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CJCZO -- GIS/Map Data -- EEMT -- Santa Catalina Mountains -- (2010-2010)


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Abstract

Yearly 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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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Santa Catalina Mountains, Santa Catalina Mountains
North Latitude
32.5946°
East Longitude
-110.6176°
South Latitude
32.3008°
West Longitude
-110.9622°

Temporal

Start Date:
End Date:

Content

ReadMe.md

CJCZO -- GIS/Map Data -- EEMT -- Santa Catalina Mountains -- (2010)


OVERVIEW

Description/Abstract

Yearly 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).

Creator/Author

Rasmussen, Craig|Durcik, Matej

CZOs

Catalina-Jemez

Contact

Craig Rasmussen; Department of Soil, Water and Environmental Science; University of Arizona; crasmuss@cals.arizona.edu

Subtitle

Effective Energy and Mass Transfer for Catalinas




SUBJECTS

Disciplines

GIS / Remote Sensing|Biology / Ecology

Topics

GIS/Map Data

Subtopic

EEMT

Keywords

EEMT|Energy|Mass transfer|Santa Catalina Mountains|Arizona

Variables

Effective Energy and Mass Transfer

Variables ODM2

Effective energy and mass transfer (EEMT)




TEMPORAL

Date Start

2010-01-01

Date End

2010-12-31




SPATIAL

Field Areas

Santa Catalina Mountains

Location

Santa Catalina Mountains

North latitude

32.59463

South latitude

32.30083

West longitude

-110.96222

East longitude

-110.61758999999999




REFERENCE

Citation

The following acknowledgment should accompany any publication or citation of these data - Logistical support and/or data were provided by the NSF-supported Jemez River Basin and Santa Catalina Mountains Critical Zone Observatory EAR-0724958.

Publications of this data

Chorover J., Troch P.A., Rasmussen C., Brooks P., Pelletier J., Breshears D.D., Huxman T., Lohse K., McIntosh J., Meixner T., Papuga S., Schaap M., Litvak M., Perdrial J. Harpold A., and Durcik M. (2011). How Water, Carbon, and Energy Drive Critical Zone Evolution: The Jemez-Santa Catalina Critical Zone Observatory . Vadose Zone Journal 10(3): 884-899 http://dx.doi.org/10.2136/vzj2010.0132

Publications using this data

Pelletier J.D., Barron-Gafford G.A., Breshears D.D., Brooks P.D., Chorover J., Durcik M., Harman C.J., Huxman T.E., Lohse K.A., Lybrand R., Meixner T., McIntosh J.C., Papuga S.A., Rasmussen C., Schaap M., Swetnam T.L., and Troch P.A. (2013). Coevolution of nonlinear trends in vegetation, soils, and topography with elevation and slope aspect: A case study in the sky islands of southern Arizona. Journal of Geophysical Research: Earth Surface 118(2): 741-758 http://dx.doi.org/10.1002/jgrf.20046

CZO ID

2561

Award Grant Numbers

National Science Foundation - EAR-0724958




COMMENTS

Comments

Detailed computation and data resources are described in the file EEMT_Radiation_Dew.pdf

Additional Metadata

Name Value
czos Catalina-Jemez
czo_id 2561
citation The following acknowledgment should accompany any publication or citation of these data - Logistical support and/or data were provided by the NSF-supported Jemez River Basin and Santa Catalina Mountains Critical Zone Observatory EAR-0724958.
comments Detailed computation and data resources are described in the file EEMT_Radiation_Dew.pdf
keywords EEMT, Energy, Mass transfer, Santa Catalina Mountains, Arizona
subtitle Effective Energy and Mass Transfer for Catalinas
variables Effective Energy and Mass Transfer
disciplines GIS / Remote Sensing, Biology / Ecology

References

Related Resources

The content of this resource serves as the data for: Chorover J., Troch P.A., Rasmussen C., Brooks P., Pelletier J., Breshears D.D., Huxman T., Lohse K., McIntosh J., Meixner T., Papuga S., Schaap M., Litvak M., Perdrial J. Harpold A., and Durcik M. (2011). How Water, Carbon, and Energy Drive Critical Zone Evolution: The Jemez-Santa Catalina Critical Zone Observatory . Vadose Zone Journal 10(3): 884-899 http://dx.doi.org/10.2136/vzj2010.0132

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation EAR-0724958

How to Cite

Rasmussen, C., M. Durcik (2019). CJCZO -- GIS/Map Data -- EEMT -- Santa Catalina Mountains -- (2010-2010), HydroShare, http://www.hydroshare.org/resource/1b1f6f97db1245e78a01edfede3b1710

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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