Abstract

Solar radiation information is used in crop growth, boundary layer, entomological and plant pathological models, and in determining the potential use of active and passive solar energy systems. Yet solar radiation is among the least measured meteorological variables.

A semi-physical model based on standard meteorological data was developed to estimate solar radiation received at the earth's surface. The radiation model includes the effects of Rayleigh scattering, absorption by water vapor and permanent gases, and absorption and scattering by aerosols and clouds. Cloud attenuation is accounted for by assigning transmission coefficients based on cloud height and amount. The cloud transmission coefficients for various heights and coverages were derived empirically from hourly observations of solar radiation in conjunction with corresponding cloud observations at West Lafayette, Indiana. The model was tested with independent data from West Lafayette and Indianapolis, Madison, WI, Omaha, NE, Columbia, MO, Nashville, TN, Seattle, WA, Los Angeles, CA, Phoenix, AZ, Lake Charles, LA, Miami, FL, and Sterling, VA. For each of these locations a 16% random sample of days was drawn within each of the 12 months in a year for testing the model. Excellent agreement between predicted and observed radiation values was obtained for all stations tested. Mean absolute errors ranged from 1.05 to 1.80 MJ m⁻² day⁻¹ and root-mean-square errors ranged from 1.31 to 2.32 MJ m⁻² day⁻¹. The model's performance judged by relative error was found to be independent of season and cloud amount for all locations tested.

Abstract

The increased interest in the climatology of solar radiation dictates a need for a distribution to fit daily solar radiation totals which tend to have negatively-skewed probability distributions. Even daily mean solar radiation for weekly periods tends to have non-normal distributions. The generalized lambda distribution, which includes a wide variety of curve shapes, is discussed for fitting these data. The underlying probability distribution is a generalization of the lambda distribution from three to four parameters. Using the weekly averages of daily solar radiation totals for each of 12 weeks during the growing season and daily totals for the week 5-11 July at West Lafayette, Indiana, it is shown that the generalized lambda distribution model fits the data well. Some results concerning percentiles and quantiles, parameter estimates, and goodness-of-fit tests are also discussed.

Abstract

Concern about climatic change and its effects on man has been increasing. Climatic changes affect the production of food and the allocation of energy resources. Proper interpretation of climatic change and the effect of weather on fuel use and crop production requires a homogeneous data base. A methodology is presented for removing non-climatic variability from monthly mean temperature records caused by changes in time of observation, station location, instrumentation and observer, using as an example climatological records for June, July and August from 1930 to 1976 in Indiana. Divisional and state mean temperature adjustments to the published figures were calculated. Divisional temperature corrections were usually negative, with an extreme correction of −1.5°F applied to the published Central Division temperatures in 1942–44 and 1950. State mean June, July and August corrections were negative every year, with an extreme correction value of −0.8°F in 1949. Even with the temperature corrections included, Indiana June, July and August mean temperatures showed a decrease of approximately 3°F from 1930 to 1976.

Abstract

Historical series of mean temperatures for climatological divisions and the state of Indiana contain systematic biases, the greatest being about −1.0°C in the Central Division in the 1940s. When these data are used in weather-management, crop-yield models they translate into biases in yield simulation and prediction The magnitude and distribution of the yield prediction bias depends on the mean temperature bias and the type of model. The effect of the mean temperature bias on corn (Zea mays L.) yield production was examined for two models: in the first, the historical climatological series was used to fit the regression coefficients, and in the second, a priori regression coefficients were used. For each of the two models, yield simulations and predictions were made with both the original published mean temperature and with those adjusted to the climatological network base for 1976. In the fiat model, the regression-fitting process averaged the effect of the temperature bias over the entire record. The estimates of corn yield trends attributed to management were slightly greater when the adjusted temperature were used in the regression model than when the published temperatures were used. The use of the adjusted temperatures resulted in slightly higher yield predictions but the differences were generally less than 5% of the mean absolute difference between the model predictions and the yields reported by the U.S. Department of Agriculture Statistical Reporting Service. In the second model, the temperature bias was translated directly into the yield simulation, which with adjusted temperature averaged 113 kg ha⁻¹ higher than that stimulated with the original temperature date in the 1950s and 1960s. Although the yield production and simulation errors caused by the mean temperature bias is nontrivial, the temperature bias contributed a relatively small part of the total variance in the yield modeling.

Abstract

An empirical site-specific water balance model was generalized to account for cropland drainage effects on soil moisture and evapotranspiration (ET). In predicting soil moisture for well-drained (WD) soils, usually total water use is equated to infiltrated precipitation, stored soil moisture, and ET.

In much of the eastern U.S. Corn Belt, however, row crop production is on soils which are poorly drained (PD) and underlain with perched water tables. These provide an additional source of soil water, capillary flow (C) into the crop root zone. Consequently, ET from PD soils is usually greater than that from WD soils. At West Lafayette, Indiana, from 1970 through 1974, for a PD soil the shallow water table furnished about a fourth of the total water used by late-planted corn (Zea mays L.) and a fifth of the water used by early-planted corn. A soil moisture budget model accounting for shallow water table influences, developed experimentally for a tile-drained Typic Argiaquoll soil, was generalized for use with other PD soils and, by voiding the capillary component, also for WD soils. For PD soils the C component was estimated as a function of the depth (G) to the shallow water table and a relative soil moisture gradient, or deficit, 1 – PAV, where PAV is the fraction of plant available soil water in the corn root zone. In turn, changes in depth of the water table were predicted as a function of C and G, assuming a fixed basal leakage. For both PD and WD soils, daily ET was estimated and the pattern of relative soil moisture extraction from each 15 cm soil layer was established as a function of the relative soil moisture deficit in the top 30 cm and the age of the corn crop. The generalized simulation of the soil moisture balance (SIMBAL) model provided excellent results when tested on independent experimental data. It provided reasonable agreement when tested over a large area, using measurements taken at 108 location-dates in July and August in Indiana, Illinois, Minnesota and Nebraska in 1978-80, in cooperation with the Control Data Corporation AGSERV project. Scatter in the predicted versus measured PAV comparisons was attributed mainly to sampling errors in estimating the daily precipitation and pan evaporation inputs needed for the model predictions of soil moisture.