Impact of biases in gridded weather datasets on biomass estimates of short rotation woody cropping systems

Bandaru, Varaprasad; Pei, Yu; Hart, Quinn; Jenkins, Bryan M.

doi:10.1016/j.agrformet.2016.11.008

Cited by 18 publications

(7 citation statements)

References 57 publications

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“…The degree of uncertainty varies largely among the datasets depending on numerous factors (e.g., quality of inputs used in the climate and geo-statistical models, scale of the models, representation of land-atmosphere interactions) (Newman et al, 2015;Strachan and Daly, 2017). Similar to earlier findings (Bandaru et al, 2017), daily weather variables of Daymet and Prism datasets were shown to have less uncertainty as indicated by MAPE values compared to that of NLDAS and NARR datasets. The better performance of Daymet and Prism datasets could be mainly attributed to the use of meteorological station observations as part of model input and a finer model scale.…”

Section: Uncertainties In Climate Datasetssupporting

confidence: 56%

“…topographic characteristics) or (2) data modeling and assimilation techniques that model regional changes in weather based on satellite observations, land cover, local geographical characteristics and other attributes (Eum et al, 2014). For regional scale carbon flux simulations, generally gridded climate data sets are used because they offer data for each grid cell of the region of interest, while insufficient density of meteorological stations restricted the accurate representation of inherent spatial weather patterns over large regions (Bandaru et al, 2017). However, studies have been consistently reporting large uncertainties in gridded climate datasets (Van Wart et al, 2013;Bandaru et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…For regional scale carbon flux simulations, generally gridded climate data sets are used because they offer data for each grid cell of the region of interest, while insufficient density of meteorological stations restricted the accurate representation of inherent spatial weather patterns over large regions (Bandaru et al, 2017). However, studies have been consistently reporting large uncertainties in gridded climate datasets (Van Wart et al, 2013;Bandaru et al, 2017). For instance, recently Bandaru et al (2017) found higher biases in monthly weather variables (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…However, studies have been consistently reporting large uncertainties in gridded climate datasets (Van Wart et al, 2013;Bandaru et al, 2017). For instance, recently Bandaru et al (2017) found higher biases in monthly weather variables (e.g. up to 3°C in minimum temperature) of high-resolution climate datasets generally used in the U.S, which were found to result in percent errors up to 45% in biomass of hybrid poplar, a short rotation woody cropping system, simulated using 3-PG forest growth model.…”

Section: Introductionmentioning

confidence: 99%

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Climate data induced uncertainties in simulated carbon fluxes under corn and soybean systems

Bandaru

2020

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Abstract. Net carbon balance on croplands depends on numerous factors (e.g., crop type, soil, climate and management practices) and their interactions. Agroecosystem models are generally used to assess cropland carbon fluxes under various agricultural land use and land management practices because of their ability to capture the complex interactive effects of factors influencing carbon balance. For regional carbon flux simulations, generally gridded climate data sets are used because they offer data for each grid cell of the region of interest. However, studies consistently report large uncertainties in gridded climate datasets, which will affect the accuracy of carbon flux simulations. This study investigates the uncertainties in daily weather variables of commonly used high resolution gridded climate datasets in the U.S (NARR, NLDAS, Prism and Daymet), and their impact on the accuracy of simulated Net Ecosystem Exchange (NEE) under irrigated and non-irrigated corn and soybeans using the Environmental Policy Integrated Climate (EPIC) agroecosystem model and observational data at four flux tower cropland sites in the U.S Midwest region. Further, the relative significance of each weather variable in influencing the uncertainty in flux estimates was evaluated. Results suggest that daily weather variables in all gridded climate datasets display some degree of bias, leading to considerable uncertainty in simulated NEE fluxes. The gridded climate datasets produced based on interpolation techniques (i.e. Daymet and Prism) were shown to have less uncertainties, and resulted in NEE estimates with relatively higher accuracy, likely due to their higher spatial resolution and higher dependency on meteorological station observations. The Mean Absolute Percentage Errors (MAPE) values of average growing season NEE estimates for Dayment, Prism, NLDAS and NARR include 22.53 %, 23.45 %, 62.52 % and 66.18 %, respectively. The NEE under irrigation management (MAPE = 53.15 %) tends to be more sensitive to uncertainties compared to the fluxes under non-irrigation (MAPE = 34.19 %). Further, this study highlights that NEE fluxes respond differently to the individual climate variables, and responses vary with management practices. Under irrigation management, NEE fluxes are more sensitive to shortwave radiation and temperature. Conversely, under non-irrigation management, precipitation is the most dominant climate factor influencing uncertainty in simulated NEE fluxes. These findings demonstrate that careful consideration is necessary when selecting climate data to mitigate uncertainties in simulated NEE fluxes. Further, alternative approaches such as integration of remote sensing data products may help reduce the models' dependency on climate datasets and improve the accuracy in the simulated CO2 fluxes.

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Section: Uncertainties In Climate Datasetssupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Climate data induced uncertainties in simulated carbon fluxes under corn and soybean systems

Bandaru

2020

Preprint

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“…PRISM has been widely used for climatic, hydrological, agricultural and environmental applications in the U.S. (e.g. Lutz et al, 2010;Bandaru et al, 2017;Bodner and Robles, 2017). In this study, we employed monthly precipitation, maximum and minimum air temperatures from 1940 to 2013, provided by PRISM at 30 seconds (approximately 10km) resolution.…”

Section: Climatic Informationmentioning

confidence: 99%