Testing and Development of Evapotranspiration Models for India | A Simplified Approach for Estimation of Evapotranspiration

M. Abraham (u. a.)

ISBN 10: 6202015438 / ISBN 13: 9786202015431

Published by LAP LAMBERT Academic Publishing, 2017

About this Item

Testing and Development of Evapotranspiration Models for India | A Simplified Approach for Estimation of Evapotranspiration | M. Abraham (u. a.) | Taschenbuch | 148 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202015431 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 109668513

Bibliographic Details

• Title: Testing and Development of Evapotranspiration Models for India | A Simplified Approach for Estimation of Evapotranspiration
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 6202015438
• ISBN 13: 9786202015431
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 238 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The book contains comparative evaluation of existing evapotranspiration (ET) models and development of site specific evapotranspiration models for various agro-climatic regions of India. Evapotranspiration was estimated using existing models for twelve stations falling under different agro-climatic regions. Sensitivity analysis was carried out to identify more sensitive parameters to understand the relative importance of variables on ET and the impact of estimation errors due to individual variables. Uncertainty analysis was carried out for finding the input variable uncertainty to judge the goodness of fit. Modifications to existing ET equations were made based on regression analysis and genetic algorithm. New regression equations were developed with stepwise regression and using the most significant variables identified by Principal Component Analysis (PCA). Simplified models were developed for ET estimation by Artificial Neural Network (ANN) with all the climatic variables and with the variables identified by PCA. The regression equation using PCA variable was identified as the best method to estimate of ET.

About the Author

Dr. M. Abraham is Scientist-E in Centre for Remote Sensing and Geoinformatics, Sathyabama University, Chennai, Dr. S. Mohan is professor in Department of Civil Engineering, Indian Institute of Technology, Madras, and Dr. K. S. Rawat is Scientist-E in Centre for Remote Sensing and Geoinformatics, Sathyabama University, Chennai.