Evaluation and Recalibration of Empirical Constant for Estimation of Reference Crop Evapotranspiration against the Modified Penman Method

International Conference on Civil Engineering and Infrastructural Issues in Emerging Economies 2017

土木建筑工程;经济学

Sasireka, K.^1 ; Jagan Mohan Reddy, C.^1 ; Charan Reddy, C.^1 ; Ramakrishnan, K.^1

School of Civil Engineering, SASTRA University, Thanjavur

613 401, India^1

关键词: Empirical constants;    Empirical equations;    Evapotranspiration models;    Management practices;    Penman method;    Reference crop evapotranspirations;    Regression equation;    Water requirements;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/80/1/012062/pdf
DOI  :  10.1088/1755-1315/80/1/012062

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

The major demand in our country is irrigation demand. Looking to the low irrigation potential and small water resources, it is felt necessary to see that water must be used economically and efficiently. This may be achieved by using latest methods of determination of water requirements for crops and applying the proper water management practices. Evapotranspiration (ET) is a basic for calculation of water requirement for crops. The various popular empirical equations for reference crop evapotranspiration (ETr) belong to three categories namely, Temperature, Radiation based methods and Combined methods. The above methods are site specific; hence it is necessary to recalibrate the coefficients for applying them in India. In the present paper, the standard combined method namely FAO modified Penman method was used to recalibrate the constants in temperature based (TB) methods and it can also be used to determine the ETrfor the selected station. Four TB evapotranspiration models namely Blaney-Criddle, Romanenko, Kharrufa, and, Thronthwaite methods are recalibrated and the constant in each method are redefined for the data from Lekkur station, Cuddalore district in India. The result shows that, large error existed when ETrhas been calculated with original constants. Hence regression equations were developed to minimise these variations in magnitude. It was found that out of four methods the Blaney-Criddle method suits better for the selected region.