presentation_2014.bib

@presentations{Bisht_AGU2014,
  title = {Topographic controls on scaling of hydrologic and thermal processes in polygonal ground features of an Arctic ecosystem: A case study using idealized non-isothermal surface-subsurface simulations},
  author = {Gautam Bisht and William Riley and Nathan Collier and Jitendra Kumar},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B33B-0169},
  month = {December 15--19},
  year = {2014},
  abstract = {Arctic and sub-Arctic soils currently contain approximately 1700 billion metric tones of frozen organic carbon, approximately 200 times current annual anthropogenic emissions. This carbon is vulnerable to release to the atmosphere as CO2 and CH4 as high-latitude temperatures warm. Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. In a future warmer climate, the spatial distribution of soil moisture and active layer depth are expected to be key factors controlling the fate of thawed permafrost carbon. Polygonal ground structures, with high or low centers, dominate the local hydrologic environment, thereby impacting the energy balance, biogeochemical dynamics, vegetation communities, and carbon releases from the subsurface. In spite of their importance to local hydrologic and thermal processes, the impact of these microtopographic features at larger spatial scales is not well understood. Our previous work from isothermal surface-subsurface simulations has indicated that statistical moments of soil moisture follow a non-linear scaling relationship. In this study, we perform surface-subsurface non-isothermal flow simulations using PFLOTRAN for four study sites located near Barrow, AK. Simulations are performed on domains at multiple horizontal resolutions for several years. We describe the statistical moments of simulated soil moisture and soil temperature fields across spatial resolutions.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Collier_AGU2014,
  title = {Challenges to large-scale simulations of permafrost freeze-thaw dynamics},
  author = {Nathan Collier and Gautam Bisht and Jitendra Kumar},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract GC33A-0480},
  month = {December 15--19},
  year = {2014},
  abstract = {In an effort to model the dymanics of the permafrost freeze and thaw process in the Alaskan tundra, we have implemented a finite volume method which approximates the evolution of a coupled surface/subsurface mass and energy balance within PFLOTRAN--an open source, state-of-the-art massively parallel subsurface flow and reactive transport code.
While this system is studied in the literature at one scale, we encounter many undocumented pitfalls as we exercise the model at high resolution and force using realistic datasets from the field sites. These realistic simulations for field sites near Barrow, Alaska expose the model to a wide range of moisture and thermal states that are not tested in published studies. For example, the conventional upwinding of the relative permeability used in the Darcy flux computation can yield a flow into a frozen cell. We also find that infiltration, sources, and sinks must be carefully regulated as flow into frozen portions of the domain, or out of dry or frozen regions can cause unphysical states in the simulation which cause failure. Many straight-forward solutions are not smooth which produce discontinuities in the Jacobian of the nonlinear residual. These difficulties represent a current hurdle to running large-scale permafrost dynamics simulations. We describe these challenges and present approaches to overcoming them in the pursuit of a scalable scheme.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Hargrove_USIALE2014,
  title = {Utility and Behavior of National Phenoregions for Characterization of Vegetation, Habitat and Seasonal Changes},
  author = {William Hargrove and Forrest Hoffman and Jitendra Kumar and Serra J. Hoagland and Yasemin Erugner Baytok and Steve Norman},
  howpublished = {US International Association of Landscape Ecology (IALE), Anchorage, AK},
  month = {May 18--22, 2014},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.06},
  url = {http://usiale.org/anchorage2014/presentation-details/5007}
}

@presentations{Hargrove_AGU2014a,
  title = {{Predominant Environmental Factors Controlling and Predicting Phenological Seasonality Across the CONUS over the Last Decade}},
  author = {William Hargrove and Jitendra Kumar and Yasemin Erugner Baytok and Forrest Hoffman},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B53K-03},
  month = {December 15--19},
  year = {2014},
  abstract = {We used phenological ecoregions to stratify the development of multiple linear regression equations predicting Day-of-Year (DOY) for a set of five Phenological Completion Milestones across the conterminous United States for each year during the MODIS era, based on five potential ecological driver variables. The primary goal was to map the predictability of each Phenological Milestone, and to rank and map the most important driver variables each year, but the potential for predicting season DOY from prognostic GCM outputs was also evaluated.

