Evaluating the use of spatially varying versus bulk average 3D vegetation structural inputs to modelled evapotranspiration within heterogeneous land cover types

Abstract

The Western Boreal Plain of north‐central Alberta is prone to water‐deficit conditions and is hydrologically sensitive to changes in climate, natural resource extraction and disturbance. Accurate measurement and modelling of the main components of the water balance are important for ecosystem and reclamation management; however, the lack of hydro‐meteorological instrumentation found within different land cover types makes quantification of changes to the water balance difficult over large areas. Remote sensing data can provide spatial estimates of land cover distribution and leaf area index (LAI) used as inputs into land surface models. However, land surface models can often suffer from inaccuracies as a result of spatial (coarse pixel) and temporal (discrete acquisition) resolutions, mis‐classification and inaccurate representation of LAI using remote sensing data. This study uses high‐resolution (1 m × 1 m) Light Detection and Ranging‐derived vegetation parameters (land cover type, LAI and 3D vegetation frictional influences on air movement) as inputs into the Penman–Monteith evapotranspiration (ET) model along with measured hydro‐meteorological variables. Comparison with eddy covariance (EC) measurements indicated that spatially explicit ET estimates at 1 m resolution (over a 5 km × 5 km study area) provided better estimates compared with bulk average ET estimates per land cover type. ET estimates scaled using spatially variable vegetation inputs only underestimated measured fluxes by 2% and 3% at 22·5 and 3 m EC instrumentation towers, respectively. Bulk averaged ET estimates underestimated measured ET by 5% at the 3 m tower and overestimated EC by 7% at the 22·5 m EC tower. Over coarser scales, the error associated with bulk input parameters can lead to error in overall water balance estimation. Copyright © 2014 John Wiley & Sons, Ltd.

A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)

“…Schmid and Lloyd, 1999;Kim et al, 2006;Li et al, 2008;Barcza et al, 2009;Chasmer et al, 2009;Sutherland et al, 2014). As remote sensing data are increasingly available in high spatial resolution, a footprint often covers more than 1 pixel of remote sensing data; hence, there is a need for information on the crosswind spread of the footprint.…”

Eddy covariance based surface‐atmosphere exchange and crop coefficient determination in a mountainous peatland

“…Q G was calculated from measured data of the HFP and soil temperature (Equation ; Oke, ; used for peatlands in Sutherland, Chasmer, Petrone, Kljun, & Devito, ). To account for the heat storage between the peat bog surface and installation depth of the HFP, heat capacity was calculated assuming the soil is composed of organic matter, soil water, and soil gas only.…”

“…The data quality of the classes 1 to 6 is considered sufficient for long-term investigations (Foken, 2006) and was therefore chosen for this study. Chasmer, Petrone, Kljun, & Devito, 2014). To account for the heat storage between the peat bog surface and installation depth of the HFP, heat capacity was calculated assuming the soil is composed of organic matter, soil water, and soil gas only.…”

Hydroclimatic influences on peatland CO2 exchange following upland forest harvesting on the Boreal Plains

“…T soil was recorded at 2, 5 and 10 cm using thermocouples (Omega copper-constantin, Campbell Scientific Inc., Logan, Utah, USA) in a lawn and depression. Ground heat flux (Q G ) was measured according to the calorimetric method (Halliwell and Rouse, 1987;Petrone and Rouse, 2000;Petrone et al, 2007) using the soil temperature profile and heat capacity calculations for each soil layer (2 to 5 cm and 5 to 10 cm) accounting for changes in moisture content and state (Sutherland et al, 2014). Published values for heat capacities of peat soils under a range of moisture conditions were used in the calculation of ground heat flux (Q G ) Oke, 1987;Halliwell and Rouse, 1987;Petrone and Rouse, 2000;Petrone et al, 2007).…”