Thursday, June 27, 2019

QUEST will have a session at AGU this year; please consider submitting an abstract by July 31.

The AGU Fall Meeting will take place December 9-13, 2019 in San Francisco.

B064 - Improving Estimates of Ecosystem Carbon Storage

Quantifying carbon storage in terrestrial ecosystems remains challenging despite the need for strategic management of the global carbon balance. Forest biomass and forest soils can be highly heterogeneous and difficult to measure. Urban trees have been characterized using allometry from closed forests, which may result in bias. In agricultural systems, understanding the impact of land use practices on soil carbon changes remains a major research need. Fortunately, new technologies are improving estimates of natural variability, thereby reducing uncertainty. For example, terrestrial LiDAR can provide detailed characterization of live and dead wood pools. At the same time, methods for characterizing uncertainty in estimates are improving.  This session will highlight studies that are aimed at understanding the uncertainty in terrestrial carbon stocks, including quantifying spatial variability. It will also highlight novel data sets and quantitative methods used in these characterizations.

Tuesday, June 27, 2017

Announcing the Stochastic Uncertainty Estimator (SUE)

Looking for a way to start to explore uncertainty in a calculation or a model?  QUEST has launched an on-line tool for uncertainty propagation!  The Stochastic Uncertainty Estimator (SUE) is a program developed by Mark E. Harmon and his colleagues in the early 2000's. With funding from QUEST, Keith Olsen has created a web-based interface for SUE.  The following link allows you to download the software, create and run files for your projects, and download the source code (   

Friday, February 10, 2017

Two QUEST-sponsored uncertainty papers were published on Feb 6 and Feb 7:

Daly, C., M.E. Slater, J.A. Roberti, S.H. Laseter, and L.W. Swift Jr. 2017. High-resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset. International Journal of Climatology, DOI: 10.1002/joc.4986

At one time, Coweeta Hydrological Laboratory had over 100 precipitation collectors, which were used to inform interpolation of a reduced monitoring scheme.  These spatially intensive data made it possible to test the predictions of the PRISM national-scale gridded precipitation dataset and quantify sources of uncertainty.

Csavina, J., J.A. Roberti, J.R. Taylor, adn H.W. Loescher. 2017. Traceable measurements and calibration: a primer on uncertainty analysis. Ecosphere, 8(2) e01683, DOI: 10.1002/ecs2.1683

This paper describes the approach used by NEON, the National Ecological Observatory Network, to uncertainty analysis, and includes a glossary of terms.  Bone up on accuracy, precision, and trueness!

Monday, November 7, 2016

QUEST at the AGU Fall Meeting, December 12-16, 2016 in San Francisco

The QUEST Session at American Geophysical Union merged with three other proposed sessions to get a full day of talks, Monday December 12, in Moscone West 2004.
Quantifying Uncertainties and Merging Observations, Experiments, and Models for Improving Estimation, Mapping, and Forecasting of Terrestrial Ecosystem Dynamics I and II.  Session: B11J and B12C

The first three talks and one late morning talk were part of the original QUEST session:

08:00  Why Quantify Uncertainty in Ecosystem Studies: Obligation versus Discovery Tool? Mark E. Harmon, Oregon State University, Corvallis, OR, United States

08:15  High-Resolution Precipitation Mapping in a Mountainous Watershed: Ground Truth for Evaluating Uncertainty in a National Precipitation Dataset.  Christopher Daly1, Melissa E. Slater2, Joshua A Roberti2, Stephanie H. Laseter3 and Lloyd W. Swift3, (1)PRISM Climate Group, College of Engineering, Oregon State University, Corvallis, OR, United States, (2)National Ecological Observatory Network, Boulder, CO, United States, (3)Coweeta Hydrologic Laboratory, USDA Forest Service, Otto, NC, United States

08:30  What if the Hubbard Brook weirs had been built somewhere else? Spatial uncertainty in the application of catchment budgets.  Scott W Bailey, US Forest Service Durham, Durham, NH, United States

12:05  From Small Forest Inventory Plots to Regional Biomass Estimates: Dealing with Uncertainty from Inventory Sampling by Focusing on Trees Instead of Biomass. Bradley Tomasek1, Erin M Schliep2, Alan E. Gelfand1 and James S Clark1, (1)Duke University, Durham, NC, United States, (2)University of Missouri Columbia, Columbia, MO, United States

The following presentations are posters, please visit them in Moscone South from 1:40 to 6:00.  

