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Evaluation of radiation and temperature data generators in the Australian tropics and sub-tropics using crop simulation models
AGRICULTURAL AND FOREST METEOROLOGY
ELSEVIER
Agricultural and Forest Meteorology 72 (1995) 295-316
Evaluation of radiation and temperature data generators in the Australian tropics and sub-tropics using crop simulation models H. Meinke *3a P.S. Carberrya, M.R. McCaskillb,‘, M.A. Hillsa,2, I. McLeod” aQDPI/CSIRO
igricultural
Production Systems Research Unit, PO Box 102, Toowoomba, Qld.. 4350, Australia ‘Queensland Department of Primary Industries, Division of Forage and Animal Management, PO Box 46. Brisbane, Qld., 4001, Australia. ‘CSIRO. Division of Tropical Crops and Pastures, 306 Carmody Rd, St Lucia, Qld., 4067, Australia
Received 20 September 1993; accepted 17 March 1994
Abstract Long-term historical weather data are needed to conduct crop simulation analyses. However, the network of weather recording stations which collect all necessary daily weather data (commonly rainfall, solar radiation, maximum and minimum temperature) for such analyses is sparse. Frequently only rainfall is recorded. Thus, weather data generation techniques are required for three situations: (i) where only rainfall data are available, (ii) where both rainfall and temperature data are available, but radiation is missing, and (iii) where records are otherwise complete, but techniques are required to fill short periods of missing data. Three weather generation techniques are compared, termed here (i) Bristow and Campbell’s method, (ii) TAMSIM and (iii) WGEN. Methods (ii) and (iii) were used to generate temperature and radiation data to accompany recorded rainfall records, and methods (i)-(iii) to generate a solar radiation record to accompany recorded temperature and rainfall records. Data from four stations in tropical and subtropical Australia with long-term complete weather records were used to compare actual with generated data sets. Results were evaluated firstly by comparing the cumulative distribution function (CDF) of generated and actual values, and secondly by comparing CDFs calculated from the output of three crop simulation models used with the generated and actual data sets. Generally the distributions of radiation and temperature differed significantly. However, when the weather data sets were used by simulation models to estimate biomass, only 10 of the 50 CDFs differed significantly. When both temperature and radiation were generated, 30%
*Corresponding author. ’Present address: Universitas Mataram, PO Box 1106, Mataram 83125, Lombok, Indonesia. ‘Present address: Brisbane City Council, GPO Box 1434, Brisbane, Qld. 4001, Australia. 01681923/95/$09.50 0 1995 ~ Elsevier Science B.V. All rights reserved SSE! 0168-1923(94)02159-H
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of CDFs from TAMSIM and 20% of WGEN differed significantly. When only radiation was generated, 40% of CDFs generated by the Bristow and Campbell’s method, 10% of WGEN and none of TAMSIM differed significantly. All methods simulated the more temperate sites with higher precision than the wet, tropical site. Simulated yields showed a similar pattern. It was concluded that where both temperature and radiation data have to be generated, WGEN is appropriate because it contains a stochastic element and thus simulates catastrophic events such as frosts. Where only radiation generation is required, both WGEN and TAMSIM performed adequately. Where temperature or radiation data sets are complete except for occasional missing days, TAMSIM was considered to be the most appropriate. In cases were the objective is not to conduct long-term simulation analyses, Bristow and Campbell’s method appeared more appropriate because of its ability to better simulate the day to day variation in solar radiation.
1. Introduction In agricultural science and extension, simulation models of either individual crops or whole farming systems are becoming increasingly important tools for a wide range of applications, including genotypic improvement (Shorter et al., 1991), assessing the viability of new industries (Carberry and Muchow, 1992) quantifying climatic risk (Hammer and Muchow, 1991; Meinke et al., 1993a,b) and the development of decision support systems (Jamieson et al., 1992). Such models quantify climatic constraints to crop production and recommendations derived from their use assume that the future climate will fit the same distribution as the historical climate used in the analyses. Most crop models require daily climatic input to simulate crop responses (e.g. Jones and Kiniry, 1986; Amir and Sinclair, 1991; Hammer and Muchow, 1991; Carberry and Muchow, 1992; Hammer et al., 1992; Chapman et al., 1993). Whilst these climatic variables are readily available for the experiments used in model development and validation, it is difficult and, in many cases, impossible to obtain weather records suitable for simulation analysis at other locations and/or for an appropriately long period. In regions with high climatic variability it is particularly important to simulate long time sequences in order adequately to assess effects of climatic variability. To reflect seasonal variability and trends in crop production, sufficiently long weather records need to be available adequately to sample the population. For the most commonly used models, this requires long-term daily values of rainfall, solar radiation, maximum and minimum temperatures. When such weather data are available, they are frequently either incomplete (‘gappy’) or of dubious quality. Many workers have recognised this problem (e.g. McCaskill, 1990b; Clemence, 1991). This has led to the development of a range of weather generators such as WGEN (Richardson and Wright, 1984) TAMSIM (McCaskill, 1990a) and others (e.g. Brock, 1981; Larsen and Pense, 1982; Bristow and Campbell, 1984; Guenni et al., 1990; Hutchinson, 1991) which can be used to either synthesise non-existing weather data or to fill incomplete sets. In semiarid regions the major constraint to crop production is water availability
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which is strongly related to rainfall amount, frequency and distribution. This is particularly true for Australia (Angus, 1991) and has led to the development of a dense network of rainfall recording stations in towns and on major rural properties throughout the country, many of which go back 80 years or more. The density of stations with temperature records is, however, much more limited than for rainfall, and even fewer stations have records of solar radiation. A preliminary survey of data availability showed that for approximately every 100 rainfall recording stations in the Australian state of Queensland with a record length of more than 80 years, about 10 have a long-term temperature record (more than 35 years), and one has a long-term radiation record (more than 10 years). Within the long-term temperature or radiation records, there are sometimes gaps of several days when equipment was being repaired or was not read, which even for good records may account for up to 15% of the total record. However, gaps in the rainfall records are rare, and can usually be filled for simulation purposes by using a nearby station (Clarkson and Owens, 1991). Three kinds of data reconstruction are therefore required (i) to provide temperature and radiation data at stations where only rainfall has been recorded, (ii) to estimate radiation where temperature and rainfall have been recorded; and (iii) to fill short gaps in temperature and radiation data. Because long-term, recorded daily rainfall data are readily available throughout Australia (Clarkson and Owens, 1991), it is appropriate to use these values to predict temperature and solar radiation data, given the limitations associated with generating daily rainfall values (Hutchinson, 1991). Using recorded rather than predicted rainfall values as the base variable to predict other climatic variables improves the quality of the generated data substantially and overcomes some of the inadequacies discussed by Hutchinson (1991) and Chia (1990). Thus, in our efforts we concentrated on the generation of daily values of solar radiation and temperature data alone, with the main emphasis on the prediction of solar radiation data. The quality of model output can be directly related to the quality of data used as input. It follows that the testing of the sensitivity of model output to the quality of generated weather data is an essential prerequisite for simulation analyses. Notwithstanding this point, the three weather generation techniques, Bristow and Campbell (1984), TAMSIM (McCaskill, 1990a) and WGEN (Richardson and Wright, 1984) have all been used for simulation analyses in northern Australia by Meinke et al. (1993a), Carberry and Abrecht (1991) and Jones et al. (1988) respectively. Hence, the objective of this paper is to compare the performance of these three temperature and radiation generators by examining the influence of generated weather data on crop simulation output in this climatic zone of Australia. Of importance is the ability to produce weather data consistent with the long-term distribution pattern of the recorded data for a site. Clemence (1991) conducted a similar study investigating the sensitivity of the CERES-Maize model (Jones and Kiniry, 1986) to two simple weather data infilling methods at four locations in Natal (South Africa). The study, however, assumes only one sowing date (12 November), and consequently only considers the adequacy of less than one third of weather data available each year. The author also stops short of statistically comparing model output distributions.
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2. Methods 2.1. Radiation
and temperature
generation
techniques
The three methods of predicting daily temperature in this paper are based on the following principles:
and/or
radiation
data compared
2.I.I. Bristow and Campbell’s (1984) method-solar radiation prediction only This method of predicting solar radiation (Q, MI m2 day-‘) is based on a relationship between the daily, extra-terrestrial solar radiation for a specific location (Qo), the fraction of daily total atmospheric transmittance of this extra-terrestrial irradiance (7’,) and the daily range of air temperatures (AT(j)), calculated from the maximum and the two adjacent minima temperatures
Q0 = 86400So (h, sin 4 sin 0 + cos Cpcos u sin h,) /7r T, = A[1 - exp(-sAT( AT(j)
= Tmax(j)
- [Tmin(j)
+ Tmin(j
+ 1)]/2
(1)
where So is the solar constant (1360 W m-*), h, is half daylength, 4 is the latitude of the location and (Tis the solar declination (all in radians). The coefficient A represents the maximum clear sky Tt characteristic of the site, and B and C determine how soon maximum Tt is achieved as AT(j) increases. Tmax(j) and Tmin(j) represent the maximum and the minimum air temperature on dayj. To allow for reduced radiation loads under rainy conditions AT(j) is reduced on wet days by 25%. For extended rain periods it is assumed that equilibration between incoming radiation and AT occurs, thus the correction is only applied to the first 2 days of any rain period. All coefficients are determined empirically from measured solar radiation data using iterative optimisation procedures (simplex method, Numerical Algorithms Group (NAG), 1983). Values for coefficient A were determined from visually examining daily solar radiation values over time and comparing the maximum values achieved with the corresponding extraterrestrial radiation on that day. This resulted in a value for A of 0.8 for all cases except at the location ‘Myall Vale’ where for the summer seasons A had a value of 0.7 (details of climatic sites are discussed later and in Table 1). Myall Vale was the only location where fitting separate values for parameter A depending on season (as suggested by Bristow and Campbell, 1984) improved predictions. In accordance with Bristow and Campbell (1984) varying values for parameter C did not improve predictions and it was thus kept constant at a value of 2.4. Coefficient B was fitted to summer (15 September to 14 March) and winter (15 March to 14 September) seasons individually. The values ranged in summer from 0.0045 (location ‘Dalby’) to 0.0062 (location ‘Lansdown’) and in winter from 0.0039 (location ‘Katherine’) to 0.0078 (location ‘Lansdown’).