Five pre-defined percent-completion milestones were established in terms of threshold percentages of maximum annual NDVI to gauge seasonal phenological progression. Unlike other Start-of-Season metrics, these Phenological Completion Milestones do not attempt to correspond with particular observable events on the ground. Nor are they species-specific, but instead describe emergent ecosystem characteristics of Land Surface Phenology. While arbitrary, they can be readily applied at national scales. The Day-of-Year (DOY) when the 20%, 80%, maximum, 80% and 20% Phenological Milestones are reached in each MODIS cell correspond roughly to start of spring, summer, peak, fall and winter, respectively.

We used Daymet (daymet.ornl.gov) to obtain interpolated daily 1km2 values from 2000 - 2013 for daily minimum and maximum temperature, precipitation, day length, and vapor pressure deficit (VPD). We accumulated annual heat sum above 5 C and cold sum below 5 C since Jan 1, and precip totals after Mar 1. Daily values were used for day length and VPD.

A set of statistically-defined national phenological ecoregions, or "phenoregions," were created at multiple levels of division using a parallel clustering algorithm to group cells having a similar annual phenological profile of MODIS NDVI. Each resultant phenoregion contains vegetation that showed similar phenological timing in each year. A multiple linear regression based on values accumulated up to that DOY was developed to predict DOY when a particular Phenological Milestone was achieved within each phenoregion. Resulting maps of R-squared, primary driver, and residuals were coherent and interpretable. The desert southwest "spring" was highly predictable, and was controlled predominantly by accumulated precipitation.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Hargrove_AGU2014b,
  title = {Version 5 of Forecasts; Forecasts of Climate-Associated Shifts in Tree Species},
  author = {William Hargrove and Jitendra Kumar and Kevin M. Potter and Forrest Hoffman},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B51G-0106},
  month = {December 15--19},
  year = {2014},
  abstract = {Version 5 of the ForeCASTS tree range shift atlas (www.geobabble.org/~hnw/global/treeranges5/climate_change/atlas.html) now predicts global shifts in the suitable ranges of 335 tree species (essentially all woody species measured in Forest Inventory Analysis (FIA)) under forecasts from the Parallel Climate Model, and the Hadley Model, each under future climatic scenarios A1 and B1, each at two future dates (2050 and 2100). Version 5 includes more Global Biodiversity Information Facility (GBIF) occurrence points, uses improved heuristics for occurrence training, and recovers occurrence points that fall in water.

A multivariate clustering procedure was used to quantitatively delineate 30 thousand environmentally homogeneous ecoregions across present and 8 potential future global locations at once, using global maps of 17 environmental characteristics describing temperature, precipitation, soils, topography and solar insolation. Occurrence of each tree species on FIA plots and in GBIF samples was used to identify a subset of suitable ecoregions from the full set of 30 thousand. This subset of suitable ecoregions was compared to the known current present range of the tree species. Predicted present ranges correspond well with existing ranges for all but a few of the 335 tree species. The subset of suitable ecoregions can then be tracked into the future to determine whether the suitable home range remains the same, moves, grows, shrinks, or disappears under each model/scenario combination.

A quantitative niche breadth analysis allows sorting of the 17 environmental variables from the narrowest, most important, to the broadest, least restrictive environmental factors limiting each tree species. Potential tree richness maps were produced, along with a quantitative potential tree endemism map for present and future CONUS. Using a new empirical imputation method which associates sparse measurements of dependent variables with particular clustered combinations of the environmental driver variables, and then estimates values for unmeasured clusters, we interpolated FIA measurements of productivity into continuous maps showing productivity across each tree's entire present and future ranges.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Hoffman_ClimateDataWorkshopNCAR_2014,
  title = {{Representativeness-based Sampling Network Design for NGEE and Identifying Phenoregions for the Conterminous U.S.}},
  author = {Forrest Hoffman and Jitendra Kumar and Damian M. Maddalena and William Hargrove},
  howpublished = {Fourth Workshop on Understanding Climate Change from Data, National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA. (Invited)},
  month = {June 30--July 2},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.07}
}

