Evapotranspiration Remote Sensing and Modeling Part 1 pdf

EVAPOTRANSPIRATION – REMOTE SENSING AND MODELING Edited by Ayse Irmak Evapotranspiration – Remote Sensing and Modeling Edited by Ayse Irmak Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Dragana Manestar Technical Editor Teodora Smiljanic Cover Designer InTech Design Team Image Copyright Selyutina Olga, 2011. Used under license from Shutterstock.com First published December, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Evapotranspiration – Remote Sensing and Modeling, Edited by Ayse Irmak p. cm. ISBN 978-953-307-808-3 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface IX Chapter 1 Assessment of Evapotranspiration in North Fluminense Region, Brazil, Using Modis Products and Sebal Algorithm 1 José Carlos Mendonça, Elias Fernandes de Sousa, Romísio Geraldo Bouhid André, Bernardo Barbosa da Silva and Nelson de Jesus Ferreira Chapter 2 Evapotranspiration Estimation Based on the Complementary Relationships 19 Virginia Venturini, Carlos Krepper and Leticia Rodriguez Chapter 3 Evapotranspiration Estimation Using Soil Water Balance, Weather and Crop Data 41 Ketema Tilahun Zeleke and Leonard John Wade Chapter 4 Hargreaves and Other Reduced-Set Methods for Calculating Evapotranspiration 59 Shakib Shahidian, Ricardo Serralheiro, João Serrano, José Teixeira, Naim Haie and Francisco Santos Chapter 5 Fuzzy-Probabilistic Calculations of Evapotranspiration 81 Boris Faybishenko Chapter 6 Using Soil Moisture Data to Estimate Evapotranspiration and Development of a Physically Based Root Water Uptake Model 97 Nirjhar Shah, Mark Ross and Ken Trout Chapter 7 Impact of Irrigation on Hydrologic Change in Highly Cultivated Basin 125 Tadanobu Nakayama VI Contents Chapter 8 Estimation of Evapotranspiration Using Soil Water Balance Modelling 147 Zoubeida Kebaili Bargaoui Chapter 9 Evapotranspiration of Grasslands and Pastures in North-Eastern Part of Poland 179 Daniel Szejba Chapter 10 The Role of Evapotranspiration in the Framework of Water Resource Management and Planning Under Shortage Conditions 197 Giuseppe Mendicino and Alfonso Senatore Chapter 11 Guidelines for Remote Sensing of Evapotranspiration 227 Christiaan van der Tol and Gabriel Norberto Parodi Chapter 12 Estimation of the Annual and Interannual Variation of Potential Evapotranspiration 251 Georgeta Bandoc Chapter 13 Evapotranspiration of Partially Vegetated Surfaces 273 L.O. Lagos, G. Merino, D. Martin, S. Verma and A. Suyker Chapter 14 Evapotranspiration – A Driving Force in Landscape Sustainability 305 Martina Eiseltová, Jan Pokorný, Petra Hesslerová and Wilhelm Ripl Chapter 15 Critical Review of Methods for the Estimation of Actual Evapotranspiration in Hydrological Models 329 Nebo Jovanovic and Sumaya Israel Chapter 16 Development of Hybrid Method for the Modeling of Evaporation and Evapotranspiration 351 Sungwon Kim Chapter 17 Modelling Evapotranspiration and the Surface Energy Budget in Alpine Catchments 377 Giacomo Bertoldi, Riccardo Rigon and Ulrike Tappeiner Chapter 18 Stomatal Conductance Modeling to Estimate the Evapotranspiration of Natural and Agricultural Ecosystems 403 Giacomo Gerosa, Simone Mereu, Angelo Finco and Riccardo Marzuoli Chapter 19 A Distributed Benchmarking Framework for Actual ET Models 421 Yann Chemin Contents VII Chapter 20 Possibilities of Deriving Crop Evapotranspiration from Satellite Data with the Integration with Other Sources of Information 437 Gheorghe Stancalie and Argentina Nertan Chapter 21 Operational Remote Sensing of ET and Challenges 467 Ayse Irmak, Richard G. Allen, Jeppe Kjaersgaard, Justin Huntington, Baburao Kamble, Ricardo Trezza and Ian Ratcliffe Chapter 22 Adaptability of Woody Plants in Aridic Conditions 493 Viera Paganová and Zuzana Jureková Preface The transfer of liquid water from soil to vapor in the atmosphere (Evapotranspiration) is one of the most profound and consequential processes on Earth. Evapotranspiration (ET), along with evaporation from open water, supplies vapor to the atmosphere to replace that condensed as precipitation. The flux of water through plants via transpiration transports minerals and nutrients required for plant growth and creates a beneficial cooling process to plant canopies in many climates. At the global scale, ET measures nearly one hundred trillion cubic meters per year and is the largest component of the hydrologic cycle, following precipitation. The large spatial variability in water consumption from land surfaces is strongly related to vegetation type, vegetation amount, soil water holding characteristics, and of course, precipitation or irrigation amount. There are very strong feedbacks from all of these factors and consequent ET rates. In this