Emery R. Boose

Hurricane Impacts to Tropical and Temperate Forest Landscapes

Hurricanes represent an important natural disturbance process to tropical and temperate forests in many coastal areas of the world. The complex patterns of damage created in forests by hurricane winds result from the interaction of meteorological, physiographic, and biotic factors on a range of spatial scales. To improve our understanding of these factors and of the role of catastrophic hurricane wind as a disturbance process, we take an integrative approach. A simple meteorological model (HURRECON) utilizes meteorological data to reconstruct wind conditions at specific sites and regional gradients in wind speed and direction during a hurricane. A simple topographic exposure model (EXPOS) utilizes wind direction predicted by HURRECON and a digital elevation map to estimate landscape—level exposure to the strongest winds. Actual damage to forest stands is assessed through analysis of remotely sensed, historical, and field data. These techniques were used to evaluate the characteristics and impacts of two important hurricanes: Hurricane Hugo (1989) in Puerto Rico and the 1938 New England Hurricane, storms of comparable magnitude in regions that differ greatly in climate, vegetation, physiography, and disturbance regimes. In both cases patterns of damage on a regional scale were found to agree with the predicted distribution of peak wind gust velocities. On a landscape there was also good agreement between patterns of forest damage and predicted exposure in the Luquillo Experimental Forest in Puerto Rico and the town of Petersham, Massachusetts. At the Harvard and Pisgah Forests in central New England the average orientation of wind—thrown trees was very close to the predicted peak wind direction, while at Luquillo there was also good agreement, with some apparent modification of wind direction by the mountainous terrain. At Harvard Forest there was evidence that trees more susceptible to windthrow were felled earlier in the storm. This approach may be used to study the effects of topography on wind direction and the relation of forest damage to wind speed and duration; to establish broad—scale gradients of hurricane frequency, intensity, and wind direction for particular regions; and to determine landscape—level exposure to long—term hurricane disturbance at particular sites.

Patterns of forest damage resulting from catastrophic wind in central New England, USA

The effect of catastrophic winds on a forested landscape in central Massachusetts was examined to investigate the factors controlling the geographic pattern of damage. The study area, Tom Swamp Tract, Harvard Forest, comprises a valley and adjoining hillslopes supporting second-growth hardwood and conifer stands. Much of the study used records and maps that were analysed cartographically with a geographic information system (GIS). Areally, forest damage was distributed fairly evenly among different damage classes ranging from no damage to more than 75% of stems broken or uprooted. However, there was a negative exponential size distribution of contiguous areas of the same damage intensity, with a preponderance less than 2 ha; these areas ranged from less than 0.04 ha to more than 35 ha; hurricane damage exhibited a continuum ranging from minor damage of individual trees to extensive blow-down of broad areas of forest (...)

LANDSCAPE AND REGIONAL IMPACTS OF HURRICANES IN NEW ENGLAND

Hurricanes are a major factor controlling ecosystem structure, function, and dynamics in many coastal forests, but their ecological role can be understood only by assessing impacts in space and time over a period of centuries. We present a new method for reconstructing hurricane disturbance regimes using a combination of historical research and computer modeling. Historical evidence of wind damage for each hurricane in the selected region is quantified using the Fujita scale to produce regional maps of actual damage. A simple meteorological model (HURRECON), parameterized and tested for selected recent hurricanes, provides regional estimates of wind speed, direction, and damage for each storm. Individual reconstructions are compiled to analyze spatial and temporal patterns of hurricane impacts. Long-term effects of topography on a landscape scale are then simulated with a simple topographic exposure model (EXPOS). We applied this method to the region of New England, USA, examining hurricanes since European settlement in 1620. Results showed strong regional gradients in hurricane frequency and intensity from southeast to northwest: mean return intervals for F0 damage on the Fujita scale (loss of leaves and branches) ranged from 5 to 85 yr, mean return intervals for F1 damage (scattered blowdowns, small gaps) ranged from 10 to >200 yr, and mean return intervals for F2 damage (extensive blowdowns, large gaps) ranged from 85 to >380 yr. On a landscape scale, mean return intervals for F2 damage in the town of Petersham, Massachusetts, ranged from 125 yr across most sites to >380 yr on scattered lee slopes. Actual forest damage was strongly dependent on land use and natural disturbance history. Annual and decadal timing of hurricanes varied widely. There was no clear century-scale trend in the number of major hurricanes. The historical-modeling approach is applicable to any region with good historical records and will enable ecologists and land managers to incorporate insights on hurricane disturbance regimes into the interpretation and conservation of forests at landscape to regional scales.

