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# Introduction

Increasingly, U.S. Geological Survey (USGS) scientists are called upon
to develop fundamental understanding of inherently complex environmental
systems and processes. Their objectives are to use this understanding to
develop more accurate and meaningful models, assessments, forecasts and
predictions of how these complex systems and processes may react to
specific natural changes and human alterations and then to be able to
effectively communicate this increased knowledge and understanding of
complex systems to support society in meaningful ways.

The complexity and diversity of the earth and biological systems and
processes we seek to understand lead directly to a set of significant
scientific challenges in how we acquire, interpret, and communicate the
*information* represented or contained in these systems and processes.
Many physical, chemical and biological systems and processes\-\--from
DNA to entire ecosystems---can be most effectively elucidated and
understood when viewed as systems of information and computational
elements. It follows that the concepts, principles, and techniques of
information/computer science can be used in an interdisciplinary way
with our natural science capabilities to help USGS scientists solve
formidable natural science research problems and to better communicate
complex concepts. This is the realm of Environmental Information
Science.

"Environmental Information Science" is defined here as the development
and application of interdisciplinary approaches---integrating natural,
computer, and information science---to advance understanding and yield
new insights into environmental phenomena at all levels of complexity.

While not explicitly called environmental information science, this
research direction has been highlighted in several recent USGS science
program planning documents (e.g., Bohlen at al 1998; U.S. Geological
Survey 1999); in the results of the January 2001 USGS workshop on "The
USGS in 2050: Envisioning the Scientific Frontier" (see
<http://www.usgs.gov:8888/director/frontier>) and in reports from the
National Research Council (National Research Council 1999; 2001a; 2001b)
and the National Science Foundation (Pfirman et al 2003). The need for a
more focused, Bureau-wide emphasis in this interdisciplinary area has
also been identified as a high priority objective in the Bureau's
long-term "Information Strategy" and in the 5-year program plan for the
Bureau's Enterprise Information Program.

The NRC report on *Future Roles and Opportunities for the U.S.
Geological Survey* (NRC 2001a) emphasizes that:

> The questions posed to the agency increasingly call for multifaceted,
> analytical, and integrative investigations of complex processes and
> systems. By evolving into a natural science and information agency,
> the USGS will be able to play a leadership role in the elucidation of
> the geological, hydrological, geographical, and biological processes
> that are important to the nation.... The USGS should give more
> attention than it has in the past to integrative data analysis,
> problem solving, and information dissemination...the USGS should do
> more to interpret what the data mean...In the twenty-first century,
> the USGS should emphasize that system modeling is a powerful tool for
> integrative science. System modeling would enable the USGS, in
> coordination with other agencies and partners, to develop a greater
> understanding of complex science problems that involve natural and
> human systems...

Achieving scientific understanding of inherently complex, interdependent
systems is not easy. These systems, processes, and phenomena are often
multi-dimensional in space and time and involve non-linear interactions
of myriad physical and biological factors. Spatial scales of interest
may span orders of magnitude from sub-microscopic to global levels;
temporal scales may range from instantaneous to millennia. Issues
involving the movement, processing, or cycling of materials and living
organisms within and across systems are pervasive. Systems and processes
are frequently difficult to observe and study in their natural state
owing to their irregular and infrequent occurrence, physical remoteness,
extremes of size, and/or hazards posed to humans. In some cases, direct
observation or measurement can alter the system being observed.
Sometimes such overwhelming amounts of data are available that
traditional techniques are insufficient to extract useful information.
In other studies, extremely limited amounts of data are collected at
small scales and/or for very limited time periods, for which standard
statistical approaches for extrapolation or interpolation over larger
areas or longer time periods are not adequate to capture the inherent
complexity of the systems being studied. Undertaking the
interdisciplinary research needed to address this complexity is even
more challenging due to the need to accommodate the different
techniques, cultures, languages, and ontologies of different scientific
disciplines.

Further challenges involve the effective communication of this
environmental complexity to better inform public policy and
decision-making (Sarewitz, Byerly, and Pielke 2000). Tremendous
information-related challenges exist in how USGS scientists can
effectively communicate and represent our understanding of these
multi-faceted, multi-dimensional systems and processes, of the
likelihood of potential outcomes, and of scientific uncertainty itself
to a diverse assortment of different constituencies and audiences,
ranging from scientists and resource managers, policy and decision
makers, teachers and students, to the interested citizen.

Many of the scientific challenges outlined above are fundamentally
related to the complexity, multi-dimensionality and non-linearity of the
*information* represented in the systems and processes we study.
Information science [^1] by focusing on the informational elements of
complex systems as specific objects of study, can offer natural
scientists complementary approaches that can help address these
challenges. Examples of information science-based capabilities that can
be applied to the study of complex environmental systems include
algorithm development, multi-dimensional modeling, multivariate
statistical modeling, computational complexity, machine learning, neural
networks, chaos theory, emergent properties, pattern recognition,
artificial intelligence, advanced visualization, and analysis of the
basic properties and structure of information itself. The specific
examples provided throughout this plan show how USGS natural science and
computer/information science are already intersecting to forge new
directions in this type of interdisciplinary research. These
illustrations share a common thread in that information science holds
part of the key to solving these diverse, and sometimes intractable,
numerical, conceptual and integrative research questions. Advanced
information and computer science can also significantly enhance USGS
ability to communicate scientific understanding of complex systems and
processes into the decision-making process with the result of achieving
greater societal benefit and impact with our science.

This plan proposes a "Future Science Direction" for the USGS that will
focus on the development of an enhanced interdisciplinary capability
linking natural science and computer/information science to address key
research questions facing the USGS today and over the next 25-30 years.
This future science direction would build on existing USGS activities
and expertise in this interdisciplinary area, recognizing that some
environmental information science-type research is currently underway in
all four of the major USGS science disciplines. Creating a more
bureau-wide focus on this subject and providing a long-term research
agenda for the bureau as a whole can help leverage these independent
ongoing activities, share techniques and approaches across science
programs and disciplines, and move the entire effort ahead in a
significant way even without a significant influx of new funds. It will
better position the USGS to take advantage, as an integrated part of our
natural science research, of the significant evolutionary advancements
in computer science and technology that are likely to occur over the
next 25-30 years.