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Once the coefficients are determined, daily radiation values can be predicted providing daily temperature and rainfall data are available. The program does not include a stochastic element. 2.1.2. TAMSIM (McCaskill, 1990a)-solar radiation and temperature prediction Daily weather data from each site were used to calculate coefficients for the function P = a + bcos(t9) + csin(B) + dcos(28) + esin(2B) +fRj_i
+ gRj + hRj+,
(2)
where P is the meteorological parameter (maximum temperature, minimum temperature or radiation), 0 is the day number converted to a radian form (19= day number x 2n/365), R’ is the rainfall converted to a binary form (0 for no rain and 1 for rain), the subscriptsj - 1, j andj + 1 refer to the previous, current and following day, and coefficients a-h are calculated empirically by multiple regression analysis. This method does not contain a stochastic element. For each site, three weather data sets were calculated using this method: (a) predicted solar radiation, but actual temperature (TAMSIM radiation); (b) predicted solar radiation and temperature (TAMSIM radiation and temperature); and (c) recorded solar radiation and temperature when available, but missing periods (less then 1% of the record) filled using this method. These data sets are referred to as ‘actual’, and were used to derive coefficients for methods (2.1.1.) and (2.1.3.). 2.1.3. WGEN (Richardson, 1981; Richardson and Wright, 1984)-solar temperature prediction
radiation and
In the program WGEN the prediction of solar radiation and maximum and minimum temperature is considered to be a continuous multivariate stochastic process with daily means and standard deviations varying depending on wet or dry days. The program calculates smoothly varying daily mean variable values by fitting single sine curves to monthly parameter values which need to be supplied for each site. At least 5 years of actual climate records are recommended to derive these parameter values. The time series of each variable is reduced to a time series of residual elements by removing the periodic means and standard deviations. This results in residual series which are stationary in their mean and standard deviations, having a mean of zero and a standard deviation of unity. Serial correlations for each time series of residual elements and cross correlations between each pair of variables are determined. Missing values are then generated using the following calculation
xp,i(j) = A&,i-1 (A + B~p,i(j)
(3)
where X,,,(j) and XP,i_l(j) are 3 x 1 matrices for days i and i - 1 of year p whose elements are residuals of maximum temperature (j = l), minimum temperature (j = 2) and solar radiation (j = 3); Ep,i(j) is a 3 x 1 matrix of independent random components, normally distributed with a mean of zero and a variance of unity and A and B are 3 x 3 matrices whose elements are defined such that the new sequences have the desired serial correlation and cross-correlation coefficients. These newly
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determined residuals are then multiplied by the standard deviation and the mean is added to determine the final values of the variable (Richardson, 1981). For more details and an in-depth discussion of underlying processes see Richardson (1981, 1984) and Richardson and Wright (1984). 2.2. Weather data Four Australian climate stations with long-term measured and reliable daily weather data (i.e. daily maximum and minimum temperatures and solar radiation) were identified (Table 1). Record length differed among climate stations. Dalby had the shortest record (3804 days or 10.4 years) and Katherine had the longest (11840 days or 32.4 years). The stations were purposely selected to cover a wide geographical area and several climatic zones in Australia (Table 1). Katherine represents the monsoonal tropics, Lansdown the coastal dry tropics, Dalby an area in the summer rainfall dominated cropping region of sub-tropical Australia, and Myall Vale, although summer rainfall dominated, a cropping system also strongly influenced by winter rainfall and roughly located on the border between the sub-tropical and temperate climate zones. The geographical spread ensures that the method finally selected to generate missing weather data can be used confidently across these climatic zones. Actual radiation and temperature data were used to calculate the necessary input parameter values for the various weather data prediction techniques. Actual recorded rainfall data from each site was used as a base input variable for each of the weather generators. Cumulative distribution functions (CDFs) of each predicted variable were calculated from these five weather files at each of the four locations. These CDFs were then compared with CDFs of the actual, recorded data and tested for significant differences using the Kolmogorov-Smirnov Test (Conover, 1971). Details of this statistical test are described later. Table 1 Geographic location of sites and their yearly climatic averages for maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rad) and rainfall (Cook and Russell 1983; Bureau of Meteorology 1988; McCaskill 1992); starting year of climate files and total number of years for which complete records are available.
Latitude. “S Longitude, “E Elevation, m Tmax, “C Tmin, “C Rad, MJ mm2 day-’ Rain, mm Start of climate records No. of years
Katherine
Lansdown
Dalby
Myall Vale
14.5 132.3 108 34.1 19.5 22.5 955 1960 33
19.4 146.5 61 30.1 18.2 19.9 724 1973 20
21.2 151.2 344 26.0 12.2 19.5 614 1982 11
30.1 149.8 210 26.2 11.5 18.0 660 1973 20
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2.3. Sensitivity tests using crop simulation models 2.3.1. Models used
To evaluate the sensitivity of crop models to recorded versus generated weather data, we used three crop models with each of the recorded and predicted weather files. All three crop models are based on a daily time-step and simulate crop growth and development dynamically as a function of temperature and solar radiation. To test the data sets under a range of different conditions we chose models of a summer crop (maize; Carberry and Abrecht, 1991) a winter crop (wheat; Amir and Sinclair, 1991) and a crop which can be grown under most environmental conditions within our region of interest, but is sensitive to frost around flowering (sunflower, Chapman et al., 1993). Although all models require the same environmental input data they differ in their design complexity and can be classed as (i) complex (maize), (ii) intermediate (sunflower) and (iii) simple (wheat). Functions describing development are mainly driven by temperature and sometimes photoperiod whereas functions describing crop growth are controlled by temperature, solar radiation, water and nitrogen availability (except for the sunflower model which does not simulate nitrogen responses). Crop phenology, a developmental process, is divided into several growth stages, the duration of which are predicted based on daily temperature and in some cases photoperiod. Leaf area development in maize and sunflower is described as functions of initiation, appearance, expansion and senescence of leaves. These functions are sensitive to photoperiod, temperature, water stress and, in the case of maize, nitrogen availability. In the wheat model crop leaf area is simulated as a function of accumulated daily temperature, water and nitrogen availability. Potential dry matter production is predicted from leaf area index, a light extinction coefficient and the crop’s radiationuse efficiency. Potential transpiration is predicted as a function of daily biomass accumulation, a transpiration efficiency coefficient and predicted daily vapour pressure deficit. Plant water uptake is dependent on the available water range of the soil. Daily biomass accumulation, transpiration and leaf growth are decreased below potential values once the fraction of available soil water declines below threshold values. In each model accumulated biomass is partitioned to a yield component, the rate of which depending on climatic conditions at and after anthesis. Soil evaporation, rainfall infiltration, soil water drainage and, in the case of the maize and sunflower model, runoff are also simulated in the respective water balance routines. The sensitivity of the functions describing crop growth and development to climatic input parameters differs widely. In the context of this paper only their integrated effect in the form of biomass and yield prediction is assessed. 2.3.2. Simulations In Australia insufficient plant available soil water commonly overwrites any effects of variation in temperature and radiation (Meinke et al., 1993a). To eliminate this large influence of water availability on crop growth all models were compared under optimal conditions (i.e. water and nutrient non-limiting for plant growth). No effects
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Table 2 Sowing dates used for each crop and location
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in simulation
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study
Crop
Katherine
Lansdown
Dalby
Myall Vale
Sunflower
15 15 15 15
15 15 15 15
1 Jan 1 Mar 15 May 15 Jul
1 Jan 1 Mar 15 May 15 Jul
Maize
15 Dee 15 Feb
15 Ott 1 Jan
15 Ott 1 Jan
Wheat
_ _
15 May 15 Jul
15 May 15 Jul
Dee Feb May Jul
Dee Feb May Jul
15 Dee 15 Feb
of pests or diseases were considered. The effect of different methods of weather data generation on (i) days to physiological maturity (ii) total biomass production and (iii) grain biomass production was evaluated for a range of simulated sowing dates for all crop models (Table 2). The chosen sowing dates do not necessarily reflect common practices, but were rather chosen to enable comparisons of model sensitivity to weather data generation and to test the generation procedures at various times of the year. Specifically, we included sowing dates which were likely to trigger model responses to (a) extremely high temperatures (maize sown on 15 December at Katherine and Lansdown) and (b) very low temperatures, i.e. frost, (sunflower sown on 1 March at Dalby and Myall Vale). In the maize model, maximum soil surface temperature for a bare soil is predicted from solar radiation and air temperature (input data) and evaporative flux and soil water content (calculated by the model) using a function developed by Ross et al. (1985). Excessive soil surface temperature (greater than 55C) during the emergence phase reduces population which in turn can impact on biomass production (Carberry and Abrecht, 1991). This is an intrinsic feature of the maize model and, whilst yield potential in the other models was simulated simply by turning their respective water balances off, in the maize model this was achieved by always applying irrigation before soil water content reached levels which would trigger reduction in biomass accumulation. Thus, the high soil temperature function was evoked only in between irrigations after the surface layer had dried out. Sunflower’s sensitivity to frost around flowering (Lovett et al., 1979) is accounted for in the model. If the daily minimum temperature drops to 0°C or below during the sensitive phenological stage, then the model halts at that point (Chapman et al., 1993). The wheat model (Amir and Sinclair, 1991) does not contain any specific responses to extreme temperatures. However, because wheat cannot be successfully grown at the two tropical sites (Katherine and Lansdown) only sunflower and maize simulations were conducted at these locations. In total, 168 long-term simulations were conducted and their CDFs statistically compared between the generated and recorded weather files. We investigated three model variables (days to maturity, total biomass at maturity and final grain yield) which resulted in 504 comparisons. For ease of presentation we combined different
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sowing dates for a model at a site into one CDF. This simplification did not alter our final conclusions. Additionally, we only present data for total above ground biomass and grain yield. The effects of weather data generation on predictions of days to physiological maturity will only be discussed briefly. To compare distributions of each estimated climate variable with recorded data the Kolmogorov-Smirnov test was used to identify significant differences between distributions. However, for ease of interpretation of the data, relative frequency distributions are presented in Figs. l-3 rather than the CDFs tested. Relative frequency distributions were calculated by assigning the value ‘1’ to the category with the highest number of values and expressing all other categories relative to this highest category. This was appropriate so that outcomes could be compared across sites varying in record length. 2.4. Statistics An appropriate statistical test to compare CDFs of actual and generated meteorological data, as well as CDFs of model outputs using such data, was needed. The Kolmogorov-Smirnov two-sample, two-sided test (also referred to as Smirnov test) was deemed to be the most appropriate test to compare two samples drawn from unknown and possibly different distributions. While other tests may also be appropriate (e.g. Mann-Whitney test, parametric t test) they may not detect differences other than differences between the two means or medians thus ignoring differences in distributions such as the extremes. The advantage of Smirnov’s test is that it detects all types of differences that may exist between two distribution functions (Conover, 1971). The test detects the largest difference between two distributions, calculates a test statistic for that difference and then compares it with a tabulated test statistic at a given probability level. The smaller the calculated test statistic is relative to the tabulated test statistic, the smaller are the differences between the distributions in question.
