This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

This repository contains the data required to reproduce all analyses presented for the manuscript:

MuSpAn: A Toolbox for Multiscale Spatial Analysis

The data is organised into two main folders:

1. domains_for_figs_2_to_6 (MuSpAn domains)

   • Four domains of increasing size from regions within a healthy mouse colon (10x Genomics Colon Atlas panel).

   • Four samples of AKPT mouse tumors (10x Genomics 480 custom panel).

2. misc_checkpoint_data (Metadata - analysis checkpointing)

   • Colormap dictionaries for consistent visualization with the published figures.

   • Checkpointing files to support analyses requiring extended computation times.

   • Annotation data used for MuSpAn labeling.

The MuSpAn domains were created and saved using v1.2.0 of MuSpAn. This data is to be used with the associate python notebooks which can be found at:

https://github.com/joshwillmoore1/Supporting_material_muspan_paper

These notebooks both reproduce the analysis conducted in the study and serve as example material for MuSpAn usage, fully explained and linked to relevent documentation.

The Bluff GIS Determination Tool is an ArcGIS script that determines if a bluff is present, locates the toe and top of bluff on a map, creates a plot of elevation vs. distance, and produces an Excel spreadsheet showing the data analysis. There are two versions of the tool, one for determining a shoreland bluff (consistent with the shoreland rule bluff definition) and one for determining a Mississippi River Corridor Critical Area bluff (consistent with the MRCCA rule bluff definition).

Technical Requirements
The user will need the following to run this tool:
System Requirements:
- ArcGIS Pro
- Spatial Analyst
Input Data Requirements:
- DEM (You can download 1-meter and 3-meter DEMs from MnTOPO: http://arcgis.dnr.state.mn.us/maps/mntopo )

For step-by-step instructions on how to use the tool, please view Bluff GIS Determination Tool Guide.pptx

This dataset shows 2 m depth contours within the study area. The contours were created using the contour feature within the Spatial Analyst Toolbox in ArcMap (v 10.2.2). Contours were manually edited where necessary to clean up artifacts in the dataset. The input dataset was bathymetry data processed to 50 cm horizontal resolution that was collected June 11th-16th, 2015.

pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. recently discovered, pockmarks along the aquitaine slope within the french eez, were manually mapped although two semi-automated methods were tested without convincing results. in order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition gazcogne1, 2013. bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the kongsberg em302 ship-borne multibeam echosounder of the r/v le suroît at a speed of ~8 knots, operated at a frequency of 30 khz and calibrated with ©sippican shots. precision of seafloor backscatter amplitude is +/- 1 db. multibeam data, processed using caraibes (©ifremer), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. the present table includes 11 morphological attributes extracted from a geographical information system project (mercator 44°n conserved latitude in wgs84 datum) and additional parameters related to seafloor backscatter amplitudes. pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on gazcogne1 and bobgeo2 bathymetric datasets.the pockmark area and its perimeter were calculated with the “calculate geometry” tool of arcmap 10.2 (©esri) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). a first method to calculate pockmark internal depth developed by gafeira et al. was tested (gafeira j, long d, diaz-doce d (2012) semi-automated characterisation of seabed pockmarks in the central north sea. near surface geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). this method is based on the “fill” function from the hydrology toolset in spatial analyst toolbox arcmap 10.2 (©esri), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. the difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. for the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark.latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “zonal geometry as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). pockmark elongation was calculated as the ratio between the major and minor axis length.cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “zonal statistics as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri). slope was calculated from bathymetry with “slope” function from spatial analyst toolbox in arcgis 10.2 (©esri) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

The solar radiation layers are simulations of solar radiation based on the Digital Surface Model. The simulation considers the topographic situation (surrounding, slope, exposition) as well as time-based variation of the sun radiation for a specific geographic location. The result is a raster visualization of the sun duration per pixel (with 1 m ground resolution). The simulation is configured to return the sun hours per pixel for a given day. Currently 3 days were calculated: 15/02 (winter), 15/05 (spring) and 15/08 (summer).

