Atmospheric deposition working group meets in Oregon

The LTER synthesis working group on quantifying uncertainty in wet atmospheric deposition convened last week in Oregon.   Participants met May 21-23 at HJ Andrews Experimental Forest to share current research and coordinate future efforts.  The first day included presentations on long-term collection of deposition data, variability in monsoon precipitation (Sevilleta LTER), uncertainty in data gap filling, a hierarchical Bayesian analysis of precipitation patterns, and an analysis of monitoring intensity.  Presentations were followed up with discussion and planning sessions.

The following day included a presentation by Dr. Chris Daly (OSU) on the PRISM model, and the uncertainties associated with large-scale spatiotemporal precipitation estimates and predictions.  The rest of the time was devoted to planning papers and future workshops.  Products coming out of this meeting will include a paper on uncertainty sources in unreplicated measurements (small watersheds), a paper on the effects of monitoring intensity on uncertainty, and a paper on gap filling in long-term hydrologic datasets.  Also coming out of this meeting are plans to host a workshop on uncertainty in ecosystem studies at this year’s LTER All Scientists Meeting in September (more on this when the date is confirmed).  After discussing precipitation for two days, participants were given a rainy tour of HJ Andrews Experimental Forest.

People in attendance: Dr. John Campbell (USFS), Dr. Ruth Yanai (SUNY-ESF), Dr. Shannon LaDeau (Cary Institute), Doug Moore (Sevilleta LTER), Adam Skibbe (Konza LTER), Craig See (SUNY-ESF)
Attending remotely (via video conference call): Dr. Kathleen Weathers (Cary Institute), Mathew Petrie (University of New Mexico), Dr. Mark Green (Plymouth State), Stephanie Laseter (USFS), Dr. Jennifer Knoepp (USFS), Carrie Rose Levine (SUNY-ESF)

Precipitation working group (left to right): John Campbell, Shannon Ladeau, Ruth Yanai, Doug Moore, Craig See

Uncertainty in precipitation inputs to ecosystems

Measuring the amount and chemistry of rainfall at a precipitation station is relatively straightforward.  However, estimating the input of rain water and solutes to ecosystems requires interpolation between the precipitation stations.  Various methods of interpolation are used in precipitation and atmospheric deposition studies (Garcia et al. 2008, Weathers et al. 2006), but the uncertainty in the interpolation is rarely reported or used in estimating uncertainty in deposition estimates.  

John Campbell conducted a preliminary analysis of spatial uncertainty in rainfall amounts using data from the Hubbard Brook Experimental Forest in New Hampshire. Hubbard Brook uses eleven precipitation gauges to estimate annual precipitation for six adjacent experimental watersheds. Thiessen polygons (Viessman and Lewis 1996) are used to define the area characterized by each of these eleven precipitation estimates, and precipitation to each watershed is calculated as the sum of the areas contributed by each polygon.  He compared this to several other interpolation methods (spline, inverse distance weighting, kriging, and regression modeling) and found differences of less than 1% across the methods for annual precipitation of the nine watersheds at Hubbard Brook (Figure 1).  The error associated with model selection is thus likely to be small.  The error within the models describing precipitation amounts (e.g. model or parameter error in the regression) has yet to be estimated.  Accounting for uncertainty in solute deposition is further complicated by the spatial and temporal mismatch between volume and chemistry samples, with fewer samples typically collected for solute chemistry than for rainfall volume.  Additional challenges to be addressed in estimates of atmospheric inputs are associated with the difficulty of monitoring dry deposition and cloud deposition and their interaction with vegetation structure.

Shannon LaDeau will develop a hierarchical regression model that can accommodate the spatial and temporal mismatches in precipitation volume and chemistry observations, using weekly data sampled from five watersheds at Hubbard Brook.  The regression will estimate monthly and annual wet deposition of solutes for each watershed and probability distributions for inference on predictive covariates. The model will also partition uncertainty due to measurement error, missing data, and poor model fit and will provide estimates of environmental stochasticity.  Methods for spatial interpolation of regressions will be explored and impacts on annual budgets compared, including spatial analyses packages in ArcView, R (e.g, ModelMap package) and Bayesian kriging methods in OpenBUGS (i.e., geoBugs). 

We also have support from the LTER Network Office for a Synthesis Working Group to further develop approaches to estimating uncertainty in precipitation fluxes.  We will extend our model protocol and results to similar data from other LTER sites.