iSTEM 2011
Idaho National Laboratory leads the Idaho Science, Technology, Engineering and Math network. This video is an overview of the iSTEM program
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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iSTEM Summer Institute: An Integrated Approach to Teacher Professional Development inSTEM
The importance of STEM education to our national prosperity and global competitiveness was recently reinforced by the Obama administration support for Change the Equation. Change the Equation is a multi-entity initiative formed in response to the rapidly increasing demand for STEM related careers and the potential lack of preparation by many Americans to be employed in these positions. To address the issue many are calling for increased emphasis on K-12 STEM education, as early preparation in STEM provides the foundation essential for further learning and competencies (National Research Council, 2007). Achieving and sustaining depth and breadth of K-12 STEM education is inextricably linked to ongoing professional development of K-12 educators. The need for teacher continuing education in STEM education and the link between teacher effectiveness and student preparation in STEM was the impetus behind our i- STEM professional development summer institute. The i-STEM initiative is a collaborative effort between business, industry, government, K-12, and higher education. Although the organization is working on a number of projects, including policy, research, communication, and collaborations, the i-STEM group has directed significant resources toward K-12 educator professional development opportunities in STEM. Our report focuses on the structure and impact of the intensive four-day i-STEM residential professional development institute which we designed to increase the capacity of grade 4-9 teachers to teach STEM content. We structured the summer institute using the outcome of a survey we conducted of grade 4-9 teachers’ to assess their STEM professional development needs, the extant literature on teacher development, the increasing need for a STEM informed society, and our desire to use evidence based practices to enhance teacher capacity to teach STEM content. We developed this investigation to determine if our summer institute influenced the participating teachers comfort with teaching STEM, efficacy for teaching STEM, content knowledge of STEM, inquiry implementation in STEM, and perceptions of STEM education. These parameters in-part have been gathered previously for specific areas of math or science education, but we are not aware of any study in which these variables have been attended to and assessed in the context of enhancing inservice teacher preparation to teach STEM. Therefore, our project provides a unique contribution to the literature because of our focus on STEM education and our adaptation and use of an array of assessment tools to measure the impact on our participants’ perceptions of teaching STEM, affective perspectives, and knowledge of the related content. Further, we are responding to the position of Putnam and Borko (2000) who contend there has been a dearth of attention paid toward creating teacher professional development experiences consistent with the teacher learning and investigations of the impact of the experience on the participating educators. Before we present our research and results, we discuss the relevant literature establishing the justification for our study. Following the presentation of our study results we discuss the related implications and directions for future research. We conclude with a discussion of study limitations and some closing remarks of our study contributions to the field of teacher professional development in STEM education.
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Continent-wide tree fecundity driven by indirect climate effects
Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.
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Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation
Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard brute-force MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.
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Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance
Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types.
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Cutting Out the Middleman: Calibrating and Validating an Ecosystem Model Using Remotely Sensed Surface Reflectance
Explore the source record for details and available documents.
Surface reflectance↗
Beyond Ecosystem Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model Integration
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.
Istem Fer↗
Cutting out the Middleman: Calibrating and Validating a Dynamic Vegetation Model (ED2-PROSPECT5) Using Remotely Sensed Surface Reflectance
Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure. However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations. Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance. In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”). In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products. We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.
Alexey N Shiklomanov↗
Business, Education Partnerships -- Bridging the Paradigm Divide
The authors discuss the integrated science, technology, engineering, and mathematics (i-STEM) curriculum in business and industry comparing it with the traditional STEM K-12 curriculum in the U.S. Topics discussed includes limitations associated with the traditional STEM education, advantages of i-STEM such as enhancing professional development of educators to enhance their capacity to make youth capable for i-STEM careers, and i-STEM tools such as a project-based learning.
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