John F. Griffiths

An assessment of the weather generator (WXGEN) used in the erosion/productivity impact calculator (EPIC)

Abstract The erosion/productivity impact calculator (EPIC) is an important planning model that is used to assess the impact of different farming techniques or changing climates on agriculture. A necessary input is a long series of daily weather which can either be observed or stochastic. The primary objective of this study is an assessment of simulated weather produced by the ‘weather generator’ (WXGEN), a component model of EPIC. Five strategically located Texas first-order stations were chosen for comprehensive testing, ten 30-year sequences of weather being generated for each site using data incorporated into the model as parameters. Statistics were extracted from these simulations, and were compared with similar statistics obtained from observed 1958–1987 records. More than 20 000 statistical tests were carried out, each with a null hypothesis of similarity. For almost every series of tests at the 0.05 level of significance, over 15% fell into the rejection region, indicating that sequences of weather generated by WXGEN are often unrealistic. If stochastic weather is used this possibly affects the accuracy of predictions made by EPIC. Two problem areas were identified, the exclusive use of first-order Markovian procedures, and the independent nature of wind speed and direction simulation. However, in spite of reservations about WXGEN output, it is probably the only currently available stochastic model that can deliver the necessary weather input to operate EPIC and, with some modification, has the potential to satisfy input requiremens of other types of model.

Simulated meteorological input for agricultural models

Many agricultural models need an input of daily weather, and a common way of accommodating this is by simulation. Long daily records are rare for agricultural sites, but it is usually possible to obtain climatic parameters to use as input for a s stochastic model. An additional advantage to simulated weather is that many likely sequences can be generated, whereas observation furnishes only a single realization. The objective of this study was to develop a model to simulate weather records, that places emphasis on realistic variability, sequences, extremes and cross-correlation, as well as mean conditions. The model was designated Simulated Meteorological Input for Agricultural Models (SIMIAM), and relies on selection of air mass as the first stochastic step. All required variables are then generated using air mass statistics. The days wet or dry character is ascertained by first-order Markov chain and, if wet, precipitation amount simulated in a two-parameter gamma distribution. A weakly-stationary generating system, that incorporates matrices of cross and auto-correlation coefficients, is used to simulate all other variables, including winds. Amarillo, Texas records were used to demonstrate model feasibility. Significantly different air masses were shown to be identifiable by gradient level wind directions, and their sequence was adequately modeled with a first-order Markovian process. SIMIAM was tested for Amorillo, Oklahoma City, and New Orleans, using 15 years of record to compute input (1961–1975), and comparing simulations against an independent 15 years of observations (1976–1990). Results indicated that the study objectives were mostly satisfied for the two inland cities. As input was compiled from discontinuous, multiple, area sources, these tests were robust. They were also considered to demonstrate model applicability, not only to point locations, but to areas of the south central United States. Test results for New Orleans were less acceptable, and further study is clearly necessary to achieve model transportability to different climatic zones.

Climatic Variability at Ten Stations Across the United States

Abstract Ten stations are chosen for a study of climatic variability in the continental United States, using as the main criteria good geographical distribution, long-period records (since before 1900), and available daily, monthly and annual values of maximum and minimum temperatures and precipitation. Regression lines of decadal variances calculated from annual temperature indicate significant trends of variances, and thus variability, at several stations. A slight increase in precipitation variability is evident across the entire region. Analyses of ranked monthly maximum and minimum temperatures reveal several distinct discontinuities in the data. In most instances, station relocations coincide with the discontinuities.

A survey of agrometeorological disasters in South China

Abstract The paper discusses climatic features of tropical and sub-tropical provinces of China. Warmth and abundant rainfall allow for intensive agricultural development. However, the region does suffer weather situations that are devastating to crops. A number of these, such as drought, flooding, typhoons, cold waves and hail are discussed, and their climatology presented. The definitions are given of drought, flood-waterlogging, cold-wave and cold-dews wind as these are used in China. The latter phenomenon is a fall season condition with low temperature and either dry and windy, or cloudy and drizzly which is very destructive of late season rice. Information on the occurrences of these adverse situations are presented together with their distribution in South China.