The solar radiation analysis is based on the solar radiation toolset of the ESRI ArcMap toolbox. A detailed documentation can be found in the corresponding documentation by ESRI: http://desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/area-solar-radiation.htm

The analysis used the following parameters:

- Input raster: Digital Surface model provided by the Administration de la navigation aérienne (ANA) based on a LiDAR flight from 2017. (DSM available here : https://data.public.lu/fr/datasets/digital-surface-model-high-dem-resolution/ )

- Latitude : 49.46 °

- Time configuration : Time Within a day (for 3 dates: 15/02 winter, 15/05 spring and 15/08 summer)

- Hour interval: 0.5 – The solar radiation was calculated in 30 min. intervals and summed up per day.

- Slope and aspect input : The slope and aspect rasters are calculated from the input digital surface model

- Calculation directions: 32, which is adequate for a complex topography.

- Diffuse proportion : 0.3 for a generally clear sky conditions.

- Transmittitivity : 0.5 for a generally clear sky.

- Output raster: The result is an output raster representing the duration of direct incoming solar radiation.

The Seabed Landform Classification Toolset is a GIS toolbox designed to classify seabed landforms on continental and island shelf settings. The user is guided through a series of classification steps within an ArcGIS toolbox to classify prominent seabed features termed ‘seabed landforms’, which characterise the morphology of the seabed surface. Seabed landforms include reefs/banks, peaks, plains, scarps, channels and depressions. Plain areas can additionally be classified into high and low features at localised and broad scales to capture features within plain surfaces. Common variables for seabed classification are utilised, including slope, bathymetric position index and ruggedness, and a series of procedures are applied to identify reef outcrops and minimise noise. The classification approach applies a whole-seascape classification which is aimed to offer a flexible and user-friendly approach to extract key seabed features from high-resolution shelf bathymetry data.

This toolset was developed using ESRI ArcGIS Desktop 10.8 and requires an Advanced licence with Spatial Analyst and 3D Analyst and extensions. It utilises scripts within the Benthic Terrain Modeler toolset (Walbridge et al. 2018) and Geomorphometry and Gradients Metrics Toolbox (Evans et al., 2014).

Please read the User Guide and supporting documentation for information on how to run the toolset. A web explainer is available at: https://arcg.is/1Tqmv50

The Seabed Landform Classification Toolset is also available for download on GitHub (https://github.com/LinklaterM/Seabed-Landforms-Classification-Toolset/).

The toolset was developed by the Coastal and Marine Team, NSW Department of Climate Change, Energy, the Environment and Water (formerly NSW Department of Planning and Environment), funded by NSW Climate Change Fund through the Coastal Management Funding Package and the Marine Estate Management Authority.

Please cite this toolset as: Linklater, M, Morris, B.D. and Hanslow, D.J. (2023) Classification of seabed landforms on continental and island shelves. Frontiers of Marine Science, 10, https://doi.org/10.3389/fmars.2023.1258556.

Other toolsets utilised by the Seabed Landform Classification Toolset include: Benthic Terrain Modeler: Walbridge, S., Slocum, N., Pobuda, M., and Wright, D. J. (2018). Unified geomorphological analysis workflows with Benthic Terrain Modeler. Geosciences 8, 94. Geomorphometry and Gradients Metrics Toolbox: Evans, J., Oakleaf, J., and Cushman, S. (2014). An ArcGIS Toolbox for Surface Gradient and Geomorphometric Modeling, Version 2.0-0. https://github.com/jeffreyevans/GradientMetrics.

The digital economy (DE) has become a major breakthrough in promoting industrial upgrading and an important engine for high-quality economic growth. However, most studies have neglected the important driving effect of regional economic and social (RES) development on DE. In this paper, we discuss the mechanism of RES development promoting the development of DE, and establish a demand-driven regional DE development model to express the general idea. With the help of spatial analysis toolbox in ArcGIS software, the spatial development characteristics of DE in the Yangtze River Delta City Cluster (YRDCC) is explored. We find the imbalance of spatial development is very significant in YRDCC, no matter at the provincial level or city level. Quantitative analysis reveals that less than 1% likelihood that the imbalanced or clustered pattern of DE development in YRDCC could be the result of random chance. Geographically weighted regression (GWR) analysis with publicly available dataset of YRDCC indicates RES development significantly promotes the development of DE.

