Evaluation of WGEN for generating long term weather data for crop simulations

Soltani, Afshin; Latifi, N; Nasiri, Maryam

doi:10.1016/s0168-1923(00)00100-3

Cited by 23 publications

(10 citation statements)

References 21 publications

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“…To overcome the inadequate length (5 yr) of HC1 data, WGEN was used to provide long term weather series for use with CERES-Wheat; WGEN (Richardson & Wright 1984) is a widely used stochastic weather generator that requires mean monthly weather data of T max and T min , precipitation and R g to generate daily weather at a site. There are no significant differences between crop simulation output obtained with WGEN generated and actual weather data (Soltani et al 2000, Hartkamp et al 2003; the latter study also demonstrated the suitability of WGEN for generating adequate long-term time series even when model parameters were derived from relatively short-term (<10 yr) daily weather data.…”

Section: Crop Simulationsmentioning

confidence: 64%

Estimation of solar radiation and its application to crop simulation models in Greece

Mavromatis¹

2008

Clim. Res.

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Lack of data on site-specific daily solar radiation (R g ) is a significant impediment for most crop modeling applications. For this reason, 5 methods for estimating R g were tested: the Ångström-Prescott equation (AP), the Supit -van Kappel formula (SK) and 3 temperature-based methods: Campbell-Donatelli model, Hargreaves equation, and piecewise multiple linear regressions with a breakpoint (PLR). To overcome the lack of long and continuous time series of R g measurements at multiple sites, satellite-derived R g from the HelioClim-1 database were tested against observations from 2 stations, and then interpolated for 12 additional stations. When sunshine duration data were available, the AP equation was best, because it (1) produced intercepts and slopes closest to zero and unity, respectively and (2) had the lowest relative RMSE (9 to 18.6%). When cloud cover observations and data on maximum and minimum temperatures were available, the SK equation was equally effective as AP in most assessment metrics. When the only data for a site were daily maximum and minimum temperatures, the PLR approach with a breakpoint, which reflects the value at which the response of daily R g changes as a function of the extraterrestrial solar radiation R A and the diurnal temperature range, performed best. The mean relative RMSE of the PLR approach was < 3.7% higher than that of AP. The SK equation provided the most suitable simulation of measured R g for the CERES-Wheat crop model, while among the temperature-based methods PLR produced the smallest yield errors. Future validation efforts should explore the validity of the PLR model in other regions and under regimes with greater availability of R g data KEY WORDS: Solar radiation estimation · Crop simulation models · Piecewise multiple linear regression · GreeceResale or republication not permitted without written consent of the publisher

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Section: Crop Simulationsmentioning

confidence: 64%

Estimation of solar radiation and its application to crop simulation models in Greece

Mavromatis¹

2008

Clim. Res.

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“…The output from weather generators is synthetic data, thus it is necessary to investigate the output to assess their representativeness. There are several evaluations of weather generators in the literature: Soltani et al [88] assessed WGEN capabilities. WGEN was found to be representative of both current and future weather.…”

Section: Limitations Of Weather Generatorsmentioning

confidence: 99%

“…There are several evaluations of weather generators in the literature: Soltani et al. 88 assessed WGEN capabilities. WGEN was found to be representative of both current and future weather.…”

Section: Synthetic Weathermentioning

confidence: 99%

A review of current and future weather data for building simulation

Herrera

Natarajan

Coley

et al. 2017

Building Services Engineering Research and Technology

148

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This article provides the first comprehensive assessment of methods for the creation of weather variables for use in building simulation. We undertake a critical analysis of the fundamental issues and limitations of each methodology and discusses new challenges, such as how to deal with uncertainty, the urban heat island, climate change and extreme events. Proposals for the next generation of weather files for building simulation are made based on this analysis. A seven-point list of requirements for weather files is introduced and the state-of-the-art compared to this via a mapping exercise. It is found that there are various issues with all current and suggested approaches, but the two areas most requiring attention are the production of weather files for the urban landscape and files specifically designed to test buildings against the criteria of morbidity, mortality and building services system failure. Practical applications: Robust weather files are key to the design of sustainable, healthy and comfortable buildings. This article provides the first comprehensive assessment of their technical requirements to ensure buildings perform well in both current and future climates.

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“…For example, short periods of heat stress can be critical (Matsui & Horie 1992;Ferris et al 1998;Vara Prasad et al 2000), but may not be adequately represented in averaged data. Short-time-scale detail is often regenerated from the average GCM and RCM output with 'weather generator' software which uses information on the statistics of weather variability (Soltani et al 2000). The high temporal resolution data produced by the climate models is, therefore, often effectively discarded and then regenerated using statistical techniques.…”

Section: Difficulties With Applying Climate Model Output To Offline Imentioning

confidence: 99%

Integrated approaches to climate–crop modelling: needs and challenges

Betts

2005

Phil. Trans. R. Soc. B

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This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.

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Evaluation of WGEN for generating long term weather data for crop simulations

Cited by 23 publications

References 21 publications

Estimation of solar radiation and its application to crop simulation models in Greece

Estimation of solar radiation and its application to crop simulation models in Greece

A review of current and future weather data for building simulation

Integrated approaches to climate–crop modelling: needs and challenges

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