Estimating solar radiation for crop modeling using temperature data from urban and rural stations

Abstract

Lack of site-specific global solar radiation (GSR) data is a significant impediment for most crop model applications. First, several empirical methods for estimating GSR from observed maximum and minimum temperature were evaluated using data from urban (National Climatic Data Center [NCDC] data set) and rural (Florida Automated Weather Network [FAWN] data set) sites in Florida. Next, the spatial structure of empirical model parameters was investigated and the possibility of employing spatially interpolated coefficients developed at urban or rural sites to predict solar radiation at rural locations, where most crops are grown and often crop modeling is required to address various issues, was assessed. Finally, the effects of observed and estimated GSR on simulated potential yield and evapotranspiration with CROPGRO-Peanut and CERES-Maize models at 6 crop-location combinations were evaluated. The model developed by Donatelli and Bellocchi (DB) achieved the most accurate estimations of GSR across Florida. The quality of those predictions varied with environment setting (urban-rural) and latitude. RMSE (root mean square error) between observed and fitted daily GSR were in the range of 3.1-4.1 and 3.2-4.9 MJ m -2 d -1 for FAWN and NCDC evaluation sub-sets, respectively. Radiation for FAWN sites was efficiently estimated by the coefficients interpolated from rural sites but was substantially overestimated by 17.6%, on monthly basis, when urban sites supplied the model empirical parameters. NCDC sites were site specific and suitable sources of solar radiation at urban environments only. Input of estimated with the DB model GSR into a crop model would be a realistic option at sites where only temperature and precipitation are available.

Sensitivity of APSIM/ORYZA model due to estimation errors in solar radiation

“…f 2 (i) =c 1 [sin(i×c 2 ×�/180)+cos(i×f 3 (c 2 )×�/180)] (7) where T nc is the summer night temperature factor to avoid underestimation of solar radiation in summer (Bechini et al, 2000); i= day of year, c 1 and c 2 are empirical model parameters for general seasonal factors (Mavromatis and Jagtap, 2005). In the equation defining ƒ 2 (i), f 3 is calculated as:…”

Estimating daily global solar radiation during the growing season in Northeast China using the Ångström–Prescott model

“…The methods must be based on readily available meteorological variables or other tools. A survey of existing literature on this topic reveals five major method categories for estimating solar radiation: (1) radiative transfer models (de La Casinière et al 1997;Gueymard 2001), (2) stochastic generation (Hansen 1999;Wilks and Wilby 1999), (3) satellite-derived estimates (Olseth and Skartveit 2001;Pinker et al 1995;Qin et al 2011), (4) artificial intelligence techniques (Mohandes et al 1998), and (5) empirical associations (Bindi and Miglietta 1991;Bristow and Campbell 1984;Mavromatis and Jagtap 2005). Among these methods, empirical models are not only effective and reliable but also economical.…”

Estimation of solar radiation and its application to crop simulation models in Greece

“…Mavromatis & Hansen 2001), linear interpolation techniques (Grant et al 2004, Soltani et al 2004), higher order statistics (Safi et al 2002), neural networks (Reddy & Ranjan 2003), and empirical approaches (e.g. Mavromatis & Jagtap 2005). Stochastic weather generators can be used for risk analysis, but not for crop model validation and simulation for a specific period, as the weather generators cannot generate data to match the actual weather at a particular time of interest (e.g.…”