The regional Ozark aquifer potentiometric-surface map shows the altitude at which the water level would have risen in tightly cased wells and represents conditions during the period from November 2014 through January 2015. Water levels were measured during this period to ensure that wells had adequate time to recover from previous summer pumping and prior to the start of the 2015 summer pumping season. Groundwater-level data from 178 wells cased completely in and open to the Ozark aquifer are available from the USGS National Water Information System (NWIS; data available at http:// waterdata.usgs.gov/nwis). Streams and springs in the study area represent the intersection of the groundwater table with land surface; these features were used in the construction of the potentiometric-surface map. In Arkansas and Missouri, where the Ozark aquifer crops out, altitudes of select gaining stream reaches, compiled from previous reports on gaining and losing streams (data available at http://dx.doi.org/10.5066/F7W9577Q) and select springs (data available at ftp://msdis.missouri.edu/pub/Inland_Water_Resources/MO_2010_ Springs_shp.zip), were calculated from 10-meter digital elevation data (Knierim and others, 2015; Missouri Department of Natural Resources and others, 2010). After collecting and processing the data, a potentiometric surface was generated by using the interpolation method TopotoRaster in ArcMap. This tool is specifically designed for the creation of hydrologically correct digital elevation models while imposing constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2011). Once the raster surface was created, 100-ft contours were generated by using Contour (Spatial Analyst), which is a spatial analyst tool (available through ArcGIS Spatial Analyst Toolbox) that creates a linefeature class of contours (isolines) from the raster surface (Esri, 2008). Contours were manually adjusted based on topographical influence, a comparison with the regional map of Imes and Emmett (1994), and data point water-level altitudes to more accurately represent the potentiometric surface.

The digital economy (DE) has become a major breakthrough in promoting industrial upgrading and an important engine for high-quality economic growth. However, most studies have neglected the important driving effect of regional economic and social (RES) development on DE. In this paper, we discuss the mechanism of RES development promoting the development of DE, and establish a demand-driven regional DE development model to express the general idea. With the help of spatial analysis toolbox in ArcGIS software, the spatial development characteristics of DE in the Yangtze River Delta City Cluster (YRDCC) is explored. We find the imbalance of spatial development is very significant in YRDCC, no matter at the provincial level or city level. Quantitative analysis reveals that less than 1% likelihood that the imbalanced or clustered pattern of DE development in YRDCC could be the result of random chance. Geographically weighted regression (GWR) analysis with publicly available dataset of YRDCC indicates RES development significantly promotes the development of DE.

This dataset consist of inputs and intermediate results from the coastal scenario modelling. It is an analysis of the bio-physical factors that best explain the changes in QLUMP land use change between 1999 and 2009 along the Queensland coastal region for the classifications used in the future coastal modelling.

Methods:

The input layers (variables etc) were produced using a range of sources as shown in Table 1. Source datasets were edited to produce raster dataset at 50m resolution and reclassified to suit the needs for the analysis.

The analysis was made using the IDRISI Land Use Change Modeler using multi-layer perceptron neural network with explanatory power of bio-physical variables. In this process a range of bio-physical layers such as slope, rainfall, distance to roads etc (see full list in Table 1) are used as potential explanatory variables for the changes in the land use. The neutral network is trained on a subset of the data then tested against the remaining data, thereby giving an estimate of the accuracy of the prediction. This analysis produces suitability maps for each of the transitions between different land use classifications, along with a ranking of the important bio-physical factors for explaining the changes.

The 1999 - 2009 Land use change was analysed with of which 4 were found to be the strongest predictors of the change for various transitions between one land use and another. This dataset includes the rasters of the 4 best predictors along with a sample of the highest accuracy transition probability maps.

Format:

Table 1 (Table 1 NERP 9_4 e-atlas dataset) This table contains the list of names, short descriptions, data source and data manipulation for the input rasters for the land use change model

All GIS files are in GDA 94 Albers Australia coordinate system.

