HPC-EPIC for high resolution simulations of environmental and sustainability assessment

Nichols, Jeffrey A.; Kang, Suhyun; Post, Wilfred M.; Wang, D.; Bandaru, Varaprasad; Manowitz, David H.; Zhang, X.; Izaurralde, R. C.

doi:10.1016/j.compag.2011.08.012

Cited by 32 publications

(27 citation statements)

References 4 publications

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“…(), and Nichols et al . (). For the initial design and testing of the global platform, we simplified some management practices to facilitate analysis of model functions.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, Nichols et al . () demonstrated the potential of parallel processing in modeling agricultural systems, achieving a 40‐fold increase in speed by running 140 000 simulations concurrently on a Linux‐based computing cluster.…”

Section: Introductionmentioning

confidence: 92%

“…Wang et al (2005) designed generic software architecture for implementing spatially explicit ecosystem models on computing grids. Recently, Nichols et al (2011) demonstrated the potential of parallel processing in modeling agricultural systems, achieving a 40-fold increase in speed by running 140 000 simulations concurrently on a Linux-based computing cluster.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global simulation of bioenergy crop productivity: analytical framework and case study for switchgrass

et al. 2013

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A global energy crop productivity model that provides geospatially explicit quantitative details on biomass potential and factors affecting sustainability would be useful, but does not exist now. This study describes a modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling. We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation, and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate (HPC-EPIC) model simulated a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock production potentials and effects across the globe. This modeling platform can assess soil C sequestration, net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus, energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding metrics of sustainability.

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“…(), and Nichols et al . (). For the initial design and testing of the global platform, we simplified some management practices to facilitate analysis of model functions.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Global simulation of bioenergy crop productivity: analytical framework and case study for switchgrass

et al. 2013

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“…This time consuming task necessitated the development of a parallel computing facility to improve the modeling efficiency. The parallel computing component of the GAMS was constructed by revising and re-structuring modules of software developed by Nichols et al (2011);Zhang et al (2013a). It seamlessly combines Python (python.org), mpi4py (Dalcin et al, 2011) and OpenMPI (www.open-mpi.org) to make use of hundreds of processors simultaneously.…”

Section: Parallelized Epic Execution and Output Data Analysismentioning

confidence: 99%

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Zhang

Izaurralde

Manowitz

et al. 2015

Environmental Modelling & Software

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a b s t r a c tAccurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO 2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatiallyexplicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems to each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000e2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies.

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“…Although high resolution model simulations are made possible by advanced computing facilities [4], accurate forecasting can't be guaranteed [5] which has been a primary concern while applying the NWP model in research and operational forecasting of weather. This task is even more challenging over the Himalayas due to the role of complex terrain, where general theory and findings from elsewhere may not be fully transferrable [6].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of WRF Cloud Microphysics to Simulations of a Convective Storm Over the Nepal Himalayas

Shrestha¹,

Connolly

Gallagher

2017

TOASCJ

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Background:This paper investigates sensitivity of bulk microphysical parameterization (BMP) schemes within the Weather Research and Forecasting (WRF) model to simulate a convective storm that generally evolves during pre-monsoon season (March -May) across the foothills of the Himalayas. Method:Four mixed-phase BMP schemes (Morrison, Lin, WDM6, and WSM6), which are parameterized with an increasing complexity from single to double moments of particle distribution to represent cloud processes, are used with an explicit convection permitting grid resolution (3 km x 3 km). Experiments are set up to simulate a convective storm that occurred in the late afternoon of 18 th May 2011 and compared with i) Satellite-based tropical rainfall measuring mission (TRMM) 3B42 v7 data, and ii) Ground-based observations at Nagarkot (27.7°N, 85.5°E), Nepal. Result:Our results show that the simulated storm characteristics are not overly sensitive to the chosen BMP schemes. In general, all the BMP schemes produce similar rainfall characteristics and compares reasonably well with the observations across Siwalik Hills and Middle Mountains, which act as a topographic barrier to low level circulations and receive more rain. The schemes, however, show negative bias across central Nepal including the Kathmandu Valley, albeit the magnitude and spatial distribution of bias are different between the schemes. In contrast, upper level total water condensate and cloud fraction show a strong sensitivity to the BMP schemes. Conclusion:Overall, the Morrison scheme, in addition to warm clouds which also predict double moment distribution of all hydrometeors in the cold-cloud processes, a dominant cloud forming process in the Himalayas, accurately represents the mechanism and outperforms the simplified schemes based on root mean square error (RMSE) analysis.

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HPC-EPIC for high resolution simulations of environmental and sustainability assessment

Cited by 32 publications

References 4 publications

Global simulation of bioenergy crop productivity: analytical framework and case study for switchgrass

Global simulation of bioenergy crop productivity: analytical framework and case study for switchgrass

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Sensitivity of WRF Cloud Microphysics to Simulations of a Convective Storm Over the Nepal Himalayas

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