@presentations{Hoffman_AGU2014,
  title = {Multivariate Spatio-Temporal Clustering: A Framework for Integrating Disparate Data to Understand Network Representativeness and Scaling Up Sparse Ecosystem Measurements},
  author = {Forrest Hoffman and Jitendra Kumar and Damian M. Maddalena and Zachary Langford and William Hargrove},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract IN44A-06},
  month = {December 15--19},
  year = {2014},
  abstract = {Disparate in situ and remote sensing time series data are being collected to understand the structure and function of ecosystems and how they may be affected by climate change. However, resource and logistical constraints limit the frequency and extent of observations, particularly in the harsh environments of the arctic and the tropics, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent variability at desired scales. These regions host large areas of potentially vulnerable ecosystems that are poorly represented in Earth system models (ESMs), motivating two new field campaigns, called Next Generation Ecosystem Experiments (NGEE) for the Arctic and Tropics, funded by the U.S. Department of Energy. Multivariate Spatio-Temporal Clustering (MSTC) provides a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. We applied MSTC to down-scaled general circulation model results and data for the State of Alaska at a 4 km2 resolution to define maps of ecoregions for the present (2000–2009) and future (2090–2099), showing how combinations of 37 bioclimatic characteristics are distributed and how they may shift in the future. Optimal representative sampling locations were identified on present and future ecoregion maps, and representativeness maps for candidate sampling locations were produced. We also applied MSTC to remotely sensed LiDAR measurements and multi-spectral imagery from the WorldView-2 satellite at a resolution of about 5 m2 within the Barrow Environmental Observatory (BEO) in Alaska. At this resolution, polygonal ground features—such as centers, edges, rims, and troughs—can be distinguished. Using these remote sensing data, we up-scaled vegetation distribution data collected on these polygonal ground features to a large area of the BEO to provide distributions of plant functional types that can be used to parameterize ESMs. In addition, we applied MSTC to 4 km2 global bioclimate data to define global ecoregions and understand the representativeness of CTFS-ForestGEO, Fluxnet, and RAINFOR sampling networks. These maps identify tropical forests underrepresented in existing observations of individual and combined networks.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Hoffman_USIALE2014,
  title = {Representativeness-Based Sampling Network Design for the Arctic},
  author = {Forrest Hoffman and Jitendra Kumar and Richard Mills and William Hargrove},
  howpublished = {US International Association of Landscape Ecology (IALE), Anchorage, AK},
  month = {May 18--22, 2014},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.06},
  url = {http://usiale.org/anchorage2014/presentation-details/4989}
}

@presentations{Hoffman_TES2014,
  title = {Representativeness-Based Sampling Network Design and Scaling Strategies for Measurements in Arctic and Tropical Ecosystems},
  author = {Hoffman, Forrest M. and Jitendra Kumar and Zachary Langford and Damian Maddalena and Nathan Collier and Victoria Sloan and Richard T. Mills and William W. Hargrove},
  howpublished = {U.S. Department of Energy Joint Terrestrial Ecosystem Science and Subsurface Biogeochemistry Research Principal Investigator Meeting, Bolger Center, Potomac, Maryland, USA.},
  month = {May 6--7, 2014},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.06}
}

@presentations{Kumar_ESM2014,
  title = {Multi-scale Modeling of Hydrologic and Biogeochemical Processes in Arctic Ecosystems},
  author = {Jitendra Kumar and Nathan Collier and Fengming Yuan and Gautam Bisht and Guoping Tang and Xiaofeng Xu and Peter Thornton},
  howpublished = {U.S. Department of Energy Climate Modeling Principal Investigator Meeting, Bolger Center, Potomac, Maryland, USA.},
  month = {May 12--14, 2014},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.06}
}

