Future Science Direction: Environmental 

Information Science Plan

January 2005

By Anne F. Frondorf, David R. Boldt, Deborah R. Hutchinson, William G. Miller, 

Douglas R. Posson, and Stuart A. Sipkin

Open-File Report 2005–1180

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

U.S. Geological Survey, Reston, Virginia: 2005

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iii

Contents

Introduction.....................................................................................................................................................1

Scientific Challenges and Research Questions .......................................................................................3

Environmental Information Science: Major Scientific Challenges .......................................................3

Challenge #1: Acquisition and Extraction of Information from Environmental Systems

and Processes .........................................................................................................................3

Challenge #2: Synthesis, Analysis, Interpretation, and Modeling of Information on

Environmental Systems and Processes ..............................................................................4

Challenge #3: Communication and Representation of Information on Environmental

Systems and Processes.........................................................................................................4

Defining the Unique Role and Potential Contribution of the USGS in Environmental

Information Science ........................................................................................................................5

Example 1: Modeling the Spread of Invasive Species ...................................................................5

Example 2: Sediment Transport Modeling in the Coastal Ocean..................................................6

Responsiveness to NRC Report on Grand Challenges in Environmental Sciences ...........................6

Past and Current USGS Activities in Environmental Information Science .........................................7

Example 3: New Methods for Analyzing Seismograms..................................................................7

Building an Environmental Information Science Workforce .................................................................8

Scientific Information Infrastructure Requirements................................................................................9

Example 4: Mapping the Sea Floor ..................................................................................................10

Opportunities for Collaboration .................................................................................................................10

Example 5: Understanding Earthquake Fault Zone Structure .....................................................11

Implementing a USGS Environmental Information Science Research Plan .....................................12

Summary........................................................................................................................................................12

References....................................................................................................................................................13

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

Agenda.............................................................................................................................................14

Appendix II: Examples of Scientific Questions ......................................................................................15

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. A 

fundamental USGS goal is 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, testing, and application of interdisciplinary 

approaches—integrating physical, biological, computer, and 

information sciences—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 and others, 1998; USGS, 1999), in the results of the 

January 2001 USGS workshop on “USGS in 2050: Envisioning the Scientific Frontier” (see http://internal.usgs.gov/

director/frontier) and in reports from the National Research 

Council (NRC) (NRC, 1999, 2001a, 2001b) and the National 

Science Foundation (Pfirman and others, 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 multidimensional in space 

and time and involve nonlinear interactions of myriad physical and biological factors. Spatial scales of interest may span 

orders of magnitude from submicroscopic to global levels; 

temporal scales may range from instantaneous to millennia. 

Future Science Direction: Environmental Information 

Science Plan

January 2005 

By Anne F. Frondorf, David R. Boldt, Deborah R. Hutchinson, William G. Miller, Douglas R. Posson, and 

Stuart A. Sipkin
 Future Science Direction: Environmental Information Science Plan

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 issues involve the effective communication of 

this environmental complexity to better inform public policy 

and decisionmaking (Sarewitz, Byerly, and Pielke, 2000). 

USGS scientists face a range of challenges in how they can 

effectively communicate their new understanding of these 

extremely complex, multidimensional systems and processes; 

of the relative likelihood of potential outcomes; and of the 

nature of scientific uncertainty itself, to a diverse assortment of 

different constituencies and audiences, ranging from scientists 

and resource managers, policy and decisionmakers, teachers 

and students, to the interested citizen. 

Many of the scientific questions outlined above are 

fundamentally related to the complexity, multidimensionality 

and nonlinearity of the information represented in the systems 

and processes we study. Information science1, 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 questions. Examples of 

information science-based capabilities that can be applied to 

the study of complex environmental systems include algorithm 

development, multidimensional 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 

1 Note that “information science” is being used generically in this plan to 

represent both the scientific disciplines of computer science and information 

science.

understanding of complex systems and processes into the 

decisionmaking 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 information science and technology that are 

likely to occur over the next 25–30 years. 

