Climate and Air Quality Indicators of Risk to Forest Health:
An On-line GIS for Integrating Climate and FHM Information
I. Problem Area Reference
The proposed study applies to RWU FS-NC-4401 Problem Area 2: Improve understanding of the
relationships among global and regional atmospheric weather and climatic processes and landscape-level
ecosystem health. Specifically, the study focuses on identifying some of the key regional climate and air
quality indicators of risk to forest health and developing a geographic information system (GIS) to integrate
climate and forest health information.
II. Introduction
Since the mid 1970s, forest losses in the Northeast U.S. have increased significantly. Damage by pests,
for example, has increased 54% (Powell et al. 1993). Dieback (Allen et al. 1992) attributable to varied
causes is 2.6 times higher than pre-1975 levels (Auclair 1997). Total natural mortality since 1975 has
reached levels that are 1.4 fold higher than historic norms (Powell et al. 1993). Although some of the
increase is associated with the larger total tree volume, there is the strong perception that disruptive
weather, air quality factors, and the outbreak of exotic pest species have played a significant role. The
response by the USDA Forest Service (USDA-FS) and State agencies has included a large and increasing
commitment to forest health monitoring, and to research into better techniques of health management.
The most recent networks such as the USDA-FS Forest Health Monitoring (FHM) Program (Heggem et
al. 1993) currently have no capability to anticipate or fully explain climate and air quality impacts. The
system of field plots serves as a passive system that allows interpretation and response only after welldefined patterns emerge. In contrast, managers often need frequent, updated assessments of current and
developing conditions on which to base management decisions and respond to public concerns. No
methodology has been developed to indicate when a forest population is at risk to specific local and
regional climate and air pollution stressors. As a first step in attempting to integrate (in a GIS-format)
climate and air quality information with information derived from the FHM Detection Monitoring Plots and
ultimately provide predictions of potential forest health conditions based on weather, climate, and air
quality forecasts, we propose to develop risk indices of four key climate and air quality variables relevant
to tree injury (damage) and loss (reduced growth, mortality) in the north central and northeastern U.S.
These variables include: (1) winter and early spring thaw-freeze occurrence and severity, (2) winter and
early spring root-freeze occurrence and severity, (3) extreme low temperature occurrence, and (4) regional
episodes of high surface ozone concentrations. This study will set the foundation and framework for future
studies that will develop additional climate and air quality risk indices relevant to forest health in the north
central and northeastern U.S. (e.g. NOx and SOx deposition patterns, extreme precipitation events, drought
occurrence). The ozone component of this study will also provide a vital link to a component of the North
Central Research Station’s (NCRS) ongoing Landscape Change Integrated Program, namely landscape
change impacts on tropospheric ozone risk to forest health in the western Great Lakes region.
III. Objectives
1.
Develop spatially-referenced (GIS) and time-specific estimates of the risk of winter and early
spring thaw-freeze episodes, winter and early spring root-freeze occurrence, extreme low
temperature events, and growing season high surface ozone concentrations that can impact forest
health in the north central and northeastern U.S. (FHM-North region).
2.
Correlate spatial and temporal patterns of the climate and air quality events listed in Objective 1
with observed patterns of crown dieback and ozone bioindicators obtained from FHM Detection
Monitoring Plots in the FHM-North region.
3.
Incorporate information from Objectives 1-2 into the NCRS’s Atmospheric Disturbance
Climatology System, a web-based GIS for displaying patterns of atmospheric-related disturbances
relevant to forest health in the FHM-North region.
IV. Methods
Task 1: Climate, Air Quality, Ecosystem, and Forest Health Database Development
Meteorological Data: Daily maximum and minimum surface air temperature data will be obtained
from the National Climatic Data Center’s (NCDC) Summary of the Day dataset, based on NCDC’s
TD-3200 file as corrected by the Validated Historical Daily Data Project. This dataset provides daily
maximum and minimum temperature observations for about 1000 Cooperative Observers Network
stations throughout the north central and northeastern U.S. for the period 1948-1998. Snow depth data
for reporting stations in the north central and northeastern U.S. through 1996 will also be obtained
from the NCDC.