book, Evapotranspiration is defined as the aggregate sum of evaporation (E) direct from the soil surface and the surfaces of plant canopies and transpiration (T), where T is the evaporation of water from the plant system via the plant leaf, stem and root-soil system. In addition to consuming enormous amounts of water, ET substantially modifies the Earth’s energy balance through its consumption of enormous amounts of energy during conversion of liquid water to vapor. Each cubic meter of water evaporated requires 2.45 billion Joules of energy. That consumption of energy cools the evaporating surface and reduces the heating of air by the surface. On a global basis, the cooling effect to the land surface is measured in trillions of GigaJoules per day. Much of that ‘latent’ energy absorbed by ET later reenters the surface energy balance when the vapor recondenses as precipitation. Even though the magnitude of ET is enormous over the Earth’s surface, and even though ET has such high bearing on vegetation growth and health, its spatial distribution and magnitudes are poorly understood and poorly quantified. Although man has been able to estimate general magnitudes of ET via its strong correlation with precipitation for centuries, it has only been during the past thirty years, with the advent of satellites and remote sensing technologies, along with sophisticated modeling approaches, that we have been able to view and quantify the complex and variable geospatial structure of ET. The combination of thermally-equipped satellites, such as Landsat, AVHRR, MODIS and ASTER, and the improved ability to simulate X Preface the energy balance at the Earth’s surface has enabled a substantial revolution in ‘mapping’ of ET over large, variable landscapes. This edition of Evapotranspiration contains 23 chapters, covering a broad range of topics related to the modeling and simulation of ET, as well as to the remote sensing of ET. Both of these areas are at the forefront of technologies required to quantify the highly spatial ET from the Earth’s surface. The chapters cover mechanics of ET simulation, including ET from partially vegetated surfaces and the modeling of stomatal conductance for natural and agricultural ecosystems, ET estimation using soil water balance, weather data and vegetation cover, ET estimation based on the Complementary Relationship, and adaptability of woody plants in conditions of soil aridity. Modeling descriptions include chapters focusing on distributed benchmarking frameworks for ET models, Hargreaves and other temperature-radiation based methods, Fuzzy-Probabilistic calculations, a hybrid-method for modeling evaporation and ET, and estimation of ET using water balance modeling. One chapter provides a critical review of methods for estimation of actual ET in hydrological models. In addition to that, six chapters describe modeling applications for determining ET patterns in alpine catchments, ET assessment and water resource management planning under shortage conditions, estimation of the annual and interannual variation of potential ET, impacts of irrigation on hydrologic change in a highly cultivated basin, ET of grasslands and pastures in north-eastern part of Poland, and climatological aspects of water balance components for Croatia. Remote sensing based approaches are described in five chapters that include deriving crop ET from satellite data, integration with other information sources and an assessment of ET using MODIS products with energy balance algorithms. Importantly, the book includes two chapters describing an overview of recommended guidelines for operational remote sensing of ET, and a review of operational remote sensing- based energy balance models including SEBAL and METRIC, and specific challenges and insights for their application. These 23 chapters represent the current state of the art in ET modeling and remote sensing applications, and provide valuable insights and experiences of developers and appliers of the technologies that have been gained over decades of development work, experimentation and modeling. This text provides valuable background information and theory for university students and courses on ET, as well as guidance and ideas for those that apply these modern methods. I wish to express my thanks to the authors of all chapters for making these timely and very useful contributions available, and to all anonymous reviewers of chapters. I also wish to thank Mr Baburao Kamble, University of Nebraska, for assistance in the handling of chapter manuscripts during reviews and for providing technical assistance. Dr. Ayse Irmak School of Natural Resources and Civil Engineering, Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln, USA [...]