Manoj Kc1,2, Kim Winton3, Michael A Langston3 and Yiqi Luo1, (1)University of Oklahoma Norman Campus, Norman, OK, United States, (2)New Mexico Institute of Mining and Technology, Dept. Earth & Environmental Science, Hydrology Program,, Socorro, NM, United States, (3)USGS South Central Climate Science Center, Norman, OK, United States

B13E-0666 How to Avoid Errors in Error Propagation: Prediction Intervals and Confidence Intervals in Forest Biomass.  Paul Lilly1, Ruth D. Yanai2, Hannah L Buckley3, Bradley S Case3, Richard C Woollons4, Robert J Holdaway5 and James Johnson6, (1)Spatial Informatics Group, LLC, Alameda, CA, United States, (2)SUNY College of Environmental Science and Forestry, Syracuse, NY, United States, (3)Lincoln University, Lincoln, New Zealand, (4)University of Canterbury, Christchurch, New Zealand, (5)Landcare Research NZ, Lincoln, New Zealand, (6)University College Dublin, Dublin, Ireland

B13E-0667 Quantifying uncertainty in carbon and nutrient pools of coarse woody debris.  Craig Robert See, University of Minnesota, Minneapolis, MN, United States, John L Campbell, USDA Forest Service, Northern Research Station, Vallejo, CA, United States, Shawn Fraver, University of Maine, School of Forest Resources, Orono, ME, United States, Grant M Domke, US Forest Service St. Paul, St. Paul, MN, United States, Mark E. Harmon, Oregon State University, Corvallis, OR, United States, Jennifer D Knoepp, Coweeta Hydrologic Laboratory, U.S. Forest Service, Otto, NC, United States and Christopher W Woodall, US Forest Service Durham, Northern Research Station, Durham, NH, United States

B13E-0668 Uncertainty Propagation in Predictions of Hydraulic Parameters Based on the Pedotransfer Functions. Boris Faybishenko, Tetsu K Tokunaga, Yongman Kim and Deb Agarwal, Lawrence Berkeley National Laboratory, Berkeley, CA, United States

B13E-0669 Uncertainty in a certain world: standardized approach to evaluating uncertainty in measurement results. Janae Lynn Csavina and Joshua A Roberti, National Ecological Observatory Network, Boulder, CO, United States

B13E-0670 Sources of variability in tissue chemistry in northern hardwood species. Yang Yang1, Ruth D. Yanai2, Farrah Roxanne Fatemi3, Carrie R Levine4, Paul Lilly5 and Russell Briggs2, (1)State University of New York Environmental Science and Forestry, Environmental Science, Syracuse, NY, United States, (2)SUNY College of Environmental Science and Forestry, Syracuse, NY, United States, (3)St. Michael’s College, Colchester, VA, United States, (4)University of California Berkeley, Berkeley, CA, United States, (5)Spatial Informatics Group, LLC, Alameda, CA, United States

B13E-0671 Interannual variability, correlated errors, and trend detection of evapotranspiration at AmeriFlux sites.  Angela Jean Rigden and Guido Salvucci, Boston University, Earth and Environment, Boston, MA, United States

B13E-0672  ‘spup’ – An R Package for Analysis of Spatial Uncertainty Propagation and Application to Trace Gas Emission Simulations.  Kasia Sawicka1, Lutz Breuer2, Tobias Houska2, Ignacio Santabarbara Ruiz3 and Gerard B.M. Heuvelink1, (1)Wageningen University, Soil Geography and Landscape group, Wageningen, Netherlands, (2)Justus Liebig University Giessen, Institute for Landscape Ecology and Resources Management, Giessen, Germany, (3)Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Karlsruhe, Germany

B13E-0673  Evaluating Random Error of Long-term, Multi-plot Flux Gradient Measurements of Agricultural Greenhouse Gas Emissions.  Shannon E Brown1, Claudia Wagner-Riddle1 and Muhammad Firdaus Sulaiman2, (1)University of Guelph, School of Environmental Sciences, Guelph, ON, Canada, (2)University of Guelph, Guelph, ON, Canada

Olivia Bartlett, University of New Hampshire Main Campus, Durham, NH, United States, Scott W Bailey, US Forest Service Durham, Durham, NH, United States and Mark J Ducey, University of New Hampshire (UNH), Department of Natural Resources & Environment, Durham, NH, United States