HUMAN OR NATURAL DISTURBANCE: LANDSCAPE‐SCALE DYNAMICS OF THE TROPICAL FORESTS OF PUERTO RICO

Increasingly, ecologists are recognizing that human disturbance has played an important role in tropical forest history and that many assumptions concerning the relative importance of natural processes warrant re-examination. To assess the historical role of broad-scale human vs. natural disturbance on an intensively studied tropical forest we undertook a landscape-level analysis of forest dynamics in the Luquillo Experimental Forest (LEF; 10871 ha) in eastern Puerto Rico. Using aerial photographs (1936 and 1989), GIS, a model of topographic exposure to hurricane winds, and historical data, we sought to: (1) document historical changes in extent, cover, and type of forest vegetation; (2) evaluate the distribution of land use and hurricane impacts; (3) assess the contributions of these processes in controlling current vegetation patterns; and (4) relate these results to ongoing ecological, conservation, and natural resource discussions. With >1000 m of relief in the LEF, the broad vegetation zones of Tabon...

LANDSCAPE AND REGIONAL IMPACTS OF HURRICANES IN PUERTO RICO

Puerto Rico is subject to frequent and severe impacts from hurricanes, whose long-term ecological role must be assessed on a scale of centuries. In this study we applied a method for reconstructing hurricane disturbance regimes developed in an earlier study of hurricanes in New England. Patterns of actual wind damage from historical records were analyzed for 85 hurricanes since European settlement in 1508. A simple meteorological model (HURRECON) was used to reconstruct the impacts of 43 hurricanes since 1851. Long-term effects of topography on a landscape scale in the Luquillo Experimental Forest (LEF) were simulated with a simple topographic exposure model (EXPOS). Average return intervals across Puerto Rico for F0 damage (loss of leaves and branches) and F1 damage (scattered blowdowns, small gaps) on the Fujita scale were 4 and 6 years, respectively. At higher damage levels, a gradient was created by the direction of the storm tracks and the weakening of hurricanes over the interior mountains. Average ...

Ecosystem Processes and Human Influences Regulate Streamflow Response to Climate Change at Long-Term Ecological Research Sites

Analyses of long-term records at 35 headwater basins in the United States and Canada indicate that climate change effects on streamflow are not as clear as might be expected, perhaps because of ecosystem processes and human influences. Evapotranspiration was higher than was predicted by temperature in water-surplus ecosystems and lower than was predicted in water-deficit ecosystems. Streamflow was correlated with climate variability indices (e.g., the El Niño—Southern Oscillation, the Pacific Decadal Oscillation, the North Atlantic Oscillation), especially in seasons when vegetation influences are limited. Air temperature increased significantly at 17 of the 19 sites with 20- to 60-year records, but streamflow trends were directly related to climate trends (through changes in ice and snow) at only 7 sites. Past and present human and natural disturbance, vegetation succession, and human water use can mimic, exacerbate, counteract, or mask the effects of climate change on streamflow, even in reference basins. Long-term ecological research sites are ideal places to disentangle these processes.