Research partnerships between the USGS and other government agencies,
academic institutions, and private industry will also be an essential
part of this future science direction. This is a way for the USGS to
take greater advantage of the extensive computer/information science
expertise and funding resources of potential partners, and to work
together with partners on these cross-cutting, societally relevant
research challenges.

One of the greatest challenges that the USGS will face in pursuing this
future science direction is to ensure it is effectively linked to and
integrated with the scientific goals and activities of the USGS science
programs. This new research direction should not come at the expense of
discontinuing the invaluable foundation of USGS national databases that
maintain data about resources and hazards (e.g., earthquakes,
streamflow, topography, biological resources, etc.) or our basic
research into physical, chemical and biological processes that remain
poorly understood. Rather, the strength of the environmental information
science research direction is to use information science to build
bridges across and within the natural science disciplines to reveal
their complex interrelationships. Environmental information science
research is at the core of integrated systems science and helps lay the
foundation for many conceptual systems models. Maintaining this vision
of using environmental information science to further scientific
understanding of natural processes and human alteration, and recognizing
but not becoming mired in technological requirements, will be essential
to the success of the initiative.

# 

# Scientific Challenges and Research Questions

In addition to Environmental Information Science, the USGS has
identified seven other Future Science Directions as areas of
environmental science requiring special attention from the USGS and our
partners in the scientific research community because of their
importance to the Nation. Each of these USGS Future Science Directions
has, in turn, specifically noted the importance of information science
and computational techniques to the resolution of pressing questions in
each area of inquiry. Shown here, in the words of the scientists
involved, are brief examples of some of these issues, highlighting their
reliance on environmental information science and computer modeling:

[Earthquake Hazards]{.underline}:

> Work carried \[by the earthquake hazards program\] includes the
> development and publication of National Seismic Hazard Maps, national
> databases on potentially active faults and seismicity, hazard
> assessments in urban areas, regional earthquake probability reports,
> and the development of earthquake scenarios. The National Seismic
> Hazard Maps, which display earthquake ground shaking amplitudes for a
> given probability of occurrence nationwide (including Alaska and
> Hawaii), are the most important product of the USGS earthquake
> program. The term "map" can be somewhat misleading in that the product
> is really a probabilistic model calculation of earthquake shaking
> nationwide, based on seismic and geodetic monitoring, fault mapping
> and analysis, and observations of seismic wave attenuation. This model
> calculation forms the basis for the seismic elements of the
> International Building Code upon which all state, regional, and local
> building codes are based. The model gives, for any specific location
> (latitude and longitude or zip code), quantitative estimates of the
> probable level of ground shaking, in terms of horizontal acceleration
> and velocity, at several periods (or frequencies) over various
> exposure times (50, 100, and 250 years) and on a given soil condition.

[Ecosystem Health, Sustainability and Land Surface Change]{.underline}:

> The goal of \[Ecosystem Health, Sustainability, and Land Surface
> Change\] is to document past and present changes to the land surface,
> to develop conceptual ecosystem models, to develop a methodology for
> predictive modeling of ecosystem health and sustainability, and to
> assess the condition and status of our Nations ecosystems and the
> resources they provide. This goal is framed by three overarching
> questions that form the integral relationship between ecosystem health
> and sustainability and land surface change:

-   In the absence of humans, how would the form and function of an
    ecosystem evolve?

-   In the presence of humans, assuming that we maintain the current
    configuration of the landscape, how will the form and functions of
    the natural elements of ecosystems evolve?

-   In the presence of humans, and assuming continued land use change,
    what will happen to the form and functions of the natural remnants
    of ecosystems?

[Energy]{.underline}:

> An integrated model for the coal system has not yet been developed as
> a scientific tool. However, the Energy Program seeks to identify
> parameters within the multidisciplinary aspects of coal geology that
> can be integrated into a single common model for the classification of
> coal and associated deposits into coal systems. Coal systems analysis
> will seek to understand the geologic factors that control carbon
> burial, preservation and coalification and to understand coal's
> potential to generate and store methane and other hydrocarbons.
> Additionally, we seek to understand the geologic controls on coal
> quality that impact its use as a fuel and on the potential migration
> of potentially hazardous elements and compounds from coal into the
> environment through surface and shallow groundwater or into the
> atmosphere with combustion. The coal systems model will provide a tool
> to help scientists understand the complex nature of the many different
> types of coal and how the various disciplines of coal geology can be
> used together to address the environmental problems associated with
> coal utilization. Increased knowledge of coal quality parameters will
> aid in assessing those resources available for alternative uses of
> coal such as synfuels, for new combustion technologies such as clean
> coal technologies, and for those resources amenable to new cleaning
> techniques.

[Ground Water Resources]{.underline}:

> Society needs to be able to forecast the response of ground-water
> systems to the ever- increasing stresses that are being imposed upon
> them. Hydrologic forecasting has been listed as one of the grand
> challenges in the environmental sciences. In order to make the most
> efficient use of the ground-water resource, we need to be able to
> predict, for example, changes in water levels, discharge, and the
> movement of contaminants. In order to make informed forecasts, all
> available data regarding a ground-water system must be brought
> together and analyzed simultaneously in a framework that defines the
> physics and chemistry of how water and solutes move in the subsurface.
> This is best done through the use of computer simulation models.
> Although assumptions must always be made in constructing these models,
> and the amount of data will always be limited, no other tool serves to
> integrate the available knowledge of a system to the same extent. As
> computer speed and storage continues to increase, these models should
> be able to represent more effectively the hydrogeologic framework and
> processes. Future modeling efforts also need to better quantify the
> uncertainty inherently associated with forecasting.

[Invasive Species]{.underline}:

> Develop models that will enable users to rapidly assess and predict
> risks to native species, ecosystem functions, and resource management
> systems associated with the introduction and spread of invasive
> species in terrestrial and aquatic ecosystems and complex landscapes.
> The adage holds that "a ounce of prevention is worth a pound of cure."
> Small populations of invasive species are far easier and less costly
> to control. Invasive species and habitat surveys, high-resolution
> mapping, and new spatial models based on cross-disciplinary research
> are needed to set cost-efficient priorities for control, restoration,
> and future research.