1999.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 1999 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

2009.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 2009 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes (with addition of tourism land use) then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

Rainfall.rst This layer shows the average annual rainfall (in mm) sourced from the Average Yearly Rainfall Isohyets Queensland dataset (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The data was re-classified and resampled at 50m resolution.

Slope.rst This layer shows the slope (in degrees) value at 50m pixel resolution (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The slope was derived from the Australian Digital Elevation Model in ArcGIS (using the Slope tool of the 3D analyst Tools) at a 200m resolution. The data was resampled at 50m resolution.

SeaDist.rst This layer shows the distance (in m) to the nearest coastline (including estuaries) at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the “Mainland coastline” feature in the GBR features dataset available from GBRMPA.

UrbanDist.rst This layer shows the distance (in m) to the nearest pixel of urban land use at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the QLUMP 2009 dataset on the selected urban polygons.

Transition_potential_Other_to_DryHorticulture.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Horticulture. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 92% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_horticulture.docx This shows the results of the analysis of change from land use Others to rain-fed horticulture between 1999 and 2009 using four variables: Distance to existing horticulture, Rainfall, Soil type and Slope.

Transition_potential_Other_to_Drysugar.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Sugar cane. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 84% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_sugar.docx This shows the results of the analysis of change from land use Others to rain-fed sugar between 1999 and 2009 using three variables: Rainfall, Soil type and Slope.

Transition_potential_Other_to_Forestry.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Forestry. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 73% was calculated during testing.

Land Change Modeler MLP Model Results_Forestry.docx This shows the results of the analysis of change from land use Others to Forestry between 1999 and 2009 using three variables: Rainfall, Soil type and Proximity to existing forestry.

Transition_potential_Other_to_Urban.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Urban. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 75% was calculated during testing.

Land Change Modeler MLP Model Results_Urban.docx This shows the results of the analysis of change from land use Others to Urban between 1999 and 2009 using two variables: Slope and Proximity to existing urban areas.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The products are designed with the goal of facilitating ecologically-based natural resources management and research. The vector (polygon) map is in digital format within a geodatabase structure that allows for complex relationships to be established between spatial and tabular data, and allows much of the data to be accessed concurrently. Each map unit has multiple photo attachments viewable easily from within the geodatabase, linked to their actual location on the ground. The Geographic Information System (GIS) format of the map allows user flexibility and will also enable updates to be made as new information becomes available (such as revised NVC codes or vegetation type names) or in the event of major disturbance events that could impact the vegetation. Unlike previous vegetation maps created by SODN, the map for Saguaro National Park was not created via in-situ mapping. Instead, we employed a remote sensing approach aided by our robust field dataset. The final version of the map was created in summer 2016. The map was created using the image-classification toolbox included in the spatial analyst extension for ArcMap (ESRI 2017). Using these tools, we performed a supervised classification with the maximum-likelihood classifier. This tool uses a set of user-defined training samples (polygons) to classify imagery by placing pixels with the maximum likelihood into each map class. We used a pixel size equivalent to the coarsest raster included in the classification, 30 meters.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Residual contributing catchments for selected stream gauges and simulation nodes in the Hunter subregion. Generated from the Geoscience Austraila 9 second flow direction grid. Catchments exist as a shapefile file. ## Purpose Residual catchment boundaries are used in river system modelling. ## Dataset History Selected gauge locations and simulation nodes were used as pour points to generate contributing catchment areas from the GA 9 second flow direction raster. The catchment delineation was achieved using the ArcGIS 10.1 Spatial Analyst WATERSHED tool from the Hydrology toolbox. Resultant watershed raster was also converted to a shapefile (no generalisation). ## Dataset Citation Bioregional Assessment Programme (XXXX) HUN AWRA-R simulation catchments v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/845f31df-5027-4ad7-9b2f-1fb03a874ad8. ## Dataset Ancestors * Derived From HUN AWRA-R simulation nodes v01 * Derived From National Surface Water sites Hydstra * Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

The folder Lake_Points contains the different lake-shapefiles. Each file has bathymetry- and shoreline-points for the interpolation. Shoreline points have heights of 0 meter. For the interpolation we used the toolbox Spatial Analyst of ArcMap 10.2. The interpolation method we used was "natural neighbour" with a fixed resolution of 0.5m for each lake. (The suggested value of ArcMap differs for each lake and depends on user inputs which are not reproducible and the volume calculations should be comparable between the lakes.) All interpolations are calculated with positive values (column depth in the shapefiles). The resulting raster was clipped by a shoreline polygonshape, created with the shoreline points.

