scholarly journals A Maximum Entropy Production Evaporation – Transpiration Product for Australia

2019 ◽  
Author(s):  
Olanrewaju Abiodun ◽  
Okke Batelaan ◽  
Huade Guan ◽  
Jingfeng Wang
Keyword(s):  
Entropy Production ◽  
Maximum Entropy ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Production Model ◽  
Model Parameters ◽  
Drought Period ◽  
Maximum Entropy Production ◽  
Millennium Drought ◽  
Mean Square Errors

Abstract. The aim of this research is to develop evaporation and transpiration products for Australia based on the maximum entropy production model (MEP). We introduce a method into the MEP algorithm of estimating the required model parameters over the entire Australia through the use of pedotransfer function, soil properties and remotely sensed soil moisture data. Our algorithm calculates the evaporation and transpiration over Australia on daily timescales at the 5 km2 resolution for 2003–2013. The MEP evapotranspiration (ET) estimates are validated using observed ET data from 20 Eddy Covariance (EC) flux towers across 8 land cover types in Australia. We also compare the MEP ET at the EC flux towers with two other ET products over Australia; MOD16 and AWRA-L products. The MEP model outperforms the MOD16 and AWRA-L across the 20 EC flux sites, with average root mean square errors (RMSE), 8.21, 9.87 and 9.22 mm/8 days respectively. The average mean absolute error (MAE) for the MEP, MOD16 and AWRA-L are 6.21, 7.29 and 6.52 mm/8 days, the average correlations are 0.64, 0.57 and 0.61, respectively. The percentage Bias of the MEP ET was within 20 % of the observed ET at 12 of the 20 EC flux sites while the MOD16 and AWRA-L ET were within 20 % of the observed ET at 4 and 10 sites respectively. Our analysis shows that evaporation and transpiration contribute 38 % and 62 %, respectively, to the total ET across the study period which includes a significant part of the “millennium drought” period (2003–2009) in Australia. The data (Abiodun et al., 2019) is available at https://doi.org/10.25901/5ce795d313db8.

scholarly journals Application of the Maximum Entropy Production Model of Evapotranspiration over Partially Vegetated Water-Limited Land Surfaces

2018 ◽  
Vol 19 (6) ◽  
pp. 989-1005 ◽  
Author(s):  
Islem Hajji ◽  
Daniel F. Nadeau ◽  
Biljana Music ◽  
François Anctil ◽  
Jingfeng Wang
Keyword(s):  
Water Stress ◽  
Entropy Production ◽  
Maximum Entropy ◽  
Land Surface ◽  
Soil Evaporation ◽  
Stress Conditions ◽  
Production Model ◽  
Weighting Coefficient ◽  
Maximum Entropy Production ◽  
Land Surfaces

Abstract The maximum entropy production (MEP) model based on nonequilibrium thermodynamics and the theory of Bayesian probabilities was recently developed to model land surface fluxes, including soil evaporation and vegetation transpiration. This model requires few input data and ensures the closure of the surface energy balance. This study aims to test the capability of such a model to realistically simulate evapotranspiration (ET) over a wide range of climates and vegetation covers. A weighting coefficient is introduced to calculate total ET from soil evaporation and vegetation transpiration over partially vegetated land surfaces, resulting in the MEP-ET model. Using this coefficient, the model outputs are compared with in situ observations of ET at eight FLUXNET sites across the continental United States. Results confirm the close agreement between the MEP-ET predicted daily ET and the corresponding observations at sites characterized by moderately limited water availability. Poor ET results were obtained under high water stress conditions. A regulation parameter was therefore introduced in the MEP-ET model to properly take into account the effects of soil water stress on stomata, yielding the generalized MEP-ET model. This parameter considerably reduced model biases under water stress conditions for various heterogeneous land surface sites. The generalized MEP-ET model outperforms several popular ET models, including Penman–Monteith (PM), modified Priestley–Taylor–Jet Propulsion Laboratory (PT-JPL), and air-relative-humidity-based two-source model (ARTS) at all test sites.

scholarly journals ESD Reviews: Thermodynamic optimality in Earth sciences. The missing constraints in modeling Earth system dynamics?

