Potentials and limitations of modelling vertical distributions of root water uptake of an Austrian pine forest on a sandy soil

Musters, P.A.D.; Bouten, W.; Verstraten, J.M.

doi:10.1002/(sici)1099-1085(200001)14:1<103::aid-hyp913>3.0.co;2-5

Cited by 23 publications

(16 citation statements)

References 18 publications

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“…Parameter optimization using a modified-microGA was implemented to identify the soil hydraulic properties as unknown parameters whose variation has large effects on the model outputs [Musters et al, 2000;Hupet et al, 2002;Ritter et al, 2003]. Several major input parameters related to soil moisture dynamics were selected as shown in Table 2 (…”

Section: Soil Parameters Of the Hydrological Modelsmentioning

confidence: 98%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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Various hydrological models have been developed for estimating root zone soil moisture dynamics. These models, however, incorporated their own parameterization approaches indicating that the output from the different model inherent structures might include uncertainties because we do not know which model structure is correct for describing the real system. More recently, multimodel approaches using a Bayesian Model Averaging (BMA) scheme can improve the overall predictive skill while individual models retain their own uncertainties for simulated soil moisture based on a single set of weights in modeling under different land surface wetness conditions (e.g., wet, moderately wet, and dry conditions). In order to overcome their limitations, we developed a BMA-based multimodel simulation approach based on various soil wetness conditions for estimating effective surface soil moisture dynamics (0-5 cm) and quantifying uncertainties efficiently based on the land surface wetness conditions. The newly developed approach adapts three different hydrological models (i.e., Noah Land Surface Model, Noah LSM; Soil-Water-Atmosphere-Plant, SWAP; and Community Land Model, CLM) for simulating soil moisture. These models were integrated with a modified-microGA (advanced version of original Genetic Algorithm (GA)) to search for optimized soil parameters for each model. Soil moisture was simulated from the estimated soil parameters using the hydrological models in a forward mode. It was found that SWAP performed better than others during wet condition, while Noah LSM and CLM showed a good agreement with measurements during dry condition. Thus, we inferred that performance of individual models with different model structures can be different with land surface wetness. Taking into account the effects of soil wetness on different model performances, we categorized soil moisture measurements and estimated different weights for each category using the BMA scheme. Effective surface soil moisture dynamics were obtained by aggregating multiple weighted soil moisture. Our findings demonstrated that the effective soil moisture estimates derived by this approach showed a better match with the measurements compared to the original models and single-weighted outputs. Multimodel simulation approach based on land surface wetness enhances the ability to predict reliable soil moisture dynamics and reflects the strengths of different hydrological models under various soil wetness conditions.

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Section: Soil Parameters Of the Hydrological Modelsmentioning

confidence: 98%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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“…The site has a shallow groundwater table fluctuating around the top of the cover sand during the winter and 1-2 m deeper in dry summers. More detailed information on the research site is given by Musters and Bouten (1999) and Musters et al (2000).…”

Section: Site Descriptionmentioning

confidence: 99%

“…Model parameterisations are based on extensive monitoring programmes and model calibrations (Bouten et al, 1992a,b;Musters et al, 2000). An overview of the specific model parameterisation for the locations is given in Table 2 and Figs.…”

Section: Specific Model Information For the Different Sitesmentioning

confidence: 99%

Towards understanding tree root profiles: simulating hydrologically optimal strategies for root distribution

Wijk

Bouten

2001

Hydrol. Earth Syst. Sci.

