Carbon dynamics in the Amazonian Basin: Integration of eddy covariance and ecophysiological data with a land surface model

Masri, Bassil El; Barman, Rahul; Meiyappan, Prasanth; Song, Yang; Liang, Miaoling

doi:10.1016/j.agrformet.2013.03.011

Cited by 57 publications

(70 citation statements)

References 83 publications

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“…• spatial resolution at multiple temporal resolutions, ranging from half hourly to yearly [5]. Each grid is occupied by combination of plant functional types (PFTs), bare ground, and glaciers for a total of 28 different PFTs types [6].…”

Section: Background a Isam: Integrated Science Assessment Modelmentioning

confidence: 99%

“…ISAM has two main components: (1) a biogeophysical module representing sunlit-shaded photosynthesis schemes [7], energy, and soil/snow hydrology [8], [9], [7] and (2) a biogeochemical module, where assimilated carbon is allocated to vegetation, litter, and soil carbon pools [5], [7], [10]. The carbon cycle and nitrogen cycle are coupled together.…”

Section: Background a Isam: Integrated Science Assessment Modelmentioning

confidence: 99%

See 1 more Smart Citation

Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer

Miller

Robson

Masri

et al. 2014

2014 43rd International Conference on Parallel Processing

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Abstract-We present the progression of developments necessary to scale the ISAM land surface model from single nodes and small clusters with unusually large per-node memory to much larger systems with more common configurations. These efforts include load balancing, conventional library-based output parallelization to reduce memory load, and parallel-in-time data input. On Hopper, a Cray XE6 machine, the result was strong scaling from 256 cores to 16k cores with an efficiency of 32.9%. On Edison, a Cray XC30 machine, the code strong scales from 256 cores to 16k cores with an efficiency of 51.4%. These largescale gains, and the associated performance increases at smaller scale, enable greater scientific productivity for the users of ISAM and open the possibilities of increased resolution in time and space and greater physical fidelity for the simulated processes while remaining computationally feasible.

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Section: Background a Isam: Integrated Science Assessment Modelmentioning

confidence: 99%

Section: Background a Isam: Integrated Science Assessment Modelmentioning

confidence: 99%

Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer

Miller

Robson

Masri

et al. 2014

2014 43rd International Conference on Parallel Processing

Self Cite

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“…Climate change causes a biomass production change in natural ecosystems (Jafari, 2013). Therefore, prediction of NPP in natural areas, and the understanding of the effect of climate change on global ecosystems, products and the sustainability of services are fundamental issues that many researchers have addressed (Fang et al, 2003;Ei-Masri et al, 2013;Hemming et al, 2013;Piao et al, 2005;Zhao et al, 2010). Liang et al (2015) investigated the spatial and temporal patterns of annual, seasonal and monthly changes of the NPP index.…”

Section: Introductionmentioning

confidence: 99%

“…Land degradation and its manifestation in drylands, desertification, are still widespread, jeopardizing livelihoods and sustainable development (Fleskens and Stringer, 2014;Reynolds and Stafford, 2002). Drylands (arid, semiarid and dry subhumid areas) cover approximately 41 % of the Earth's surface and approximately 10 to 20 % of these regions are experiencing degradation and desertification processes, resulting in a decline in agricultural productivity, loss of biodiversity and the breakdown of ecosystems (Keesstra, 2007).…”

Section: Introductionmentioning

confidence: 99%

Desertification of forest, range and desert in Tehran province, affected by climate change

et al. 2016

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Abstract. Climate change has been identified as a leading human and environmental crisis of the twenty-first century. Drylands throughout the world have always undergone periods of degradation due to naturally occurring fluctuation in climate. Persistence of widespread degradation in arid and semiarid regions of Iran necessitates monitoring and evaluation. This paper aims to monitor the desertification trend in three types of land use, including range, forest and desert, affected by climate change in Tehran province for the 2000s and 2030s. For assessing climate change at Mehrabad synoptic station, the data of two emission scenarios, including A2 and B2, were used, utilizing statistical downscaling techniques and data generated by the Statistical DownScaling Model (SDSM). The index of net primary production (NPP) resulting from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images was employed as an indicator of destruction from 2001 to 2010. The results showed that temperature is the most significant driving force which alters the net primary production in rangeland, forest and desert land use in Tehran province. On the basis of monitoring findings under real conditions, in the 2000s, over 60 % of rangelands and 80 % of the forest were below the average production in the province. On the other hand, the longterm average changes of NPP in the rangeland and forests indicated the presence of relatively large areas of these land uses with a production rate lower than the desert. The results also showed that, assuming the existence of circumstances of each emission scenarios, the desertification status will not improve significantly in the rangelands and forests of Tehran province.

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“…While the climate is affected by the expansion of agriculture land, climate change simultaneously affects agriculture. Many crops show positive responses to elevated carbon dioxide and low levels of warming, but higher levels of warming often negatively affect growth and yield (Hatfield et al, 2008;Kucharik and Serbin, 2008;Urban et al, 2012) The overall aim of this study is to evaluate dynamic crop growth processes in a land surface model, the Integrated Science Assessment Model (ISAM) El-Masri et al, 2013), to understand and address the interactions between C3/C4 crop growth and seasonal dynamics of carbon, water, and energy fluxes. Our implementation focuses on the corn-soybean rotation, which is the most common crop rotation practice around the world (Nafziger, 2012).…”

Section: Introductionmentioning

confidence: 99%

Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

2013

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Abstract. Worldwide expansion of agriculture is impacting the earth's climate by altering carbon, water, and energy fluxes, but the climate in turn is impacting crop production. To study this two-way interaction and its impact on seasonal dynamics of carbon, water, and energy fluxes, we implemented dynamic crop growth processes into a land surface model, the Integrated Science Assessment Model (ISAM). In particular, we implemented crop-specific phenology schemes and dynamic carbon allocation schemes. These schemes account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, root, stem, and grain pools. The dynamic vegetation structure simulation better captured the seasonal variability in leaf area index (LAI), canopy height, and root depth. We further implemented dynamic root distribution processes in soil layers, which better simulated the root response of soil water uptake and transpiration. Observational data for LAI, above-and belowground biomass, and carbon, water, and energy fluxes were compiled from two AmeriFlux sites, Mead, NE, and Bondville, IL, USA, to calibrate and evaluate the model performance. For the purposes of calibration and evaluation, we use a corn-soybean (C4-C3) rotation system over the period 2001-2004. The calibrated model was able to capture the diurnal and seasonal patterns of carbon assimilation and water and energy fluxes for the corn-soybean rotation system at these two sites. Specifically, the calculated gross primary production (GPP), net radiation fluxes at the top of the canopy, and latent heat fluxes compared well with observations. The largest bias in model results was in sensible heat flux (SH) for corn and soybean at both sites. The dynamic crop growth simulation better captured the seasonal variability in carbon and energy fluxes relative to the static simulation implemented in the original version of ISAM. Especially, with dynamic carbon allocation and root distribution processes, the model's simulated GPP and latent heat flux (LH) were in much better agreement with observational data than for the static root distribution simulation. Modeled latent heat based on dynamic growth processes increased by 12-27% during the growing season at both sites, leading to an improvement in modeled GPP by 13-61 % compared to the estimates based on the original version of the ISAM.

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Carbon dynamics in the Amazonian Basin: Integration of eddy covariance and ecophysiological data with a land surface model

Cited by 57 publications

References 83 publications

Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer

Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer

Desertification of forest, range and desert in Tehran province, affected by climate change

Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

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