Estimating regional effects of climate change and altered land use on biosphere carbon fluxes using distributed time delay neural networks with Bayesian regularized learning

Neural Networks : the Official Journal of the International Neural Network Society
Andres SchmidtBeverly E Law

Abstract

The ability to accurately predict changes of the carbon and energy balance on a regional scale is of great importance for assessing the effect of land use changes on carbon sequestration under future climate conditions. Here, a suite of land cover-specific Distributed Time Delay Neural Networks with a parameter adoption algorithm optimized through Bayesian regularization was used to model the statewide atmospheric exchange of CO2, water vapor, and energy in Oregon with its strong spatial gradients of climate and land cover. The network models were trained with eddy covariance data from 9 atmospheric flux towers. Compared to results derived with more common regression networks utilizing non-delayed input vectors, the performance of the DTDNN models was significantly improved with an average increase of the coefficients of determination of 64%. The optimized models were applied in combination with downscaled climate projections of the CMIP5 project to calculate future changes in the cycle of carbon, associated with a prescribed conversion of conventional grass-crops to hybrid poplar plantations for biofuel production in Oregon. The results show that under future RCP8.5 climate conditions the total statewide NEP increases by 0.87 ...Continue Reading

References

Jul 28, 2005·Neural Networks : the Official Journal of the International Neural Network Society·Yurong LiuXiaohui Liu
Sep 12, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Yurong LiuXiaohui Liu
Jan 26, 2013·Ecology Letters·Matthias PeichlMatthew Saunders
Oct 22, 2013·Environmental Science & Technology·Tara W HudiburgBeverly E Law

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