Ozone Data: Hourly surface ozone concentration data will be obtained from the Environmental
Protection Agency’s Aerometric Information Retrieval System (AIRS) for the period 1982-present for
AIRS reporting stations in the north central and northeastern U.S.
Ecosystem Data: In addition to climate and air quality stress data required for risk predictions, specific
ecosystem parameter data are also required. Forest age information is important because tree
populations are often most vulnerable to climatic stress at or near maturity. Young trees, however, can
be affected when freezing and drought conditions are severe (Auclair et al. 1992, 1996). Forest type
information is critical for assessing site conditions and different species sensitivities to climate and air
quality stressors. Soil texture, depth, and drainage provide a valuable indication of rooting habit.
Superficial roots can be more exposed to freezing, drought, and nutrient stresses, while drainage can be
a good indicator of the likelihood and rapidity of soil frost penetration. Topography (position on slope,
elevation, aspect) is important in assessing the exposure to climate extremes and air drainage. All of
these parameters will be acquired from the USDA-FS electronic databases that form part of the FHM
Program network.
Forest Health: Of the 23 parameters routinely monitored in the FHM plots, we will limit our efforts
for this study by focusing on two key parameters that can reflect climatic and air quality stress: crown
dieback and ozone bioindicators. These data will be obtained from the USDA-FS electronic databases
that form part of the FHM Program network.
Task 2: Development of Climatic and Air Quality Indices
We will structure a set of climate and air quality indicators related to thaw-freeze stress, root-freeze
stress, extreme cold, and elevated surface ozone concentrations, which are all factors that can lead to crown
dieback or biomass loss in the FHM-North region (Auclair 1997, Auclair et al. 1992, Auclair et al. 1996,
Auclair et al. 1997, Karnosky et al. 1996, Laurence et al. 2000). These indicators include the following
extreme climatic and air quality conditions:
1.
Thaw-freeze stress – Winter and early spring (December-March) occurrences of daily maximum
air temperature  0 to10 ºC for at least 1-3 consecutive days, followed by daily minimum air
temperatures  -10 to -20 ºC for at least one day within the succeeding 3-10 days since the last day
of thaw.
2.
Root-freeze stress – The number of days in winter and early spring (December-March) in which
the snow depth is  5 cm, and the daily minimum air temperature is  -10 ºC.
3.
Extreme cold – The number of days per year with minimum air temperatures  -30 ºC.
4.
Elevated surface ozone concentrations – Three-month cumulative hourly surface ozone
concentrations  60 ppb over the maximum three months of the year (June-August or JulySeptember).
Extensive research is recently available (Auclair 2000, Auclair et al. 2000a, Auclair et al. 200b) on the
use of climate predictors to model dieback episodes in the Northern Hardwoods. Acute fluctuations in
climate are known to cause injuries to the tree’s water transport system, namely sapwood cavitation and
root injuries (Cochard and Tyree 1990, Sperry et al. 1988, Tyree and Sperry 1989). Some of these extreme
conditions are known from Forest Insect and Disease Detection Surveys (USDA Forest Service 19501998), and/or have been described in the literature on an extensive set of freeze induction experiments in
the laboratory (Pomerleau 1991, Sperry et al. 1988) and on treatment plots in the field (Pomerleau 1991,
Robitaille et al. 1995, Sperry et al. 1988). Recent research also suggests that surface ozone pollution as an
air quality indicator could be used as a predictor to model reductions in forest productivity or biomass
(Karnosky et al. 1996, Laurence et al. 2000). Combinations of the set of climate and air quality indicators
of forest dieback or biomass loss will also be explored and then verified and calibrated against the FHM
plot observations of dieback and ozone injury.