... MOD09 and MYD09 data (Surface Reflectance – GHK / 500 m and GQK / 250 m) and MOD11A1 and MYD11A1 data (Surface Temperature - LST) were used in this research, totalizing 24 scenes over the ‘tile’ h14/v 11 corresponding to Julian Day 218 th, 227th, 230th, 241st, 255th, 285th, 320th and 339th in 2005 and 15 th, 36th, 63rd , 10 2nd, 11 6th, 13 9th, 16 6th, 18 6th, 18 9th, 19 0th, 19 1st, 200th, 201st, 205th, 208th and. .. ‘Dr Leonel Miranda’ – UFRRJ, (geographical coordinates: 21 17 ’ 36” S and 41 48’ 09” W) contains 1 Anemometer, 1 Barometer, 1 Termohygrometer, 1 Piranometer and 1 Pluviometer and recording values every minute and stored an average every 10 minutes All geographical coordinates are related to Datum WGS 84 – zone 24, with average altitude of 11 m The localization of the surface stations, where meteorological... coordinates: 21 24’ 48” S and 41 44’ 48” W) is an automatic station Is equipped with 1 Anemometer, 1 Barometer, 1 Termohygrometer, 1 Piranometer and 1 Pluviometer All sensor are connected to a datalogger model DL 12 – V 2.00 – Thies Clima, recording values every minute and stored an average every 10 minutes The Agrosystem model install at the Meteorological Station of the Experimental Campus ‘Dr Leonel Miranda’... evapotranspiration for the dry period in the Fluminense North Region, Rio de Janeiro State DJ 2005 218 Fig 10 Images of the daily evapotranspiration for the humid period in the Fluminense North Region, Rio de Janeiro State DJ 2006 015 15 16 Evapotranspiration – Remote Sensing and Modeling Allen et al (20 01) , using images of LANDSAT in the basin of river Bear, North-East region of the U.S.A., observed that SEBAL... Wageningen, The Netherlands 273p Bastiaanssen, W.G.M.; Pelgrum, H.; Wang, J.; Ma, Y.; Moreno, J.; Roerink, G J.; van der Val, T., 19 98 A remote sensing surface energy balance algorithm for land (SEBAL) :Part 2 validation, Journal of Hidrology, v, 212 - 213 : 213 -229 Assessment of Evapotranspiration in North Fluminense Region, Brazil, Using Modis Products and Sebal Algorithm 17 Frota, P.C.E., 19 78 Estudo do calor... values of BOC and Rn24h tent to be in agreement with the values mentioned by Alados et al (2003) Thus, the radiation balance for the daily period (Rn24h) was ultimately determined for each pixel of the study scene by the equation: Rn24h = 0, 911 1* (1 – chart of albedo) * Rs↓24h -23, 918 (11 ) 3 .1. 2 Determination of the ET24h values Based on charts of Rn, G, H, LE, Ts and α and values of ETo24h and EToinst,... mean, maximum and minimum values obtained in charts of daily evapotranspiration (ET24h) estimated with the “H_Pesagro’ proposition, expressed in mm day -1, are showed in Table 2 10 Evapotranspiration – Remote Sensing and Modeling Table 2 Statistical data of daily evapotranspiration charts (ET 24h) of the study area using the ‘H_Pesagro’ proposition w/ Rn 24hs and w/ ETr_F, in mm day -1 Average mean... coordinates: 21 48’ 31, 2” S and 41 10 ’ 46,2” W) The micrometeorological stations installed in both areas (sugar cane and coconut) were equipped with the following sensor: 1 Net radiometer NR Lite (Kipp and Zonen), 2 Piranometer LI 200 (Li-Cor), 2 Probe HMP45C-L (Vaissala), 2 Met One Anemometer (RN Yong) and 3 HFP01SC_L Soil Healt Flux Plat (Hukseflux) All data from were collected every minute and average... Products and Sebal Algorithm 11 Fig 5 Correlation between values of ET24h estimated with the method FAO (PM_FAO56) with data collected at PESAGRO station and values of ET24h estimated by SEBAL with propositions “H_Classic” w/Rn24h (A), “H_Classic” w/ETr_F (B), “H_Pesagro” w/Rn24h (C) and “H_Pesagro” w/ETr_F (D) observed in pixel from Pesagro, expressed in mm day -1 12 Evapotranspiration – Remote Sensing and. .. equation 09 it is possible to obtain the ET24h expressed in mm day -1 from the equation: ET24 h  ETrF * ETo24 (10 ) In the present work, four values of ET24hSEBAL were estimated for the same day, applying equations 5 and 10 to the ‘H_Classic’ and H_Pesagro’ propositions 3 Results and discusion 3 .1 Daily evapotranspiration (ET24h) 3 .1. 1 Determination of Rn24h values To determine Rn24h charts, an adaptation . ‘tile’ h14/v 11 corresponding to Julian Day 218 th, 227th, 230th, 241st, 255th, 285th, 320th and 339th in 2005 and 15 th, 36th, 63rd , 10 2nd, 11 6th, 13 9th, 16 6th, 18 6th, 18 9th, 19 0th, 19 1st, 200th,. ‘Dr. Leonel Miranda’ – UFRRJ, (geographical coordinates: 21 17 ’ 36” S and 41 48’ 09” W) contains 1 Anemometer, 1 Barometer, 1 Termohygrometer, 1 Piranometer and 1 Pluviometer and recording. cane (geographical coordinates: 21 43’ 21, 8” S and 41 24’ 26 ,1 W), and ‘dwarf green’ coconut irrigated (geographical coordinates: 21 48’ 31, 2” S and 41 10 ’ 46,2” W). The micrometeorological