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TwitterYearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).
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TwitterYearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).
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TwitterAttachment regarding request by George Farrell, Jr. for a Conditional Use B-1 Business Permit for expansion of the self storage business located across McGhee Rd and for flex office buildings (various business uses) with related storage located at the corner of Farrington Point Rd (SR 1008) and McGhee Rd (SR 1717), Williams Township.
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TwitterAttachment regarding public hearing request by Rocky McCampbell to rezone approximately 1 acre of a 4.03 acre tract located at 1115 Mt. Carmel Church Rd., Parcel No. 64812, Williams Township, from R-1 Residential to Conditional Use Neighborhood Business (CU-NB).
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TwitterData represents maximum up-estuary intrusion of the 1 ppt salinity contour. The lines represent 1000 ppt chloride per million, measured 1½ hours after high tide. This data contains contours for 1921-1990.
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TwitterAttachment regarding a request by Tanya Matzen, Vice President, on behalf of NNP Briar Chapel, LLC for subdivision Final Plat review and approval of Briar Chapel, Phase 14, consisting of 89 lots on 31.45 acres, located off Catullo Run, Baldwin Township, parcels #89624.
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TwitterAttachment regarding public hearing request by the Chatham County Board of Commissioners for text amendments to the Subdivision Regulations to modify the subdivision process and standards.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This abstract contains links to public ArcGIS maps that include locations of carbonate springs and some of their characteristics. Information for accessing and navigating through the maps are included in a PowerPoint presentation IN THE FILE UPLOAD SECTION BELOW. Three separate data sets are included in the maps:
Several base maps are included in the links. The US carbonate map describes and categorizes carbonates (e.g., depth from surface, overlying geology/ice, climate). The carbonate springs map categorizes springs as being urban, specifically within 1000 ft of a road, or rural. The basis for this categorization was that the heat island effect defines urban as within a 1000 ft of a road. There are other methods for defining urban versus rural to consider. Map links and details of the information they contain are listed below.
Map set 1: The WQP map provides three mapping options separated by the parameters available at each spring site. These maps summarize discrete water quality samples, but not data logger availability. Information at each spring provides links for where users can explore further data.
Option 1: WQP data with urban and rural springs labeled, with highlight of springs with or without NWIS data https://www.arcgis.com/home/item.html?id=2ce914ec01f14c20b58146f5d9702d8a
Options 2: WQP data by major ions and a few other solutes https://www.arcgis.com/home/item.html?id=5a114d2ce24c473ca07ef9625cd834b8
Option 3:WQP data by various carbon species https://www.arcgis.com/home/item.html?id=ae406f1bdcd14f78881905c5e0915b96
Map 2: The worldwide carbonate map in the WoKaS data set (citation below) includes a description of carbonate purity and distribution of urban and rural springs, for which discharge data are available: https://www.arcgis.com/apps/mapviewer/index.html?webmap=5ab43fdb2b784acf8bef85b61d0ebcbe.
Reference: Olarinoye, T., Gleeson, T., Marx, V., Seeger, S., Adinehvand, R., Allocca, V., Andreo, B., Apaéstegui, J., Apolit, C., Arfib, B. and Auler, A., 2020. Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater. Scientific Data, 7(1), pp.1-9.
Map 3: Karst and spring data from selected states: This map includes sites that members of the RCN have suggested to our group.
https://uageos.maps.arcgis.com/apps/mapviewer/index.html?webmap=28ed22a14bb749e2b22ece82bf8a8177
This data set is incomplete (as of October 13, 2022 it includes Florida and Missouri). We are looking for more information. You can share data links to additional data by typing them into the hydroshare page created for our group. Then new sites will periodically be added to the map: https://www.hydroshare.org/resource/0cf10e9808fa4c5b9e6a7852323e6b11/
Acknowledgements: These maps were created by Michael Jones, University of Arkansas and Shishir Sarker, University of Kentucky with help from Laura Toran and Francesco Navarro, Temple University.
TIPS FOR NAVIGATING THE MAPS ARE IN THE POWERPOINT DOCUMENT IN THE FILE UPLOAD SECTION BELOW.
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TwitterAttachment regarding a request by Sears Design Group, P.A. on behalf of Galloway Ridge, Inc., located off US 15-501 N, Williams Township, for a revision to the existing conditional use permit (CU-PUD for Fearrington Village) for an expansion of services and renovations to existing buildings.
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TwitterData presented has been aggregated into 2020 US Census Block unit geographies by the South Carolina Broadband Office (SCBBO). The data represents a generalized overview of state-managed broadband investments made from 2021 to 2024 through competitively awarded grant programs administered by the SCBBO. Note, this map is for general reference only, not all locations within a funded census block were necessarily included in a project. Location-specific details regarding any funded area are tracked at a project level. Additional South Carolina broadband information may be found at https://ors.sc.gov/broadband/office/investments/state or www.scdigitaldrive.org. The SCBBO is neither responsible nor liable for damages or injuries caused by failure of performance, error, omission, inaccuracy, inaccessibility, incompleteness or any other errors of this information period or formatting on this map. Submit comments or questions to broadband@ors.sc.gov
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Abbreviations: AWC (available water-holding capacity), GDD (growing degree-days), BEDD (biologically effective degree-days), FFD (frost-free days), PPT (growing season precipitation).