Task 3: GIS and Graphical Output
Using appropriate interpolation and graphical mapping software (e.g. ArcInfo, Transform) within the
framework of the NCRS’s Atmospheric Disturbance Climatology System
(http://climate.usfs.msu.edu/climatology/), the initial and interim meteorological, air quality, ecosystem,
and FHM parameter data from Task 1 and the climate and air quality indicator data from Task 2 will be
mapped across the FHM-North region (e.g. see Fig. 1). The array of climatic, air quality, ecosystem, and
forest health data will be assembled in GIS layers and as time-series plots using optional graphical displays
showing one or several parameters simultaneously. Graphs will be developed showing current conditions
relative to historical values over various periods from 1970 to the present. We will use the GIS graphical
mapping software to identify spatial correlations in all the mapped variables and to illustrate the query
structure (e.g., request: map all FHM plots in New England states in sugar maple forest type, with severe
crown dieback, and with root-freeze index values greater than 7).
Task 4: Analysis and Calibration
A significant part of the proposed effort will consist of verifying and calibrating the climate and air
quality indicators by testing modeled indicator values against actual FHM Detection Monitoring Plot
observations. For example, the extent and severity of crown dieback observed on FHM plots in the 19911994 period in New England (Fig. 2) can be compared to thaw-freeze (Fig. 1) and other dieback indicator
levels. To achieve this, both the spatial correlations and/or the time series trends can be used. The effect of
successive modifications of the indicators on the strength of correlation can be used as a means of testing
for the sensitivity of trees to acute climate or air quality events. For example, successive modification in the
severity of winter thaw, such as 2 ºC decreases from +10 ºC thaw to +2 ºC thaw can be used to verify and
refine the efficacy of thaw-freeze as an indicator of observed dieback. Using this approach, we noted in a
pilot study that winter thaws of just +2 ºC gave a better correlation to observed dieback than did a winter
thaw of +10 ºC.
Task 5: Demonstration Workshop and Publications
The investigators will publish the methods and findings of the study in a series of peer-reviewed
scientific journals and at appropriate scientific conferences and workshops. A crucial aspect of this study
will be the transfer of new knowledge and techniques to FHM staff. We propose a workshop that brings
together key FHM managers and researchers, where the concepts, scientific basis, datasets, essential
algorithms, and graphical output of the study will be presented and discussed.
V. Application of Research Results
The research results from this study will improve our understanding of some of the relationships
between regional climate and air quality factors and regional forest health indicators. These relationships
will be presented in the form of a user-friendly geographic information system for displaying maps of
specific climate and weather variables along with maps of specific forest health indicators (i.e. crown
dieback and ozone bioindicators) derived from the Forest Health Monitoring Detection Monitoring Plot
data. The maps will provide and indication of the spatial and temporal variability of key climate stressors
and forest health indicators across the north central and northeastern U.S. The maps provided within the
geographic information system will help forest managers in identifying where forest resources in the north
central and northeastern U.S. are currently at risk to specific climate and air quality stressors.
IX. References
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recommendations. In P.D. Manion and D. Lachance (eds), Forest Decline Concepts, American
Phytopathological Society Press, St. Paul, MN.
Auclair, A.N.D. 1997. Retrospective analysis of forest dieback, extreme climatic fluctuations, and global
climate changes from 1910 to 1995. 23p. Interim Task Report, Office of Global Programs, National
Oceanic and Atmospheric Administration, Silver Spring, MD.
Auclair, A.N.D. 2000. Biome-wide and century-scale patterns of dieback in Northern Hardwoods:
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Northern Hardwoods: Analysis of climatic impacts and the development of indicators of the risk of major
dieback episodes. Climatic Research (in progress).
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Concepts, American Phytopathological Society Press, St. Paul, MN.
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of mature sugar maples (Acer saccharum). Canadian J. Forest Res. 25: 577-587.
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(Acer saccharum). American J. Botany 75: 1212-1218.
Tyree, M.T., and J.S. Sperry. 1989. Vulnerability of xylem to cavitation and embolism. Annals of Plant
Physiology and Molecular Biology 40: 19-38.
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reports, Forest Pest Management, Washington, DC.