@presentations{Kumar_TES2014,
  title = {Multi-scale Modeling of Hydrologic and Biogeochemical Processes in Arctic Ecosystems},
  author = {Jitendra Kumar and Nathan Collier and Fengming Yuan and Gautam Bisht and Guoping Tang and Xiaofeng Xu and Peter Thornton},
  howpublished = {U.S. Department of Energy Joint Terrestrial Ecosystem Science and Subsurface Biogeochemistry Research Principal Investigator Meeting, Bolger Center, Potomac, Maryland, USA.},
  month = {May 6--7, 2014},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.06}
}

@presentations{Kumar_AGU2014b,
  title = {{Detecting and Tracking Shifts in National Vegetation Composition,Including Donors and Recipients, Across the MODIS Era}},
  author = {Jitendra Kumar and William Hargrove and Steve Norman and Forrest Hoffman},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B31A-0008},
  month = {December 15--19},
  year = {2014},
  abstract = {Forested landscapes are ecologically and economically important, and understanding their dynamics is important for land--management decision making. Forest ecosystems are also under stress, and may be changing due to interannual variability and long term change in climate, natural and anthropogenic disturbance, including human use and management. Detecting and tracking shifts in vegetation is important for land--management, conservation planning, monitoring recovery, managing and monitoring forest structure and composition, maintaining species and habitat diversity and many other purposes.

We used MODIS NDVI to create phenological ecoregions, or "phenoregions" having similar annual phenology using a unsupervised clustering method over the period 2000--2012. These statistically derived phenoregions were reclassified to National Land Cover Database (NLCD) classes using the "Mapcurves" algorithm. Interannual transitions in phenologically defined classes are indicator of disturbance and recovery. Because the area within the CONUS is fixed, land cover area changes are a zero-sum game. Changes in one land cover class must be accompained by compensating changes in other classes.

We demonstrate a full-circle national-scale accounting system which can track not only area changes in land cover classes, but can show which other compensatory land cover class area changes accompanied them. Area changes in the vegetation distributions, as well as compensatory gains, losses, and trades in area of other land cover types, were mapped and tracked annually during 2000--2012 period at MODIS resolution. The types or labels of the classes used in the accounting can easily be changed to sets of land cover types that maximize the utility of the tracking. For any particular "focus" land cover type, results show which other land covers were donors or recipients of area changes, showing ecologists and land managers alike what vegetation types were given up or gained to offset particular increases or losses.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Kumar_HPCGeospatial_2014,
  title = {Empirical Mining of Large Data Sets to Solve Practical Ecological Problems},
  author = {Jitendra Kumar and Forrest Hoffman and William Hargrove},
  howpublished = {High Performance Computing and Geospatial Analytics Workshop, Argonne National Laboratory (Invited)},
  month = {April 29--30},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.07}
}

@presentations{Kumar_USIALE2014,
  title = {Mapping plant functional type distributions in Arctic ecosystems using multi-spectral remote sensing and vegetation survey datasets},
  author = {Jitendra Kumar and Forrest Hoffman and Victoria Sloan and Richard Norby},
  howpublished = {US International Association of Landscape Ecology (IALE), Anchorage, AK},
  month = {May 18--22, 2014},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.06},
  url = {http://usiale.org/anchorage2014/presentation-details/5029}
}

