Advancing simulations of water fluxes, soil moisture and drought stress by using the LWF-Brook90 hydrological model in R
•Sensitivity analysis of LWF-Brook90 revealed a distinct parameter importance ranking•Bayesian calibration indicated high water use for a poplar short rotation forest•The LWF-Brook90 SVAT model excellently captures temporal variations of soil moisture•The LWFBrook90R package facilitates complex stat...
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| Veröffentlicht in: | Agricultural and forest meteorology Jg. 291; S. 108023 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
15.09.2020
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| ISSN: | 0168-1923, 1873-2240 |
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| Abstract | •Sensitivity analysis of LWF-Brook90 revealed a distinct parameter importance ranking•Bayesian calibration indicated high water use for a poplar short rotation forest•The LWF-Brook90 SVAT model excellently captures temporal variations of soil moisture•The LWFBrook90R package facilitates complex statistical analysis and parallelization•The modelling case study with code examples demonstrates the utility of the R package
Soil vegetation atmosphere transport (SVAT) models are important for the quantification of water fluxes, soil water availability, drought stress and their uncertainties under climate change. We present LWFBrook90R, an enhanced implementation of the well-established, process-based SVAT model LWF-Brook90 for the R environment for statistical computing. The package provides new functions and sub-models for model parameterization, and facilitates parallel computing, sensitivity analysis and inverse calibration of the model. A case study comprising i) basic forward water balance simulations for temperate grassland vegetation, deciduous and evergreen forest, ii) a parallelized sensitivity analysis, and iii) Bayesian calibrations based on soil water storage observed in a poplar (Populus nigra × P. maximowiczii) Short Rotation Forest (SRF) demonstrates the utility of the R package. The sensitivity analysis revealed parameters affecting plant-available soil water storage capacity and the vegetation's timing and level of water demand to be most important for the annual course of simulated soil water storage, with seasonal and interannual differences in parameter importance rankings. The subsequent calibration yielded a very high agreement between daily simulated and observed soil water storage (0-200 cm soil depth) for the calibration and validation datasets, with Nash-Sutcliffe efficiencies of 0.97 and 0.95, respectively. The final model predicted high though realistic rates of annual evapotranspiration (2011: 844 ± 3.8 mm y-1, 2012: 733 ± 4.5 mm y-1) for the poplar SRF, regularly exceeding grass reference evaporation (ET0) by 20-47% during the months of the growing season. However, basing calibrations solely on observed soil water storage probably resulted in biased partitioning of evapotranspiration towards interception losses. The integration of the LWF-Brook90 hydrological model into R with its wide variety of extensions was successfully tested and may provide efficient, reliable and reproducible water balance predictions by facilitating complex statistical analyses and large-scale applications of the model. |
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| AbstractList | •Sensitivity analysis of LWF-Brook90 revealed a distinct parameter importance ranking•Bayesian calibration indicated high water use for a poplar short rotation forest•The LWF-Brook90 SVAT model excellently captures temporal variations of soil moisture•The LWFBrook90R package facilitates complex statistical analysis and parallelization•The modelling case study with code examples demonstrates the utility of the R package
Soil vegetation atmosphere transport (SVAT) models are important for the quantification of water fluxes, soil water availability, drought stress and their uncertainties under climate change. We present LWFBrook90R, an enhanced implementation of the well-established, process-based SVAT model LWF-Brook90 for the R environment for statistical computing. The package provides new functions and sub-models for model parameterization, and facilitates parallel computing, sensitivity analysis and inverse calibration of the model. A case study comprising i) basic forward water balance simulations for temperate grassland vegetation, deciduous and evergreen forest, ii) a parallelized sensitivity analysis, and iii) Bayesian calibrations based on soil water storage observed in a poplar (Populus nigra × P. maximowiczii) Short Rotation Forest (SRF) demonstrates the