Parameterization of Root Water Uptake Models Considering Dynamic Root Distributions and Water Uptake Compensation

Cai, Gaochao; Vanderborght, Jan; Couvreur, Valentin; Mboh, Cho Miltin; Vereecken, Harry

doi:10.2136/vzj2016.12.0125

Cited by 78 publications

(120 citation statements)

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“…Root length densities were therefore expressed in units of length per surface. To calculate the total root length below a unit surface area, both root length and root counts per image surface were considered in Cai et al (2017). We assumed that root lengths in the images were proportional to root counts.…”

Section: Root Observationmentioning

confidence: 99%

“…For crops with small lateral variations in root length density (RLD), this 3-D model could be upscaled to a 1-D model (Couvreur et al, 2014) which shows similarities to the models of Nimah and Hanks (1973) and of Amenu and Kumar (2008). Cai et al (2017) obtained the root hydraulic parameters of the 1-D upscaled…”

mentioning

confidence: 99%

“…Measuring sap flow with the thermoelectric method is a direct and in situ technique which 10 was discovered by Huber (1932). It was used to estimate transpiration for different trees species (Granier et al, 1996;Cermak et al, 2004;Massai and Remorini, 2000) and crops (Chabot et al, 2005;Langensiepen et al, 2014;Cohen et al, 1993). Due to limitations of sensor installation on small and vulnerable crop stems, sap flow measurements on crops with small stem diameters of less than 5 mm are practically challenging.…”

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confidence: 99%

“…The experimental set up of the plots was described in detail in Cai et al (2016) and the model setup and the inverse modeling procedure that was used to determine the parameters by Cai et al (2017). For more detailed information on the setup and the inverse modeling procedure, we refer the reader to these publications.…”

mentioning

confidence: 99%

“…2 mm) viewed by the camera in the estimation of absolute total root length. The root counts were associated with a soil volume that corresponds with the diameter (height, 64 mm) and the radius (width, 32 mm) of the tube, and the image width (depth, 16.5 mm) to obtain an estimate of root length density (Cai et al, 2017). The root densities were subsequently integrated over depth to obtain the total root length below a unit surface area.…”

mentioning

confidence: 99%

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Root growth, water uptake, and sap flow of winter wheat in response to different soil water availability

Cai

Vanderborght

Langensiepen

et al. 2017

Preprint

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Abstract. How much and where water is taken up by roots from the soil profile are important questions that need to be answered to close the soil water balance equation and to describe water fluxes in the soil-plant-atmosphere system. Physicallybased root water uptake (RWU) models that relate RWU to transpiration, root density, and water potential distributions have been developed but far less used or tested. This study aims at evaluating the simulated RWU of winter wheat by the empirical Feddes-Jarvis (FJ) model and the physically-based Couvreur (C) model for different soil water conditions and soil textures 5 against sap flow measurements. Soil water content (SWC), water potential, and root development were monitored noninvasively at six soil depths in two rhizotron facilities that were constructed in two soil textures: stony vs. silty with each three water treatments: sheltered, rainfed, and irrigated. Soil and root parameters of the two models were derived from inverse modeling and simulated RWU was compared with sap flow measurements for validation. The different soil types and water treatments resulted in different crop biomass, root densities and root distributions with depth. The two models simulated the 10 lowest RWU in the sheltered plot of the stony soil where RWU was also lower than the potential RWU. In the silty soil, simulated RWU was equal to the potential uptake for all treatments. The variation of simulated RWU among the different plots agreed well with measured sap flow but the C model predicted the ratios of the transpiration fluxes in the two soil types slightly better than the FJ model. The root hydraulic parameters of the C model could be constrained by the field data but not the water stress parameters of the FJ model. This was attributed to differences in root densities between the different soils and 15 treatments which are accounted for by the C model whereas the FJ model only considers normalized root densities. The impact of differences in root density on RWU could be accounted for directly by the physically-based RWU model but not by empirical models that use normalized root density functions.

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Section: Root Observationmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Root growth, water uptake, and sap flow of winter wheat in response to different soil water availability

Cai

Vanderborght

Langensiepen

et al. 2017

Preprint

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Calibrating the Spatiotemporal Root Density Distribution for Macroscopic Water Uptake Models Using Tikhonov Regularization

Yue

2018

Water Resources Research

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Macroscopic root water uptake models proportional to a root density distribution function (RDDF) are most commonly used to model water uptake by plants. As the water uptake is difficult and labor intensive to measure, these models are often calibrated by inverse modeling. Most previous inversion studies assume RDDF to be constant with depth and time or dependent on only depth for simplification. However, under field conditions, this function varies with type of soil and root growth and thus changes with both depth and time. This study proposes an inverse method to calibrate both spatially and temporally varying RDDF in unsaturated water flow modeling. To overcome the difficulty imposed by the ill‐posedness, the calibration is formulated as an optimization problem in the framework of the Tikhonov regularization theory, adding additional constraint to the objective function. Then the formulated nonlinear optimization problem is numerically solved with an efficient algorithm on the basis of the finite element method. The advantage of our method is that the inverse problem is translated into a Tikhonov regularization functional minimization problem and then solved based on the variational construction, which circumvents the computational complexity in calculating the sensitivity matrix involved in many derivative‐based parameter estimation approaches (e.g., Levenberg‐Marquardt optimization). Moreover, the proposed method features optimization of RDDF without any prior form, which is applicable to a more general root water uptake model. Numerical examples are performed to illustrate the applicability and effectiveness of the proposed method. Finally, discussions on the stability and extension of this method are presented.

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Modelling root water uptake under deficit irrigation and rewetting in Northwest China

et al. 2020

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The spatial and temporal distribution of root water uptake (RWU) under deficit irrigation are critical factors for crop growth. The SWAP (soil–water–atmosphere–plant) model was applied to analyze the pattern of RWU for winter wheat (Triticum aestivum L.) under three irrigation levels: no water deficit (100% evapotranspiration [ET]), moderate water deficit (80% ET) and severe water deficit (60% ET). The 2–yr experiments indicated that SWAP was highly accurate (mean relative error [MRE] <21.7%, root mean square error [RMSE] <0.07 cm3 cm−3) in simulating the soil water content (SWC). Root water uptake was significantly (P < 0.01) different in the 0‐ to 60‐cm soil layer. The 0‐ to 60‐cm soil layer was the main source of RWU, and the average value accounted for 89.4% of the total root zone. Water stress had the greatest adverse effect on heading to grain filling, reducing RWU by 0.0026 cm3 cm−3 d−1. The critical SWC was 67.9% of the field capacity, when the RWU dropped to 95% of the control treatment. After rewetting, compensation and hysteresis effects on RWU were observed. The ranking of RWU recovery ability after rewetting was: emergence to jointing > jointing to heading > grain filling to maturity > heading to grain filling. Recovery time of RWU was 2 to 11 d and gradually increased with growth stage. The simplified RWU model established using path analysis and regression performed well (R2 = 0.836; P < 0.01) for RWU. This provided a more convenient way to accurately estimate RWU with fewer variables.

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Parameterization of Root Water Uptake Models Considering Dynamic Root Distributions and Water Uptake Compensation

Cited by 78 publications

References 137 publications

Root growth, water uptake, and sap flow of winter wheat in response to different soil water availability

Root growth, water uptake, and sap flow of winter wheat in response to different soil water availability

Calibrating the Spatiotemporal Root Density Distribution for Macroscopic Water Uptake Models Using Tikhonov Regularization

Modelling root water uptake under deficit irrigation and rewetting in Northwest China

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