3. Results and discussion 3.1. Comparison
of recorded and predicted
weather data
When comparing weather data generation techniques the differing objectives which led to their development need to be considered. Bristow and Campbell’s (1984) method was developed to demonstrate that a useful relationship exists between solar irradiance and the range of daily temperature extremes. Thus, the method does not contain a stochastic element and estimates the day-to-day variation between solar radiation based on recorded rainfall and temperature; long-term distribution patterns have not been considered in its development. Richardson (1981) developed WGEN to simulate rainfall, solar radiation and temperature data as input into mathematical models which evaluate long-term hydrological changes. The stochastic element within WGEN aims to simulate distributions of weather data identical to
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1 0.8 0.6
(a)
0.4 0.2 0
1 0.8 0.6 0.4
(b)
0.2 0
1 0.8 0.6 0.4 0.2 0 20
10
30
40
Solar Radiation
-Actual
- - - BRISTOW
__
TAMSIM
WGEN
Fig. 1. Comparison of solar radiation distributions (MJ me2 day-‘) generated using either Bristow and Campbell’s method, TAMSIM or WGEN with distributions calculated from recorded data at (a) Katherine, (b) Lansdown, (c) Dalby and (d) Myall Vale.
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those of the original data. Because of this feature it is inappropriate to use WGEN to estimate day-to-day variation. TAMSIM (McCaskill, 1990a) has been developed to estimate missing temperature and solar radiation values from actual rainfall data in cases where stochasticity of the non-rainfall variable is not required. It is less complex than either of the other methods in its approach and, because it does not contain a stochastic element, can be used to assess day-to-day variations. The different underlying objectives became particularly apparent when the weather data generators were compared using actual vs. predicted data, a method which examines the adequacy of prediction of daily variation in the data. Such a comparison showed that Bristow and Campbell’s method would be preferable to TAMSIM if the objective was to reproduce radiation data for a given day (data not presented). It would not be appropriate to subject WGEN generated data to such a test because of its stochastic feature. For our objectives other testing procedures are required. 3.1 .I. Solar radiation Statistical comparison showed that all CDFs of generated solar radiation data differed significantly from the recorded data (Table 3, Figs. l(a)-(d)). WGEN, however, had the lowest Smirnov test statistic at all sites except Katherine, indicating that the distribution of WGEN-generated data differed least from the recorded data. When radiation data were classified into three groups, i.e. low (less than 8 MJ m* day-‘), medium (8-26 MJ m* day-‘) and high (greater than 26 MJ m2 day-‘), WGEN simulated all categories well except for low values at Dalby were the number of days in this category was overpredicted. TAMSIM also performed adequately but tended to underpredict both the low and high extremes. Bristow and Campbell’s method performed well at Myall Vale, the most temperate site, but overpredicted Table 3 Comparison temperature distributions Variable
of simulated versus actual solar radiation (Rad), maximum temperature (Tmax) and minimum (Tmin) distributions. Values followed by * have distributions differing significantly from of actual data at P < 0.05 using Smirnov’s test. Site
Prediction
method
Bristow
TAMSIM
WGEN
P 0.05
0.15* 0.14* 0.12* 0.11*
0.10* 0.13* 0.11* 0.11*
0.17* 0.08* 0.06* 0.08*
0.02 0.02 0.03 0.02
Radiation
Katherine Lansdown Dalby Myall Vale
Tmin
Katherine Lansdown Dalby Myall Vale
0.11* 0.11* 0.13* 0.11*
0.03* 0.05* 0.02 0.05*
0.02 0.02 0.03 0.02
Tmax
Katherine Lansdown Dalby Myall Vale
0.10* 0.11* 0.12* 0.10*
0.06* 0.08* 0.05* 0.07*
0.02 0.02 0.03 0.02
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the high categories at the three other sites and underpredicted the medium category at the two tropical sites. Bristow and Campbell (1984) acknowledged that their method required further refinements adequately to accommodate summer rainfall areas. 3.1.2. Minimum temperature (Figs. 2(a)-(d)) Prediction of minimum temperatures is important particularly when using models which predict potential frost damage to crops. There were significant differences between simulated and actual minimum temperature distributions in virtually all cases, but the performance of WGEN, as judged by the Smirnov statistic, was two to six times better than for TAMSIM (Table 3). Minimum temperature distributions from TAMSIM tended to be deficient in values at the low and high ends of the range (Figs. 2 (a)-(d)). This is to be expected because TAMSIM does not contain a stochastic element. When minimum temperature data are missing or large gaps in minimum temperature records need to be filled, WGEN can produce a satisfactory data set, given the right input parameters. However, TAMSIM can still be useful in cases where only small gaps need to be filled and the stochastic feature of WGEN is either not required or even not desired (McCaskill, 1990b). 3.1.3. Maximum temperature (Figs. 3(a)-(d)) Similar to minimum temperature, maximum predicted best using WGEN although CDFs recorded data at the 95% level (Table 3). As prediction, TAMSIM did not predict extreme short-term gaps. 3.2. Sensitivity
tests using crop simulation
temperatures and their extremes were were significantly different from the in the case of minimum temperature values well but could be useful to fill
models
The CDFs of simulated number of days to physiological maturity, total above ground biomass production and grain yield for all sites and models were statistically compared and the ones significantly different from those using the recorded weather data were identified. The total number of simulations conducted at each site for each model differed depending on the number of sowing dates per year and number of complete seasons which could be simulated with the available weather data sets for different sowing dates. At Dalby, for instance, the sunflower model was run for four sowing dates, three of those dates resulting in the simulation of ten seasons each and one sowing date resulting in the simulation of 11 seasons, i.e. the total number of simulated seasons (n) was 41 (Table 4). 3.2.1. Efects on development As expected, none of the radiation data generators had any influence on the predicted number of days to physiological maturity. Similarly, when temperature data was simulated using WGEN no significant differences were detected. However, when TAMSIM was used for temperature generation, the distributions differed significantly in each case because of TAMSIM’s inability to simulate extreme events (data not presented).
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l0.8
-
0.6
-
0.4
-
0.2
-
11 0.8
-
l0.8
-
l0.8
-
0.6 Id)
0.4 0.2 0-l
-
-10
0
10
Minimum
-Actual
__
20
30
Temperature
TAMSIM
-) j’--WGEN
Fig. 2. Comparison of minimum temperature distributions (Tmin, “C) generated using either TAMSIM or WGEN with distributions calculated from recorded data at (a) Katherine, (b) Lansdown, (c) Dalby and (d) Myall Vale.
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1
0.8 0.6 (a)
0.4 0.2 0
0.8 J 0.6 (d)
0.4 0.2
-
0 i 0
10
20
30
Maximum
-Actual
__
40
60
Temperature
TAMSIM
--e
WGEN
Fig. 3. Comparison of maximum temperature distributions (Tmax, “C) generated using either TAMSIM or WGEN with distributions calculated from recorded data at (a) Katherine, (b) Lansdown, (c) Dalby and (d) Myall Vale.
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Table 4 Kolmogorov-Smirnov test statistics for the comparison of the distributions of total above ground biomass and grain yield (values in italics), generated by crop simulation using actual weather data, with distributions containing n number of elements each generated using five methods of weather data generation. The comparisons were conducted for three crops at four sites. Values followed by * differ significantly at P < 0.05. Site
P 0.05
n
Katherine Sunflower
129
0.17
Maize
64
0.24
Lansdown Sunflower
70
0.23
Maize
36
0.32
Dalby Sunflower
41
0.30
Wheat
20
0.43
Maize
20
0.43
Myall Vale Sunflower
70
0.23
Wheat
34
0.33
Maize
36
0.32
Weather
data generation
method
Bristow rad
TAMSIM rad
WGEN rad
TAMSIM rad, temp
WGEN rad, temp
0.19* O.lF 0.19 0.17
0.09 0.08 0.17 0.23
0.05 0.05 0.25* 0.24*
0.16 0.13 0.27* 0.27*
0.12 0.08 0.25* 0.22
0.26* 0.27* 0.25 0.27
0.11 0.09 0.28 0.28
0.11 0.08 0.25 0.25
0.19 0.19 0.44* 0.42*
0.20 0.17 0.19 0.2I
0.27 0.28 0.65* 0.65* 0.25 0.18
0.10 0.11 0.15 0.05 0.25 0.25
0.12 0.15 0.15 0.15 0.20 0.20
0.15 0.26 0.30 0.26 0.30 0.27
0.12 0.14 0.20 0.26 0.20 0.26
0.20 0.20 0.59* 0.51* 0.14 0.13
0.07 0.08 0.24 0.18 0.19 0.21
0.10 0.11 0.24 0.24 0.22 0.12
0.11 0.14 0.15 0.20 0.33* 0.33*
0.06 0.07 0.35* 0.23 0.19 0.14
3.2.2. Efects on growth In our discussion we will concentrate on biomass production rather than yield. Although grain yield is generally a reflection of total biomass, as shown in Table 4, it is conceivable that certain climatic conditions during grain filling could result in large discrepancies. However, our results indicate that grain yield might be less responsive to the methods of weather data generation than biomass. In two instances, i.e. maize at Katherine and wheat at Myall Vale, significant differences caused by the generation of temperature and radiation data using WGEN were found for biomass but not for yield (Table 4). We have chosen the location ‘Myall Vale’ as an example to demonstrate how similar or dissimilar were the biomass CDFs for the three models. Figs. 4(a)-(c) show the CDFs for maize (a), sunflower (b) and wheat (c) for the three methods of
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0.8 0.6
0
s s ic h
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l0.8
-
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-
L : ‘E 0 _jE
5
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Total Biomass (g m-2) -Actual
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Fig. 4. Comparison of cumulative distribution functions (CDFs) of simulated total biomass production (g me2) at Myall Vale using either Bristow and Campbell’s method, TAMSIM or WGEN to predict daily solar radiation. The three methods are compared with simulations conducted using recorded weather data (Actual) and using three simulation models: (a) a maize model, (b) a sunflower model and (c) a wheat model.
