Inaccuracies in weather data and their effects on crop growth simulation results. I. Potential production

Nonhebel, Sanderine

doi:10.3354/cr004047

Cited by 24 publications

(27 citation statements)

References 12 publications

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“…Two crop scenarios, A and C, accounted for most of the error in predicting simulated maize yield (Table 3). Often the effects of errors in radiation in the performance of a crop model tend to cancel each out despite the non-linearity in the plants response to radiation (Nonhebel 1994). In this study, however, the sign of the simulated yield errors concurred with the respective of PET in 8 out of 9 cases (Table 3).…”

Section: Crop Simulation Resultsmentioning

confidence: 46%

Estimating solar radiation for crop modeling using temperature data from urban and rural stations

Mavromatis¹,

Jagtap²

2005

Clim. Res.

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Lack of site-specific global solar radiation (GSR) data is a significant impediment for most crop model applications. First, several empirical methods for estimating GSR from observed maximum and minimum temperature were evaluated using data from urban (National Climatic Data Center [NCDC] data set) and rural (Florida Automated Weather Network [FAWN] data set) sites in Florida. Next, the spatial structure of empirical model parameters was investigated and the possibility of employing spatially interpolated coefficients developed at urban or rural sites to predict solar radiation at rural locations, where most crops are grown and often crop modeling is required to address various issues, was assessed. Finally, the effects of observed and estimated GSR on simulated potential yield and evapotranspiration with CROPGRO-Peanut and CERES-Maize models at 6 crop-location combinations were evaluated. The model developed by Donatelli and Bellocchi (DB) achieved the most accurate estimations of GSR across Florida. The quality of those predictions varied with environment setting (urban-rural) and latitude. RMSE (root mean square error) between observed and fitted daily GSR were in the range of 3.1-4.1 and 3.2-4.9 MJ m -2 d -1 for FAWN and NCDC evaluation sub-sets, respectively. Radiation for FAWN sites was efficiently estimated by the coefficients interpolated from rural sites but was substantially overestimated by 17.6%, on monthly basis, when urban sites supplied the model empirical parameters. NCDC sites were site specific and suitable sources of solar radiation at urban environments only. Input of estimated with the DB model GSR into a crop model would be a realistic option at sites where only temperature and precipitation are available.

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Section: Crop Simulation Resultsmentioning

confidence: 46%

Estimating solar radiation for crop modeling using temperature data from urban and rural stations

Mavromatis¹,

Jagtap²

2005

Clim. Res.

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“…Nonhebel (1994a) found that inaccuracies in solar radiation measurement of 10% and of daily temperature of 1°C in data used within a crop simulation model resulted in yield estimation errors of up to 1 t ha −1 . Maintaining meteorologically appropriate, synchronised relationships between individual weather variables is essential for models that represent entities with non-linear responses to driving variables such as biological systems (Nonhebel 1994b) and hydro-chemical processes (Soulsby 1995;Creed et al 1996). Thermal time accumulation, which depends not only on the mean daily temperature but the difference between daily maximum and minimum temperatures, is the key driver of plant and insect phenological development (Arnold and Monteith 1974;Jarvis et al 2003, respectively).…”

Section: Related Researchmentioning

confidence: 98%

Evaluating regional climate model estimates against site-specific observed data in the UK

Rivington¹,

Miller²,

Matthews³

et al. 2008

Climatic Change

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This paper compares precipitation, maximum and minimum air temperature and solar radiation estimates from the Hadley Centre's HadRM3 regional climate model (RCM), (50×50 km grid cells), with observed data from 15 meteorological station in the UK, for the period 1960-90. The aim was to investigate how well the HadRM3 is able to represent weather characteristics for a historical period (hindcast) for which validation data exist. The rationale was to determine if the HadRM3 data contain systematic errors and to investigate how suitable the data are for use in climate change impact studies at particular locations. Comparing modelled and observed data helps assess and quantify the uncertainty introduced to climate impact studies. The results show that the model performs very well for some locations and weather variable combinations, but poorly for others. Maximum temperature estimations are generally good, but minimum temperature is overestimated and extreme cold events are not represented well. For precipitation, the model produces too many small events leading to a serious under estimation of the number of dry days (zero precipitation), whilst also over-or underestimating the mean annual total. Estimates represent well the temporal distribution of precipitation events. The model systematically over-estimates solar radiation, but does produce good quality estimates at some locations. It is concluded that the HadRM3 data are unsuitable for detailed (i.e. daily time step simulation model based) site-specific impacts studies in their current form. However, the close similarity between modelled and observed data for the historical case raises the potential for using simple adjustment methods and applying these to future projection data.

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“…Mearns et al (1996) found that 380 simulated wheat yields were sensitive to changes in both temperature and precipitation, which 381 depended on soil characteristics. Nonhebel (1994a) found that temperature and solar radiation data 382 errors generated up to 35% overestimation of yield. In water-limited conditions, the model was 383 sensitive to inaccuracies in precipitation and solar radiation data, but when there was sufficient 384 water, it was sensitive to errors in temperature and solar radiation data (1994b).…”

Section: Relative Importance Of Rainfall Temperature and Yield Datamentioning

confidence: 99%

The relative importance of rainfall, temperature and yield data for a regional-scale crop model

Watson

Challinor

2013

Agricultural and Forest Meteorology

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Paper:Watson, J and Challinor, AJ (2013 Abstract 21When projecting future crop production, the skill of regional scale (> 100km resolution) crop models 22 is limited by the spatial and temporal accuracy of the calibration and weather data used. The skill of 23 climate models in reproducing surface properties such as mean temperature and rainfall patterns is 24 of critical importance for the simulation of crop yield. However, the impact of input data errors on 25 the skill of regional scale crop models has not been systematically quantified. We evaluate the 26 impact of specific data error scenarios on the skill of regional-scale hindcasts of groundnut yield in 27 the Gujarat region of India, using observed input data with the GLAM crop model. Two methods 28 were employed to introduce error into rainfall, temperature and crop yield inputs at seasonal and 29 climatological timescales: (1) random temporal resequencing, and (2) biasing values. 30 31We find that, because the study region is rainfall limited, errors in rainfall data have the most 32 significant impact on model skill overall. More generally, we find that errors in inter-annual 33 variability of seasonal temperature and precipitation cause the greatest crop model error. Errors in 34 the crop yield data used for calibration increased Root Mean Square Error by up to 143%. Given that 35 cropping systems are subject both to a changing climate and to ongoing efforts to reduce the yield 36 gap, both potential and actual crop productivity at the regional scale need to be measured. 37 38We identify three key endeavours that can improve the ability to assess future crop productivity at 39 the regional-scale: (i) increasingly accurate representation of inter-annual climate variability in 40 climate models; (ii) similar studies with other crop models to identify their relative strengths in 41 dealing with different types of climate model error; (iii) the development of techniques to assess 42 potential and actual yields, with associated confidence ranges, at the regional scale. 43

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Inaccuracies in weather data and their effects on crop growth simulation results. I. Potential production

Cited by 24 publications

References 12 publications

Estimating solar radiation for crop modeling using temperature data from urban and rural stations

Estimating solar radiation for crop modeling using temperature data from urban and rural stations

Evaluating regional climate model estimates against site-specific observed data in the UK

The relative importance of rainfall, temperature and yield data for a regional-scale crop model

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