[Rivers]{.underline}:

> Goal 4: Predict Effects of Natural Changes and Human Actions on Rivers
>
> Integrate modeling and monitoring activities more fully.

-   Understand and describe scale issue to provide meaningful
    predictions at appropriate scales.

-   Predict the effect of river plan-form changes on ecosystem
    conditions.

While these are not the only issues for environmental information s
cience, USGS research should be directed at extending and deepening our
understanding of complex environmental systems, processes, and
phenomena, such as those identified in the Future Science Directions
highlighted above, by focusing on three major scientific challenges:

**Environmental Information Science: Major Scientific Challenges**

**Challenge #1: Acquisition or Extraction of Information from**

**Environment Systems and Processes**

This challenge focuses on the need to significantly advance our
capabilities to extract information from complex systems and processes
in three major ways: 1) increasing the amount of meaningful information
acquired, 2) increasing the quality of information acquired, and 3)
increasing the efficiency with which information is acquired, used, and
re-used (e.g., by reducing or eliminating the need for direct human
intervention in observations and measurements, increasing real-time data
acquisition capabilities, and using previously collected data in totally
new applications). This challenge includes improving our ability to
observe and measure environmental systems in totally new ways, such as
through the use of computer-designed, deployed and operated sensor
networks, robotics, wireless technologies, or the use of molecules,
cells, and organisms as programmable biological or chemical
sensors/messengers. USGS scientists can use "virtual laboratories"
within computers to conduct experiments and repeatedly "observe"
phenomena (such as earthquakes, volcanoes, or the spread of
wildlife-transmitted human diseases) that cannot be readily or
realistically observed *in situ*. We can also significantly increase our
ability to extract previously "hidden" or "un-used" data and information
from existing data sets for use in new applications and to discover new
cross-disciplinary patterns in data (emergent properties, patterns and
trends, etc.).

**Challenge #2: Synthesis, Analysis, Interpretation, and Modeling of**

**Information on Environmental Systems and Processes**

This challenge focuses on the need to significantly advance our
capabilities to use the information we have extracted from complex
environmental systems and processes to better elucidate how these
systems and processes function at a fundamental level, to understand how
and why they react to specific natural and human-induced changes, and to
develop more accurate predictions of, or scenarios for, possible future
outcomes. This challenge includes: 1) improving our ability to integrate
complex multi-disciplinary and multi-dimensional data; 2) accelerating
the scientific process itself and expanding our knowledge of complex
systems by allowing scientists to collaborate in entirely new ways
(e.g., enabling scientists to work more effectively together across
disciplinary "language" barriers or facilitating more real-time, remote
collaborations); and 3) developing a new generation of advanced modeling
techniques and algorithms (e.g., 3- and 4-dimensional modeling, more
accurate and reliable statistical modeling and forecasting, more
rigorous approaches for quantifying and incorporating uncertainty in
predictive models through risk analysis, auto-calibration of models, and
advanced spatial analysis).

Challenge #3: Communication and Representation of Data and Information
on

Environmental Systems and Processes

This challenge focuses on the need to significantly advance our
capabilities to communicate our increased understanding of complex
environmental systems and processes to a diverse set of audiences and
constituencies. This challenge includes: 1) effectively representing
inherent information-related properties such as complexity, uncertainty,
or credibility to widely different audiences; 2) developing more
advanced approaches for visualization and simulation of complex
multi-dimensional systems and phenomena; 3) increasing the ability to
communicate newly acquired data and information in real-time or
near-real time; 4) increasing the speed with which research methods can
be provided as operational analysis tools; and 5) design and development
of a new generation of scientific data and information products and
delivery services (e.g., new approaches for continuously measuring how
scientific data and information are used and understood by different
customers so that products and delivery services can be continuously
refined).

The 2003 National Science Foundation report entitled *Revolutionizing
Science and Engineering Through Cyberinfrastructure: Report of the
National Science Foundation Blue-Ribbon Advisory Panel on
Cyberinfrastructure* (Atkins et al 2003) stated the effects of the
computing sciences on the future of the scientific method. An
abbreviated list of these effects on conduct of research in the natural
sciences include:

-   The classic two approaches to scientific research:
    theoretical/analytical and experimental/observational, have been
    extended to computer simulation and modeling to explore new
    possibilities and to achieve new precision.

-   The enormous speedups of computers and networks have enabled
    simulations of far more complex systems and phenomena, as well as
    visualizing the results from many perspectives.

-   Advanced computing is no longer restricted to a few research groups
    in a few fields such as weather prediction and high-energy physics,
    but pervades scientific and engineering research, including the
    biological, chemical, social, and environmental sciences, medicine,
    and nanotechnology.

-   Crucial data collections in the social, biological, and physical
    sciences are now online and remotely accessible -- modern genome
    research would be impossible without such databases, and soon
    astronomical research will be similarly redefined through the
    National Virtual Observatory.

-   Groups can collaborate across institutions and time zones, sharing
    data, complementary expertise, ideas, and access to special
    facilities without travel.

-   Scientists can combine raw data and new models from many sources,
    and utilize the most up-to-date tools to analyze, visualize, and
    simulate complex interrelations.

-   Scientists can collect and make widely available far more
    information (the outputs of all major observatories and astronomical
    satellites, satellite and land-based weather data, three-dimensional
    images of anthropologically important objects), leading to a
    qualitative change in the way research is done and the type of
    science that results.

-   Scientists can work across traditional disciplinary boundaries:
    environmental scientists will take advantage of climate models,
    physicists will make direct use of astronomical observations, social
    scientists will analyze interactive behavior of scientists as well
    as others.

-   Scientists can simulate more complex and exciting systems (cells and
    organisms rather than proteins and DNA; the entire earth system
    rather than air, water, land, and snow independently).

-   Scientists can visualize the results of complex data sets in new and
    exciting ways, and create techniques for understanding and acting on
    these observations.

-   Scientists can work routinely with colleagues at distant
    institutions, even ones that are not traditionally considered
    research universities, and with junior scientists and students as
    genuine peers, despite differences in age, experience, race, or
    physical limitations.