@presentations{Kumar_AGU2014a,
  title = {Remote sensing to inform Plant Functional Type (PFT) distributions in the Community Land Model},
  author = {Jitendra Kumar and Zachary Langford and Fengming Yuan and Forrest Hoffman},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B53I-03},
  month = {December 15--19},
  year = {2014},
  abstract = {Sensitive Arctic ecosystems are vulnerable to change as warming climate impacts the hydrological, thermal, biogeochemical and plant physiological processes on the landscape, leading to geomorphic, biophysical and biogeochemical changes. In particular, Arctic vegetation is expected to exhibit significant shifts in community composition, phenology, distribution and productivity under a changing climate. Modeling of vegetation communities, often represented as Plant Functional Types (PFTs) in Earth System Models (ESMs), requires accurate characterization of their distributions on the landscape as input to ESMs. The unique spectral characteristics exhibited by vegetation can be sensed by remote sensing platforms and used to characterize and distinguish different vegetation types. In this study we employ multi-spectral remote sensing from WorldView--2 and LIDAR--derived digital elevation models to characterize the Arctic tundra vegetation communities near Barrow, Alaska. Using field vegetation surveys at a number of sites, we derived statistical relationships between vegetation distributions and spectral data, which were then employed to estimate the distributions of evergreen shrub, deciduous shrub, grass, sedge, forb, moss and lichen PFTs for the Barrow Environmental Observatory. Plant physiological parameters for these tundra-specific PFTs were implemented in the Community Land Model (CLM). We will present CLM results from simulations employing different distributions of these new PFTs, created using different subsets of remote sensing and in situ vegetation data, to test the sensitivity of the model to a range of predicted distributions.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Langford_AGU2014,
  title = {Mapping plant functional type distributions in Arctic ecosystems using WorldView-2 satellite imagery and unsupervised clustering},
  author = {Zachary Langford and Jitendra Kumar and Victoria Sloan and Richard Norby and Stan Wullschleger},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B41I-0166},
  month = {December 15--19},
  year = {2014},
  abstract = {The Arctic has emerged as an important focal point for the study of climate change. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivity under changing climate. Modeling of Arctic tundra vegetation requires representation of the heterogeneous tundra landscape, which includes representation of individual plant functional types (PFT). Vegetation exhibits unique spectral characteristics that can be harnessed to discriminate plant types and develop quantitative vegetation indices, such as the Normalized Difference Vegetation Index. We have combined high resolution multi-spectral remote sensing from the WorldView-2 satellite with LiDAR-derived digital elevation models to characterize the tundra landscape in four 100m X 100m sites within the Barrow Environmental Observatory, a 3021 hectare research reserve located at the northern most location on the Alaskan Arctic Coastal Plain. Classification of landscape PFT's using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season (June - August) to collect vegetation surveys from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. We will describe two versions of PFT upscaling from WorldView-2 imagery: 1) a version computed from multiple imagery through the growing season and 2) a version computed from a single image in the middle of the growing season. This approach allowed us to test the degree to which including phenology helps predict PFT distribution. Ground-truthing was performed using both sets of PFT estimates to characterize uncertainty. Early results show uncertainty exists in wet and inundated areas where bryophyte moss are overestimated. Further investigation will be done for areas of uncertainty and improving our upscaling algorithms.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Maddalena_AGU2014,
  title = {Landscape Characterization and RepresentativenessAnalysis for Understanding Sampling Network Coverage},
  author = {Damian M. Maddalena and Forrest Hoffman and Jitendra Kumar},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B51B-0029},
  month = {December 15--19},
  year = {2014},
  abstract = {The need to understand sensitive forested systems is amplified under climate change. Relative change can have large impacts on these sensitive systems due to low variability of climate variables under current climate regimes. Our regional and global understanding of these systems begins with understanding the data collected for scientific investigation. Sampling networks rarely conform to spatial and temporal ideals, often comprised of network sampling points which are unevenly distributed and located in less than ideal locations due to access constraints, budget limitations, or political conflict. Quantifying the global, regional, and temporal representativeness of these networks by quantifying the coverage of network infrastructure highlights the capabilities and limitations of the data collected, facilitates upscaling and downscaling for modeling purposes, and improves the planning efforts for future infrastructure investment under current conditions and future modeled scenarios. The current analysis utilizes multivariate spatiotemporal clustering (MSTC) and representativeness analysis with 4 km^2 global bioclimate data for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and CTFS-ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables globally and quantitative representativeness maps of individual and combined networks within ecological domains, regionally, globally, and through time.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Norman_USIALE2014,
  title = {A diagnostic and predictive tool for landscape fire regimes},
  author = {Steve Norman and Jitendra Kumar and William Hargrove},
  howpublished = {US International Association of Landscape Ecology (IALE), Anchorage, AK},
  month = {May 18--22, 2014},
  year = {2014},
  owner = {jkumar},
  timestamp = {2014.07.06},
  url = {http://usiale.org/anchorage2014/presentation-details/5183}
}

