Solution of Three‐Constraint Entropy‐Based Velocity Distribution

Barbé, Donald E.; Cruise, J. F.; Singh, Vijay P.

doi:10.1061/(asce)0733-9429(1991)117:10(1389)

Cited by 43 publications

(16 citation statements)

References 4 publications

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“…The maximum entropy formulation was developed by [40,41]. The principle of maximum entropy (POME) proposes that if inferences are to be made based on incomplete information, they should be drawn from the probability distribution that has maximum entropy permitted by the information that is already available [42]. Based on this concept, Al-Hamdan and Cruise [22] developed an entropy-based model for simulating soil moisture profiles corresponding to all phases of the physical process.…”

Section: Entropy Applied To Soil Moisture Statesmentioning

confidence: 99%

“…Relationships between the Lagrange multipliers and the constraints and boundary conditions have also been developed, as in the first case, with the resulting system of nonlinear equations solved for the multipliers by a technique given by Barbe et al [42]. Finally, Al-Hamdan and Cruise [22] demonstrated that the third case (dynamic) can be handled by a combination of the wet and dry profiles.…”

Section: Development Of Soil Moisture Profiles From Pomementioning

confidence: 99%

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Modeling Soil Moisture Profiles in Irrigated Fields by the Principle of Maximum Entropy

Mishra

Ellenburg

Al‐Hamdan

et al. 2015

Entropy

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Vertical soil moisture profiles based on the principle of maximum entropy (POME) were validated using field and model data and applied to guide an irrigation cycle over a maize field in north central Alabama (USA). The results demonstrate that a simple two-constraint entropy model under the assumption of a uniform initial soil moisture distribution can simulate most soil moisture profiles that occur in the particular soil and climate regime that prevails in the study area. The results of the irrigation simulation demonstrated that the POME model produced a very efficient irrigation strategy with minimal losses (about 1.9% of total applied water). However, the results for finely-textured (silty clay) soils were problematic in that some plant stress did develop due to insufficient applied water. Soil moisture states in these soils fell to around 31% of available moisture content, but only on the last day of the drying side of the irrigation cycle. Overall, the POME approach showed promise as a general strategy to guide irrigation in humid environments, such as the Southeastern United States.

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Section: Entropy Applied To Soil Moisture Statesmentioning

confidence: 99%

Section: Development Of Soil Moisture Profiles From Pomementioning

confidence: 99%

Modeling Soil Moisture Profiles in Irrigated Fields by the Principle of Maximum Entropy

Mishra

Ellenburg

Al‐Hamdan

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…However, estimation of two-dimensional velocity distribution is not always simple and may require as many as six parameters (Chiu & Chiou 1986). The probability density function of the velocity was then obtained by applying the maximum entropy principle (Barbé et al 1991;Chiu 1987Chiu ,1988Chiu ,1989. Using this probabilistic formulation, the mean velocity, u^, can be expressed as a linear function of the maximum velocity, u,"ax, through a dimensionless entropy parameter M (Chiu 1991).…”

Section: Introductionmentioning

confidence: 99%

Velocity profiles assessment in natural channels during high floods

2011

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The accuracy of three different approaches for velocity profiles assessment during high floods, when the velocity points sampling is carried out only in the upper portion of the flow area, has been investigated. The first two methods assume the classical logarithmic law with additional terms, to take account of the dip-phenomenon in the velocity profile. The third one is based on the entropy theory and uses the maximum flow velocity occurring in the flow area. A sample of velocity measurements carried out at Pontelagoscuro gauged section (Po River, Italy), has been considered for the analysis. Six flood events have been selected and the accuracy of the investigated methods has been evaluated in terms of mean error in estimating both the mean velocity along each sampled vertical and the mean flow velocity. For high floods, the logarithmic law and the entropie approach were found quite accurate; however, the ability of the latter in reproducing the velocity profiles only by sampling the maximum flow velocity has been shown.Therefore, a procedure for velocity measurements based on the entropie approach has been proposed. The procedure allows one to both to shorten remarkably the time of the velocity sampling and to quickly estimate the discharge.

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Section: Development Of Soil Moisture Profiles From Alexi Awmentioning

confidence: 99%

A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

et al. 2013

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Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. 3332The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.

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Solution of Three‐Constraint Entropy‐Based Velocity Distribution

Cited by 43 publications

References 4 publications

Modeling Soil Moisture Profiles in Irrigated Fields by the Principle of Maximum Entropy

Modeling Soil Moisture Profiles in Irrigated Fields by the Principle of Maximum Entropy

Velocity profiles assessment in natural channels during high floods

A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

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