The three Environmental Information Science research challenges
described above require scientific insight, strong links to the
disciplinary sciences of the USGS, and the capacity to focus our science
on environmental issues of greatest significance to society, including
those identified in the other USGS Future Science Directions. Some of
the scientific issues that need to be addressed to meet these challenges
include:

1)  Issues of scale play a significant role. Scales of analyses are
    > expanding from local to global as additional computational power
    > and seamless datasets become more readily available.

2)  Complexities associated with scientific domains are complicated by
    > complexities caused by data gathering and sampling, leading to
    > complexities associated with data volume (Gahegan 2001).
    > Understanding is often complicated by local variation.

> 3\) Increase in dimensionality is allowing geometric models to be
> treated as n-dimensional manifolds, instead of the more traditional
> simple curves and surfaces. The demand to store and provide direction,
> in addition to location and time, increases the expressive power of
> systems requiring at least 7 degrees of freedom.
>
> 4\) Linear analyses in the form of photogrammetry and map algebra are
> common. The increase in dimensionality leads to the ability to study
> non-linear systems. Examples include unexpectedly organized behaviors
> of non-linear differential equations (solitons) and unexpectedly
> disorganized behaviors (chaos). These non-linear methods allow us to
> study many facets of complexity.

5\) Development of standard semantics for expressing scientific domain
activities.

> 6\) Limitations of mathematics, formal reasoning and computation
> (computability) need to be explored and understood. Not all problems
> have solutions that can be computed or modeled (Chaitin 1998).
>
> 7\) Need to develop more appropriate (intelligent) methods for
> organizing complex, multi-disciplinary data for efficient storage and
> retrieval.

Appendix I provides an initial framework for a long-term environmental
information science research agenda for the USGS. A more detailed
research agenda would be developed in close collaboration with the USGS
natural science programs. Appendix II lists examples of some scientific
questions that could be addressed, in the context of specific USGS
natural science investigations, through an interdisciplinary research
emphasis in environmental information science.

**Defining the Unique Role and Potential Contribution of the USGS in
Environment Information Science**

The development of an interdisciplinary USGS research emphasis in
environmental information science is predicated on the recognition that
a fundamental *advancement* in the bureau's capabilities to understand,
model, and predict complex environmental systems and processes can be
accomplished through a more focused effort to link and apply information
science principles and methods together with the Bureau's extensive
natural science capabilities. The unique set of information-related
challenges inherent to the study and understanding of multidisciplinary
environmental systems and processes (i.e., multiple levels and types of
complexity, multi-dimensional in space and time, multiple types of flux
of materials and organisms, difficulties in measurement/observation *in
situ*, etc.) represent an intersection of environmental science and
information science. These are true interdisciplinary research
challenges, unique to the mission of USGS, which cannot be addressed by
the mere application of existing computer/information technology.

The USGS is uniquely positioned to make a significant contribution over
the next 25-50 years in this type of interdisciplinary environmental
information science research. First and foremost, the natural science
research challenges facing the USGS over the next 50 years\-\--such as
the need for improved predictive capabilities for natural hazards and
disasters, modeling of diminishing ground and surface water supplies,
discovery of potential new energy and mineral resources, and preventing
the spread of invasive species\-\--will provide the underlying research
context for the environmental information science research agenda.
Secondly, as noted above, the needed research must, by definition, be
truly interdisciplinary---pushing the boundaries of both natural and
information science. The bureau has a strong existing multi-disciplinary
scientific workforce, including natural scientists in all major
disciplines and a relatively small cadre of experienced computer and
information scientists. The Bureau can also build on its existing
collaborative relationships and research partnerships with other Federal
agencies, academic scientists, and private industry, all of which have
significant strengths and resources to complement USGS in-house
capabilities. Finally, the USGS has a significant long-term investment
(totaling hundreds of millions of dollars) in scientific data collection
networks, data sets, analyses and models, and information products that
can form the foundation for our environmental information science
research activities.

While other Federal agencies, including NSF, NASA, EPA, NOAA, USDA, DOE,
and DOD, support and conduct environmental information science research
activities, there is no other single Federal agency that is engaged in
information science research specifically directed at enhancing
understanding, prediction, interpretation, and communication of the
breadth of complex environmental systems and processes that USGS
scientists study. Similarly, most academic research in
computer/information science has not been traditionally directed at the
special problems and challenges of the environmental science domain, but
has instead focused on other topical/thematic areas (such as genomics,
materials science, or defense/intelligence applications).

It is not likely, in the near future, that the USGS will be in a
position to invest any significant new resources (funding or staffing)
into this interdisciplinary research focus area. While it is essential
for the USGS to provide leadership in this interdisciplinary area by
most effectively utilizing and enhancing our own core capabilities, it
is also critical that we take maximum advantage of opportunities for
collaboration with other government and non-government entities in order
to leverage their more extensive information science research
capabilities, resources, and interests.

**Responsiveness to NRC Report on Grand Challenges in Environmental
Sciences**

The National Research Council (NRC) Committee on Grand Challenges in the
Environmental Sciences (NRC 2001b) identifies eight Grand Challenges:
Biogeochemical Cycles, Biological Diversity and Ecosystem Functioning,
Climate Variability, Hydrologic Forecasting, Infectious Disease and the
Environment, Institutions and Resource Use, Land-Use Dynamics, and
Reinventing the Use of Materials. The Grand Challenges are each
multidisciplinary, drawing upon expertise in the biological, chemical,
physical, and social sciences. The report states, "New training, new
organizations, and new funding are needed to bring together
multidisciplinary teams ...". Further, the report recognizes that "...
grand challenges in the environmental sciences may be different from
other research activities in that they could require special efforts to
develop measurement techniques, databases, or conceptual frameworks ..."