B13E-0675 A geospatial framework for improving the vertical accuracy of elevation models in Florida’s coastal Everglades.  Hannah Cooper1, Caiyun Zhang1 and Matthew Sirianni2, (1)Florida Atlantic University, Boca Raton, FL, United States, (2)Florida Atlantic University, Geosciences, Davie, FL, United States

B13E-0676 Quantifying the impact of the longitudinal dispersion coefficient parameter uncertainty on the physical transport processes in rivers.  Vivian Veronica Camacho Suarez, James Shucksmith and Alma Schellart, University of Sheffield, Sheffield, United Kingdom

B13E-0677 Forested hillslope water budget uncertainty: understanding the pathway from precipitation to biota to stream discharge. Heather Nicole Speckman1, Daniel Beverly2, Jason Mercer2, Suman Chitrakar3, Drew Thayer3, Bradley Carr4, Andrew Parsekian5 and Brent E Ewers2, (1)University of Wyoming, WyCEHG, Laramie, WY, United States, (2)University of Wyoming, Botany, Laramie, WY, United States, (3)University of Wyoming, Laramie, WY, United States, (4)University of Wyoming, Department of Geology and Geophysics, Laramie, WY, United States, (5)University of Wyoming, Geology, Laramie, WY, United States

Friday, November 4, 2016

Uncertainty Workshop at the ILTER OSM

QUEST sponsored an Uncertainty Workshop at the ILTER Open Science Meeting in South Africa October 9-13.  The workshop consisted of four modules:  (1) Measurement Uncertainty, presented by Hank W. Loescher and Janae L. Csavina; (2) Experimental Design for Long-Term Monitoring, presented by Christina L. Staudhammer;  (3) Monte Carlo Error Propagation, presented by Oswaldo Carrillo and Ruth D. Yanai; and (4) Uncertainty quantification: analysis of NEON and other biodiversity network data with hierarchical Bayes, presented by James S. Clark.   The workshop was well received by the 66 delegates who attended.  More information, including the materials for each of the modules, can be found here.

Friday, September 2, 2016

SSSAJ Special Issue: Quantifying Uncertainty in Forest Ecosystem Studies

We are now accepting contributions to a special issue of Soil Science Society of America Journal called "Quantifying Uncertainty in Forest Ecosystem Studies." 

Papers may address any source of uncertainty in forest ecosystems, including spatial and temporal variation, measurement error, model uncertainty, and model selection error.  Topics may include pools and turnover rates in soil, necromass, and living biomass. Papers that address how these uncertainties influence monitoring designs or affect management and policy decisions are also welcome.  

Submit your paper through Manuscript Central.  Select Quantifying Uncertainty in Forest Ecosystem Studies in the menu for the type of submission.

Monday, June 27, 2016

Uncertainty Analysis, 2016 Fall AGU Meeting: Abstracts due by Aug 3

Uncertainty Enthusiasts,

Please consider submitting an abstract to the following session at the 2016 Fall AGU meeting.

B065. Quantifying uncertainty in ecosystem studies:  Methods and applications

Uncertainty analysis is an emerging field in ecosystem science and there is growing recognition that including uncertainty in ecosystem studies is required for sound science. Uncertainty is essential for determining the significance of observed differences and for analyzing trends over time or making predictions. Recognizing which sources contribute the most uncertainty can improve efficiency in ecosystem monitoring efforts, allowing sampling designs to maximize information gained relative to the resources expended. Despite the many benefits of uncertainty analysis, in ecosystem studies it is not uncommon for uncertainties to be overlooked because of difficulties in characterizing multiple sources of uncertainty and because of the complexity of the calculations. This session will highlight methods and applications of uncertainty analysis in observations, experiments, modeling, and synthesis in ecosystem studies. The objective is to demonstrate how estimates of uncertainty can add scientific rigor to analyses and improve the interpretation of data for decision making.

Abstracts can be submitted to this session at and are due by Aug. 3.

Session ID: 13494
Section/Focus Group: Biogeosciences (B)
Cross-Listed: Hydrology (H)

The AGU meeting will be held 12-16 December 2016 in San Francisco (  This session is being organized by QUEST (Quantifying Uncertainty in Ecosystem Studies), a Research Coordination Network funded by the NSF.

Thank you,
John Campbell (USFS)

Ruth Yanai (SUNY-ESF)