Effective SVAT-model parameters through inverse stochastic modelling and second-order first moment propagation

Kunstmann, Harald

doi:10.1016/j.jhydrol.2007.09.032

Cited by 6 publications

(4 citation statements)

References 29 publications

(37 reference statements)

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“…They act as boundary condition for the WRF model and are updated every 6 h. Spatially, the datasets are interpolated (coarser datasets) or regridded and averaged (finer datasets) to the model grid at 7 km resolution. While the averaging implies a loss of information for LAI, it preserves a proper grid-scale representation of the variables affected by VF and ALB (Kunstmann 2008).…”

Section: Generation and Implementation Of The Dynamical Datasetsmentioning

confidence: 99%

Feedback of observed interannual vegetation change: a regional climate model analysis for the West African monsoon

et al. 2016

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over higher (lower) VF is 12 % higher than by chance. We attribute this behaviour to horizontal circulations driven by differential heating. Over more vegetated regions, the divergence of moist air together with lower sensible heat fluxes hinders the initiation of deep convection during the day. During the night, mature convective systems cause an increase in the number of rainy hours over these regions. We identify this feedback in both water-and energy-limited regions of West Africa. The inclusion of observed dynamical surface information improved the spatial distribution of modelled rainfall in the Sahel with respect to observations, illustrating the potential of satellite data as a boundary constraint for atmospheric models.

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Section: Generation and Implementation Of The Dynamical Datasetsmentioning

confidence: 99%

Feedback of observed interannual vegetation change: a regional climate model analysis for the West African monsoon

et al. 2016

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“…[8] and [9] instead of using Eq. [2], [3], and [5] (Kunstmann, 2008; Wang et al, 2009). The direct relationship of soil variables to soil moisture was previously shown by Lee et al (2014) and became the basis of this inversion.…”

Section: Methodsmentioning

confidence: 99%

“…An alternative is an inverse method (Li et al, 2012;Mohanty, 2013;Zhou, 2011). Kunstmann (2008) and Intsiful and Kunstmann (2008) previously applied a stochastic p. 2 of 9 inverse method to the estimation of several SVAT input parameters. Gutmann and Small (2007) also found that an inverse modeling is more accurate than soil texture-based PTFs (Santanello et al, 2007).…”

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confidence: 99%

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Lee

2014

Vadose Zone Journal

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In highly stratified soils as on the Tibetan Plateau, uncertainty associated with a vertical profile of soil and hydraulic properties largely restricts the performance of Soil Vegetation Atmosphere Transfer (SVAT) model. In lieu of commonly used pedotransfer functions (PTFs) or artificial neural networks (ANNs), soil hydraulic properties in this study were inverted from an Ensemble Kalman filter (EnKF) analysis of Synthetic Aperture Radar (SAR) surface soil moisture. The calibrated SVAT scheme using inverted soil hydraulic variables C1 and θgeq was better matched with in situ field measurements than the uncalibrated SVAT scheme using soil maps–based PTFs on a local point scale. It was shown that the inverse calibration of two soil hydraulic variables solved the forecast bias (underestimation) in surface soil moisture due to the assumption of vertical homogeneity and the site‐specificity of empirical PTFs. Additionally, at a SAR spatial scale, the calibrated SVAT scheme appropriately captured a high vertical gradient between surface and subsurface soil moisture, while the uncalibrated SVAT scheme could not. This suggests that it is possible to infer the SVAT soil hydraulic variables that are the main error source in SVAT scheme from the SAR soil moisture data assimilation analysis.

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“…Использование усредненных значений метеорологических величин в уравнениях переноса, безусловно, оправдано применительно к прогностическим моделям мезо-и макроуровня, но на микроуровне такое упрощение не всегда является корректным. В связи с этим предлагается учитывать турбулентные свойства атмосферы, приводящие к возникновению хаотических пульсаций воздуха, и использовать для этого стохастические методы [17][18][19][20][21][22][23]…”

Section: Introductionunclassified

Модель Адвективных Изменений Влажности Воздуха Со Стохастическими Параметрами

Zadorozhnii¹,

Семенов²,

Ulshin³

et al. 2019

sait

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В работе приведено решение лежащего в основе модели переноса влаги дифференциального уравнения, коэффициентами которого являются случайные процессы. Распределение проекции мгновенного вектора скорости аппроксимировано законом Веге – Изинга, параметры которого идентифицированы на основе бионической модели адаптивного поискового поведения. Предложены явные формулы для математического ожидания и второй моментной функции решения соответствующего уравнения переноса влаги. Определена оценка степени влияния случайных факторов на систему, в случае замены случайного коэффициента уравнения его математическим ожиданием. Приведен пример, в случае гауссова распределения горизонтальной компоненты скорости ветра, позволяющий определить математическое ожидание и вторую моментную функцию в рамках модельных представлений.

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Effective SVAT-model parameters through inverse stochastic modelling and second-order first moment propagation

Cited by 6 publications

References 29 publications

Feedback of observed interannual vegetation change: a regional climate model analysis for the West African monsoon

Feedback of observed interannual vegetation change: a regional climate model analysis for the West African monsoon

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Модель Адвективных Изменений Влажности Воздуха Со Стохастическими Параметрами

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