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generating solar radiation; Figs. 5(a)-(c) show the corresponding CDFs for methods generating both solar radiation and temperature. Low maize biomass yields (less than 1600 g m-2) apparent in Figs. 4(a) and 5(a) were caused by the function which predicts lethal conditions for emerging seedlings because of excessive soil surface temperatures. Only simulations conducted using TAMSIM radiation and temperature data (Fig. 5(a)) did not show the low population response because of TAMSIM’s inability to reproduce extreme temperatures. According to Table 4, none of the sunflower CDFs differed from the original (Figs. 4(b) and 5(b)). Solar radiation values produced using Bristow and Campbell’s method resulted in a significantly different wheat CDF (Fig. 4(c)). This was caused by an overprediction of radiation values in the 20-28 MJ m2 day-’ range (clearly visible on Fig. l(d)), which occurred mainly in winter and spring, i.e. during the wheat growing season. Similarly, maize using TAMSIM radiation and temperature and wheat using WGEN radiation and temperature, resulted in significantly different CDFs (Figs. 5(a) and 5(c)). The significantly different wheat simulation using WGEN radiation and temperature (Fig. 5(c)) was caused entirely by the 15 May sowing date (data not presented) for which in some years the predicted temperatures were higher than the recorded temperatures resulting in a faster developmental rate. Consequently, crop duration was shortened by up to 20 days which led to lower biomass yields. This difference, however, was not carried through to grain yield. The same effect was evident in sunflower (18 days maximum difference), but did not result in significantly different biomass or grain yields. Briefly, from the ten biomass comparisons conducted for each method (2 sites x 3 models + 2 sites x 2 models), four comparisons differed significantly for Bristow and Campbell’s method, none for TAMSIM radiation, one for WGEN radiation, three for TAMSIM radiation and temperature, and two for WGEN radiation and temperature files. The results for grain yields were similar, except for WGEN radiation and temperature files which did not result in any significant differences in grain yield (Table 4). When both variables, radiation and temperature, were predicted, WGEN clearly outperformed TAMSIM because of the ability of generating the extreme temperatures of the distribution (Figs. 2 and 3). However, caution is still warranted when predicting both variables using WGEN as the maize simulations at Katherine show: under these conditions the maize model using recorded weather data predicted that high soil surface temperatures would reduce plant population in 45% of all cases. Using WGEN generated weather data the model predicted a reduced population in 65% of cases and using TAMSIM in 25% of cases. These differences resulted in significantly different output CDFs for both methods as shown in Table 4. For the 1 March sowing at Dalby the frost damage routine of the sunflower model was evoked twice during a 10 year run when using recorded data. Using WGEN radiation and temperature had the same result, but the actual years in which frost was predicted, differed. At Myall Vale 4 out of 19 years resulted in predicted frost damage but only one such case was predicted when using WGEN radiation and temperature data. However, this did not affect the overall shape of the biomass CDF (Fig. 5(b)), Generally, the prediction of extreme minimum temperatures using
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__
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1200
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TAMSIM
-/_.--I WGEN
Fig. 5. Comparison of cumulative distribution functions (CDFs) of simulated total biomass (g m-*) production at Myall Vale using either TAMSIM or WGEN to predict daily solar radiation, maximum and minimum temperatures. The two methods are compared with simulations conducted using recorded weather data (Actual) and using three simulation models: (a) a maize model, (b) a sunflower model and (c) a wheat model.
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WGEN seemed adequate (Fig. l(d)). This result demonstrates that WGEN generated data is adequate when only the long-term distribution of simulation results are to be considered. The method is not appropriate when crop performance in a specific season is to be evaluated, nor is it appropriate to use such weather data for the analysis of frost risks using climatic indices such as the southern oscillation index (Stone et al., 1993). Although there is general evidence of global warming, spatial and temporal variation of the supporting data is considerable (Karl et al., 1993). Techniques used to generate temperature data do not explicitly account for global warming. However, by calibrating a temperature data generator using actual, recent temperature data to re-create historic records (the most likely scenario) a very slight bias towards overpredicting the historic temperature data might be introduced. Such an overprediction would reflect the most recent temperature records, thus adjusting historic values to a present climate. Therefore, simulation models used with such data sets would produce probability distributions which are more relevant to today’s agricultural production because they would quantify today’s climatic variability more accurately. In other words, global warming is implicitly accounted for through the calibration process on a case by case basis.
4. Conclusions We have demonstrated how crop simulation models can be used to assess the adequacy and quality of weather data generation. Weather data is used in crop simulation modelling not only to predict crop growth and development in response to these environmental variables but also to flag catastrophic events as in the case of the maize model (heat damage) and the sunflower model (frost damage). Often such effects can only be assessed by using the weather data as input into the simulation models in question because models act as data filters and integrate the effect of deviations of generated from actual data. Model complexity did not appear to influence model sensitivity to differences in environmental input data. Both TAMSIM radiation and WGEN radiation performed adequately and the overall performance of the two methods appeared to be similar. Bristow and Campbell’s method was inappropriate for our objectives, because output from two of three crop models was sensitive to this method of prediction. Although we regard WGEN as the appropriate method for the long-term generation of temperature data, we also found TAMSIM useful in instances where data contained only small gaps. In such cases the absence of a stochastic element in TAMSIM was advantageous because it avoided the possibility of rare, extreme events being introduced during short gaps (less than 5 successive days) in the data sets. Our results indicate that (i) When only rainfall and temperature data are available, both TAMSIM WGEN are suitable methods of generating radiation data.
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(ii) When only rainfall data are available, WGEN is the most suitable method of generating temperature and radiation data, because extreme events such as frosts are included in the temperature data sets. (iii) Where the temperature record contains short gaps (less than 5 days), TAMSIM is a suitable method of data reconstruction, because extreme events are not introduced during these gaps. (iv) Bristow and Campbell’s method of generating solar radiation resulted in overprediction of high (greater than 26 MJ mz day-‘) solar radiation values at three sites and an underprediction of medium (8-26 MJ m* day-‘) values at the two tropical sites. These effects carried through to the final simulation results. We have used these methods to generate long-term weather locations throughout Eastern Australia from existing rainfall data. The data sets are currently used for simulation analyses assessments (e.g. Meinke et al., 1993b).
data sets for 53 and temperature and climatic risk
Acknowledgements This project was financially supported by GRDC and RIRDC. We also wish to thank Dr Keith Bristow for his helpful comments on the manuscript.
References Amir, J. and Sinclair, T.R., 1991. A model of the temperature and solar-radiation effects on spring wheat growth and yield. Field Crops Res., 28: 47758. Angus, J.F., 1991. The evolution of methods for quantifying risk in water limited environments. In: R.C. Muchow and J.A. Bellamy (Editors), Climatic Risk in Crop Production: Models and Management for the Semiarid Tropics and Subtropics. C.A.B. International, Wallingford, UK, pp. 39-54. Bristow, K.L. and Campbell, G.S., 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric. For. Meteorol., 3 1: 159- 166. Brock, T.D., 1981. Calculating solar radiation for ecological studies. Ecol. Model., 14: l-19. Bureau of Meteorology, 1988. Climatic Averages Australia. Australian Government Publishing Service, Canberra, Australia, 532 pp. Carberry, P.S. and Abrecht, D.G., 1991. Tailoring crop models to the semiarid tropics. In: R.C. Muchow and J.A. Bellamy (Editors), Climatic Risk In Crop Production: Models and Management for the Semiarid Tropics and Subtropics. C.A.B. International, Wallingford, UK, pp. 1577182. Carberry, P.S. and Muchow, R.C., 1992. A simulation model of kenaf for assisting fibre industry planning in Northern Australia. III. Model description and validation. Aust. J. Agric. Res., 43: 152771545. Chapman, S.C., Hammer, G.L. and Meinke, H., 1993. A crop simulation model for sunflower. I. Model development. Agron. J., 85: 725-735. Chia, E., 1990. Some fundamental questions in stochastic simulation of weather sequences, with applications to ANUCLOUD. Math. Comput. Sim., 32: 101-106. Clarkson, N.M. and Owens, D.T., 1991. Rainman-rainfall Information for Better Management. Queensland Department of Primary Industries, Brisbane, 49 pp. Clemence, B.S.E., 1991. A simple method for supplementing long-term weather records in a maizemodelling exercise. S. Afr. J. Plant Soil, 8(l): 27-30.