In addition to Environmental Information Science, the 

USGS has identified seven other Future Science Directions 

(Coastal Environments; Earthquake Hazards; Ecosystem 

Health, Sustainability and Land Surface Change; Energy; 

Ground-Water Resources; Invasive Species; and Rivers) as 

topics meriting special attention from the USGS and from our 

partners in the scientific research community over the next 

several decades, because of their importance to the Nation. 

Each of these seven Future Science Directions has, in turn, 

specifically noted the importance of information science and 

computational techniques to the resolution of some of the 

most pressing scientific questions in each area of inquiry. 

It is believed that by facilitating the development of a more 

focused, Bureau-wide research agenda in this interdisciplinary 

area, this Environmental Information Science Future Science 

Direction plan can help address those information science 

requirements which are common to all the other seven subject 

areas. 

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 crosscutting, 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 of 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 focused on mere 

technological requirements, will be essential to the success of 

the initiative. 

Scientific Challenges and Research 

Questions

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 and others, 

2003) describes how current and future developments in the 

computing and information sciences can help transform the 

conduct of research in the physical, biological, and social sciences. These changes 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 increase in processing speed 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 actual 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, and social scientists will 

analyze interactive behavior of scientists.

• Scientists can simulate more complex and exciting 

systems (cells and organisms rather than proteins and 

DNA, and 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. 

Within this overall context of scientific evolution and 

change, USGS research in the area of environmental information science should be directed at extending and deepening our 

understanding of complex environmental systems, processes, 

and phenomena by focusing on the three major scientific challenges relating to the acquisition, analysis, and communication of information.

Environmental Information Science: 

Major Scientific Challenges

Challenge #1: Acquisition and Extraction of 

Information from Environmental 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 

Environmental Information Science: Major Scientific Challenges 
 Future Science Direction: Environmental Information Science Plan

with which information is acquired, used, and reused (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 

“unused” 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 multidisciplinary and multidimensional 

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., three- and four-dimensional 

modeling, more accurate and reliable statistical modeling 

and forecasting, more rigorous approaches for quantifying 

and incorporating uncertainty in predictive models through 

risk analysis, autocalibration of models, and advanced spatial 

analysis).

Challenge #3: Communication and 

Representation of 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 multidimensional 

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 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 

seven USGS Future Science Directions. Some of the scientific 

issues that will need to be addressed to meet these challenges 

include:

(1) Issues of scale play a significant role. Scales of 

analyses are rapidly expanding from local to global as 

additional computational power and seamless data sets 

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, instead of the more 

traditional simple curves and surfaces. The demand to 

store and provide direction, in addition to location and 

time, increases requirements for the expressive power 

of systems, requiring at least seven degrees of freedom.

(4) Linear analyses in the form of photogrammetry and 

map algebra are common. The increase in dimensionality of many problems leads to requirements for the 

ability to study nonlinear systems. Examples include 

unexpectedly organized behaviors of nonlinear differential equations (solitons) and unexpectedly disorganized behaviors (chaos). These nonlinear 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, multidisciplinary 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 

Environmental 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 complex environmental systems 

and processes (i.e., multiple levels and types of complexity, 

multidimensional 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 

multidisciplinary 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 

Example 1: Modeling the Spread of Invasive Species

One of the major long-term science goals identified in the USGS Invasive Species Future 

Science Direction plan is to improve our capabilities to rapidly assess and predict risks of 

invasive species on native species, ecosystem functions, and resource management systems. To meet this goal, USGS scientists are developing computer models of the distribution and spread of selected invasive species. Complex, multivariable data sets, including 

remote-sensing data from satellites and a variety of other biological and physical variables 

measured on the ground, are used to create these models. The long-range goal of these 

studies is to eventually develop an “ecological forecasting” capability, similar to weather 

forecasting, where certain key ecological variables would be tracked constantly in order 

to provide information on where and how fast invasive species are moving so that species 

invasions can be suppressed before they become unmanageable. 