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TwitterAttachment regarding a request by Kim and Annette Ringeisen, to rezone property from R-1 Residential to CD-RB Conditional District Regional Business, on Parcel No. 63764, located at 3215 Mt. Gilead Church Rd., approximately 7.14 acres, Baldwin Township, for a special events venue to host weddings, receptions, anniversaries, reunions, company socials, photography, and other similar events.
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TwitterAttachment regarding
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TwitterData presented has been aggregated into 2020 US Census Block unit geographies by the South Carolina Broadband Office (SCBBO). The data represents a generalized overview of state-managed broadband investments made from 2021 to 2024 through competitively awarded grant programs administered by the SCBBO. Note, this map is for general reference only, not all locations within a funded census block were necessarily included in a project. Location-specific details regarding any funded area are tracked at a project level. Additional South Carolina broadband information may be found at https://ors.sc.gov/broadband/office/investments/state or www.scdigitaldrive.org. The SCBBO is neither responsible nor liable for damages or injuries caused by failure of performance, error, omission, inaccuracy, inaccessibility, incompleteness or any other errors of this information period or formatting on this map. Submit comments or questions to broadband@ors.sc.gov
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TwitterBased on SC Broadband Office (SCBBO) analysis of FCC Broadband Data Collection (fcc.gov), Jun. 30, 2023 (as of Mar. 19, 2024), submissions that were audited through the SC BEAD Challenge process which concluded on Jun. 30, 2024. The SC BEAD Challenge process relied upon FCC BSL Fabric Jun. 30, 2023, Version 3.2 (pub. Jul. 21, 2023). Satellite and mobile broadband services are excluded. Population and K-12 estimates are derived from residential unit level data based on the FCC BSL fabric. Broadband investment data based on SCBBO actual BSL contract data in the case of state-managed funds (when available) and best-available federal data in the case of FCC and US Department of Agriculture (USDA) managed investments. County-level investments are based upon data provided to the SCBBO. The SCBBO is neither responsible nor liable for damages or injuries caused by failure of performance, error, omission, inaccuracy, inaccessibility, incompleteness or any other errors of this information period or formatting on this slide. This data should be used for general reference purposes only. Additional broadband information regarding South Carolina may be found at www.scdigitaldrive.org. Submit comments or questions to broadband@ors.sc.gov
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TwitterAttachment regarding a request by Kim and Annette Ringeisen, to rezone property from R-1 Residential to CD-RB Conditional District Regional Business, on Parcel No. 63764, located at 3215 Mt. Gilead Church Rd., approximately 7.14 acres, Baldwin Township, for a special events venue to host weddings, receptions, anniversaries, reunions, company socials, photography, and other similar events.
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TwitterAttachment regarding request by Chad Abbot, P.E. for subdivision First Plat review and approval of Ridgecrest Estates, consisting of 28 lots on 49.41 acres, located off Hamlets Chapel Road, SR-1525, parcels #1798.
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TwitterBased on SC Broadband Office (SCBBO) analysis of FCC Broadband Data Collection (fcc.gov), Jun. 30, 2023 (as of Mar. 19, 2024), submissions that were audited through the SC BEAD Challenge process which concluded on Jun. 30, 2024. The SC BEAD Challenge process relied upon FCC BSL Fabric Jun. 30, 2023, Version 3.2 (pub. Jul. 21, 2023). Satellite and mobile broadband services are excluded. Population and K-12 estimates are derived from residential unit level data based on the FCC BSL fabric. Broadband investment data based on SCBBO actual BSL contract data in the case of state-managed funds (when available) and best-available federal data in the case of FCC and US Department of Agriculture (USDA) managed investments. County-level investments are based upon data provided to the SCBBO. The SCBBO is neither responsible nor liable for damages or injuries caused by failure of performance, error, omission, inaccuracy, inaccessibility, incompleteness or any other errors of this information period or formatting on this slide. This data should be used for general reference purposes only. Additional broadband information regarding South Carolina may be found at www.scdigitaldrive.org. Submit comments or questions to broadband@ors.sc.gov
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TwitterAttachment regarding public Hearing request by Mark Moldenhauer for a Conditional Use District [CUD] Rezoning from R-1 Residential to CU Light Industrial on Parcel No. 67592, located at 1971 Lystra Rd, on approximately 1 acre of a 5.032 acre tract.
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TwitterAttachment regarding a request by Cathleen Rubens to repeal Section 3.4.2 of the Chatham County –Town of Cary Joint Land Use Plan. The section establishes a 400 foot undisturbed buffer adjacent to Corps of Engineers property when public utilities are utilized for a development in the portion of the plan area south of Lewter Shop Road and Martha’s Chapel Road.
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TwitterYearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).