Framework for Standardizing Less Data-Intensive Methods of Reference Evapotranspiration Estimation

• Published in: Water resources management Vol. 32; no. 13; pp. 4159 - 4175
• Main Authors: Singh, Laishram Kanta, Jha, Madan K., Pandey, Mohita
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2018; Springer Nature B.V
• Subjects: Atmospheric Sciences; Civil Engineering; Climatic conditions; Data; Developing countries; Earth and Environmental Science; Earth Sciences; Environment; Evapotranspiration; Frameworks; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrologic cycle; Hydrological cycle; Hydrology/Water Resources; Irrigation water; LDCs; Meteorological data; Methods; Relative humidity; Standardization; Temperature; Weather; Wind speed; Reference evapotranspiration; Standardization framework; Temperature-based ET; Data-scarce condition; methods
• DOI: 10.1007/s11269-018-2022-5
• ISSN: 0920-4741

Evapotranspiration is one of the vital components of water cycle and its accurate estimation is the key to sustainable management of irrigation water. The FAO Penman-Monteith (FAO-PM) method is recommended as the standard method for computing reference evapotranspiration (ET o ) as well as for evaluating other indirect methods. However, due to the lack of weather data such as radiation, relative humidity and wind speed in many regions of the world, especially in developing countries, the FAO-PM method is difficult to use. To address this issue, a fairly robust methodology is proposed in this study to standardize two popular less data-intensive (temperature-based) ET o methods, viz., Hargreaves-Samani (HS) and Penman-Monteith Temperature (PMT) against the FAO-PM method. To achieve this goal, the daily and monthly biases of these two methods were adjusted using the weather data of 14 locations for the 1979–2003 period. Subsequently, the performance of the standardized (de-biased) less data-intensive methods were verified using salient statistical and graphical indicators for the 2004–2013 period. The results indicated that the HS and PMT methods underestimate ET o on a monthly time step by 9.62 and 14.77%, respectively. However, the performances of these methods significantly improve after the standardization. The estimates of ET o by the standardized less data-intensive methods were found to be in close agreement with those by the standard FAO-PM method, thereby suggesting the usefulness and applicability of the proposed framework in data-scarce situations irrespective of agro-climatic conditions.