As many as 40 different ecological variables, such as carbon or nitrogen levels in soil 

or reflectance values from satellite data, play some factor into the creation of these models. 

USGS scientists can apply computer-based statistical analysis on each individual variable 

to identify those variables that are statistically important. But the complexity of invasive 

species systems and ecological processes calls for the use of combinatorial assessments, 

where all possible variations of variables are considered simultaneously and the most 

accurate models are produced quickly so that scientists can design and refine models in 

real time or near real time as new data come in. Because the number of possible models 

increases exponentially with the number of variables, massive computation is required for 

data sets involving more than just a few variables. 

Serial processing algorithms, designed for running on individual personal computers or workstations, can take more than a month of constant processing to analyze one 

data set. Scientists at the USGS Fort Collins Science Center are investigating the use of 

high-performance computing (using a super computer or Beowulf cluster) to perform the 

same invasive species analysis in a matter of minutes. The activity requires computer scientists to work closely with the biologists to transform complex existing ecological model 

algorithms from serial processing to parallel processing and to test and adjust the models 

on the high-performance computing platform. This technique has important potential to 

advance USGS understanding and modeling of other complex systems and phenomena.

Defining the Unique Role and Potential Contribution of the USGS in Environmental Information Science 
 Future Science Direction: Environmental Information Science Plan

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 demonstrate 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 nongovernment 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 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. 

Because of its emphasis on meeting the three major scientific 

challenges described above, the development of an interdisciplinary USGS research emphasis in environmental information 

science can directly address key components and threads of 

these Grand Challenges. 

Example 2: Sediment Transport Modeling in 

the Coastal Ocean

One of the major challenges of coastal science identified in 

the USGS Coastal Environments Future Science Direction 

is to understand, and to be able to accurately model, the 

processes that cause coastal change. Central to that challenge is the need to develop effective models of sediment 

transport.

USGS scientists, along with colleagues from other 

Federal agencies, academic institutions, and private industry, are leading an effort to build a well-tested, publicly 

available model of sediment transport for the coastal ocean. 

Not only are many of the processes involved nonlinear (for 

example, currents alter bottom topography, which in turn 

alters currents), but scientists also still lack fundamental 

understanding of some of the basic processes involved (e.g., 

aggregation and cohesion of fine sediments). Sediment 

transport occurs on spatial scales ranging from centimeters 

for sand ripples to hundreds of kilometers along beaches. 

Relevant time scales range from seconds for individual 

waves to decades for sea-level rise or inlet migration. Tools 

for making detailed observations at the short time scales 

(e.g., current meters) are too expensive or too intrusive to 

employ at large spatial scales, and tools that measure over 

large spatial areas (e.g., satellite sensors) provide data that 

are not immediately useful for modeling purposes. 

 A critical component of this USGS-led multidisciplinary modeling effort focuses on information science 

questions, including: how to store and access observations 

that are inherently difficult to quantify (e.g., the expert 

knowledge contained in maps), how to infer model input 

needed on a fine grid from sparse observations, how to 

compare results with reality to identify model limitations, 

how to design model runs to mitigate model limitations, 

how to test and verify model accuracy across the entire 

spectrum of spatial and temporal scales on which coastal 

change occurs, and how to communicate the model results 

effectively and efficiently to managers and decision­makers. 
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 on 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, and 

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 5 years, USGS disciplines and 

Example 3: New Methods for Analyzing Seismograms

The USGS is the nexus of global and national real-time earthquake data. Large quantities of earthquake data are collected 

regularly to increase understanding of how to monitor and estimate damage from these natural disasters. Data are received in 

real time by satellite, Internet, and phone lines. USGS seismologists perform routine analysis of data where it is crucial to be 

both fast and accurate. Processed information is distributed via pagers, e-mail, and bulletins. Recently, there have been a number of initiatives that will increase the amount of real-time data flowing into USGS systems. The Advanced National Seismic 

System (ANSS) is one of these initiatives, and is the centerpiece of the Earthquake Hazards Future Science Directions goal to 

“provide timely and critical earthquake information through improved earthquake monitoring and notification.” The current 

systems that receive, process, and output this information are already strained.