@presentations{Norman_AGU2014,
  title = {A Global Classification of Contemporary Fire Regimes},
  author = {Steve Norman and Jitendra Kumar and William Hargrove and Forrest Hoffman},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract GC31A-0440},
  month = {December 15--19},
  year = {2014},
  abstract = {Fire regimes provide a sensitive indicator of changes in climate and human use as the concept includes fire extent, season, frequency, and intensity. Fires that occur outside the distribution of one or more aspects of a fire regime may affect ecosystem resilience. However, global scale data related to these varied aspects of fire regimes are highly inconsistent due to incomplete or inconsistent reporting. In this study, we derive a globally applicable approach to characterizing similar fire regimes using long geophysical time series, namely MODIS hotspots since 2000. K-means non-hierarchical clustering was used to generate empirically based groups that minimized within-cluster variability. Satellite-based fire detections are known to have shortcomings, including under-detection from obscuring smoke, clouds or dense canopy cover and rapid spread rates, as often occurs with flashy fuels or during extreme weather. Such regions are free from preconceptions, and the empirical, data-mining approach used on this relatively uniform data source allows the region structures to emerge from the data themselves. Comparing such an empirical classification to expectations from climate, phenology, land use or development-based models can help us interpret the similarities and differences among places and how they provide different indicators of changes of concern. Classifications can help identify where large infrequent mega-fires are likely to occur ahead of time such as in the boreal forest and portions of the Interior US West, and where fire reports are incomplete such as in less industrial countries.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Shu_AGU2014,
  title = {{Data Mining Approach for Evaluating Vegetation Dynamics in Earth System Models (ESMs) Using Satellite Remote Sensing Products}},
  author = {Shijie Shu and Forrest Hoffman and Jitendra Kumar and William Hargrove and Atul Jain},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B53G-06},
  month = {December 15--19},
  year = {2014},
  abstract = {Uncertainties in data retrieved from remote sensor present challenges to using such observational products to constrain Earth system model (ESM) results. While simple statistics can be applied to compare models with observations, advanced data mining methods, like unsupervised cluster analysis, offer powerful tools for summarizing model-data differences in the spatial and temporal patterns of ecological characteristics. We compared modeled land surface phenology with MODIS 16-day composited Normalized Difference Vegetation Index (NDVI) (MOD13C1) and Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g products spanning years 2001 to 2012. Annual traces of NDVI from two ESMs (CESM-CLM and ISAM) were calculated using modeled radiation from the output of historical simulations and corrected to better match observed properties by considering instrumental bandwidths and solar angle. Multivariate Spatio-Temporal Clustering (MSTC) was applied to annual traces of MODIS and GIMMS NDVI to create phenological regions (phenoregions) and analyzed using Mapcurves, a tool designed for comparing categorical maps, to check the consistency of the spatial patterns of observations before assessing model output. To evaluate modeled phenology, MSTC was first applied to obtain representative centroids of modeled NDVI and separately of satellite NDVI. The Mapcurves method was applied to compare the spatial patterns of modeled phenology to remote sensing observations. Next, modeled NDVI were projected onto the centroids defining phenoregions of observed NDVI, and observed NDVI were projected onto the centroids of modeled NDVI. Mapcurves was then applied to compare the spatial patterns of these classifications. Results showed a general agreement in the spatial pattern of phenoregions from models to satellite observations, except in high-latitude regions and agricultural areas. MSTC averages out small deviations between modeled and observed phenology, which are exhibited across all biome types. However, Mapcurves results showed a relatively low goodness of fit score for modeled phenology projected onto observations. This study demonstrates the utility of a data mining approach for cross-validation of observations and evaluation of model performance.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Song_AGU2014,
  title = {{Above-belowground Carbon Allocation in Earth System Models}},
  author = {Xia Song and Forrest Hoffman and Xiaofeng Xu and Colleen Iversen and Jitendra Kumar},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract A21G-3117},
  month = {December 15--19},
  year = {2014},
  abstract = {Above-belowground carbon allocation is a critical mechanism for vegetation growth and its adaptation to the changing environment. The model representation of carbon allocation mechanisms significantly influence the simulated carbon stock and land-atmosphere exchange in Earth System Models (ESMs). Few previous studies, however, have investigated and evaluated the above-belowground carbon allocation in ESMs. In this study, we analyzed carbon density in belowground, total vegetation (above + belowground), and belowground:vegetation ratios of eleven ESMs from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), which were used for the latest IPCC Assessment Report (AR5).