The use of precipitation data to identify soil moisture patterns and the growing seasons in Eastern Kenya

Abstract Eight stations with 36 years of rainfall record were selected in the dry region of Kenya, east of 37°E longitude, so as to study the nature of the moisture patterns during the Short and the Long rains (October through June). The rainfall data were reduced to soil moisture based upon area-wide assumptions concerning soil characteristics. The start, end and duration of the rainy seasons were determined and tested for normality of distribution using a 10 mm soil moisture threshold value at each station. Mean-year conditions were computed and comparisons were made between the two growing seasons at each station and among all the stations. Stations were grouped according to the magnitude of the coefficient of determination between the start and duration of the seasons with sufficient soil moisture for plant growth, and regression equation were developed to predict season duration using the starting dates. Equations are also derived to estimate the probable length of the season for various levels of soil moisture requirements. The important findings that have relevance for the farmer in the area so as to assist in his decision-making are: (1) The distributions of the start, end and duration of the growing seasons are not significantly skewed or significantly different from the normal distribution; (2) the start and end times of the Short rains season show a southward trend; (3) the Short rains growing season is found to last longer, be more reliable, and to have a higher soil moisture content than the Long rains season for stations located south of the equator; and (4) the opposite is true for stations north of the equator.

Some techniques for predicting winter wheat yields in major wheat-producing crop districts of Texas and Oklahoma.

Abstract Wheat yields are analyzed as a differential time function of agricultural technology and the fluctuating environmental conditions as indicated by the Palmer Drought Index severity variables. The use of arctangent curves is introduced to represent the three time trends in agricultural technology in the recent history (1931–1975) of the area. Multiple regression equations for estimating and predicting deviations of wheat yields from the technological curves using weather (environmental) variables were generated and critically studied. In several instances, the partial regression coefficients for estimating and predicting the weather component of the wheat yields were found to be inconsistent in sign and magnitude, and to be agronomically meaningless. Idealized estimators of the same form are presented which have virtually the same correlation coefficients, standard error of estimates and predictiveness. The rationale and a probable method for obtaining the ultimate parameters (similar to the idealized estimators) for forecasting the winter wheat yields are suggested and discussed.

Problems with environmental data: A case study in climatology

Environmental data, including climatic data, are often being used to an accuracy for which they were not intended. This is becoming more apparent with the use of sophisticated statistical techniques in which computers play a major role. The example of data used in large‐scale secular temperature changes is given here because our studies have cast doubt on the representativeness and reliability of the data used. A method is suggested for finding the best candidates for climatic reference stations in the U.S.A., so that from these a good database can result.

Changes in high cloud conditions

Changes in high cloud (any cirriform or cloud layer above 20,000 ft. or 6.1 km) were analyzed by comparing data from 1966–70 to 1950–54 for eight cities in the United States. Six stations showed increases in high cloud while two had minor decreases. Areas with high‐cloud increases (comparing 1966–70 to 1950–54) have the following characteristics: increased jet traffic; strong upper air flow; and high‐cloud coverage of less than half of the sky. The largest increases in high clouds occurred in the late afternoon. Also, increases were noted when there were either no low clouds or considerable low cloudiness.

A problem in climate change studies, extrapolation from point to area data: a case study in Texas, USA

An attempt is made to try to assess the error arising from using single point values to represent large areas. Correlation coefficients (r) greater than 0.86 were deemed necessary in order to sufficiently represent an area. Using several carefully selected stations across Texas, correlation values were computed using individual stations against their respective divisional averages, the statewide average as well as between the single stations. Correlation values were found to be greatest during the winter months for both temperature and precipitation with the summer months exhibiting the lowest correlations. Also, station to station correlations were found to be directional dependent as inland stations exhibited low correlations when correlated with coastal stations. It appears that mean monthly values of temperature at a station can give a reasonable estimate of the larger area mean, especially during the cooler months and with minimal topography, while precipitation r values were so low as to discourage ...

A survey of agroclimatic divisions of China on the national, provincial and county scales

Climate shows distinct variability on all spatial scales and the method of identifying areas with analogous climates is widely used. The technique is usually developed with some specific application in mind, human comfort, building types or, as in this case, agriculture. The paper addresses the topic as it applies in China, presenting first the method of basic climatic divisions and then explaining the threshold values used in agroclimatic classifications. Temperature is a major variable used in the classification, as is precipitation, but at the county level many other aspects are introduced. China has also devised classifications for many specific crops, and rice is selected for special consideration.