Comparing the Performance of the Maximum Entropy Production Model With a Land Surface Scheme in Simulating Surface Energy Fluxes

2019 ◽  
Vol 124 (6) ◽  
pp. 3279-3300 ◽  
Author(s):  
M. Alves ◽  
B. Music ◽  
D. F. Nadeau ◽  
F. Anctil
Keyword(s):  
Surface Energy ◽  
Entropy Production ◽  
Maximum Entropy ◽  
Land Surface ◽  
Production Model ◽  
Energy Fluxes ◽  
Land Surface Scheme ◽  
Maximum Entropy Production ◽  
Surface Energy Fluxes ◽  
Surface Scheme

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 Remotely sensed land-surface energy fluxes at sub-field scale in heterogeneous agricultural landscape and coniferous plantation

2014 ◽  
Vol 11 (18) ◽  
pp. 5021-5046 ◽  
Author(s):  
R. Guzinski ◽  
H. Nieto ◽  
R. Jensen ◽  
G. Mendiguren
Keyword(s):  
Surface Energy ◽  
Spatial Resolution ◽  
Land Surface ◽  
Agricultural Landscape ◽  
Remotely Sensed ◽  
Energy Fluxes ◽  
Surface Energy Fluxes ◽  
Coniferous Plantation ◽  
Polar Orbiting Satellite ◽  
Agricultural Site

Abstract. In this study we evaluate a methodology for disaggregating land surface energy fluxes estimated with the Two-Source Energy Balance (TSEB)-based Dual-Temperature Difference (DTD) model which uses day and night polar orbiting satellite observations of land surface temperature (LST) as a remotely sensed input. The DTD model is run with MODIS input data at a spatial resolution of around 1 km while the disaggregation uses Landsat observations to produce fluxes at a nominal spatial resolution of 30 m. The higher-resolution modelled fluxes can be directly compared against eddy covariance (EC)-based flux tower measurements to ensure more accurate model validation and also provide a better visualization of the fluxes' spatial patterns in heterogeneous areas allowing for development of, for example, more efficient irrigation practices. The disaggregation technique is evaluated in an area covered by the Danish Hydrological Observatory (HOBE), in the west of the Jutland peninsula, and the modelled fluxes are compared against measurements from two flux towers: the first one in a heterogeneous agricultural landscape and the second one in a homogeneous conifer plantation. The results indicate that the coarse-resolution DTD fluxes disaggregated at Landsat scale have greatly improved accuracy as compared to high-resolution fluxes derived directly with Landsat data without the disaggregation. At the agricultural site the disaggregated fluxes display small bias and very high correlation (r ≈ 0.95) with EC-based measurements, while at the plantation site the results are encouraging but still with significant errors. In addition, we introduce a~modification to the DTD model by replacing the "parallel" configuration of the resistances to sensible heat exchange by the "series" configuration. The latter takes into account the in-canopy air temperature and substantially improves the accuracy of the DTD model.

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 A Maximum Entropy Production Hypothesis for Time Varying Climate Problems: Illustration on a Conceptual Model for the Seasonal Cycle

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 966
Author(s):  
Vincent Labarre ◽  
Didier Paillard ◽  
Bérengère Dubrulle
Keyword(s):  
Energy Balance ◽  
Entropy Production ◽  
Conceptual Model ◽  
Maximum Entropy ◽  
Seasonal Cycle ◽  
Ground Temperature ◽  
Time Varying ◽  
Energy Fluxes ◽  
Maximum Entropy Production ◽  
Energy Balance Models

We investigated the applicability of the maximum entropy production hypothesis to time-varying problems, in particular, the seasonal cycle using a conceptual model. Contrarily to existing models, only the advective part of the energy fluxes is optimized, while conductive energy fluxes that store energy in the ground are represented by a diffusive law. We observed that this distinction between energy fluxes allows for a more realistic response of the system. In particular, a lag is naturally observed for the ground temperature. This study therefore shows that not all energy fluxes should be optimized in energy balance models using the maximum entropy production hypothesis, but only the fast convective (turbulent) part.

Using Spatial Eddy Covariance to Investigate Energy Balance Closure over a Heterogeneous Ecosystem

2020 ◽  
Author(s):  
Brian Butterworth ◽  
Ankur Desai ◽  
Sreenath Paleri ◽  
Stefan Metzger ◽  
David Durden ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Energy Balance ◽  
Surface Energy ◽  
Atmospheric Boundary Layer ◽  
Eddy Covariance ◽  
Land Surface ◽  
Surface Heterogeneity ◽  
Energy Fluxes ◽  
Surface Energy Fluxes ◽  
Land Surface Heterogeneity

<p>Land surface heterogeneity influences patterns of sensible and latent heat flux, which in turn affect processes in the atmospheric boundary layer. However, gridded atmospheric models often fail to incorporate the influence of land surface heterogeneity due to differences between the temporal and spatial scales of models compared to the local, sub-grid processes. Improving models requires the scaling of surface flux measurements; a process made difficult by the fact that surface measurements usually find an imbalance in the energy budget.</p><p>The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) was an observational experiment designed to investigate how the atmospheric boundary layer responds to scales of spatial heterogeneity in surface-atmosphere heat and water exchanges. The campaign was conducted from June – October 2019, measuring surface energy fluxes over a heterogeneous forest ecosystem as fluxes transitioned from latent heat-dominated summer through sensible heat-dominated fall. Observations were made by ground, airborne, and satellite platforms within the 10 x 10 km study region, which was chosen to match the scale of a typical model grid cell. The spatial distribution of energy fluxes was observed by an array of 20 eddy covariance towers and a low-flying aircraft. Mesoscale atmospheric properties were measured by a suite of LiDAR and sounding instruments, measuring winds, water vapor, temperature, and boundary layer development. Plant phenology was measured in-situ and mapped remotely using hyperspectral imaging.</p><p>The dense set of multi-scale observations of land-atmosphere exchange collected during the CHEESEHEAD field campaign permits combining the spatial and temporal distribution of energy fluxes with mesoscale surface and atmospheric properties. This provides an unprecedented data foundation to evaluate theoretical explanations of energy balance non-closure, as well as to evaluate methods for scaling surface energy fluxes for improved model-data comparison. Here we show how fluxes calculated using a spatial eddy covariance technique across the 20-tower network compare to those of standard temporal eddy covariance fluxes in order to characterize of the spatial representativeness of single tower eddy covariance measurements. Additionally, we show how spatial EC fluxes can be used to better understand the energy balance over heterogeneous ecosystems.</p>

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