Running through the list of the most important areas for research in
each Grand Challenge area are several common threads with direct
relevance to an interdisciplinary USGS environmental information science
research emphasis. These themes include: development of tools for rapid
assessment, development of theoretical models, quantification of data,
and development of long-term databases. The report also identifies
several crosscutting implementation issues, which must be addressed in
furthering Grand Challenge research. These include focusing on
elucidating the fundamental complexity of environmental phenomena;
building capacity for interdisciplinary, problem-oriented research; need
for interagency support of grand challenge research; and the need to
improve the usefulness or societal relevance of environmental science
research. Given these themes, it is suggested that any progress in
developing a USGS environmental information science research program can
potentially make a fundamental contribution to meeting these Grand
Challenges. The NRC report, however, makes it quite clear that such
progress, at least within the context of addressing the Grand
Challenges, must be in the service of advancing natural science
research.

**Past and Current USGS Activities in Environmental Information
Science**

In developing a new research emphasis in environmental information
science, the USGS can build on several current activities as well as
some past experience in this area. As described in the narrative
examples given throughout this plan, USGS scientists in different
disciplines are currently applying specific advanced information science
methods (e.g., artificial intelligence, genetic algorithms, scientific
visualization, high performance computing) to help address specific
natural science questions. However, this type of information science
work is usually pursued within the scope and confines of an individual
discipline-based research project (e.g., on invasive species or
earthquakes), thus limiting opportunities for synthesis, integration,
and sharing of the *information science* methods and results across USGS
programs and disciplines.

Additionally, there have been competitive research funding opportunities
identified through research prospectuses in USGS science disciplines
that include at least a partial focus on projects that link information
science research and specific USGS science questions (e.g., in the
Geology and Geography Disciplines). Within the last five years, USGS
Disciplines and individual science programs have also been involved in
collaborative funding of selected extramural information science
research projects by joining with partners. Some examples include, but
are not limited to: the Geology Discipline has initiated a research
partnership with NSF including participation in the Geosciences Network
(GEON) research initiative; the Geography and Biology disciplines have
supported several selected research projects through NSF's "Digital
Government" program); Geography has supported selected projects through
NIMA's University Research (NURI) program; Biology and Geography have
collaborated with NASA and NSF to sponsor a workshop and special
research funding competition on "Research Directions in Biodiversity and
Ecosystem Informatics" (Maier et al 2001) and Biology is collaborating
with NASA to apply high performance computing resources to invasive
species modeling questions. Again, while these activities have been
successful on an individual basis, there have been only limited
opportunities for cross-discipline and cross-program integration and
synthesis in this subject area. A recognized Bureau-wide research focus
in environmental information science would provide the mechanism to help
better link, leverage, and grow these existing activities across the
Bureau.

On an individual basis, USGS scientists have also been involved as
co-investigators or consultants on individual information
science-related research projects funded by other Federal agencies
(including NSF's Information Technology Research (ITR) Program and
NASA's High Performance Computing Research Program).

The USGS also has a growing, interdisciplinary research community using
high performance computing capabilities to address specific natural
science research questions. Beginning in the late 1980\'s, the USGS
began to explore alternatives to make high performance computing
capabilities more accessible to USGS scientists. Early activities
included partnerships through which USGS scientists had access to
resources at Los Alamos and San Diego supercomputer centers. More
recently, USGS scientists in all

four major disciplines have been experimenting with use of Beowulf
clusters[^2] to provide relatively low-cost, in-house access to high
performance computing for selected research applications. Since 1999,
the USGS has installed several Beowulf clusters at various USGS
locations around the U.S. In addition, in FY 2001, the Director's
Venture Capital fund supported a project to explore the potential use of
distributed, Internet-based supercomputing for USGS natural science
applications.

**Building an Environmental Information Science Workforce**

Over the long-term as this interdisciplinary research emphasis matures,
an effective USGS environmental information science workforce should
consist of an interdisciplinary combination of professionals in three
major categories: natural scientist-integrators, computer and
information scientists, and information technologists and computer
engineers. Across this workforce must be represented the skill set
capable of synthesizing the scientific results and interacting with
decision makers to bring new knowledge of complex systems and processes
to bear.

**Natural Scientist-Integrators** should be the bridge between the main
body of USGS natural scientists, social scientists, and
computer/information scientists. This group represents an essential core
competency for USGS in building and maintaining an environmental
information science workforce. These individuals are natural scientists
who, in addition to being highly skilled in their respective natural
science disciplines, have also acquired (through training and work
experience) advanced skills in information science. They are thus able
to communicate and work equally well with scientists from all domains.
These individuals need to be intimately familiar with specific USGS
natural science research challenges focusing on the acquisition,
analysis, and communication of information from complex systems and
processes. They are the individuals who will work directly with
information scientists to formulate, plan and carry out specific USGS
environmental information science research projects. Many of these
individuals already are working within the USGS across all four major
disciplines. An important contribution of the environmental information
science effort will be to provide more opportunities and mechanisms for
these individuals to communicate with each other, share and exchange
information, and collaborate on interdisciplinary projects.

**Computer and information scientists** are needed to supply the
state-of-the-art expertise necessary to develop and apply information
science research approaches to environmental information science
questions. They should be highly skilled in the major information
science sub-disciplines identified in the research agenda (e.g.,
computational methods, adaptive systems, knowledge representation,
cognitive sciences). These are the individuals who will be working at
the leading edge of information science research and who can bring this
expertise to bear on addressing specific natural science research
questions. For the most part, this component of the USGS environmental
information science workforce can be best developed through
collaboration and partnership with leading researchers at academic
institutions, other government agencies and the private sector. This is
probably the most effective way to bring cutting edge information
science thinking and techniques into the USGS in a flexible and
responsive way. While this area will not represent a significant future
growth area for the internal USGS workforce, the USGS should strive to
develop and maintain a relatively modest in-house complement of
computer/information scientists. These individuals can help give
continuity to longer-term USGS environmental information science
research projects, can apply techniques used successfully in one area to
similar challenges in other USGS disciplines, and can help articulate
and convey new information science research questions arising from USGS
issues to the broader information science research community.

