Sources of solar radiation estimates and their effect on daily potential evaporation for use in streamflow modeling

Lindsey, S.; Farnsworth, R.K.

doi:10.1016/s0022-1694(97)00046-2

Cited by 33 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They reported that the R G overestimation by 4 per cent caused an error in the potential evapotranspiration of between 1.6 and 3.6 per cent depending on the time of the year [80]. Lindsey and Farnsworth [79] found that the use of cloud cover as an R G predictor usually led to an underestimation of the potential evaporation from a water surface by 14 per cent on average. In some cases these authors reported an error as large as 39 per cent, which is a significantly worse performance than any of the seven methods tested in our study.…”

Section: Discussionmentioning

confidence: 99%

Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

Trnka

Eitzinger

Kapler

et al. 2007

Sensors

View full text Add to dashboard Cite

The results of previous studies have suggested that estimated daily global radiation (RG) values contain an error that could compromise the precision of subsequent crop model applications. The following study presents a detailed site and spatial analysis of the RG error propagation in CERES and WOFOST crop growth models in Central European climate conditions. The research was conducted i) at the eight individual sites in Austria and the Czech Republic where measured daily RG values were available as a reference, with seven methods for RG estimation being tested, and ii) for the agricultural areas of the Czech Republic using daily data from 52 weather stations, with five RG estimation methods. In the latter case the RG values estimated from the hours of sunshine using the Ångström-Prescott formula were used as the standard method because of the lack of measured RG data. At the site level we found that even the use of methods based on hours of sunshine, which showed the lowest bias in RG estimates, led to a significant distortion of the key crop model outputs. When the Ångström-Prescott method was used to estimate RG, for example, deviations greater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 per cent of cases. The precision of the yield estimates and other crop model outputs was lower when RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that RG estimates based on diurnal temperature range or its combination with daily total precipitation produced a bias of to 30 per cent in the mean winter wheat grain yields in some regions compared with simulations in which RG values had been estimated using the Ångström-Prescott formula. In contrast to the results at the individual sites, the methods based on the diurnal temperature range in combination with daily precipitation totals showed significantly poorer performance than the methods based on the diurnal temperature range only. This was due to the marked increase in the bias in RG estimates with altitude, longitude or latitude of given region. These findings in our view should act as an incentive for further research to develop more precise and generally applicable methods for estimating daily RG based more on the underlying physical principles and/or the remote sensing approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

Trnka

Eitzinger

Kapler

et al. 2007

Sensors

View full text Add to dashboard Cite

show abstract

“…So preparation of a mathematical model for evaporation with consideration of all of the effective ecological factors is a difficult task; therefore if it is prepared, it faces so many errors or needs more information; the preparation of all of these information are difficult and time consuming. (Jain et al, 1999) However, as the evaporation process is strongly nonlinear in nature, researchers should emphasize on the accuracy of used methodologies in modeling it (Lindsey & Farnsworth, 1997;Xu & Singh, 1998;Bruton et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Chari¹,

Nemati²,

Afrasiab³

et al. 2012

JAS

View full text Add to dashboard Cite

The relation between water table depth and evaporation rate from bare soil is of great importance in arid and semi-arid areas. In such areas, due to over irrigation, the water table is very close to the ground surface which leads to salinization of the soil. In this study a physical water table model was used to estimate evaporation rate in sandy loam, loam and clay loam soils under greenhouse conditions for 40, 60 and 80 cm water table depth. The evaporation from bare soil, evaporation from free surface, soil surface moisture (with using TDR) and maximum and minimum daily temperature were measured for 74 days in this study. In the next step, several nonlinear models have been efficiently developed with the aid of the Gamma test (GT), including local linear regression, two layer back propagation, and conjugate gradient descent and BFGS neural network to simulate the evaporation from soil. And finally, for evaluation of the models, the root mean square error and mean absolute error and larger determination coefficient were calculated. The results showed a suitable correlation between the predicted values and the test measures.

show abstract

“…Linacre (1977) used the different temperatures to predict the PE in Australia. However, because the evaporation process is strongly nonlinear in nature, some researchers should emphasize the estimation of accurate evaporation in the research field using modeling techniques (Lindsey and Farnsworth, 1997;Xu and Singh, 1998;Bruton et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling

Kim

2008

Journal of Hydrology

205

View full text Add to dashboard Cite

Sources of solar radiation estimates and their effect on daily potential evaporation for use in streamflow modeling

Cited by 33 publications

References 5 publications

Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling

Contact Info

Product

Resources

About