2019 ◽  
Author(s):  
Martijn Westhoff ◽  
Axel Kleidon ◽  
Stan Schymanski ◽  
Benjamin Dewals ◽  
Femke Nijsse ◽  
...  
Keyword(s):  
Earth Sciences ◽  
Entropy Production ◽  
Maximum Entropy ◽  
Maximum Power ◽  
Model Parameters ◽  
Earth System ◽  
Maximum Entropy Production ◽  
Flux Gradient ◽  
Conversion Rates ◽  
Maximum Power Principle

Abstract. Thermodynamic optimality principles have been often used in Earth sciences to estimate model parameters or fluxes. Applications range from optimizing atmospheric meridional heat fluxes to sediment transport and from optimizing spatial flow patterns to dispersion coefficients for fresh and salt water mixing. However, it is not always clear what has to be optimized and how. In this paper we aimed to clarify terminology used in the literature and to infer how these principles have been used and when they give proper predictions of observed fluxes and states. We distinguish roughly four classes of applications: predictions using a flux-gradient feedback, predictions using a constant thermodynamic potential boundary conditions, predictions based on information theoretical approaches and comparative studies quantifying entropy production rates from observations at different sites. Here we mainly focus on the flux-gradient feedback, since it results in clear physical limits of energy conversion rates occurring in the Earth system and its subsystems. We show that within the flux-gradient feedback application, maximum entropy production is in many cases equivalent to maximum power and maximum energy dissipation. We advocate the maximum power principle above the more widely used maximum entropy production principle because entropy can be produced by all kinds of fluxes, but only optimized fluxes performing work coincided with observations. Furthermore, the maximum power principle links to the maximum amount of free energy that can be converted into another form of energy. This clearly separates the well defined physical conversion limit from the hypothesis that a system evolves to that limit of maximum power. Although attempts have been made to fundamentally explain why a system would evolve to such a maximum in power, there is still no consensus. Nevertheless, we think that when the maximum power approach is correctly and consistently used, the positive (or negative) results will speak for themselves. We end this review with some open research questions that may guide further research in this area.

Analysis of Water Vapor Fluxes Over a Seasonal Snowpack Using the Maximum Entropy Production Model

2021 ◽  
Vol 126 (1) ◽  
Author(s):  
Islem Hajji ◽  
Daniel F. Nadeau ◽  
Biljana Music ◽  
François Anctil ◽  
Jingfeng Wang
Keyword(s):  
Water Vapor ◽  
Entropy Production ◽  
Maximum Entropy ◽  
Production Model ◽  
Maximum Entropy Production

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

The mechanics of granitoid systems and maximum entropy production rates

2010 ◽  
Vol 368 (1910) ◽  
pp. 53-93 ◽  
Author(s):  
Bruce E. Hobbs ◽  
Alison Ord
Keyword(s):  
Entropy Production ◽  
Maximum Entropy ◽  
Melt Inclusions ◽  
Selection Process ◽  
Finite Thickness ◽  
Control Valve ◽  
Entropy Production Rate ◽  
Constitutive Behaviour ◽  
Maximum Entropy Production ◽  
Physical Height

A model for the formation of granitoid systems is developed involving melt production spatially below a rising isotherm that defines melt initiation. Production of the melt volumes necessary to form granitoid complexes within 10 4 –10 7 years demands control of the isotherm velocity by melt advection. This velocity is one control on the melt flux generated spatially just above the melt isotherm, which is the control valve for the behaviour of the complete granitoid system. Melt transport occurs in conduits initiated as sheets or tubes comprising melt inclusions arising from Gurson–Tvergaard constitutive behaviour. Such conduits appear as leucosomes parallel to lineations and foliations, and ductile and brittle dykes. The melt flux generated at the melt isotherm controls the position of the melt solidus isotherm and hence the physical height of the Transport/Emplacement Zone. A conduit width-selection process, driven by changes in melt viscosity and constitutive behaviour, operates within the Transport Zone to progressively increase the width of apertures upwards. Melt can also be driven horizontally by gradients in topography; these horizontal fluxes can be similar in magnitude to vertical fluxes. Fluxes induced by deformation can compete with both buoyancy and topographic-driven flow over all length scales and results locally in transient ‘ponds’ of melt. Pluton emplacement is controlled by the transition in constitutive behaviour of the melt/magma from elastic–viscous at high temperatures to elastic–plastic–viscous approaching the melt solidus enabling finite thickness plutons to develop. The system involves coupled feedback processes that grow at the expense of heat supplied to the system and compete with melt advection. The result is that limits are placed on the size and time scale of the system. Optimal characteristics of the system coincide with a state of maximum entropy production rate.

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