**Information Technologists and Computer Engineers** are needed to
supply the technical skills required to support environmental
information science research in USGS. This group includes programmers,
system and network engineers, system and network administrators,
telecommunications specialists, database experts, and other specialists
who will be needed to support specific environmental information science
research activities, and to support an advanced scientific computing
infrastructure for the entire USGS, e.g., high performance computing,
high speed/high bandwidth data networks, scientific collaboration tools,
etc. (see further under "Scientific Information Infrastructure
Requirements," below). The USGS should build and maintain its own core
competency in the most commonly needed skill areas represented in this
group; while utilizing partnerships, contracts, etc. with other
agencies, academia, and private industry to help provide more
specialized or unique skills on a shorter-term, as-needed basis.

Beginning with these high-level categories, a detailed list of the
specific skills and capabilities needed to address the USGS
environmental information science research agenda should be developed.
This should be followed by a complete inventory and assessment of
environmental information science-related skills and capabilities
available within the current USGS workforce. This skills assessment will
allow the USGS to identify areas of existing workforce strengths and to
identify critical expertise gaps.

Gaps in skill areas identified as USGS core competency areas should be
filled through a combination of techniques, including retraining of
existing staff, recruitment of new permanent and non-permanent staff,
post-doctoral research fellowships, temporary details and assignments,
etc. It should be noted that, based on data from the USGS workforce
planning effort, about 40% of the individuals currently in the computer
scientist job series (GS-1550) and almost 25% of the individuals in the
information technology specialist series (GS-334) will be eligible for
retirement within the next five years. Within the next ten years, 60% of
the existing USGS computer scientist workforce and 45% of the existing
information technology specialist workforce will be eligible for
retirement.

Gaps in non-core competency areas should be filled through an aggressive
program of partnerships with academic institutions, other government
agencies, and private industry. Mechanisms could include: contracts;
cooperative agreements; initiation of a cooperative industrial research
program, through which the USGS could exchange scientists with private
industry for selected cooperative information science research and
development projects); and intergovernmental personnel agreements,
through which information scientists from other agencies or universities
could work directly with USGS scientists on specific projects.

As demands and competition for skilled information science professionals
in both the public and private sectors continue to increase, the USGS
will be vying for high quality information science expertise in a very
competitive market. The special challenge and opportunity to the USGS in
this area will center on our ability to better market a unique and
highly challenging set of *environmental* information science problems;
to highlight the significance, urgency and societal relevance of the
scientific issues we address as an agency; and to emphasize the
significant professional growth and development opportunities that are
available within a large, interdisciplinary, geographically distributed
science organization. These factors, if successfully conveyed and
communicated to the broader information science workforce, can
potentially offer the USGS a significant competitive advantage in this
field.

Another challenge in building and maintaining a skilled and viable
environmental information science workforce will be developing
recognition within the USGS organizational culture of environmental
information science as an important scientific focus area in which we
have the opportunity to build on and advance some existing work and
capabilities. This recognition can be encouraged through several
measures: 1) increasing opportunities for the existing community of USGS
scientists who are currently working on projects involving environmental
information science to share information and communicate among
themselves; 2) increasing opportunities for qualified USGS
computer/information scientists to move into research grade positions;
3) encouraging publication in peer-reviewed journals and facilitating
greater attendance at scientific conferences in this subject area; 4)
providing opportunities for USGS environmental information scientists to
give seminars on their research for their USGS colleagues (and inviting
noted information scientists from other agencies and organizations to
present seminars at the USGS); 5) actively disseminating information on
current environmental information science research projects through web
sites, fact sheets, and other USGS publications, and 6) developing
mechanisms for environmental information scientists to interact with
decision makers to articulate the value and impact of the knowledge
gained to society.

**Scientific Information Infrastructure Requirements**

The USGS does not currently possess an information infrastructure that
is capable of supporting the kind of scientific computing that will be
needed to support the research described in this plan. Costs for these
more advanced capabilities will not be trivial and will require the
recognition that a world-class scientific organization needs to make an
investment in a comparable scientific information infrastructure. Not
all this investment would necessarily need to come from the USGS itself
and the Bureau should collaborate with key partners to leverage their
existing infrastructures (e.g., national supercomputer facilities) and
to share costs on new infrastructure investments.

Key infrastructure requirements to support a USGS environmental
information science research program will include: 1) high
bandwidth/high speed networks (Next Generation Internet and beyond) in
order to move very large of volumes of data at high speeds to support
remote data acquisition technologies, real-time scientific
collaboration, etc.; 2) high capacity/high capability scientific data
management systems that allow extremely large amounts of data to be
stored, managed, and shared among scientists in real-time (or near
real-time); 3) high performance computing resources that are capable of
computing very complex models in a reasonably short time; and 4)
advanced scientific visualization hardware and software. As USGS
capabilities in environmental information science research mature
(within 5-10 years), it would also be appropriate to consider the
establishment of a research center or laboratory that would focus on
this subject. This center, which could be a collaborative investment
with one or more partner agencies, would house the more specialized
and/or expensive hardware and software that could be used by scientists
throughout the bureau. It would also provide a location for USGS and
non-USGS environmental information science researchers who are working
on similar issues to interact more closely on either a short-term or
more extended basis. It should be emphasized that providing a more
advanced scientific information infrastructure for the Bureau will not
only support USGS environmental information science research activities,
but will be of vital importance for the entire spectrum of USGS science
activities.

**Opportunities for Collaboration**

The USGS cannot and should not expect to take on the implementation of
the entire environmental information science research focus by itself.
The collective information science challenges outlined above offer an
attractive and rich field of study for the broader (i.e., non-USGS)
information science research community in government, academia, and the
private sector. The overall environmental information science research
agenda proposed by this Future Science Direction includes research
questions and activities on which internal USGS-initiated
interdisciplinary research projects should logically and appropriately
focus, as well as a complementary set of research questions that would
be best addressed in collaboration with other agencies and
organizations.

This "external component" of the USGS environmental information science
research activity should not, on the other hand, be merely opportunistic
or ad hoc. In order to best complement and supplement the specific
capabilities and activities of the USGS in this area, external research
activities need to be well-planned and organized, and tied to USGS
research questions. For example, an effective collaboration with the NSF
could help focus NSF-sponsored academic research on specific information
science questions at a more basic or fundamental level, with
complementary research on a more applied level (i.e., related to
specific natural science research challenges) supported through the USGS
alone or possibly in collaboration with other natural science agencies
such as the Forest Service, Natural Resources Conservation Service, or
the EPA.

