Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs

Baigorria, Guillermo A.; Jones, James W.; Shin, Dong Jik; Mishra, Ashok; O’Brien, James J.

doi:10.3354/cr00703

Cited by 95 publications

(68 citation statements)

References 27 publications

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“…Therefore, further bias correction is often required. The impacts of biases on hydrological and agriculture modeling has been studied extensively (e.g., Kunstmann et al, 2004;Baigorria et al, 2007;Ghosh and Mujumdar, 2009;Ott et al, 2013). Precipitation is an important parameter in climate studies (Schmidli et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Mao

Vogl

Laux

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over-or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer functionbased statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971-2000. The REG-NIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971-1985) and validation (1986-2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10 % (wet bias) to −1 % (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32 % March-April-May (MAM), −15 % June-July-August (JJA), 4 % September-October-November (SON) and 28 % December-January-February (DJF) to 16 % (MAM), −11 % (JJA), −1 % (SON) and −3 % (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions.

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Section: Introductionmentioning

confidence: 99%

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Mao

Vogl

Laux

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Challinor et al 2005b, Baigorria et al 2007) or the crop model (Katz 2002, Makowski et al 2006, Challinor & Wheeler 2008b), but rarely both. Comprehensive assessment of uncertainty in climate simulation is difficult due to the computational expense of climate models, the difficulty in assessing structural and parameter uncertainty, and issues relating to the probabilistic interpretation of results (e.g.…”

Section: Introductionmentioning

confidence: 99%

Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change

Challinor

Wheeler²,

Hemming³

et al. 2009

Clim. Res.

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Estimates of the response of crops to climate change rarely quantify the uncertainty inherent in the simulation of both climate and crops. We present a crop simulation ensemble for a location in India, perturbing the response of both crop and climate under both baseline (12 720 simulations) and doubled-CO 2 (171 720 simulations) climates. Some simulations used parameter values representing genotypic adaptation to mean temperature change. Firstly, observed and simulated yields in the baseline climate were compared. Secondly, the response of yield to changes in mean temperature was examined and compared to that found in the literature. No consistent response to temperature change was found across studies. Thirdly, the relative contribution of uncertainty in crop and climate simulation to the total uncertainty in projected yield changes was examined. In simulations without genotypic adaptation, most of the uncertainty came from the climate model parameters.Comparison with the simulations with genotypic adaptation and with a previous study suggested that the relatively low crop parameter uncertainty derives from the observational constraints on the crop parameters used in this study. Fourthly, the simulations were used, together with an observed dataset and a simple analysis of crop cardinal temperatures and thermal time, to estimate the potential for adaptation using existing cultivars. The results suggest that the germplasm for complete adaptation of groundnut cultivation in western India to a doubled-CO 2 environment may not exist. In conjunction with analyses of germplasm and local management practices, results such as this can identify the genetic resources needed to adapt to climate change. KEY WORDS: Adaptation · Climate change impacts · Crop growth model · General circulation modelResale or republication not permitted without written consent of the publisher

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“…In this paper the processes of interest are river flows from mountainous headwaters and agricultural production, both of which depend upon inputs of mass (precipitation) and energy (ambient temperature and incoming radiation). From a simulation standpoint, common approaches for modelling both meltwater generation from seasonal snowpack and glaciers (Ragettli et al, 2013) and crop yields (Baigorria et al, 2007;Kar et al, 2014) require both air temperature and incoming radiation in addition to precipitation as input data. Furthermore, moisture exchanges from the land surface and atmosphere depend upon the latter's vapour pressure deficit, which is commonly expressed as relative humidity.…”

Section: Selection Of Climate Variables Governing Water Resources Andmentioning

confidence: 99%

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

2015

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Abstract.A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

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Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs

Cited by 95 publications

References 27 publications

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

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