Evaluation of six equations for daily reference evapotranspiration estimating using public weather forecast message for different climate regions across China

Abstract

Accurate estimation of daily reference evapotranspiration (ET0) are vital for water resource management and irrigation decision-making. Based on the public weather forecasts, numerous models have been successfully used for daily ET0 estimating, while too many models available for selection, which causes confusion regarding model selection for specific climate regions. In this paper, the estimating performances of six ET0 equations using public weather forecast for a lead time of 1–7 days were compared for four main climatic region across China, and then, the most accurate equation was recommended for each climate region. Meanwhile, the applicability of every equation was assessed in relation to four climates, including subtropical monsoon climate (Cwa), temperate continental climate (Dfc), temperate monsoon climate (Dwa) and mountain plateau climate (HG). The Penman-Monteith Forecast (PMF) equation, which consisting of an adaptation of FAO56-PM equation using temperature and weather type forecast as inputs, provided the best ET0 estimation performance in Cwa and HG climates; and the Temperature Penman-Monteith (PMT) equation using only the temperature data, obtained the most accurate average results for the Dwa and Dfc climates. The best and the second best estimation performance for each climate usually provided by PMF and PMT equations, since they both have the same advantage of following the conceptual approach of the FAO56-PM equation; further the third and the fourth choice would be the Hargreaves-Samani (HS) and the Blaney-Criddle (BC), respectively, while the Thornwaite (TH) and the McCloud (MC) yielded high errors and may not be applicable for ET0 estimation for most climatic regions. As a whole, the PMF and PMT equations were better than the other equations and thus these two equations were recommended for daily ET0 estimation for the near-future at all climate regions across China.