Important components of an external/collaborative research program would
include extra-mural research funded solely by USGS on specific research
questions; joint funding of selected research in collaboration with
other agencies (e.g., where the USGS is able to leverage the competitive
information science research grants programs at agencies such as NSF,
NASA, or NIMA by contributing a relatively small amount of funding to
these programs in return for having USGS-relevant research questions
included in their program solicitations); and use of CRADA's with
private industry The USGS should also facilitate and encourage USGS
scientists to work as (non-funded) co-investigators on collaborative
environmental information science research projects funded solely by
external, non-USGS sources (such as NSF or NASA).

Through the combination of the scientific questions we seek to answer
and the unmatched resources represented in our extensive natural science
data collection, analysis and delivery networks and many large, diverse,
and long-term data sets, the USGS can provide the single largest and
most comprehensive "R&D test bed" for those in the computer and
information science and technology community wishing to collaborate on
issues in the environmental information science domain. Currently, this
is a natural USGS asset that remains relatively unexploited. What is
needed is coordinated access to these research opportunities by the
overall information science research community and coordinated
interdisciplinary engagement of the non-USGS natural and information
science research communities on problems that are directly relevant to
the USGS science mission.

Current funding for information science research at agencies such as
NSF, NASA, or DOE is very substantial compared to existing (and likely
future) USGS resources in this area (for example, for each of the past
several fiscal years, funding for NSF's multidisciplinary Information
Technology Research initiative has totaled more than \$150 million). The
issue is how the USGS can be more creative in articulating and
communicating ("marketing") our challenging and unique environmental
information science research questions to encourage and engage non-USGS
researchers (who can be potentially funded by these other agencies) to
work on research questions that are scientifically challenging to them
and, at the same time, of direct relevance and benefit to the USGS and
our customers (Maier et al 2001).

To meet this need, the USGS should explore specific, proactive
approaches for making the broader (non-USGS) information science
research community more aware of our unique environmental information
science research challenges and of specific opportunities for research
collaboration with USGS scientists. These could include:

A.  Highlighting availability of special USGS "challenge problems" that
    could be potential research topics for non-USGS information science
    researchers seeking outside (e.g., NSF or NASA) funding. Use of
    challenge problems to convey and communicate particularly difficult
    and intellectually challenging questions to a broader interested
    research community is a well-established technique in disciplines
    such as mathematics and physics.

B.  Highlighting availability of particularly interesting and
    challenging (e.g., very large, complex, and/or widely distributed)
    USGS data sets (e.g., NAWQA, Breeding Bird Survey, etc.) for use as
    test beds by non-USGS researchers. Information science researchers
    (including those who are largely unfamiliar with the natural
    environmental science domain) are frequently interested in having
    access to actual, substantive data sets on which to test concepts,
    apply new algorithms, etc. (An example of the success of this
    approach can be seen in the USGS-Microsoft TerraServer partnership,
    in which Microsoft researchers were interested in using USGS' large
    Digital Orthophoto Quadrangle (DOQ) data set to test new web-based
    data base technologies).

C.  Providing an interactive environmental information science research
    bulletin board "matching service" to allow USGS and non-USGS
    researchers to more effectively locate potential collaborators from
    other domains for prospective research projects.

D.  Develop a "Scientist in Residence" program in which a
    computer/information scientist from outside USGS takes a sabbatical
    to work at USGS on a specific USGS-relevant environmental
    information science problem.

It should also be recognized that development of a strong and
well-designed external component to the overall USGS environmental
information science research activity will provide additional benefits
to the Bureau by helping to increase exposure and awareness of our own
scientists to the broader information science research community (and
vice versa), thus helping to continually improve the capacity and
stature of USGS researchers in addressing these problems in the future.

**Implementing a USGS Environmental Information Science Research Plan**

This proposed Future Science Direction in USGS environmental information
science research should be implemented through a combination of
in-house, interdisciplinary research on questions of interest and
importance to USGS science and an aggressive external program of
research partnerships and collaborative activities with other government
agencies, academia, and private industry. There needs to be both a
short-term and long-term implementation and funding strategy to
strengthen the identification, recognition and support of environmental
information science as an important interdisciplinary research focus for
USGS. As noted above, there are a variety of activities currently
underway in the bureau in which USGS scientists are investigating the
use of specific advanced information science techniques or methods to
help address specific natural science questions. In the short-term
(during FY 2004 and FY 2005), a survey should be conducted to identify
these current USGS research activities and to identify ways these
activities can be more effectively integrated or leveraged to contribute
to addressing research questions included in an overall, long-term USGS
environmental information science research agenda. For FY 2005, it is
recommended that the Bureau make some (e.g., \$500,000 - \$1 million)
existing funding available through an interdisciplinary research
prospectus, similar to the Director's Venture Capital fund. This
prospectus could be used to provide seed money to help support existing
projects in USGS science programs and to initiate new projects that
focus on high priority issues in the research agenda that are of
interest to multiple USGS science programs. For the longer-term (i.e.,
potentially beginning with FY 2006), a budget initiative should be
developed to provide continuing funding for environmental information
science research and for building and maintaining an advanced scientific
information infrastructure for the entire bureau.

#### 

#### Appendix I: Framework for a Long-Term Environmental Information Science Research Agenda

Initial framework for a long-term Environmental Information Science
research agenda for the USGS. A more detailed research agenda would be
developed in close collaboration with the USGS science programs:

> **Analytical modeling**

-   Study means of qualifying and quantifying uncertainty in
    mathematical and statistical modeling, including integrated
    parameter estimation

-   Develop and study the use of generic, interpretive and predictive
    models that can be used to improve understanding of environmental
    systems and processes, including 4-dimensional models

-   Examine data handling techniques for extremely large and complex
    models to better enable modelers in model construction

> **Computational methods**

-   Investigate computational methods for characterizing hydrological,
    geological, and biological data from data-sparse areas (e.g., using
    geostatistical, Markov Chain, Monte Carlo, fuzzy logic, risk
    analysis)

-   Examine the use of neural networks and genetic algorithms for new
    applications

-   Apply the science of complexity for an enhanced understanding of
    environmental systems and processes

