Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/8/2008
Publication Date: N/A
Citation: N/A Interpretive Summary:
Technical Abstract: For large scale applications like the US National scale Conservation Effects Assessment Project (CEAP) is, soil hydraulic characteristics data are not readily available and therefore need to be estimated. Field soil water conditions are commonly approximated using laboratory soil water retention measurements taken on small soil cores. However, there are different matric potentials used worldwide to approximate field capacity (FC); most of which can be estimated using a number of different equations/techniques. Their general validity, however, is still to be established. The APEX model - used to assess the onsite environmental benefits of cropland conservation practices – uses the equations of Rawls et al. (1982) to estimate -33 kPa soil water retention as an approximation of FC. It was found earlier that -33kPa water content and in situ FC show poor and biased correlation. We also found that the Rawls et al. (1982) equations give biased estimations for US conditions. We used an improved laboratory data set as well as alternatives based on field-data to estimate FC. The estimates were then input to the APEX model to study the sensitivity of model outputs to the differences in estimates. Relative differences in output measures were not uniform but were dependent on environmental conditions and the queried output variable. Differences in model outputs using different FC estimations ranged from negligible to substantial, the choice of estimation method can have a large impact on the simulation results. When such data are available, estimations based on in-situ field data should be preferred as those are more suitable for the scales for which the simulation studies are used. When laboratory data are the only option, care should be taken to apply laboratory data based estimation techniques that use suitable data and an estimation technique that is capable of describing complex relationships in the base data.