Service-oriented approach for modeling and estimating reference evapotranspiration

Software used for estimating reference evapotranspiration (ET"0) has been developing in various directions. The main goal of this paper is to present an approach based on Service-Oriented Architecture (SOA) paradigm for modeling and estimating ET"0. The FAO-56 Penman-Monteith (FAO-56 PM) and Hargreaves equation are used for estimating monthly ET"0. The weather data for this study were obtained from CIMIS for Davis weather station. The FAO-56 PM and Hargreaves ET"0 values estimated using ET Web service were compared to corresponding CIMIS PM ET"0 estimates. The proposed model based on Web services implemented to the FAO-56 PM and Hargreaves equations has good performances and can be used in estimating ET"0 and has ability to complete missing weather data.

[1]  Ozgur Kisi,et al.  Evapotranspiration modelling from climatic data using a neural computing technique , 2007 .

[2]  C. Willmott,et al.  An Empirical Method for the Spatial Interpolation of Monthly Precipitation within California , 1980 .

[3]  K. P. Sudheer,et al.  Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique , 2003 .

[4]  Ozgur Kisi,et al.  Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation , 2007 .

[5]  Dawei Han,et al.  Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques , 2009 .

[6]  Ali Rahimikhoob,et al.  Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran , 2010 .

[7]  Anthony J. Jakeman,et al.  Performance of conceptual rainfall‐runoff models in low‐yielding ephemeral catchments , 1997 .

[8]  Slavisa Trajkovic,et al.  Estimating Reference Evapotranspiration Using Limited Weather Data , 2009 .

[9]  George H. Hargreaves,et al.  Defining and Using Reference Evapotranspiration , 1994 .

[10]  Soroosh Sorooshian,et al.  Toward improved identifiability of hydrologic model parameters: The information content of experimental data , 2002 .

[11]  N. S. Raghuwanshi,et al.  Comparative study of conventional and artificial neural network-based ETo estimation models , 2008, Irrigation Science.

[12]  Todd V. Elliott,et al.  WISE: a web-linked and producer oriented program for irrigation scheduling , 2001 .

[13]  David M. Booth,et al.  Web Services Architecture , 2004 .

[14]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .

[15]  Cort J. Willmott,et al.  On the climatic optimization of the tilt and azimuth of flat-plate solar collectors , 1982 .

[16]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[17]  Ozgur Kisi,et al.  Evapotranspiration modelling using support vector machines / Modélisation de l'évapotranspiration à l'aide de ‘support vector machines’ , 2009 .

[18]  C. Willmott ON THE VALIDATION OF MODELS , 1981 .

[19]  Milan Gocic,et al.  Software for estimating reference evapotranspiration using limited weather data , 2010 .

[20]  A. R. Khoob,et al.  Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment , 2008, Irrigation science.

[21]  Branimir Todorovic,et al.  Forecasting of Reference Evapotranspiration by Artificial Neural Networks , 2003 .

[22]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[23]  Hossein Tabari,et al.  Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression , 2010, Irrigation Science.

[24]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[25]  William O. Pruitt,et al.  Adaptation of the Thornthwaite scheme for estimating daily reference evapotranspiration , 2004 .

[26]  George H. Hargreaves,et al.  Irrigation Water Requirements for Senegal River Basin , 1985 .

[27]  Hung Soo Kim,et al.  Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling , 2008 .

[28]  Roberto Chinnici,et al.  Web Services Description Language (WSDL) Version 2.0 Part 1: Core Language , 2007 .

[29]  Özgür Kişi,et al.  Evapotranspiration modeling using a wavelet regression model , 2010, Irrigation Science.

[30]  T. McMahon,et al.  Application of the daily rainfall-runoff model MODHYDROLOG to 28 Australian catchments , 1994 .

[31]  Thomas Erl,et al.  Service-Oriented Architecture: Concepts, Technology, and Design , 2005 .

[32]  M. Trosset,et al.  Bayesian recursive parameter estimation for hydrologic models , 2001 .

[33]  Ozgur Kisi,et al.  Fuzzy Genetic Approach for Modeling Reference Evapotranspiration , 2010 .

[34]  Soroosh Sorooshian,et al.  Constraining Land Surface and Atmospheric Parameters of a Locally Coupled Model Using Observational Data , 2005 .

[35]  M. H. Diskin,et al.  A procedure for the selection of objective functions for hydrologic simulation models , 1977 .

[36]  Claudio O. Stöckle,et al.  Evaluation of estimated weather data for calculating Penman-Monteith reference crop evapotranspiration , 2004, Irrigation Science.

[37]  Narendra Singh Raghuwanshi,et al.  Estimating Evapotranspiration using Artificial Neural Network , 2002 .

[38]  M. Cobaner Evapotranspiration estimation by two different neuro-fuzzy inference systems , 2011 .

[39]  George Kuczera,et al.  Assessment of hydrologic parameter uncertainty and the worth of multiresponse data , 1998 .

[40]  Gianni Bellocchi,et al.  A software component for estimating solar radiation , 2006, Environ. Model. Softw..

[41]  Mike P. Papazoglou,et al.  Service oriented architectures: approaches, technologies and research issues , 2007, The VLDB Journal.

[42]  Slavisa Trajkovic,et al.  Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration , 2009 .