Understanding and Modeling Hydrological Cycles: From New Theories to New Models of Water and Energy Fluxes

Ocean Science and Engineering Presents Dr. Jingfeng Wang, GA Tech, School of Civil and Environmental Engineering 

Understanding and Modeling Hydrological Cycles -- From New Theories to New Models of Water and Energy Fluxes

Seeking more fundamental principles underlying the hydrological cycles than the conservation laws opens new opportunities of developing innovative hydrologic models. Based on the argument that thermodynamic equilibriums tend to be reached as quickly as possible, the hypothesis of maximum evapotranspiration (ET) was proposed and confirmed by field observations with testable predictions justifying the formulation of ET as a function of surface variables. Translation of this new physical theory into predictive power of water and energy fluxes is made possible by the application of the theory of maximum entropy production (MEP) as a physical principle and a statistical inference algorithm. The formulation of the MEP model of ET and heat fluxes builds on the Bayesian probability theory (as an inference algorithm to translate physical constraints into prediction of physical variables), information theory and boundary-layer turbulence theory. The MEP model has several unique advantages including simultaneous solution of all heat fluxes, closure of surface energy balance at all space and time scales, independent of temperature and moisture gradients, wind speed and surface roughness lengths, covering the entire range of soil moisture, and reduced sensitivities to those of model inputs and parameters without using empirical tuning parameters. The MEP model has been used as a new remote sensing algorithm of surface water-energy fluxes and an innovative physical parameterization of surface hydrology in coupled land-atmosphere-ocean models.

Webinar Link
United States: +1 (646) 749-3122
Access Code: 811-813-909

Event Details


  • Friday, February 14, 2020
    3:00 pm - Saturday, February 15, 2020
    3:59 pm
Location: Ford Environmental, Science & Technology (ES&T) Building, Rm. L1255, 3pm
Fee(s): Free

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  • Jingfeng Wang

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Emanuele Di Lorenzo