> **Knowledge representation and communication**

-   Assemble natural science ontologies to enhance access to and
    understanding of USGS data sets

-   Develop techniques for integration, interpolation and extrapolation
    of previously collected data variables from different data sets into
    new applications

-   Study use of information structures to represent complex
    environmental systems and processes

-   Study inference techniques for model development in an effort to
    make models more intelligent

-   Improve ways of representing complex data and information (e.g.,
    visualization, digital cartographic techniques)

-   Develop techniques to carry EIS knowledge into the decision-making
    arena

> **Adaptive systems**

-   Evaluate innovative remote data acquisition platforms that would
    monitor real-time natural phenomena for continual input to and
    modification of models; automatically enrich new real-time data
    streams with existing/previously collected data

-   Study of computer-based field acquisition platforms that are
    consistent with a centralized multi-dimensional, object-oriented
    natural science data base and data model

-   Investigate use of machine learning methods for understanding of
    complex environmental systems and processes

> **High-performance computing**

-   Expand upon existing methods of parallelization of current
    algorithms for enhanced performance

-   Study applications of field programmable logic

-   Develop and enhance the use of distributed systems

> **Cognitive science**

-   Investigate techniques for querying multidimensional model inputs
    and outputs to better understand the complex boundaries and
    interfaces of natural domains

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-   Evaluate and apply new scientific visualization techniques for
    understanding and communicating natural system complexity and for
    modeling system responses to natural and human-induced stresses

-   Explore virtual reality approaches to visualizing 4-dimensional
    modeling results

-   Apply techniques for understanding of spatial analysis and reasoning
    to improve representation of natural science information

**Nature of Information**

-   Study how organization arises in computer systems as a way to
    evaluate and refine computer models of environmental systems and
    processes

-   Investigate the potential of emergent properties as explanation for
    observed patterns in environmental systems. These kinds of studies
    inevitably lead to the study of self-organizing and dissipative
    systems, the study of the evolution of systems, and the nature of
    information.

**Appendix II: Examples of Scientific Questions**

Examples of some scientific questions that could be addressed, in the
context of specific USGS natural science investigations, through an
interdisciplinary research emphasis in Environmental Information
Science:

-   **What specific adaptations are required to existing tools to deal
    with explicitly spatial or spatial-temporal data in general and
    co-dependence and co-variance specifically?**

-   **How can we map geographic problems to the hypothesis space used by
    tools, be they visually or computationally based?**

-   **How can we be certain that the tools (and/or the user) examine all
    relevant portions of the hypothesis space?**

-   **How should we measure? (How do we know when useful insight has
    been created, and how can we report on reliability?)**

-   **How can existing domain knowledge be effectively harnessed in the
    search for new insights?**

-   **How should results be conveyed to the expert? How should results
    be conveyed to the non-expert?**

-   **What kinds of synergy are possible between the computer itself and
    the domain expert and how can it be optimized?**

-   **What kinds of analytical or visually based tools are most suitable
    for a given task in the cycle of knowledge discovery?**

-   **How can semantics be developed to describe models and their
    outcomes?**

-   **How can desired outcomes be mapped to their original data and
    model pairs?**

-   **How can we best analyze sensitivity of results for various steps
    in a complex model or sequence of models?**

**References**

Bohlen, Steven R., et al. 1998. Geology for a Changing World: A Science
Strategy for the Geologic Division of the U.S. Geological Survey,
2000-2010. U.S. Geological Survey Circular 1172.

Chaitin, Gregory J. 1998. The Limits of Mathematics. Springer-Verlag.
Singapore. 148 pp.

Gahegan, Mark. 2001. Data Mining and Knowledge Discovery in the
Graphical Domain.

In preprint for Workshop on Intersections Between Geospatial Information
and Information Technology, The National Academies, Computer Science and
Telecommunications Board, Arlington, VA. October 1-2, 2001.

Kottman, Cliff. 2001. White Paper on Trends in the Intersection of GIS
and Information Technology. In preprint for Workshop on Intersections
Between Geospatial Information and Information Technology, The National
Academies, Computer Science and Telecommunications Board, Arlington, VA.
October 1-2, 2001.

Maier, D., et al. 2001. Research Directions in Biodiversity and
Ecosystem Informatics. Report of an NSF, USGS, NASA Workshop, NASA
Goddard Space Flight Center, Greenbelt, MD. June 22-23, 2000.

National Research Council. 1999. Science for Decision-making: Coastal
and Marine Geology at the U.S. Geological Survey. National Academy
Press. Washington, D.C.

National Research Council. 2001a. Future Roles and Opportunities for the
U.S. Geological Survey. National Academy Press. Washington, D.C.

National Research Council. 2001b. Grand Challenges in Environmental
Sciences. National Academy Press. Washington, D.C.

Atkins, D., et al. 2003. Revolutionizing Science and Engineering Through
Cyberinfrastructure. Report of the Advisory Committee for
Cyberinfrastructure, National Science Foundation. Arlington, VA. 84 pp.

Pfirman, S., et al. 2003. Complex Environmental Systems: Synthesis for
Earth, Life and Society in the 21^st^ Century. Report of the Advisory
Committee for Environmental Research and Education, National Science
Foundation, Arlington, VA. 68 pp.

U.S. Geological Survey. 1999. Streamflow Information for the Next
Century: A Plan for the National Streamflow Information Program of the
U.S. Geological Survey. Open-File Report 99-456.

Sarewitz, D., Byerly, R., and Pielke Jr., R.A., eds., 2000. Prediction,
Science, Decision Making, and the Future of Nature. Island Press.
Washington, D.C. 405 pp.

**Environmental Information Science Future Science Direction Writing
Team**

Anne Frondorf (Lead)

David Boldt

Deborah Hutchinson

William Miller

Douglas Posson

Stuart Sipkin

Rebecca Deusser (Writer-Editor)

[^1]: Note that "information science" is being used generically in this
    plan to represent both the scientific disciplines of computer
    science and information science

[^2]: A cluster of personal computers linked together on a private local
    network to form an affordable, multi-processor, parallel computing
    system (i.e., for high performance computing applications).