Estimation of daily reference evapotranspiration from limited climatic variables in coastal regions

ABSTRACT

Generalized multi-adaptive regression splines (MARS) and genetic expression programming (GEP)-based equations were developed to estimate Reference Evapotranspiration (ET_o) in coastal regions. Following existing regression-based ET_o retrieval equations, five climatic data configurations were used to train, validate, and test the MARS and GEP models (hereafter called MARS1–MARS5 and GEP1–GEP5). The performances of the MARS and GEP models with each of the five input configurations were assessed. The generalized MARS1–MARS5 and GEP1–GEP5 models could estimate ET_o accurately in regions other than their training region. In addition, MARS1 performed better than MARS2–MARS5. Similarly, GEP1 outperformed GEP2–GEP5, implying that input configuration 1 contains the most important information about ET_o. The results also show that MARS can estimate ET_o more accurately than GEP. The findings indicate that MARS1–MARS5 and GEP1–GEP5 improved ET_o values compared with the corresponding traditional equations. Finally, sensitivity analyses were conducted to evaluate the impact of each input variable on ET_o.

KEYWORDS:

• multi-adaptive regression splines (MARS)
• genetic expression programming (GEP)
• reference evapotranspiration
• coastal regions

Editor A. Fiori; Associate Editor J. Rodrigo-Comino

Editor A. Fiori; Associate Editor J. Rodrigo-Comino

Additional information

Funding

The authors gratefully acknowledge support from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) under grants #NR203A750013G004 and #69-3A75-17-54.