Agricultural and Forest Meteorology 102 2000 1–12
Evaluation of WGEN for generating long term weather data for crop simulations
A. Soltani
a,∗
, N. Latifi
a
, M. Nasiri
b
a
Department of Agronomy, Gorgan University of Agricultural Sciences, Gorgan, Iran
b
Department of Agronomy, Ferdosi University of Mashhad, Mashhad, Iran Received 11 June 1999; received in revised form 20 December 1999; accepted 4 January 2000
Abstract
We evaluated the ability of the WGEN model to generate long term weather series in situations where the actual historic weather data record is only 3–10 years long. The series generated were used to simulate yield of irrigated and rainfed chickpea.
To do this, four 100-year samples of weather data were generated for Tabriz, Iran. The WGEN parameters used to generate data were obtained from daily actual weather data of 3 W3, 5 W5, 7 W7, and 10 W10 recent years. The actual and
generated weather series were each used as input to a chickpea crop model under irrigated and rainfed conditions at three planting dates. Results showed that the generated data are very similar to the actual data used for parameter estimation for all
base periods tested. In comparison of the generated data and the historic data the means and the distributions of weather data variables differed significantly. However, with increasing the number of years used for parameter estimation of WGEN from
3 to 10, percent of significant differences were 38, 26, 17 and 13 for W3–W10, respectively. When generated weather data were evaluated as input to a chickpea crop model, simulated yields obtained using generated data were significantly different
from that obtained using actual data in 50 and 8 of cases under irrigated and rainfed conditions, respectively. To generate data similar to long term historic data, a longer base period 10 years would be required for parameter estimation. However,
when it is required that the generated data represent recent history rather than a long term period, the WGEN can be used as a reliable source of weather data even if it’s required parameters are obtained from only 3–10 years of actual historical weather
data. © 2000 Elsevier Science B.V. All rights reserved.
Keywords: WGEN; Generating weather data; Crop simulation
1. Introduction
In agricultural science, crop simulation models are quantitative tools based on scientific knowledge
that can evaluate the effects of climatic, edaphic, hy- drologic, agronomic, and genotypic factors on crop
yield and stability Boote et al., 1996. Such mod- els are used increasingly for genotype improvement
∗
Corresponding author.
Fax: +
98-171-2220981+98-171- 2220438.
E-mail address: irgounidci.iran.com A. Soltani
Habekotte, 1997, environmental characterization and agro-ecological zonation Aggarwal, 1993, es-
timating production potential Meinke and Hammer, 1995, quantifying climatic risk Meinke et al., 1993,
evaluating optimal management practices Egli and Bruening, 1992; Muchow et al., 1994; O’Leary and
Connor, 1998 and for predicting the effects of cli- matic change and climatic variability on crop growth
and yield Matthews et al., 1997; Lal et al., 1998.
Long term weather data obtained at a specific site are usually required for simulation analyses. In regions
with high climatic variability it is particularly impor-
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2 A. Soltani et al. Agricultural and Forest Meteorology 102 2000 1–12
tant in order to assess effects of climatic variability. For the most commonly used models, this requires
long term daily values of rainfall, solar radiation, max- imum and minimum temperatures. However, many lo-
cations do not have sufficient historical weather data. Many workers have recognized this problem, and this
has led to the development of a range of weather generators such as WGEN Richarsdon and Wright,
1984, SIMMETEO Geng et al., 1986, 1988, TAM- SIM McCaskill, 1990 and others e.g. Larsen and
Pense, 1982; Bristow and Campbell, 1984; Guenni et al., 1991 which can be used to generate weather
data sets.
The quality of model output is related to the quality of weather data used as input. It follows that the test-
ing of the sensivity of model output to the quality of generated weather data is an essential prerequisite for
simulation analyses. Weather data is used in crop mod- eling not only to predict crop growth and development
in response to environmental variables but also to flag catastrophic events such as heat stress, water deficit,
frost damage and etc. Often such effects can only be assessed by using the weather data as input into the
simulation models in question because model act as a data filter and integrate the effect of deviations of
generated from actual data Richardson, 1984; Meinke et al., 1995. Meinke et al. 1995 demonstrated how
crop simulation models can be used to assess the ad- equacy and quality of weather data generation. They
showed that model complexity did not appear to influ- ence model sensivity to differences in environmental
input data.
WGEN is a well-known and widely used stochastic weather generator that requires a number of parame-
ters to generate a weather series at a site. This genera- tor has been incorporated into the WeatherMan short
for weather data manager, Pickering et al., 1994, an application program of Decision Support System for
Agrotechnology Transfer DSSAT software Tsuji et al., 1994. DSSAT is a collection of crop models and
computer programs integrated into a single software package in order to facilitate the application of crop
simulation models in research and decision making. This software is a product of the International Bench-
mark Sites Network for Agrotechnology Transfer IB- SNAT project and is distributed over the world.
Richardson 1984 and Meinke et al. 1995 tested the WGEN output applied to crop simulation models
and showed that yields obtained using generated data were not significantly different from that obtained us-
ing actual data. In these tests, parameters required by the model were obtained from long term actual data
longer than 10 years. However, we have found no reports showing the capability of WGEN for gener-
ating long term data when model parameters are de- rived from relatively short term weather data, say 3–10
years. Thus, the objective of this study was the evalua- tion of the WGEN model for generating long weather
series parameterized using short 3–10 years records of actual data to derive a specific crop model.
2. Materials and methods