Overall, results of ESMs are not consistent with observational data; both the belowground and total vegetation carbon density are underestimated in tropical/subtropical and temperate regions, while overestimated in arctic/subarctic regions. Moreover, the ratios of belowground:total vegetation carbon are underestimated in all three climate zones. The model-data discrepancies in carbon density vary substantially among biomes, while the ratios of belowground:total vegetation carbon are consistently underestimated across all major biomes expect tropical moist forests. This study indicates that the carbon allocation algorithms in current ESMs need to be improved to better simulate vegetation growth and its responses to global change.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Wullschleger_AGU2014,
  title = {Integration of Measurements and Models Across Spatial Scales for Improved Process Understanding in Arctic and Boreal Ecosystems},
  author = {Stan Wullschleger and Nathan Collier and Jitendra Kumar and Scott Painter and Peter Thornton and Cathy Wilson},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B53H-02},
  month = {December 15--19},
  year = {2014},
  abstract = {Characterizing the spatial variability of properties and processes in Arctic and boreal landscapes is critical for gaining an understanding of ecosystem functioning and for parameterizing process-rich models that simulate feedbacks to a changing climate. However, large-scale models are often poorly informed by process studies and new approaches are needed if we are to better link field and laboratory investigations to climate models. A fundamental goal of the Next-Generation Ecosystem Experiments (NGEE Arctic) project is to accelerate improvements in climate prediction through close integration of field, laboratory, and modeling activities. Geomorphological units, including thaw lakes, drained thaw lake basins, and ice-rich polygonal ground provide the organizing framework for our integrated framework for the coastal plains of the North Slope of Alaska. Process studies and observations of hydrology, geomorphology, biogeochemistry, vegetation patterns, and energy exchange and their couplings are being conducted across nested scales to populate a modeling framework and to achieve a broader goal of optimally informing process representations in global-scale models. We investigate the soil thermal regimes and their control on local scale hydrology for sites near Barrow, Alaska, through simulations at sub-meter scale resolution for low-centered, high-centered and transition polygons. We use high-resolution LiDAR and high-fidelity simulations using several models to couple surface-subsurface processes. A central focus of this challenge is to advance process understanding and predicting the evolution of permafrost thaw, degradation (i.e., thermokarst), and disturbance, and their impact on topography in a warming world and how these changes control the availability of water for biogeochemical, ecological, and physical feedbacks to the climate system.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Xu_AGU2014,
  title = {{Upscaling plot-scale methane flux to a eddy covariance tower domain in Barrow, AK: integrating in-situ data with a microbial functional group-based model}},
  author = {Xiaofeng Xu and Melanie Hahn and Jitendra Kumar and Fengming Yuan and Guoping Tang and Peter Thornton and Margaret Torn and Stan Wullschleger},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B11D-0044},
  month = {December 15--19},
  year = {2014},
  abstract = {Substantial spatial heterogeneity of methane flux has been recognized as a key uncertainty for estimating land-atmosphere exchange and further predicting the behavior of the climate system. Static-chamber method has been widely used to measure methane flux at plot-scale of tens of centimeter while it is unable for the scale beyond, and the eddy covariance technique has been used for in-situ measuring methane flux at a scale of tens of meters while it lacks of mechanistic representation of biogeochemical processes at smaller scale. How to link methane flux at these two scales while keeping primary spatial variations is critically important for accurate quantification of methane flux. A data-model integration approach is a valuable tool to scale-up methane flux while sustaining spatial heterogeneity.

