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).
Detailed computation and data resources are described in the file EEMT_Radiation_Dew.pdf
EEMT, Energy, Mass transfer, Santa Catalina Mountains, Arizona
XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.
Citation for This Dataset
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.
Citation for This Webpage
Craig Rasmussen; Matej Durcik (2010). "CZO Dataset: Santa Catalina Mountains - GIS/Map Data (2010) - EEMT." Retrieved 23 May 2019, from http://criticalzone.org/catalina-jemez/data/dataset/2561/
How Water, Carbon, and Energy Drive Critical Zone Evolution: The Jemez-Santa Catalina Critical Zone Observatory . 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): Vadose Zone Journal 10(3): 884-899
Coevolution of nonlinear trends in vegetation, soils, and topography with elevation and slope aspect: A case study in the sky islands of southern Arizona. 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): Journal of Geophysical Research: Earth Surface 118(2): 741-758
National Science Foundation EAR-0724958
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