Long-term Agricultural Research: A Research, Education, and Extension Imperative

ABSTRACT For agriculture to meet goals that include profitability, environmental integrity, and the production of ecosystem services beyond food, fuel, and fiber requires a comprehensive, systems-level research approach that is long-term and geographically scalable. This approach is largely lacking from the US agricultural research portfolio. It is time to add it. A long-term agricultural research program would substantially improve the delivery of agricultural products and other ecosystem services to a society that calls for agriculture to be safe, environmentally sound, and socially responsible.

Quantity is Nothing without Quality: Automated QA/QC for Streaming Environmental Sensor Data

Sensor networks are revolutionizing environmental monitoring by producing massive quantities of data that are being made publically available in near real time. These data streams pose a challenge for ecologists because traditional approaches to quality assurance and quality control are no longer practical when confronted with the size of these data sets and the demands of real-time processing. Automated methods for rapidly identifying and (ideally) correcting problematic data are essential. However, advances in sensor hardware have outpaced those in software, creating a need for tools to implement automated quality assurance and quality control procedures, produce graphical and statistical summaries for review, and track the provenance of the data. Use of automated tools would enhance data integrity and reliability and would reduce delays in releasing data products. Development of community-wide standards for quality assurance and quality control would instill confidence in sensor data and would improve interoperability across environmental sensor networks.

Ensuring reliable datasets for environmental models and forecasts

Abstract At the dawn of the 21st century, environmental scientists are collecting more data more rapidly than at any time in the past. Nowhere is this change more evident than in the advent of sensor networks able to collect and process (in real time) simultaneous measurements over broad areas and at high sampling rates. At the same time there has been great progress in the development of standards, methods, and tools for data analysis and synthesis, including a new standard for descriptive metadata for ecological datasets (Ecological Metadata Language) and new workflow tools that help scientists to assemble datasets and to diagram, record, and execute analyses. However these developments (important as they are) are not yet sufficient to guarantee the reliability of datasets created by a scientific process — the complex activity that scientists carry out in order to create a dataset. We define a dataset to be reliable when the scientific process used to create it is (1) reproducible and (2) analyzable for potential defects. To address this problem we propose the use of an analytic web , a formal representation of a scientific process that consists of three coordinated graphs (a data-flow graph, a dataset-derivation graph, and a process-derivation graph) originally developed for use in software engineering. An analytic web meets the two key requirements for ensuring dataset reliability: (1) a complete audit trail of all artifacts (e.g., datasets, code, models) used or created in the execution of the scientific process that created the dataset, and (2) detailed process metadata that precisely describe all sub-processes of the scientific process. Construction of such metadata requires the semantic features of a high-level process definition language. In this paper we illustrate the use of an analytic web to represent the scientific process of constructing estimates of ecosystem water flux from data gathered by a complex, real-time multi-sensor network. We use Little-JIL, a high-level process definition language, to precisely and accurately capture the analytical processes involved. We believe that incorporation of this approach into existing tools and evolving metadata specifications (such as EML) will yield significant benefits to science. These benefits include: complete and accurate representations of scientific processes; support for rigorous evaluation of such processes for logical and statistical errors and for propagation of measurement error; and assurance of dataset reliability for developing sound models and forecasts of environmental change.

Experience in using a process language to define scientific workflow and generate dataset provenance

This paper describes our experiences in exploring the applicability of software engineering approaches to scientific data management problems. Specifically, this paper describes how process definition languages can be used to expedite production of scientific datasets as well as to generate documentation of their provenance. Our approach uses a process definition language that incorporates powerful semantics to encode scientific processes in the form of a Process Definition Graph (PDG). The paper describes how execution of the PDG-defined process can generate Dataset Derivation Graphs (DDGs), metadata that document how the scientific process developed each of its product datasets. The paper uses an example to show that scientific processes may be complex and to illustrate why some of the more powerful semantic features of the process definition language are useful in supporting clarity and conciseness in representing such processes. This work is similar in goals to work generally referred to as Scientific Workflow. The paper demonstrates the contribution that software engineering can make to this domain.