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Conover, W.J., 1971. Practical Nonparametric Statistics. Wiley, New York, 462 pp. Cook, S.J. and Russell, J.S., 1983. The climate of seven CSIRO field stations in Northern Australia. CSIRO Division of Tropical Crops and Pastures Tech. Paper No 25, Commonwealth Scientific and Industrial Research Organisation (Ed.), Australia, 38 pp. Guenni, L., Charles-Edwards, D., Rose, R., Braddock, R. and Hogarth, W., 1990. Stochastic weather modelling: a phenomenological approach. Math. Comput. Sim., 32: 113-l 18. Hammer, G.L. and Muchow R.C., 1991. Quantifying climatic risk to sorghum in Australia’s semiarid tropics and subtropics: model development and simulation. In: R.C. Muchow and J.A. Bellamy (Editors), Climatic Risk in Crop Production: Models and Management for the Semiarid Tropics and Subtropics. C.A.B. International, Wallingford, UK, pp. 205-232. Hammer, G.L., Irmansyah, Meinke, H., Wright, G.C. and Bell, M.J., 1992. Development of a peanut growth model to assist in integrating knowledge from management and adaptation studies. In: G.C. Wright and K.J. Middleton (Editors), Proceedings of an ACIAR/AARD/QDPI Collaborative Review Meeting, Malang, East Java, 19-23 August 1991, ACIAR Proceedings No. 40, 108 pp. Hutchinson, M.F., 1991. Climatic analyses in data sparse regions. In: R.C. Muchow and J.A. Bellamy (Editors), Climatic Risk in Crop Production: Models and Management for the Semiarid Tropics and Subtropics. C.A.B. International, Wallingford, UK, pp. 55-71. Jamieson. A., Hammer, G.L. and Meinke, H., 1992. A computer based decision aid for sunflower, sorghum and wheat. Proceedings 13th International Sunflower Conference, Vol. I, 1992, Pisa, Italy, pp. 202-207. Jones, C.A. and Kiniry, J.R., 1986. CERES-Maize. A Simulation Model of Maize Growth and Development. Texas A & M University Press, College Station, 194 pp. Jones, C.A., Wegener, M.K., Russell, J.S., McLeod, I.M. and Williams, J.R., 1988. AUSCANE-Simulation of Australian Sugarcane with EPIC. CSIRO Division of Tropical Crops and Pastures. Technical Paper No. 29, Brisbane, 99 pp. Karl, T.R., Jones, P.D., Knight, R.W., Kukla, G., Plummer, N., Razuvayev, V., Gallo, K.P., Lindseay, J., Charlson, R.J. and Peterson, T.C., 1993. A new perspective on recent global warming: asymmetric trends of daily maximum and minimum temperature. Bull. Am. Meteorol. Sot., 74(6): 100771023. Larsen, G.A. and Pense, R.B., 1982. Stochastic simulation of daily climatic data for agronomic models. Agron. J., 74: 510-514. Lovett, J.V., Harris, H.C. and McWilliam, J.R., 1979. Sunflower. In: J.V. Lovett and A. Lazenby (Editors), Australian Field Crops Volume 2. Tropical Cereals, Oilseeds, Grain Legumes and Other Crops. Angus and Robertson, Sydney, Australia, pp. 137-160. McCaskill, M.R., 1990a. TAMSIM-a program for preparing meteorological records for weather-driven models. Tropical Agronomy Technical Memorandum No 65, 1990. CSIRO, Div. of Tropical Crops and Pastures, Brisbane, pp. 26. McCaskill, M.R., 1990b. An efficient method for generation of full climatological records from daily rainfall. Aust. J. Agric. Res., 41(3): 595-602. McCaskill, M.R., 1992. Generation of long-tetm weather data sets for Charters Towers. Tropical Agronomy Technical Memorandum No 72, 1992. CSIRO, Div. of Tropical Crops and Pastures, Brisbane, 23 pp. Meinke, H., Hammer, G.L. and Chapman, SC., 1993a. A crop simulation model for sunflower. II. Simulation analysis of production risk in a variable sub-tropical environment. Agron. J., 85: 735-742. Meinke, H., Hammer, G.L., Hills, M.A. and Hatfield, P.M., 1993b. Potential yield of peanuts in northeastern Australia. In: Australian Society of Agronomy (Editors), Seventh Australian Agronomy Conference. Adelaide, South Australia. 19924 September 1993, pp. 182-185. Numerical Algorithms Group (NAG), 1983. The Nag Fortran PC50 Library Handbook-Release 1. Richardson, C.W., 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res., 17(l): 182-190. Richardson, C.W., 1984. Weather simulation for crop management models. ASAE, Paper no 84-4541, New Orleans, Louisiana, December 11-14. Richardson, C.W. and Wright, D.A., 1984. WGEN: A model for generating daily weather variables. U.S. Dept. of Agriculture, Agricultural Res. Service, ARS-8, pp. 83. Ross, P.J., Williams, J. and McCown, R.L., 1985. Soil temperature and the energy balance of vegetative
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mulch in the semiarid tropics, I. Static analysis of the radiation balance. Aust. J. Soil Res., 23: 493-514. Shorter, R., Lawn, R.J. and Hammer, G.L., 1991. Improving genotypic adaptation in crops-a role for breeders, physiologists and modellers. Exp. Agric., 27: 155-175. Stone, R.C, Hammer, G.L. and Woodruff, D., 1993. Assessment of risk associated climate prediction in management of wheat in north-eastern Australia. In: Australian Society of Agronomy (Editor) Seventh Australian Agronomy Conference, Adelaide, South Australia, 19-24 September 1993, pp. 174-177.
ELSEVIER
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Evaluation of radiation and temperature data generators in the Australian tropics and sub-tropics using crop simulation models H. Meinke *3a P.S. Carberrya, M.R. McCaskillb,‘, M.A. Hillsa,2, I. McLeod” aQDPI/CSIRO
igricultural
Production Systems Research Unit, PO Box 102, Toowoomba, Qld.. 4350, Australia ‘Queensland Department of Primary Industries, Division of Forage and Animal Management, PO Box 46. Brisbane, Qld., 4001, Australia. ‘CSIRO. Division of Tropical Crops and Pastures, 306 Carmody Rd, St Lucia, Qld., 4067, Australia
Received 20 September 1993; accepted 17 March 1994
Abstract Long-term historical weather data are needed to conduct crop simulation analyses. However, the network of weather recording stations which collect all necessary daily weather data (commonly rainfall, solar radiation, maximum and minimum temperature) for such analyses is sparse. Frequently only rainfall is recorded. Thus, weather data generation techniques are required for three situations: (i) where only rainfall data are available, (ii) where both rainfall and temperature data are available, but radiation is missing, and (iii) where records are otherwise complete, but techniques are required to fill short periods of missing data. Three weather generation techniques are compared, termed here (i) Bristow and Campbell’s method, (ii) TAMSIM and (iii) WGEN. Methods (ii) and (iii) were used to generate temperature and radiation data to accompany recorded rainfall records, and methods (i)-(iii) to generate a solar radiation record to accompany recorded temperature and rainfall records. Data from four stations in tropical and subtropical Australia with long-term complete weather records were used to compare actual with generated data sets. Results were evaluated firstly by comparing the cumulative distribution function (CDF) of generated and actual values, and secondly by comparing CDFs calculated from the output of three crop simulation models used with the generated and actual data sets. Generally the distributions of radiation and temperature differed significantly. However, when the weather data sets were used by simulation models to estimate biomass, only 10 of the 50 CDFs differed significantly. When both temperature and radiation were generated, 30%
*Corresponding author. ’Present address: Universitas Mataram, PO Box 1106, Mataram 83125, Lombok, Indonesia. ‘Present address: Brisbane City Council, GPO Box 1434, Brisbane, Qld. 4001, Australia. 01681923/95/$09.50 0 1995 ~ Elsevier Science B.V. All rights reserved SSE! 0168-1923(94)02159-H
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of CDFs from TAMSIM and 20% of WGEN differed significantly. When only radiation was generated, 40% of CDFs generated by the Bristow and Campbell’s method, 10% of WGEN and none of TAMSIM differed significantly. All methods simulated the more temperate sites with higher precision than the wet, tropical site. Simulated yields showed a similar pattern. It was concluded that where both temperature and radiation data have to be generated, WGEN is appropriate because it contains a stochastic element and thus simulates catastrophic events such as frosts. Where only radiation generation is required, both WGEN and TAMSIM performed adequately. Where temperature or radiation data sets are complete except for occasional missing days, TAMSIM was considered to be the most appropriate. In cases were the objective is not to conduct long-term simulation analyses, Bristow and Campbell’s method appeared more appropriate because of its ability to better simulate the day to day variation in solar radiation.
1. Introduction In agricultural science and extension, simulation models of either individual crops or whole farming systems are becoming increasingly important tools for a wide range of applications, including genotypic improvement (Shorter et al., 1991), assessing the viability of new industries (Carberry and Muchow, 1992) quantifying climatic risk (Hammer and Muchow, 1991; Meinke et al., 1993a,b) and the development of decision support systems (Jamieson et al., 1992). Such models quantify climatic constraints to crop production and recommendations derived from their use assume that the future climate will fit the same distribution as the historical climate used in the analyses. Most crop models require daily climatic input to simulate crop responses (e.g. Jones and Kiniry, 1986; Amir and Sinclair, 1991; Hammer and Muchow, 1991; Carberry and Muchow, 1992; Hammer et al., 1992; Chapman et al., 1993). Whilst these climatic variables are readily available for the experiments used in model development and validation, it is difficult and, in many cases, impossible to obtain weather records suitable for simulation analysis at other locations and/or for an appropriately long period. In regions with high climatic variability it is particularly important to simulate long time sequences in order adequately to assess effects of climatic variability. To reflect seasonal variability and trends in crop production, sufficiently long weather records need to be available adequately to sample the population. For the most commonly used models, this requires long-term daily values of rainfall, solar radiation, maximum and minimum temperatures. When such weather data are available, they are frequently either incomplete (‘gappy’) or of dubious quality. Many workers have recognised this problem (e.g. McCaskill, 1990b; Clemence, 1991). This has led to the development of a range of weather generators such as WGEN (Richardson and Wright, 1984) TAMSIM (McCaskill, 1990a) and others (e.g. Brock, 1981; Larsen and Pense, 1982; Bristow and Campbell, 1984; Guenni et al., 1990; Hutchinson, 1991) which can be used to either synthesise non-existing weather data or to fill incomplete sets. In semiarid regions the major constraint to crop production is water availability
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which is strongly related to rainfall amount, frequency and distribution. This is particularly true for Australia (Angus, 1991) and has led to the development of a dense network of rainfall recording stations in towns and on major rural properties throughout the country, many of which go back 80 years or more. The density of stations with temperature records is, however, much more limited than for rainfall, and even fewer stations have records of solar radiation. A preliminary survey of data availability showed that for approximately every 100 rainfall recording stations in the Australian state of Queensland with a record length of more than 80 years, about 10 have a long-term temperature record (more than 35 years), and one has a long-term radiation record (more than 10 years). Within the long-term temperature or radiation records, there are sometimes gaps of several days when equipment was being repaired or was not read, which even for good records may account for up to 15% of the total record. However, gaps in the rainfall records are rare, and can usually be filled for simulation purposes by using a nearby station (Clarkson and Owens, 1991). Three kinds of data reconstruction are therefore required (i) to provide temperature and radiation data at stations where only rainfall has been recorded, (ii) to estimate radiation where temperature and rainfall have been recorded; and (iii) to fill short gaps in temperature and radiation data. Because long-term, recorded daily rainfall data are readily available throughout Australia (Clarkson and Owens, 1991), it is appropriate to use these values to predict temperature and solar radiation data, given the limitations associated with generating daily rainfall values (Hutchinson, 1991). Using recorded rather than predicted rainfall values as the base variable to predict other climatic variables improves the quality of the generated data substantially and overcomes some of the inadequacies discussed by Hutchinson (1991) and Chia (1990). Thus, in our efforts we concentrated on the generation of daily values of solar radiation and temperature data alone, with the main emphasis on the prediction of solar radiation data. The quality of model output can be directly related to the quality of data used as input. It follows that the testing of the sensitivity of model output to the quality of generated weather data is an essential prerequisite for simulation analyses. Notwithstanding this point, the three weather generation techniques, Bristow and Campbell (1984), TAMSIM (McCaskill, 1990a) and WGEN (Richardson and Wright, 1984) have all been used for simulation analyses in northern Australia by Meinke et al. (1993a), Carberry and Abrecht (1991) and Jones et al. (1988) respectively. Hence, the objective of this paper is to compare the performance of these three temperature and radiation generators by examining the influence of generated weather data on crop simulation output in this climatic zone of Australia. Of importance is the ability to produce weather data consistent with the long-term distribution pattern of the recorded data for a site. Clemence (1991) conducted a similar study investigating the sensitivity of the CERES-Maize model (Jones and Kiniry, 1986) to two simple weather data infilling methods at four locations in Natal (South Africa). The study, however, assumes only one sowing date (12 November), and consequently only considers the adequacy of less than one third of weather data available each year. The author also stops short of statistically comparing model output distributions.