Les couches "ensoleillement" représentent des simulations de l'ensoleillement théorique basant sur le modèle numérique de la surface. Ces simulations prennent en compte les situations topographiques (alentours, pentes, expositions) et la variation temporaire pendant l'année de l'ensoleillement théorique pour une position géographique spécifique. Le résultat est une visualisation de la durée de l'ensoleillement en heures par jour, sous forme d'une grille avec une résolution d'1 m. Actuellement, cette analyse a été calculée pour trois jours, notamment le 15/02 (hiver), 15/05 (printemps) et 15/08 (été).

L'analyse de l'ensoleillement a été réalisé avec l'outil "Rayonnement solaire zonal" du logiciel ESRI ArcMap. Une documentation détaillée est disponible sur le site web d'ESRI:

Les paramètres utilisés pour l'analyse sont les suivants :

- Raster en entrée : Modèle numérique de surface mis à disposition par l'Administration de la navigantion aérienne (ANA) se basant sur des données LiDAR de 2017.

- Latitude : 49.46 °

- Configuration du temps: Time Within a day (pour 3 dates: 15/02 hiver, 15/05 printemps et 15/08 été)

- Intervalle de temps : 0.5 Des intervalles de 30 minutes sont utilisés et les données sont accumulées par jours.

- Pente et exposition : Les rasters de pente et d'exposition sont calculés à partir du modèle numérique de surface en entrée.

- Nombre de directions azimutales : 32 directions. Ce nombre est approprié pour une topographie complexe.

- Proportion du flux du rayonnement normal global : La valeur utilisée est 0,3 pour des conditions de ciel dégagé.

- Fraction du rayonnement traversant l'atmosphère : 0,5 pour des conditions de ciel dégagé.

- Grille de la durée de l'ensoleillement directe : Raster en sortie correspondant à la durée du rayonnement solaire direct en heures par jour.

Spatial autocorrelation report of CCCDEI in YRDCC for 2018–2021.

The DNR bluff mapping tool is intended to help local governments identify bluffs in the administration of shoreland and river-related ordinances that regulate placement of structures, vegetation management and land alteration activities in bluff areas. The tool is intended to show the general locations of bluffs. A field survey is necessary to specifically locate the toe and top of bluffs and bluff impact zones for building purposes.

Technical Requirements
The user will need the following to run this tool:
System Requirements:
- ArcGIS 10.x
- Spatial Analyst
Input Data Requirements:
- LiDAR or similar data that can be used or converted into a DEM for elevation data (You can download 1-meter and 3-meter DEMs from MnTOPO: http://arcgis.dnr.state.mn.us/maps/mntopo )

For step-by-step instructions on how to use the tool, please view MN DNR Bluff Mapping Tool Guidance.pdf