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In this modelling study differences in vertical root distributions measured in four contrasting forest locations in the Netherlands were investigated. Root distributions are seen as a reflection of the plant's optimisation strategy, based on hydrological grounds. The 'optimal' root distribution is defined as the one that maximises the water uptake from the root zone over a period of ten years. The optimal root distributions of four forest locations with completely different soil physical characteristics are calculated using the soil hydrological model SWIF. Two different model configurations for root interactions were tested: the standard model configuration in which one single root profile was used (SWIF-NC), and a model configuration in which two root profiles compete for the same available water (SWIF-C). The root profiles were parameterised with genetic algorithms. The fitness of a certain root profile was defined as the amount of water uptake over a simulation period of ten years. The root profiles of SWIF-C were optimised using an evolutionary game. The results showed clear differences in optimal root distributions between the various sites and also between the two model configurations. Optimisation with SWIF-C resulted in root profiles that were easier to interpret in terms of feasible biological strategies. Preferential water uptake in wetter soil regions was an important factor for interpretation of the simulated root distributions. As the optimised root profiles still showed differences with measured profiles, this analysis is presented, not as the final solution for explaining differences in root profiles of vegetation but as a first step using an optimisation theory to increase understanding of the root profiles of trees.

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“…This was done since the water stress function by Feddes (Feddes et al, 1978) explicitly assumes that reductions in the potential root water uptake occur from some critical pressure head, even though this function already implicitly assumes that the unsaturated soil hydraulic conductivity governs this reduction. It is generally acknowledged that the critical pressure heads depend on soil type and that the reduction occurs much earlier for a coarse‐textured than a medium‐ or fine‐textured soil (i.e., that | h crit | sand < | h crit | loam ; e.g., Musters et al, 2000) since the unsaturated hydraulic conductivity curve drops much faster. This concept is used here to adjust the critical pressure heads according to our spatially variable measured hydraulic properties.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, to rigorously investigate the effect of within‐field soil hydraulic variability on transpiration and crop yield, it seems appropriate to adjust the water stress parameters according to the basic soil properties as well. Unfortunately, such an adjustment is not straightforward because water stress parameters cannot be directly measured and their indirect estimation is also very difficult (e.g., Musters et al, 2000).…”

mentioning

confidence: 99%

Impact of Within‐Field Variability in Soil Hydraulic Properties on Transpiration Fluxes and Crop Yields: A Numerical Study

Hupet

Dam²,

Vanclooster

2004

Vadose Zone Journal

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By means of numerical modeling we investigate the impact of within‐field variability in the soil hydraulic properties on actual transpiration and dry matter yield for three different climate scenarios. We first show that the sensitivity of the simulated actual transpiration and dry matter yield to soil hydraulic parameters increases with the dryness of the climate. The numerical simulations with soil independent stress factors further demonstrate that the impact of the within‐field variability in soil hydraulic properties on the simulated transpiration and dry matter yield can be very large. The generated spatial variability in transpiration and dry matter yield increases systematically with the dryness of the climate, with coefficients of variation increasing from 7 to 14.6% for actual transpiration and from 6.7 to 16% for dry matter yield. In a subsequent analysis, all agrohydrological simulations are rerun considering that the water stress parameters are spatially variable and soil dependent. While the results obtained with the adjusted water stress parameters are quite different, the spatial variability in simulated transpiration and dry matter yield still increases for drier conditions. The different results obtained, although not validated experimentally, illustrate that the use of an agrohydrological simulation model in a stochastic mode requires accurate estimates of the water stress parameters, which should be soil dependent. Finally, we show that simultaneous estimation of water stress and soil hydraulic parameters cannot be robustly performed using measurements of transpiration or dry matter yield alone. Consequently, the use of an agro‐hydrological model in a stochastic mode for a vegetated surface requires alternative strategies for specifying reliable water stress parameters. Adjustment of water stress parameters from reference unsaturated hydraulic conductivity values seems attractive, but needs more research.

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Potentials and limitations of modelling vertical distributions of root water uptake of an Austrian pine forest on a sandy soil

Cited by 23 publications

References 18 publications

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Towards understanding tree root profiles: simulating hydrologically optimal strategies for root distribution

Impact of Within‐Field Variability in Soil Hydraulic Properties on Transpiration Fluxes and Crop Yields: A Numerical Study

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