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2. Methods 2.1. Radiation
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The three methods of predicting daily temperature in this paper are based on the following principles:
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data compared
2.I.I. Bristow and Campbell’s (1984) method-solar radiation prediction only This method of predicting solar radiation (Q, MI m2 day-‘) is based on a relationship between the daily, extra-terrestrial solar radiation for a specific location (Qo), the fraction of daily total atmospheric transmittance of this extra-terrestrial irradiance (7’,) and the daily range of air temperatures (AT(j)), calculated from the maximum and the two adjacent minima temperatures
Q0 = 86400So (h, sin 4 sin 0 + cos Cpcos u sin h,) /7r T, = A[1 - exp(-sAT( AT(j)
= Tmax(j)
- [Tmin(j)
+ Tmin(j
+ 1)]/2
(1)
where So is the solar constant (1360 W m-*), h, is half daylength, 4 is the latitude of the location and (Tis the solar declination (all in radians). The coefficient A represents the maximum clear sky Tt characteristic of the site, and B and C determine how soon maximum Tt is achieved as AT(j) increases. Tmax(j) and Tmin(j) represent the maximum and the minimum air temperature on dayj. To allow for reduced radiation loads under rainy conditions AT(j) is reduced on wet days by 25%. For extended rain periods it is assumed that equilibration between incoming radiation and AT occurs, thus the correction is only applied to the first 2 days of any rain period. All coefficients are determined empirically from measured solar radiation data using iterative optimisation procedures (simplex method, Numerical Algorithms Group (NAG), 1983). Values for coefficient A were determined from visually examining daily solar radiation values over time and comparing the maximum values achieved with the corresponding extraterrestrial radiation on that day. This resulted in a value for A of 0.8 for all cases except at the location ‘Myall Vale’ where for the summer seasons A had a value of 0.7 (details of climatic sites are discussed later and in Table 1). Myall Vale was the only location where fitting separate values for parameter A depending on season (as suggested by Bristow and Campbell, 1984) improved predictions. In accordance with Bristow and Campbell (1984) varying values for parameter C did not improve predictions and it was thus kept constant at a value of 2.4. Coefficient B was fitted to summer (15 September to 14 March) and winter (15 March to 14 September) seasons individually. The values ranged in summer from 0.0045 (location ‘Dalby’) to 0.0062 (location ‘Lansdown’) and in winter from 0.0039 (location ‘Katherine’) to 0.0078 (location ‘Lansdown’).
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Once the coefficients are determined, daily radiation values can be predicted providing daily temperature and rainfall data are available. The program does not include a stochastic element. 2.1.2. TAMSIM (McCaskill, 1990a)-solar radiation and temperature prediction Daily weather data from each site were used to calculate coefficients for the function P = a + bcos(t9) + csin(B) + dcos(28) + esin(2B) +fRj_i
+ gRj + hRj+,
(2)
where P is the meteorological parameter (maximum temperature, minimum temperature or radiation), 0 is the day number converted to a radian form (19= day number x 2n/365), R’ is the rainfall converted to a binary form (0 for no rain and 1 for rain), the subscriptsj - 1, j andj + 1 refer to the previous, current and following day, and coefficients a-h are calculated empirically by multiple regression analysis. This method does not contain a stochastic element. For each site, three weather data sets were calculated using this method: (a) predicted solar radiation, but actual temperature (TAMSIM radiation); (b) predicted solar radiation and temperature (TAMSIM radiation and temperature); and (c) recorded solar radiation and temperature when available, but missing periods (less then 1% of the record) filled using this method. These data sets are referred to as ‘actual’, and were used to derive coefficients for methods (2.1.1.) and (2.1.3.). 2.1.3. WGEN (Richardson, 1981; Richardson and Wright, 1984)-solar temperature prediction
radiation and
In the program WGEN the prediction of solar radiation and maximum and minimum temperature is considered to be a continuous multivariate stochastic process with daily means and standard deviations varying depending on wet or dry days. The program calculates smoothly varying daily mean variable values by fitting single sine curves to monthly parameter values which need to be supplied for each site. At least 5 years of actual climate records are recommended to derive these parameter values. The time series of each variable is reduced to a time series of residual elements by removing the periodic means and standard deviations. This results in residual series which are stationary in their mean and standard deviations, having a mean of zero and a standard deviation of unity. Serial correlations for each time series of residual elements and cross correlations between each pair of variables are determined. Missing values are then generated using the following calculation
xp,i(j) = A&,i-1 (A + B~p,i(j)
(3)
where X,,,(j) and XP,i_l(j) are 3 x 1 matrices for days i and i - 1 of year p whose elements are residuals of maximum temperature (j = l), minimum temperature (j = 2) and solar radiation (j = 3); Ep,i(j) is a 3 x 1 matrix of independent random components, normally distributed with a mean of zero and a variance of unity and A and B are 3 x 3 matrices whose elements are defined such that the new sequences have the desired serial correlation and cross-correlation coefficients. These newly
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determined residuals are then multiplied by the standard deviation and the mean is added to determine the final values of the variable (Richardson, 1981). For more details and an in-depth discussion of underlying processes see Richardson (1981, 1984) and Richardson and Wright (1984). 2.2. Weather data Four Australian climate stations with long-term measured and reliable daily weather data (i.e. daily maximum and minimum temperatures and solar radiation) were identified (Table 1). Record length differed among climate stations. Dalby had the shortest record (3804 days or 10.4 years) and Katherine had the longest (11840 days or 32.4 years). The stations were purposely selected to cover a wide geographical area and several climatic zones in Australia (Table 1). Katherine represents the monsoonal tropics, Lansdown the coastal dry tropics, Dalby an area in the summer rainfall dominated cropping region of sub-tropical Australia, and Myall Vale, although summer rainfall dominated, a cropping system also strongly influenced by winter rainfall and roughly located on the border between the sub-tropical and temperate climate zones. The geographical spread ensures that the method finally selected to generate missing weather data can be used confidently across these climatic zones. Actual radiation and temperature data were used to calculate the necessary input parameter values for the various weather data prediction techniques. Actual recorded rainfall data from each site was used as a base input variable for each of the weather generators. Cumulative distribution functions (CDFs) of each predicted variable were calculated from these five weather files at each of the four locations. These CDFs were then compared with CDFs of the actual, recorded data and tested for significant differences using the Kolmogorov-Smirnov Test (Conover, 1971). Details of this statistical test are described later. Table 1 Geographic location of sites and their yearly climatic averages for maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rad) and rainfall (Cook and Russell 1983; Bureau of Meteorology 1988; McCaskill 1992); starting year of climate files and total number of years for which complete records are available.
