Evapotranspiration predictions: a comparison among GLEAMS,Opus, PRZM-2, and RZWQM models in a humid and thermic climate |
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| Institution: | 1. USDA-ARS, Nematodes, Weeds, and Crops Research Unit, Tifton, GA 31793, USA;2. Department of Crop and Soil Sciences, University of Georgia, Tifton, GA 31793, USA;1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China;2. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, No. 219, Ningliu Road, Nanjing 210044, China;3. University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China;4. Jiangsu Center for Collaborative innovation of Geographical Information Resources Development and Application, Nanjing 210023, China;1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;2. College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;3. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;4. CGCEO/Geography, Michigan State University, East Lansing, MI 48823, USA;5. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA;6. Climate and Water Institute, Research Center of Natural Resources, National Institute of Agricultural Technology (CIRN-INTA), Hurlingham, Argentina;7. Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China;8. CNR-Institute of Mediterranean Forest and Agricultural Systems, Via Patacca, 85, 80040-Ercolano (Napoli), Italy;9. A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia;10. Wageningen Environmental Research, Wageningen University and Research, Wageningen, The Netherlands;11. Alfred Wegener Institute for Polar and Marine Research, Telegrafenberg A43, 14473 Potsdam, Germany;12. CSIRO Land and Water, Floreat W.A. 6014, Australia;13. Research Faculty of Agriculture, Hokkaido University, Sapporo, 060-8589, Japan;14. Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland;15. Department of Biological Systems Engineering and School of Natural Resources, University of Nebraska, Lincoln, Nebraska 68583, USA;p. School of Agriculture and Environment, The University of Western Australia, Crawley, WA 6020, Australia;q. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA;r. Institute of Ecology, University of Innsbruck, Innsbruck 6020, Austria;s. Faculty of Science and Technology, Free University of Bolzano, Piazza Università 5, Bolzano, Italy;t. Physical Geography and Ecosystem Science Lund University Sölvegatan 12, SE-223 62 Lund, Sweden;u. GFZ German Research Centre for Geosciences, Section Remote Sensing, 14473 Potsdam, Germany;v. European Commission, Joint Research Centre, Ispra, Italy;w. Institute of Biometeorology, National Research Council, Via Caproni 8, 50145 Firenze, Italy;x. Department of Ecology, Faculty of Sciences, University of Granada, Granada, 18071, Spain;y. Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), 82467 Garmisch-Partenkirchen, Germany;z. Centre of Excellence PLECO, Department of Biology, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk, Belgium;1. Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Lanzhou, 730000, China;2. School of Environmental Studies, China University of Geosciences at Wuhan, 430074, China;3. Beijing Institute of Applied Meteorology, Beijing, 100029, China;1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science & Technology, Nanjing 210044, China;4. School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA;1. School of Life Science, Nanjing University, Nanjing, Jiangsu, 210037, China;2. The Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA, 02543, USA;3. Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, 97331, USA;4. Department of Biology, Macalester College, Saint Paul, MN, 55105, USA;5. School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China |
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| Abstract: | Environmental fate models are increasingly used to evaluate potential impacts of agrochemicals on water quality to aid in decision making. However, errors in predicting processes like evapotranspiration (ET), which is rarely measured during model validation studies, can significantly affect predictions of chemical fate and transport. This study compared approaches and predictions for ET by GLEAMS, Opus, PRZM-2, and RZWQM and determined effects of the predicted ET on simulations of other hydrology components. The ET was investigated for 2 years of various fallow–corn growing seasons under sprinkler irrigation. The comparison included annual cumulative daily potential ET (ETp), actual ET, and partitioning of total ET between soil evaporation (Es) and crop transpiration (Et). When measured pan evaporation was used for calculating ETp (the pan evaporation method), Opus, PRZM-2, and RZWQM predicted 74, 65, and 59%, respectively, of the 10-year average ET reported for a nearby site. When the energy-balance equations were used for calculating ETp (the combination methods), GLEAMS, Opus, PRZM-2, and RZWQM predicted 84, 105, 60, and 72% of the reported ET, respectively. The pan evaporation method predicted a similar amount of ET to the combination methods for bare soil, but predicted less ET when both Es and Et occurred. RZWQM reasonably predicted partitioning of ET to Es, while GLEAMS and Opus over-predicted this partitioning. A close correlation between soil water storage in the root zone and ET suggests that accurate soil water content predictions were fundamental to ET predictions. © |
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