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Volume 22, issue 7 | Copyright
Hydrol. Earth Syst. Sci., 22, 4097-4124, 2018
https://doi.org/10.5194/hess-22-4097-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Jul 2018

Research article | 30 Jul 2018

Testing an optimality-based model of rooting zone water storage capacity in temperate forests

Matthias J. R. Speich1,2,3,a, Heike Lischke1, and Massimiliano Zappa2 Matthias J. R. Speich et al.
  • 1Dynamic Macroecology, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
  • 2Hydrological Forecasts, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
  • 3Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
  • anow at: Biometry and Environmental Systems Analysis, University of Freiburg, 79106 Freiburg i. Br., Germany

Abstract. Rooting zone water storage capacity Sr is a crucial parameter for modeling hydrology, ecosystem gas exchange and vegetation dynamics. Despite its importance, this parameter is still poorly constrained and subject to high uncertainty. We tested the analytical, optimality-based model of effective rooting depth proposed by Guswa (2008, 2010) with regard to its applicability for parameterizing Sr in temperate forests. The model assumes that plants dimension their rooting systems to maximize net carbon gain. Results from this model were compared against values obtained by calibrating a local water balance model against latent heat flux and soil moisture observations from 15 eddy covariance sites. Then, the effect of optimality-based Sr estimates on the performance of local water balance predictions was assessed during model validation.

The agreement between calibrated and optimality-based Sr varied greatly across climates and forest types. At a majority of cold and temperate sites, the Sr estimates were similar for both methods, and the water balance model performed equally well when parameterized with calibrated and with optimality-based Sr. At spruce-dominated sites, optimality-based Sr were much larger than calibrated values. However, this did not affect the performance of the water balance model. On the other hand, at the Mediterranean sites considered in this study, optimality-based Sr were consistently much smaller than calibrated values. The same was the case at pine-dominated sites on sandy soils. Accordingly, performance of the water balance model was much worse at these sites when optimality-based Sr were used. This rooting depth parameterization might be used in dynamic (eco)hydrological models under cold and temperate conditions, either to estimate Sr without calibration or as a model component. This could greatly increase the reliability of transient climate-impact assessment studies. On the other hand, the results from this study do not warrant the application of this model to Mediterranean climates or on very coarse soils. While the cause of these mismatches cannot be determined with certainty, it is possible that trees under these conditions follow rooting strategies that differ from the carbon budget optimization assumed by the model.

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To simulate the water balance of, e.g., a forest plot, it is important to estimate the maximum volume of water available to plants. This depends on soil properties and the average depth of roots. Rooting depth has proven challenging to estimate. Here, we applied a model assuming that plants dimension their roots to optimize their carbon budget. We compared its results with values obtained by calibrating a dynamic water balance model. In most cases, there is good agreement between both methods.
To simulate the water balance of, e.g., a forest plot, it is important to estimate the maximum...
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