Latitude. “S Longitude, “E Elevation, m Tmax, “C Tmin, “C Rad, MJ mm2 day-’ Rain, mm Start of climate records No. of years
Katherine
Lansdown
Dalby
Myall Vale
14.5 132.3 108 34.1 19.5 22.5 955 1960 33
19.4 146.5 61 30.1 18.2 19.9 724 1973 20
21.2 151.2 344 26.0 12.2 19.5 614 1982 11
30.1 149.8 210 26.2 11.5 18.0 660 1973 20
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2.3. Sensitivity tests using crop simulation models 2.3.1. Models used
To evaluate the sensitivity of crop models to recorded versus generated weather data, we used three crop models with each of the recorded and predicted weather files. All three crop models are based on a daily time-step and simulate crop growth and development dynamically as a function of temperature and solar radiation. To test the data sets under a range of different conditions we chose models of a summer crop (maize; Carberry and Abrecht, 1991) a winter crop (wheat; Amir and Sinclair, 1991) and a crop which can be grown under most environmental conditions within our region of interest, but is sensitive to frost around flowering (sunflower, Chapman et al., 1993). Although all models require the same environmental input data they differ in their design complexity and can be classed as (i) complex (maize), (ii) intermediate (sunflower) and (iii) simple (wheat). Functions describing development are mainly driven by temperature and sometimes photoperiod whereas functions describing crop growth are controlled by temperature, solar radiation, water and nitrogen availability (except for the sunflower model which does not simulate nitrogen responses). Crop phenology, a developmental process, is divided into several growth stages, the duration of which are predicted based on daily temperature and in some cases photoperiod. Leaf area development in maize and sunflower is described as functions of initiation, appearance, expansion and senescence of leaves. These functions are sensitive to photoperiod, temperature, water stress and, in the case of maize, nitrogen availability. In the wheat model crop leaf area is simulated as a function of accumulated daily temperature, water and nitrogen availability. Potential dry matter production is predicted from leaf area index, a light extinction coefficient and the crop’s radiationuse efficiency. Potential transpiration is predicted as a function of daily biomass accumulation, a transpiration efficiency coefficient and predicted daily vapour pressure deficit. Plant water uptake is dependent on the available water range of the soil. Daily biomass accumulation, transpiration and leaf growth are decreased below potential values once the fraction of available soil water declines below threshold values. In each model accumulated biomass is partitioned to a yield component, the rate of which depending on climatic conditions at and after anthesis. Soil evaporation, rainfall infiltration, soil water drainage and, in the case of the maize and sunflower model, runoff are also simulated in the respective water balance routines. The sensitivity of the functions describing crop growth and development to climatic input parameters differs widely. In the context of this paper only their integrated effect in the form of biomass and yield prediction is assessed. 2.3.2. Simulations In Australia insufficient plant available soil water commonly overwrites any effects of variation in temperature and radiation (Meinke et al., 1993a). To eliminate this large influence of water availability on crop growth all models were compared under optimal conditions (i.e. water and nutrient non-limiting for plant growth). No effects
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Table 2 Sowing dates used for each crop and location
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study
Crop
Katherine
Lansdown
Dalby
Myall Vale
Sunflower
15 15 15 15
15 15 15 15
1 Jan 1 Mar 15 May 15 Jul
1 Jan 1 Mar 15 May 15 Jul
Maize
15 Dee 15 Feb
15 Ott 1 Jan
15 Ott 1 Jan
Wheat
_ _
15 May 15 Jul
15 May 15 Jul
Dee Feb May Jul
Dee Feb May Jul
15 Dee 15 Feb
of pests or diseases were considered. The effect of different methods of weather data generation on (i) days to physiological maturity (ii) total biomass production and (iii) grain biomass production was evaluated for a range of simulated sowing dates for all crop models (Table 2). The chosen sowing dates do not necessarily reflect common practices, but were rather chosen to enable comparisons of model sensitivity to weather data generation and to test the generation procedures at various times of the year. Specifically, we included sowing dates which were likely to trigger model responses to (a) extremely high temperatures (maize sown on 15 December at Katherine and Lansdown) and (b) very low temperatures, i.e. frost, (sunflower sown on 1 March at Dalby and Myall Vale). In the maize model, maximum soil surface temperature for a bare soil is predicted from solar radiation and air temperature (input data) and evaporative flux and soil water content (calculated by the model) using a function developed by Ross et al. (1985). Excessive soil surface temperature (greater than 55C) during the emergence phase reduces population which in turn can impact on biomass production (Carberry and Abrecht, 1991). This is an intrinsic feature of the maize model and, whilst yield potential in the other models was simulated simply by turning their respective water balances off, in the maize model this was achieved by always applying irrigation before soil water content reached levels which would trigger reduction in biomass accumulation. Thus, the high soil temperature function was evoked only in between irrigations after the surface layer had dried out. Sunflower’s sensitivity to frost around flowering (Lovett et al., 1979) is accounted for in the model. If the daily minimum temperature drops to 0°C or below during the sensitive phenological stage, then the model halts at that point (Chapman et al., 1993). The wheat model (Amir and Sinclair, 1991) does not contain any specific responses to extreme temperatures. However, because wheat cannot be successfully grown at the two tropical sites (Katherine and Lansdown) only sunflower and maize simulations were conducted at these locations. In total, 168 long-term simulations were conducted and their CDFs statistically compared between the generated and recorded weather files. We investigated three model variables (days to maturity, total biomass at maturity and final grain yield) which resulted in 504 comparisons. For ease of presentation we combined different
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sowing dates for a model at a site into one CDF. This simplification did not alter our final conclusions. Additionally, we only present data for total above ground biomass and grain yield. The effects of weather data generation on predictions of days to physiological maturity will only be discussed briefly. To compare distributions of each estimated climate variable with recorded data the Kolmogorov-Smirnov test was used to identify significant differences between distributions. However, for ease of interpretation of the data, relative frequency distributions are presented in Figs. l-3 rather than the CDFs tested. Relative frequency distributions were calculated by assigning the value ‘1’ to the category with the highest number of values and expressing all other categories relative to this highest category. This was appropriate so that outcomes could be compared across sites varying in record length. 2.4. Statistics An appropriate statistical test to compare CDFs of actual and generated meteorological data, as well as CDFs of model outputs using such data, was needed. The Kolmogorov-Smirnov two-sample, two-sided test (also referred to as Smirnov test) was deemed to be the most appropriate test to compare two samples drawn from unknown and possibly different distributions. While other tests may also be appropriate (e.g. Mann-Whitney test, parametric t test) they may not detect differences other than differences between the two means or medians thus ignoring differences in distributions such as the extremes. The advantage of Smirnov’s test is that it detects all types of differences that may exist between two distribution functions (Conover, 1971). The test detects the largest difference between two distributions, calculates a test statistic for that difference and then compares it with a tabulated test statistic at a given probability level. The smaller the calculated test statistic is relative to the tabulated test statistic, the smaller are the differences between the distributions in question.
3. Results and discussion 3.1. Comparison
of recorded and predicted
weather data
When comparing weather data generation techniques the differing objectives which led to their development need to be considered. Bristow and Campbell’s (1984) method was developed to demonstrate that a useful relationship exists between solar irradiance and the range of daily temperature extremes. Thus, the method does not contain a stochastic element and estimates the day-to-day variation between solar radiation based on recorded rainfall and temperature; long-term distribution patterns have not been considered in its development. Richardson (1981) developed WGEN to simulate rainfall, solar radiation and temperature data as input into mathematical models which evaluate long-term hydrological changes. The stochastic element within WGEN aims to simulate distributions of weather data identical to
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1 0.8 0.6 0.4 0.2 0 20
10
30
40
Solar Radiation
-Actual
- - - BRISTOW
__
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WGEN
Fig. 1. Comparison of solar radiation distributions (MJ me2 day-‘) generated using either Bristow and Campbell’s method, TAMSIM or WGEN with distributions calculated from recorded data at (a) Katherine, (b) Lansdown, (c) Dalby and (d) Myall Vale.
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those of the original data. Because of this feature it is inappropriate to use WGEN to estimate day-to-day variation. TAMSIM (McCaskill, 1990a) has been developed to estimate missing temperature and solar radiation values from actual rainfall data in cases where stochasticity of the non-rainfall variable is not required. It is less complex than either of the other methods in its approach and, because it does not contain a stochastic element, can be used to assess day-to-day variations. The different underlying objectives became particularly apparent when the weather data generators were compared using actual vs. predicted data, a method which examines the adequacy of prediction of daily variation in the data. Such a comparison showed that Bristow and Campbell’s method would be preferable to TAMSIM if the objective was to reproduce radiation data for a given day (data not presented). It would not be appropriate to subject WGEN generated data to such a test because of its stochastic feature. For our objectives other testing procedures are required. 3.1 .I. Solar radiation Statistical comparison showed that all CDFs of generated solar radiation data differed significantly from the recorded data (Table 3, Figs. l(a)-(d)). WGEN, however, had the lowest Smirnov test statistic at all sites except Katherine, indicating that the distribution of WGEN-generated data differed least from the recorded data. When radiation data were classified into three groups, i.e. low (less than 8 MJ m* day-‘), medium (8-26 MJ m* day-‘) and high (greater than 26 MJ m2 day-‘), WGEN simulated all categories well except for low values at Dalby were the number of days in this category was overpredicted. TAMSIM also performed adequately but tended to underpredict both the low and high extremes. Bristow and Campbell’s method performed well at Myall Vale, the most temperate site, but overpredicted Table 3 Comparison temperature distributions Variable
of simulated versus actual solar radiation (Rad), maximum temperature (Tmax) and minimum (Tmin) distributions. Values followed by * have distributions differing significantly from of actual data at P < 0.05 using Smirnov’s test. Site
Prediction
method
Bristow
TAMSIM
WGEN
P 0.05
0.15* 0.14* 0.12* 0.11*
0.10* 0.13* 0.11* 0.11*
0.17* 0.08* 0.06* 0.08*
0.02 0.02 0.03 0.02
Radiation
Katherine Lansdown Dalby Myall Vale
Tmin
Katherine Lansdown Dalby Myall Vale
0.11* 0.11* 0.13* 0.11*
0.03* 0.05* 0.02 0.05*
0.02 0.02 0.03 0.02
Tmax
Katherine Lansdown Dalby Myall Vale
0.10* 0.11* 0.12* 0.10*
0.06* 0.08* 0.05* 0.07*
0.02 0.02 0.03 0.02
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the high categories at the three other sites and underpredicted the medium category at the two tropical sites. Bristow and Campbell (1984) acknowledged that their method required further refinements adequately to accommodate summer rainfall areas. 3.1.2. Minimum temperature (Figs. 2(a)-(d)) Prediction of minimum temperatures is important particularly when using models which predict potential frost damage to crops. There were significant differences between simulated and actual minimum temperature distributions in virtually all cases, but the performance of WGEN, as judged by the Smirnov statistic, was two to six times better than for TAMSIM (Table 3). Minimum temperature distributions from TAMSIM tended to be deficient in values at the low and high ends of the range (Figs. 2 (a)-(d)). This is to be expected because TAMSIM does not contain a stochastic element. When minimum temperature data are missing or large gaps in minimum temperature records need to be filled, WGEN can produce a satisfactory data set, given the right input parameters. However, TAMSIM can still be useful in cases where only small gaps need to be filled and the stochastic feature of WGEN is either not required or even not desired (McCaskill, 1990b). 3.1.3. Maximum temperature (Figs. 3(a)-(d)) Similar to minimum temperature, maximum predicted best using WGEN although CDFs recorded data at the 95% level (Table 3). As prediction, TAMSIM did not predict extreme short-term gaps. 3.2. Sensitivity
tests using crop simulation
temperatures and their extremes were were significantly different from the in the case of minimum temperature values well but could be useful to fill
models
The CDFs of simulated number of days to physiological maturity, total above ground biomass production and grain yield for all sites and models were statistically compared and the ones significantly different from those using the recorded weather data were identified. The total number of simulations conducted at each site for each model differed depending on the number of sowing dates per year and number of complete seasons which could be simulated with the available weather data sets for different sowing dates. At Dalby, for instance, the sunflower model was run for four sowing dates, three of those dates resulting in the simulation of ten seasons each and one sowing date resulting in the simulation of 11 seasons, i.e. the total number of simulated seasons (n) was 41 (Table 4). 3.2.1. Efects on development As expected, none of the radiation data generators had any influence on the predicted number of days to physiological maturity. Similarly, when temperature data was simulated using WGEN no significant differences were detected. However, when TAMSIM was used for temperature generation, the distributions differed significantly in each case because of TAMSIM’s inability to simulate extreme events (data not presented).