This software archive is superseded by Hydrologic Toolbox v1.1.0, available at the following citation: Barlow, P.M., McHugh, A.R., Kiang, J.E., Zhai, T., Hummel, P., Duda, P., and Hinz, S., 2024, U.S. Geological Survey Hydrologic Toolbox version 1.1.0 software archive: U.S. Geological Survey software release, https://doi.org/10.5066/P13VDNAK. The U.S. Geological Survey Hydrologic Toolbox is a Windows-based desktop software program that provides a graphical and mapping interface for analysis of hydrologic time-series data with a set of widely used and standardized computational methods. The software combines the analytical and statistical functionality provided in the U.S. Geological Survey (USGS) Groundwater (Barlow and others, 2014) and Surface-Water (Kiang and others, 2018) Toolboxes and provides several enhancements to these programs. The main analysis methods are the computation of hydrologic-frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10); the computation of design flows, including biologically based flows; the computation of flow-duration curves and duration hydrographs; eight computer-programming methods for hydrograph separation of a streamflow time series, including the BFI (Base-flow index), HYSEP, PART, and SWAT Bflow methods and Eckhardt’s two-parameter digital-filtering method; and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater-recharge values from streamflow data. Several of the statistical methods provided in the Hydrologic Toolbox are used primarily for computation of critical low-flow statistics. The Hydrologic Toolbox also facilitates retrieval of streamflow and groundwater-level time-series data from the USGS National Water Information System and outputs text reports that describe their analyses. The Hydrologic Toolbox supersedes and replaces the Groundwater and Surface-Water Toolboxes. The Hydrologic Toolbox was developed by use of the DotSpatial geographic information system (GIS) programming library, which is part of the MapWindow project (MapWindow, 2021). DotSpatial is a nonproprietary, open-source program written for the .NET framework that includes a spatial data viewer and GIS capabilities. This software archive is designed to document different versions of the Hydrologic Toolbox. Details about version changes are provided in the “Release.txt” file with this software release. Instructions for installing the software are provided in files “Installation_instructions.pdf” and “Installation_instructions.txt.” The “Installation_instructions.pdf” file includes screen captures of some of the installation steps, whereas the “Installation_instructions.txt” file does not. Each version of the Hydrologic Toolbox is provided in a separate .zip file. Citations: Barlow, P.M., Cunningham, W.L., Zhai, T., and Gray, M., 2014, U.S. Geological Survey groundwater toolbox, a graphical and mapping interface for analysis of hydrologic data (version 1.0)—User guide for estimation of base flow, runoff, and groundwater recharge from streamflow data: U.S. Geological Survey Techniques and Methods 3–B10, 27 p., https://doi.org/10.3133/tm3B10. Kiang, J.E., Flynn, K.M., Zhai, T., Hummel, P., and Granato, G., 2018, SWToolbox: A surface-water toolbox for statistical analysis of streamflow time series: U.S. Geological Survey Techniques and Methods, book 4, chap. A–11, 33 p., https://doi.org/10.3133/tm4A11. MapWindow, 2021, MapWindow software, accessed January 9, 2021, at https://www.mapwindow.org/#home.

Les couches "ensoleillement" représentent des simulations de l'ensoleillement théorique basant sur le modèle numérique de la surface. Ces simulations prennent en compte les situations topographiques (alentours, pentes, expositions) et la variation temporaire pendant l'année de l'ensoleillement théorique pour une position géographique spécifique. Le résultat est une visualisation de la durée de l'ensoleillement en heures par jour, sous forme d'une grille avec une résolution d'1 m. Actuellement, cette analyse a été calculée pour trois jours, notamment le 15/02 (hiver), 15/05 (printemps) et 15/08 (été).

L'analyse de l'ensoleillement a été réalisé avec l'outil "Rayonnement solaire zonal" du logiciel ESRI ArcMap. Une documentation détaillée est disponible sur le site web d'ESRI:

Les paramètres utilisés pour l'analyse sont les suivants :

- Raster en entrée : Modèle numérique de surface mis à disposition par l'Administration de la navigantion aérienne (ANA) se basant sur des données LiDAR de 2017.

- Latitude : 49.46 °

- Configuration du temps: Time Within a day (pour 3 dates: 15/02 hiver, 15/05 printemps et 15/08 été)

- Intervalle de temps : 0.5 Des intervalles de 30 minutes sont utilisés et les données sont accumulées par jours.

- Pente et exposition : Les rasters de pente et d'exposition sont calculés à partir du modèle numérique de surface en entrée.

- Nombre de directions azimutales : 32 directions. Ce nombre est approprié pour une topographie complexe.

- Proportion du flux du rayonnement normal global : La valeur utilisée est 0,3 pour des conditions de ciel dégagé.

- Fraction du rayonnement traversant l'atmosphère : 0,5 pour des conditions de ciel dégagé.

- Grille de la durée de l'ensoleillement directe : Raster en sortie correspondant à la durée du rayonnement solaire direct en heures par jour.