utility of the R package. The sensitivity analysis revealed parameters affecting plant-available soil water storage capacity and the vegetation's timing and level of water demand to be most important for the annual course of simulated soil water storage, with seasonal and interannual differences in parameter importance rankings. The subsequent calibration yielded a very high agreement between daily simulated and observed soil water storage (0-200 cm soil depth) for the calibration and validation datasets, with Nash-Sutcliffe efficiencies of 0.97 and 0.95, respectively. The final model predicted high though realistic rates of annual evapotranspiration (2011: 844 ± 3.8 mm y-1, 2012: 733 ± 4.5 mm y-1) for the poplar SRF, regularly exceeding grass reference evaporation (ET0) by 20-47% during the months of the growing season. However, basing calibrations solely on observed soil water storage probably resulted in biased partitioning of evapotranspiration towards interception losses. The integration of the LWF-Brook90 hydrological model into R with its wide variety of extensions was successfully tested and may provide efficient, reliable and reproducible water balance predictions by facilitating complex statistical analyses and large-scale applications of the model. Soil vegetation atmosphere transport (SVAT) models are important for the quantification of water fluxes, soil water availability, drought stress and their uncertainties under climate change. We present LWFBrook90R, an enhanced implementation of the well-established, process-based SVAT model LWF-Brook90 for the R environment for statistical computing. The package provides new functions and sub-models for model parameterization, and facilitates parallel computing, sensitivity analysis and inverse calibration of the model. A case study comprising i) basic forward water balance simulations for temperate grassland vegetation, deciduous and evergreen forest, ii) a parallelized sensitivity analysis, and iii) Bayesian calibrations based on soil water storage observed in a poplar (Populus nigra × P. maximowiczii) Short Rotation Forest (SRF) demonstrates the utility of the R package. The sensitivity analysis revealed parameters affecting plant-available soil water storage capacity and the vegetation's timing and level of water demand to be most important for the annual course of simulated soil water storage, with seasonal and interannual differences in parameter importance rankings. The subsequent calibration yielded a very high agreement between daily simulated and observed soil water storage (0-200 cm soil depth) for the calibration and validation datasets, with Nash-Sutcliffe efficiencies of 0.97 and 0.95, respectively. The final model predicted high though realistic rates of annual evapotranspiration (2011: 844 ± 3.8 mm y⁻¹, 2012: 733 ± 4.5 mm y⁻¹) for the poplar SRF, regularly exceeding grass reference evaporation (ET₀) by 20-47% during the months of the growing season. However, basing calibrations solely on observed soil water storage probably resulted in biased partitioning of evapotranspiration towards interception losses. The integration of the LWF-Brook90 hydrological model into R with its wide variety of extensions was successfully tested and may provide efficient, reliable and reproducible water balance predictions by facilitating complex statistical analyses and large-scale applications of the model. |
| ArticleNumber | 108023 |
| Author | Schmidt-Walter, Paul Trotsiuk, Volodymyr Meusburger, Katrin Meesenburg, Henning Zacios, Martina |
| Author_xml | – sequence: 1 givenname: Paul orcidid: 0000-0003-2699-0893 surname: Schmidt-Walter fullname: Schmidt-Walter, Paul email: paul.schmidt-walter@nw-fva.de organization: Northwest German Forest Research Institute (NW-FVA), Grätzelstraße 2, 37079 Göttingen, Germany – sequence: 2 givenname: Volodymyr surname: Trotsiuk fullname: Trotsiuk, Volodymyr organization: Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zuercherstrasse 111, 8903 Birmensdorf, Switzerland – sequence: 3 givenname: Katrin surname: Meusburger fullname: Meusburger, Katrin organization: Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zuercherstrasse 111, 8903 Birmensdorf, Switzerland – sequence: 4 givenname: Martina surname: Zacios fullname: Zacios, Martina organization: Bavarian State Institute of Forestry (LWF), Hans-Carl-von-Carlowitz-Platz 1, 85354 Freising, Germany – sequence: 5 givenname: Henning orcidid: 0000-0002-3035-4737 surname: Meesenburg fullname: Meesenburg, Henning organization: Northwest German Forest Research Institute (NW-FVA), Grätzelstraße 2, 37079 Göttingen, Germany |
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