In this study, we take advantage of a set of in-situ measurements of methane flux at plot-scale and a flux tower domain scale, and a high-resolution dataset of vegetation distribution and meteorology data, as well as a newly-developed microbial functional group-based methane module (incorporated in CLM4.5). A footprint model is used to characterize the domain of the eddy covariance tower in which high-resolution model simulations are carried out. The ecosystem model is first parameterized with plot-scale measurements of methane flux and ecosystem properties before it is used for regional simulations across the flux tower domain. The simulated regional methane flux will be weighted by spatial contribution of land surface methane flux estimated by the footprint model, and then compared with eddy covariance measurement. The low and high boundaries of the methane flux in the domain will be estimated for its potential uncertainties during this upscaling processes by comparing with empirical modeling method. The upscaling approach with data-model integration adopted in this study is valuable as it considers spatial heterogeneity of ecosystem properties and the dynamic representativeness of tower over the season and across the spatial domain.},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@presentations{Yuan_AGU2014,
  title = {Explicitly Synchronizing Soil Water and Carbon Nitrogen Reactive Transport Using CLM-PFLOTRAN: Does Sequential or Synchronized Implementing of Soil Processes Matter to Soil C Stocks?},
  author = {Fengming Yuan and Guoping Tang and Xiaofeng Xu and Jitendra Kumar and Gautam Bisht and Glenn E Hammond and Peter Thornton and Richard Mills and Stan Wullschleger},
  howpublished = {American Geophysical Union (AGU) Fall Meeting, San Francisco, California, USA Abstract B23C-0212},
  month = {December 15--19},
  year = {2014},
  abstract = {In nature soil biophysical and biogeochemical processes are coupled spatially and temporally. However due to constrain of both understanding of complexity of process interactions and computing ability, it still remains a challenge to represent fully coupled system of soil hydrological-thermal dynamics and biogeochemical processes in land surface models (LSMs). In the Community Land Model (CLM), the land component of the Community Earth System Model (CESM), soil C-N processes are not only implemented sequentially but also asynchronously coupled to thermal and hydrological processes. PFLOTRAN is an open source, state-of-the-art massively parallel 3-D subsurface flow and reactive transport code. In this study, we extend the subsurface hydrological-thermal process coupling between CLM and PFLOTRAN to include explicitly synchronized soil biogeochemical processes. The resulting coupled CLM-PFLOTRAN model is a LSM capable of resolving 3-D soil hydrological-thermal-biogeochemical processes.

The classic CLM-CN reaction networks, degassing-dissolving of C-N relevant greenhouse gases between soil solution and air, soil N absorption and transportation processes are implemented in PFLOTRAN’s reactive-transport framework. We compare soil C stock estimates from CLM alone and coupled CLM-PFLOTRAN simulations at the Next Generation Ecosystem Experiment-Arctic field sites at the Barrow Environmental Observatory (BEO), AK. Both simulations are compared against available soil C dataset to assess importance of representing this synchronization in LSMs. Contributions of various factors to spatial variance of simulated variations from the two modeling approaches are evaluated across this polygonal coastal tundra landscape. Results indicate that two modeling approaches could produce very contrasting results, especially in the N-limit ecosystem. The developed CLM-PFLOTRAN framework will be used for regional evaluation of climate change caused ecosystem process responses and their feedbacks to climate system.

Key word: Soil C stocks, soil biogeochemistry, soil thermal-hydrology, synchronization, CLM-CN model, PFLOTRAN model, polygonal coastal tundra},
  owner = {jkumar},
  timestamp = {2015.04.08}
}

@comment{{jabref-entrytype: Presentations: req[] opt[author;howpublished;location;month;note;title;year]}}