21 research outputs found
Sampling locations in this study (circles) and monitoring stations (triangles) in northeastern USA.
<p>The base map shows the sum of dry and wet Hg deposition modeled by Yu et al. (2014).</p
Concentrations of Hg in foliage of dominant species at four sites in the northeastern USA.
<p>Tree species included American beech (BE), yellow birch (YB), red maple (RM), sugar maple (SM), red spruce (RS), white ash (WA), white pine (WP) and balsam fir (BF). Error bars represent the SE of Hg concentrations of three composited samples.</p
Biomass and Hg content of foliage, bark and bole wood in hardwood and conifer stands in this study and three published studies.
<p>Biomass and Hg content of foliage, bark and bole wood in hardwood and conifer stands in this study and three published studies.</p
Concentrations of Hg in bark of dominant species at four sites in the northeastern USA.
<p>Tree species included American beech (BE), yellow birch (YB), red maple (RM), sugar maple (SM), red spruce (RS), white ash (WA), white pine (WP) and balsam fir (BF). Error bars represents the SE of Hg concentrations of three composited samples.</p
Four sites in the northeastern USA were used in this study.
<p>Four sites in the northeastern USA were used in this study.</p
Concentrations of Hg in bole wood of dominant species at four sites in the northeastern USA.
<p>Tree species included American beech (BE), yellow birch (YB), red maple (RM), sugar maple (SM), red spruce (RS), white ash (WA), white pine (WP) and balsam fir (BF). Error bars represent the SE of Hg concentrations of three composited samples.</p
Appendix A. Table of the effects of soil nutrient concentrations on foliar resorption.
Table of the effects of soil nutrient concentrations on foliar resorption
Appendix B. Relationship between soil predictor variables.
Relationship between soil predictor variables
Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions
<div><p>The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.</p></div