismically defined ravinement surface was used as the lower structural boundary for volume calculations. However, the purpose of the current study is to estimate only the volume of sand with measured physical parameters. Therefore, the lower boundary for volumetric calculations was determined primarily by the grain size parameters of vibracore samples. The lower boundary surface was set at the depth where the sampled sand became too fine or too poorly sorted for use as beach fill. In those cases where the entire length of core contained usable sand, the boundary surface was set at the depth of maximum vibracore penetration. Because vibracore penetrations on Little and Great Gull Banks were generally within a meter of the ravinement surface, that surface was used as the lower boundary for those shoals, unless vibracore samples indicated otherwise." (Conkwright and Williams, 1996).REFS:Conkwright, R.D. and R.A.Gast. 1994a. Potential Offshore Sand Resources in Southern Maryland Shoal Fields. Coastal and Estuarine Geology File Report #95-4Conkwright, R.D. and R.A.Gast. 1994b. Potential Offshore Sand Resources in Central Maryland Shoal Fields. Coastal and Estuarine Geology File Report #95-9Conkwright, R.D. and C.P. Williams. 1996. Offshore Sand Resources in Central Maryland Shoal Fields. Coastal and Estuarine Geology File Report #96-3Conkwright, R.D., Williams, C.P., and L.B. Christiansen. 2000. Offshore Sand Resources in Northern Maryland Shoal Fields. Coastal and Estuarine Geology File Report No. 00-2Wells, D.V. 1994. Non-Energy Resources and Shallow Geological Framework of the Inner Continental Margin Off Ocean City, Maryland. Coastal and Estuarine Geology Open File Report #16USACE. 2008. Atlantic Coast of Maryland Shoreline Protection Project Final supplemental Environmental Impact Statement General Reevaluation Study: Borrow Sources for 2010-2044 Funding to compile these datasets provided by BOEM under cooperative agreement number: M14AC00007. Data processing and compilation was executed by Maryland Geological Survey.The views expressed herein are those of the authors and do not necessarily reflect the views of the Bureau of Ocean Energy Management (BOEM) or any of its sub-agencies. This geodatabase was created to provide planners and managers access to data about aggregate resources off the coast of Maryland’s coastline. This geodatabase should not be used for navigational purposes.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Feature Service Link: https://mdgeodata.md.gov/imap/rest/services/Geoscientific/MD_OffshoreOceanResources/FeatureServer/2

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using seasonal and day-of-week adjustment factors.Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan.Update Cycle: AADT & CAADT volumes are created and released every year.Transportation Data Management System (TDMS) AADT Calculation HelpTraffic Monitoring Program

Traffic volumes; measured and calculated amounts of vehicle traffic that travel the section of road.=For more information on this layer, you can use the Data Dictionary available in both web and spreadsheet format.

The purpose of this layer is to support resilience planning by mapping potential swale infrastructure aligned with Broward County’s Tier 1 and Tier 2 adaptation strategies. The dataset guides capital planning, stormwater retrofitting, and adaptation prioritization under multiple future climate scenarios.The depth assumed for the swales is 7 inches (the County standard is a minimum of 6 inches). 4H:1V side slopes. For the volume calculations, a top width of 5 feet and two sets of parallel swales along each side of the road were assumed. For the storage area, a useful depth of 2 feet was used.Eq_Length - Length of swale used for cost calculation to include average length of both sides of road (10 ft wide)