FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226197349> ?p ?o ?g. }

• W4226197349 endingPage "103341" @default.
• W4226197349 startingPage "103341" @default.
• W4226197349 abstract "Net carbon balance on croplands depends on numerous factors (e.g., crop type, soil, climate) and their interactions. Agroecosystem models are generally used to assess cropland carbon fluxes 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 uncertainties in climate datasets, which affect the accuracy of carbon flux simulations. The objectives were to 1) determine the uncertainties in daily weather variables of commonly used high resolution gridded climate datasets in the U.S (NARR, NLDAS, Prism and Daymet); 2) estimate 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 3) understand the relative sensitivity of the NEE to various climate variables. The observational data at four flux tower cropland sites in the U.S Midwest region were used to quantify the uncertainties in the gridded weather datasets, and EPIC simulations were performed at each flux tower site using each gridded climate dataset. Further, sensitivity analysis using Extended Fourier Amplitude Sensitivity Test (EFAST) was conducted. Results suggest that daily weather variables in all gridded climate datasets display some degree of bias, leading to considerable uncertainty in simulated NEE. 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 (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 responds differently to the individual climate variables and management. Under irrigation management, NEE are more sensitive to temperature. Conversely, under non-irrigation, precipitation is the most dominant factor influencing NEE uncertainty. These findings demonstrate that careful consideration is necessary when selecting climate data to mitigate uncertainties in simulated NEE. 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." @default.
• W4226197349 created "2022-05-05" @default.
• W4226197349 creator A5061635161 @default.
• W4226197349 date "2022-02-01" @default.
• W4226197349 modified "2023-09-27" @default.
• W4226197349 title "Climate data induced uncertainties in simulated carbon fluxes under corn and soybean systems" @default.
• W4226197349 cites W1593674196 @default.
• W4226197349 cites W1660836766 @default.
• W4226197349 cites W1898919587 @default.
• W4226197349 cites W1966334841 @default.
• W4226197349 cites W1983274176 @default.
• W4226197349 cites W1983404400 @default.
• W4226197349 cites W1985797798 @default.
• W4226197349 cites W1995047010 @default.
• W4226197349 cites W1997444062 @default.
• W4226197349 cites W2000786281 @default.
• W4226197349 cites W2001525024 @default.
• W4226197349 cites W2007852692 @default.
• W4226197349 cites W2010951708 @default.
• W4226197349 cites W2016769009 @default.
• W4226197349 cites W2017449108 @default.
• W4226197349 cites W2019022785 @default.
• W4226197349 cites W2031831872 @default.
• W4226197349 cites W2033481223 @default.
• W4226197349 cites W2034870975 @default.
• W4226197349 cites W2039323753 @default.
• W4226197349 cites W2042906203 @default.
• W4226197349 cites W2054935105 @default.
• W4226197349 cites W2059017190 @default.
• W4226197349 cites W2069214604 @default.
• W4226197349 cites W2069360801 @default.
• W4226197349 cites W2075653514 @default.
• W4226197349 cites W2088765131 @default.
• W4226197349 cites W2094692226 @default.
• W4226197349 cites W2105103805 @default.
• W4226197349 cites W2106241015 @default.
• W4226197349 cites W2115431454 @default.
• W4226197349 cites W2126105956 @default.
• W4226197349 cites W2134825671 @default.
• W4226197349 cites W2159568833 @default.
• W4226197349 cites W2161994757 @default.
• W4226197349 cites W2166096127 @default.
• W4226197349 cites W2170272752 @default.
• W4226197349 cites W2173251738 @default.
• W4226197349 cites W2296185332 @default.
• W4226197349 cites W2345400954 @default.
• W4226197349 cites W2394515166 @default.
• W4226197349 cites W2462583090 @default.
• W4226197349 cites W2463898247 @default.
• W4226197349 cites W2546396646 @default.
• W4226197349 cites W2557094964 @default.
• W4226197349 cites W2597675998 @default.
• W4226197349 cites W2609842019 @default.
• W4226197349 cites W2619780113 @default.
• W4226197349 cites W2734608208 @default.
• W4226197349 cites W2920706557 @default.
• W4226197349 cites W2972837290 @default.
• W4226197349 cites W2989812594 @default.
• W4226197349 cites W4231254910 @default.
• W4226197349 cites W4241793634 @default.
• W4226197349 doi "https://doi.org/10.1016/j.agsy.2021.103341" @default.
• W4226197349 hasPublicationYear "2022" @default.
• W4226197349 type Work @default.
• W4226197349 citedByCount "0" @default.
• W4226197349 crossrefType "journal-article" @default.
• W4226197349 hasAuthorship W4226197349A5061635161 @default.
• W4226197349 hasConcept C107054158 @default.
• W4226197349 hasConcept C127313418 @default.
• W4226197349 hasConcept C127413603 @default.
• W4226197349 hasConcept C132651083 @default.
• W4226197349 hasConcept C153294291 @default.
• W4226197349 hasConcept C168754636 @default.
• W4226197349 hasConcept C18903297 @default.
• W4226197349 hasConcept C205649164 @default.
• W4226197349 hasConcept C21200559 @default.
• W4226197349 hasConcept C24326235 @default.
• W4226197349 hasConcept C39432304 @default.
• W4226197349 hasConcept C41156917 @default.
• W4226197349 hasConcept C49204034 @default.
• W4226197349 hasConcept C86803240 @default.
• W4226197349 hasConcept C91586092 @default.
• W4226197349 hasConceptScore W4226197349C107054158 @default.
• W4226197349 hasConceptScore W4226197349C127313418 @default.
• W4226197349 hasConceptScore W4226197349C127413603 @default.
• W4226197349 hasConceptScore W4226197349C132651083 @default.
• W4226197349 hasConceptScore W4226197349C153294291 @default.
• W4226197349 hasConceptScore W4226197349C168754636 @default.
• W4226197349 hasConceptScore W4226197349C18903297 @default.
• W4226197349 hasConceptScore W4226197349C205649164 @default.
• W4226197349 hasConceptScore W4226197349C21200559 @default.
• W4226197349 hasConceptScore W4226197349C24326235 @default.
• W4226197349 hasConceptScore W4226197349C39432304 @default.
• W4226197349 hasConceptScore W4226197349C41156917 @default.
• W4226197349 hasConceptScore W4226197349C49204034 @default.
• W4226197349 hasConceptScore W4226197349C86803240 @default.
• W4226197349 hasConceptScore W4226197349C91586092 @default.
• W4226197349 hasFunder F4320306101 @default.
• W4226197349 hasLocation W42261973491 @default.

• next