Technical Note: A comparison of model and empirical measures of catchment-scale effective energy and mass transfer

Abstract

Abstract. Recent work suggests that a coupled effective energy and mass transfer (EEMT) term, which includes the energy associated with effective precipitation and primary production, may serve as a robust prediction parameter of critical zone structure and function. However, the models used to estimate EEMT have been solely based on long-term climatological data with little validation using direct empirical measures of energy, water, and carbon balances. Here we compare catchment-scale EEMT estimates generated using two distinct approaches: (1) EEMT modeled using the established methodology based on estimates of monthly effective precipitation and net primary production derived from climatological data, and (2) empirical catchment-scale EEMT estimated using data from 86 catchments of the Model Parameter Estimation Experiment (MOPEX) and MOD17A3 annual net primary production (NPP) product derived from Moderate Resolution Imaging Spectroradiometer (MODIS). Results indicated positive and significant linear correspondence (R 2 = 0.75; P < 0.001) between model and empirical measures with an average root mean square error (RMSE) of 4.86 MJ m −2 yr −1 . Modeled EEMT values were consistently greater than empirical measures of EEMT. Empirical catchment estimates of the energy associated with effective precipitation (E PPT ) were calculated using a mass balance approach that accounts for water losses to quick surface runoff not accounted for in the climatologically modeled E PPT . Similarly, local controls on primary production such as solar radiation and nutrient limitation were not explicitly included in the climatologically based estimates of energy associated with primary production (E BIO ), whereas these were captured in the remotely sensed MODIS NPP data. These differences likely explain the greater estimate of modeled EEMT relative to the empirical measures. There was significant positive correlation between catchment aridity and the fraction of EEMT partitioned into E BIO (F BIO ), with an increase in F BIO as a fraction of the total as aridity increases and percentage of catchment woody plant cover decreases. In summary, the data indicated strong correspondence between model and empirical measures of EEMT with limited bias that agree well with other empirical measures of catchment energy and water partitioning and plant cover.

The global distribution of Earth's critical zone and its controlling factors

“…The calculation of EEMT (J m −2 s −1 or W m −2 ) is briefly introduced as follows (for details see Rasmussen and Gallo []): where E PPT is heat energy related to effective precipitation energy and mass transfer and E BIO is net primary production energy and mass transfer: where F is available water mass flux (base flow used in this study) to move into and through the subsurface (kg m −2 s −1 ), c w is the specific heat of water (J kg −1 K −1 ), and Δ T = T ambient − T ref (K) with T ambient is the ambient temperature at time of water flux and T ref is set at 273.15 K, and where NPP is carbon mass flux as net primary production (kg m −2 s −1 ) and h BIO is specific enthalpy (J kg −1 ) at a fixed value of 22 × 10 6 J kg −1 .…”

Influence of climate variability on water partitioning and effective energy and mass transfer in a semi-arid critical zone

“…EEMT has also been incorporated in geomorphic and pedogenic models on granitic rocks to describe landscape attributes and regolith thickness (Pelletier and Rasmussen, 2009a, b). Rasmussen and Tabor (2007) demonstrated that regolith depth on stable low-gradient slopes increased exponentially with increasing EEMT. Similarly, Pelletier et al (2013) found that high EEMT values are associated with large above-ground biomass, deeper soils, and longer distance to the valley bottoms across hillslopes in the Santa Catalina Mountains in southern Arizona.…”

“…For instance, strong correlations were found between EEMT, soil carbon, and clay content in soils on igneous parent materials from California and Oregon (Rasmussen et al, 2005). Furthermore, transfer functions were successfully determined between EEMT and pedogenic indices, including pedon depth, clay content, and chemical indices of soil alteration along an environmental gradient on residual igneous parent material (Rasmussen and Tabor, 2007). EEMT has also been incorporated in geomorphic and pedogenic models on granitic rocks to describe landscape attributes and regolith thickness (Pelletier and Rasmussen, 2009a, b).…”

“…Previous research has shown that EEMT can become a tool to predict regolith depth, rate of soil production, and soil properties (Rasmussen et al, 2005(Rasmussen et al, , 2011Pelletier and Rasmussen, 2009a, b;Rasmussen and Tabor, 2007). For instance, strong correlations were found between EEMT, soil carbon, and clay content in soils on igneous parent materials from California and Oregon (Rasmussen et al, 2005).…”

“…We applied two different methods to estimate E ppt and E bio . Following a similar methodology described in Rasmussen and Gallo (2013), the term EEMT emp was empirically estimated at the catchment scale based on baseflow estimations and average basin-scale net primary productivity (NPP) derived from MODIS satellite data. In comparison, EEMT model was estimated at the catchment scale based on long-term climate records from PRISM, developed by the climate group at Oregon State University (http://www.wcc.nrcs.usda.gov/ ftpref/support/climate/prism/) and described in Rasmussen et al (2005Rasmussen et al ( , 2011.…”