Modelling hourly and daily open‐water evaporation rates in areas with an equatorial climate

Abstract

Evaporation rate estimation is important for water resource studies. Previous studies have shown that the radiation-based models, mass transfer models, temperature-based models and artificial neural network (ANN) models generally perform well for areas with a temperate climate. This study evaluates the applicability of these models in estimating hourly and daily evaporation rates for an area with an equatorial climate. Unlike in temperate regions, solar radiation was found to correlate best with pan evaporation on both the hourly and daily time-scales. Relative humidity becomes a significant factor on a daily time-scale. Among the simplified models, only the radiation-based models were found to be applicable for modelling the hourly and daily evaporations. ANN models are generally more accurate than the simplified models if an appropriate network architecture is selected and a sufficient number of data points are used for training the network. ANN modelling becomes more relevant when both the energy-and aerodynamics-driven mechanisms dominate, as the radiation and the mass transfer models are incapable of producing reliable evaporation estimates under this circumstance.

Comparison of LLR, MLP, Elman, NNARX and ANFIS Models—with a case study in solar radiation estimation

“…Reflected energy is the short or longwave radiation reflected by a surface and is a function of the radiative properties of that surface (Monteith and Unsworth, 1990;Glickman, 2000). Solar radiation is more important than other variables used in estimating reference evapotranspiration (Tan et al, 2007), hydrological and soil water balance models and crop growth simulation models (Ball et al, 2004).…”

Modeling monthly pan evaporations using fuzzy genetic approach

“…Recent experiments have reported that ANN may offer a promising alternative in the hydrological context (Cancelliere et al, 2002;Kisi, 2005, 2006;Cobaner et al, 2009;Guven and Kisi, 2011;Keskin and Terzi, 2006;Kisi, 2008aKisi, ,b, 2009aKisi, ,b, 2010Kisi and Yildirim, 2005a,b;Kumar et al, 2004;Piri et al, 2009;Sudheer et al, 2002;Supharatid, 2003;Tabari et al, 2010;Tan et al, 2007;Tayfur, 2002). Sudheer et al (2002) used a feed forward ANN to estimate PE and found that the ANN compared favorably to other conventional approach.…”

Data-Based Evapotranspiration Modeling

“…However, in recent decades, several computational techniques and data-based statistical techniques have been successfully applied in the field of evaporation modeling using models such as ANN, Fuzzy, Neuro-fuzzy, SVM, and hybrid models [14,18,25,28]. Tan et al [26] and Piri et al [21] used ANN models for modeling daily pan/open water evaporation in dry countries. Kisi and Cimen [15] used SVM for evapotranspiration modeling, utilizing several meteorological data from three stations (Windsor, Oakville, and Santa Rosa) in central California, USA.…”