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l0.8
-
0.6
-
0.4
-
0.2
-
11 0.8
-
l0.8
-
l0.8
-
0.6 Id)
0.4 0.2 0-l
-
-10
0
10
Minimum
-Actual
__
20
30
Temperature
TAMSIM
-) j’--WGEN
Fig. 2. Comparison of minimum temperature distributions (Tmin, “C) generated using either TAMSIM or WGEN with distributions calculated from recorded data at (a) Katherine, (b) Lansdown, (c) Dalby and (d) Myall Vale.
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1
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0.4 0.2
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0 i 0
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Maximum
-Actual
__
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60
Temperature
TAMSIM
--e
WGEN
Fig. 3. Comparison of maximum temperature distributions (Tmax, “C) generated using either TAMSIM or WGEN with distributions calculated from recorded data at (a) Katherine, (b) Lansdown, (c) Dalby and (d) Myall Vale.
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Table 4 Kolmogorov-Smirnov test statistics for the comparison of the distributions of total above ground biomass and grain yield (values in italics), generated by crop simulation using actual weather data, with distributions containing n number of elements each generated using five methods of weather data generation. The comparisons were conducted for three crops at four sites. Values followed by * differ significantly at P < 0.05. Site
P 0.05
n
Katherine Sunflower
129
0.17
Maize
64
0.24
Lansdown Sunflower
70
0.23
Maize
36
0.32
Dalby Sunflower
41
0.30
Wheat
20
0.43
Maize
20
0.43
Myall Vale Sunflower
70
0.23
Wheat
34
0.33
Maize
36
0.32
Weather
data generation
method
Bristow rad
TAMSIM rad
WGEN rad
TAMSIM rad, temp
WGEN rad, temp
0.19* O.lF 0.19 0.17
0.09 0.08 0.17 0.23
0.05 0.05 0.25* 0.24*
0.16 0.13 0.27* 0.27*
0.12 0.08 0.25* 0.22
0.26* 0.27* 0.25 0.27
0.11 0.09 0.28 0.28
0.11 0.08 0.25 0.25
0.19 0.19 0.44* 0.42*
0.20 0.17 0.19 0.2I
0.27 0.28 0.65* 0.65* 0.25 0.18
0.10 0.11 0.15 0.05 0.25 0.25
0.12 0.15 0.15 0.15 0.20 0.20
0.15 0.26 0.30 0.26 0.30 0.27
0.12 0.14 0.20 0.26 0.20 0.26
0.20 0.20 0.59* 0.51* 0.14 0.13
0.07 0.08 0.24 0.18 0.19 0.21
0.10 0.11 0.24 0.24 0.22 0.12
0.11 0.14 0.15 0.20 0.33* 0.33*
0.06 0.07 0.35* 0.23 0.19 0.14
3.2.2. Efects on growth In our discussion we will concentrate on biomass production rather than yield. Although grain yield is generally a reflection of total biomass, as shown in Table 4, it is conceivable that certain climatic conditions during grain filling could result in large discrepancies. However, our results indicate that grain yield might be less responsive to the methods of weather data generation than biomass. In two instances, i.e. maize at Katherine and wheat at Myall Vale, significant differences caused by the generation of temperature and radiation data using WGEN were found for biomass but not for yield (Table 4). We have chosen the location ‘Myall Vale’ as an example to demonstrate how similar or dissimilar were the biomass CDFs for the three models. Figs. 4(a)-(c) show the CDFs for maize (a), sunflower (b) and wheat (c) for the three methods of
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s s ic h
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400
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800
1000
’ 1200
1400
1600
Total Biomass (g m-2) -Actual
- - - BRISTOW
__
TAMSIM
WGEN
Fig. 4. Comparison of cumulative distribution functions (CDFs) of simulated total biomass production (g me2) at Myall Vale using either Bristow and Campbell’s method, TAMSIM or WGEN to predict daily solar radiation. The three methods are compared with simulations conducted using recorded weather data (Actual) and using three simulation models: (a) a maize model, (b) a sunflower model and (c) a wheat model.
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generating solar radiation; Figs. 5(a)-(c) show the corresponding CDFs for methods generating both solar radiation and temperature. Low maize biomass yields (less than 1600 g m-2) apparent in Figs. 4(a) and 5(a) were caused by the function which predicts lethal conditions for emerging seedlings because of excessive soil surface temperatures. Only simulations conducted using TAMSIM radiation and temperature data (Fig. 5(a)) did not show the low population response because of TAMSIM’s inability to reproduce extreme temperatures. According to Table 4, none of the sunflower CDFs differed from the original (Figs. 4(b) and 5(b)). Solar radiation values produced using Bristow and Campbell’s method resulted in a significantly different wheat CDF (Fig. 4(c)). This was caused by an overprediction of radiation values in the 20-28 MJ m2 day-’ range (clearly visible on Fig. l(d)), which occurred mainly in winter and spring, i.e. during the wheat growing season. Similarly, maize using TAMSIM radiation and temperature and wheat using WGEN radiation and temperature, resulted in significantly different CDFs (Figs. 5(a) and 5(c)). The significantly different wheat simulation using WGEN radiation and temperature (Fig. 5(c)) was caused entirely by the 15 May sowing date (data not presented) for which in some years the predicted temperatures were higher than the recorded temperatures resulting in a faster developmental rate. Consequently, crop duration was shortened by up to 20 days which led to lower biomass yields. This difference, however, was not carried through to grain yield. The same effect was evident in sunflower (18 days maximum difference), but did not result in significantly different biomass or grain yields. Briefly, from the ten biomass comparisons conducted for each method (2 sites x 3 models + 2 sites x 2 models), four comparisons differed significantly for Bristow and Campbell’s method, none for TAMSIM radiation, one for WGEN radiation, three for TAMSIM radiation and temperature, and two for WGEN radiation and temperature files. The results for grain yields were similar, except for WGEN radiation and temperature files which did not result in any significant differences in grain yield (Table 4). When both variables, radiation and temperature, were predicted, WGEN clearly outperformed TAMSIM because of the ability of generating the extreme temperatures of the distribution (Figs. 2 and 3). However, caution is still warranted when predicting both variables using WGEN as the maize simulations at Katherine show: under these conditions the maize model using recorded weather data predicted that high soil surface temperatures would reduce plant population in 45% of all cases. Using WGEN generated weather data the model predicted a reduced population in 65% of cases and using TAMSIM in 25% of cases. These differences resulted in significantly different output CDFs for both methods as shown in Table 4. For the 1 March sowing at Dalby the frost damage routine of the sunflower model was evoked twice during a 10 year run when using recorded data. Using WGEN radiation and temperature had the same result, but the actual years in which frost was predicted, differed. At Myall Vale 4 out of 19 years resulted in predicted frost damage but only one such case was predicted when using WGEN radiation and temperature data. However, this did not affect the overall shape of the biomass CDF (Fig. 5(b)), Generally, the prediction of extreme minimum temperatures using
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,
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Total Biomass -Actual
__
( 1000
1200
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(g m-2)
TAMSIM
-/_.--I WGEN
Fig. 5. Comparison of cumulative distribution functions (CDFs) of simulated total biomass (g m-*) production at Myall Vale using either TAMSIM or WGEN to predict daily solar radiation, maximum and minimum temperatures. The two methods are compared with simulations conducted using recorded weather data (Actual) and using three simulation models: (a) a maize model, (b) a sunflower model and (c) a wheat model.
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WGEN seemed adequate (Fig. l(d)). This result demonstrates that WGEN generated data is adequate when only the long-term distribution of simulation results are to be considered. The method is not appropriate when crop performance in a specific season is to be evaluated, nor is it appropriate to use such weather data for the analysis of frost risks using climatic indices such as the southern oscillation index (Stone et al., 1993). Although there is general evidence of global warming, spatial and temporal variation of the supporting data is considerable (Karl et al., 1993). Techniques used to generate temperature data do not explicitly account for global warming. However, by calibrating a temperature data generator using actual, recent temperature data to re-create historic records (the most likely scenario) a very slight bias towards overpredicting the historic temperature data might be introduced. Such an overprediction would reflect the most recent temperature records, thus adjusting historic values to a present climate. Therefore, simulation models used with such data sets would produce probability distributions which are more relevant to today’s agricultural production because they would quantify today’s climatic variability more accurately. In other words, global warming is implicitly accounted for through the calibration process on a case by case basis.
4. Conclusions We have demonstrated how crop simulation models can be used to assess the adequacy and quality of weather data generation. Weather data is used in crop simulation modelling not only to predict crop growth and development in response to these environmental variables but also to flag catastrophic events as in the case of the maize model (heat damage) and the sunflower model (frost damage). Often such effects can only be assessed by using the weather data as input into the simulation models in question because models act as data filters and integrate the effect of deviations of generated from actual data. Model complexity did not appear to influence model sensitivity to differences in environmental input data. Both TAMSIM radiation and WGEN radiation performed adequately and the overall performance of the two methods appeared to be similar. Bristow and Campbell’s method was inappropriate for our objectives, because output from two of three crop models was sensitive to this method of prediction. Although we regard WGEN as the appropriate method for the long-term generation of temperature data, we also found TAMSIM useful in instances where data contained only small gaps. In such cases the absence of a stochastic element in TAMSIM was advantageous because it avoided the possibility of rare, extreme events being introduced during short gaps (less than 5 successive days) in the data sets. Our results indicate that (i) When only rainfall and temperature data are available, both TAMSIM WGEN are suitable methods of generating radiation data.
and
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(ii) When only rainfall data are available, WGEN is the most suitable method of generating temperature and radiation data, because extreme events such as frosts are included in the temperature data sets. (iii) Where the temperature record contains short gaps (less than 5 days), TAMSIM is a suitable method of data reconstruction, because extreme events are not introduced during these gaps. (iv) Bristow and Campbell’s method of generating solar radiation resulted in overprediction of high (greater than 26 MJ mz day-‘) solar radiation values at three sites and an underprediction of medium (8-26 MJ m* day-‘) values at the two tropical sites. These effects carried through to the final simulation results. We have used these methods to generate long-term weather locations throughout Eastern Australia from existing rainfall data. The data sets are currently used for simulation analyses assessments (e.g. Meinke et al., 1993b).
data sets for 53 and temperature and climatic risk
Acknowledgements This project was financially supported by GRDC and RIRDC. We also wish to thank Dr Keith Bristow for his helpful comments on the manuscript.
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