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Water Requirements for Horticultural Crops under Micro Irrigation
Published in Megh R. Goyal, Basamma K. Aladakatti, Pradeep Kumar, Engineering Interventions in Sustainable Trickle Irrigation, 2018
Jayakumar Manickam, Surendran Udayar Pillai, Rajavel Manickam
Bowen ratio is the ratio of temperature to vapor pressure gradients. The AtiAe”“-and energy balance concepts were not incorporated at an early date into methods for estimating ET as they were for estimating evaporation from water surfaces. In contrast to the development of largely empirical methods in the United States, Penman34 in the United Kingdom took a basic approach and related ET to energy balance and rates of sensible heat and water vapor transfer. Penman equation was based on the physics of the processes, and it laid the foundation for current ET-estimating methodology using standard weather measurements of solar radiation, air temperature, humidity, and wind speed. The Penman equation35–37 stands out as the most commonly applied physics-based equation. Later, a surface resistance term was added.30, 38 The modern combination equation applied to standardized surfaces is currently referred to as the Penman-Monteith equation (PM). It represents the state of the art in estimating hourly and daily ET.
Evapotranspiration
Published in Stephen A. Thompson, Hydrology for Water Management, 2017
The Penman equation estimates reference crop evapotranspiration for periods of one day to one month. When calibrated for local conditions this is considered one of the most accurate methods. As with the Jensen-Haise method it gives estimates of Etr for use with alfalfa-based crop coefficients. The Penman equation is again: () Etr=ΔΔ+γEn+ΔΔ+γEa
Moisture modeling and durability assessment of building envelopes
Published in Jan L.M. Hensen, Roberto Lamberts, Building Performance Simulation for Design and Operation, 2019
Aytaç Kubilay, Xiaohai Zhou, Dominique Derome, Jan Carmeliet
The Penman equation (Penman 1948) is a common method used in the field of agricultural and environmental physics for predicting potential evaporation from a surface. It considers both energy balance and convective vapor transport. The Penman equation for calculating the potential evaporation from a vertical surface is as follows (Zhou et al. 2016):
Projected changes of regional lake hydrologic characteristics in response to 21st century climate change
Published in Inland Waters, 2021
Zachary J. Hanson, Jacob A. Zwart, Stuart E. Jones, Alan F. Hamlet, Diogo Bolster
VIC uses the well-known Penman equation and Penman-Monteith equation to simulate lake (open water) evaporation and evapotranspiration over land, respectively. Evaporation in the simulations is directly affected by warming (via increases in the saturated vapor pressure), decreasing separation between daily minimum air temperature (Tmin) and daily maximum air temperature (Tmax) used in the VIC model to estimate attenuation of solar radiation by clouds (using the MTCLIM model incorporated in VIC; Thornton and Running 1999), and increasing Tmin, which is used to estimate the dewpoint temperature in VIC (also using MTCLIM). Ice cover is also a significant driver of lake evaporation, and changes in this variable are explicitly simulated by the LWB model (discussed earlier). Changes in vegetation and wind speed also potentially affect evapotranspiration, but changes in these variables were not included in our future projections (climate and land cover uncertainties discussed later).
Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt
Published in Engineering Applications of Computational Fluid Mechanics, 2019
Sinan Q. Salih, Mohammed Falah Allawi, Ali A. Yousif, Asaad M. Armanuos, Mandeep Kaur Saggi, Mumtaz Ali, Shamsuddin Shahid, Nadhir Al-Ansari, Zaher Mundher Yaseen, Kwok-Wing Chau
Evaporation from the lake occurs as a result of the vapor pressure difference between the lake’s surface and the atmosphere. It is driven by the availability of energy required for the evaporation process (Burt, Mutziger, Allen, & Howell, 2005; Shirgure & Rajput, 2011). Therefore, a number of meteorological factors influences the evaporation process, including air temperature, water temperature, solar radiation, humidity and wind (Priestley & Taylor, 1972; Sartori, 2000). Numerous models have been implemented for the measurement of evaporative losses from water bodies, which can be categorized as experimental tests, physical methods and artificial intelligence (AI) models. Among the experimental methods, the pan evaporation is most widely used as it is reasonably simple and inexpensive (Koza, 1992). Long-term recordings can be obtained by installing an evaporation pan for a long period, which is considered to provide the most creditable data on evaporation losses (Kişi, 2006). A regression coefficient derived from pan evaporation data is used for measuring the evaporative losses from open water bodies (Cooley, 1983). However, pan evaporation is time consuming and subject to large uncertainties. Considering the limitations of in-situ measurement, many empirical models have been developed for the estimation of evaporation from meteorological variables, which are considered the most suitable methods for the measurement of evaporative loss in the absence of pan evaporation data (Allawi, Jaafar, Mohamad Hamzah, Ehteram, et al., 2018). For example, the Penman equation is especially used to estimate evaporation losses from open water bodies (Penman, 1948). These empirical methods are constructed considering the static initial conditions (Adamala, Raghuwanshi, Mishra, & Tiwari, 2014). Although several empirical models are available for the estimation of evaporation, their potential is not satisfactory owing to the nonlinear association of evaporation with meteorological variables, and non-stationarity and stochasticity in the evaporation process (Baydarolu & Koçak, 2014). Hence, it is difficult to derive reliable physical–empirical models to represent the physical mechanism of the evaporation process. As a result, new machine learning models to simulate the evaporation process are always being explored by climate and hydrology scientists.