This situation is being rectified by the development, as part of the ANSS, of several new systems that will help to 

quickly analyze and process incoming data. New techniques devoted to handling large amounts of data, such as automated 

visualization tools, are explored and developed at the USGS. Visualization tools are used to extract key information from 

large volumes of data. For example, seismic waveform data, which help seismologists determine earthquake sources, is 

strongly affected by many variables, such as epicentral distance, earth structure, and focal depth. The USGS continues to 

develop tools to look at seismograms as a suite, rather than individually. One new method developed for this purpose is 

global stacking of broadband seismograms, where the characteristics of many seismograms are filtered into a single image. 

Another method involves phase picking algorithms, which are used to quickly identify and time phase arrivals in large seismic databases using an automated algorithm. These techniques will help identify secondary seismic arrivals and minimize 

arrival misidentification. Both of these tools help seismologists to extract information without having to look at hundreds of 

seismograms, and to process massive data sets in a timely manner. These new tools provide opportunities to improve USGS 

estimates of earthquake source parameters and to share information with the public faster in order to save lives.

There is also promise in polarization analysis, where particle motions help to determine wave interactions and polarization of seismic arrivals. Such information helps scientists to constrain earthquake source parameters. This type of analysis is 

still developmental. The USGS also hopes to develop expert systems using artificial intelligence that can immediately recognize aspects of data that presently only a skilled seismic analyst can recognize. This technology will require further development of artificial intelligence systems that far outpaces our current computational capabilities. When these capabilities are 

realized, artificial intelligence systems will most likely have the power to interpret seismic information faster and more accurately than seismologists. These systems may also help in making decisions that contribute to earthquake hazard mitigation.

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 following: 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 the National Geospatial-Intelligence 

Agency’s (NGA) 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 and others, 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. 

Past and Current USGS Activities in Environmental Information Science 
 Future Science Direction: Environmental Information Science Plan

On an individual basis, USGS scientists have also been 

involved as coinvestigators 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 1980s, 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 using 

Beowulf clusters2 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. 

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.

Natural Scientist-Integrators should be the bridge 

between the main body of USGS natural scientists, social 

scientists, and computer/information scientists. 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. 

This area represents an essential core competency for 

USGS in building and maintaining an environmental information science workforce. 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, 

2 A cluster of personal computers linked together on a private local 

network to form an affordable, multiprocessor, parallel computing 

system (i.e., for high-performance computing applications).

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. 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. They 

should be highly skilled in the major information science 

subdisciplines identified in the research agenda (e.g., computational methods, adaptive systems, knowledge representation, 

and cognitive sciences). 

For the most part, this does not represent a core competency area for USGS, and this component of the USGS 

environmental information science workforce can, therefore, 

be best developed through collaboration and partnership with 

leading researchers at academic institutions, other government 

agencies, and the private sector. This is 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 

science 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 
and other USGS publications; and (6) developing mechanisms 

for environmental information scientists to interact with decisionmakers to articulate the value and impact of their gained 

knowledge 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. Some of the key infrastructure-related 

requirements to support a USGS environmental information 

science research program include: the ability to move very 

large of volumes of data at high speeds and to support remotedata acquisition technologies and real-time scientific collaboration; ability to store, manage, and distribute extremely large 

amounts of data in real time or near real time; ability to compute very complex, multidimensional models in reasonably 

short times; and the ability to support scientific visualization 

of complex data sets and phenomena. These requirements, in 

turn, call for continuing investment in infrastructure components such as: high-bandwidth/high-speed networks (Next 

Generation Internet and beyond); high-capacity/high-capability scientific data management systems; high-performance or 

grid-computing resources; and advanced scientific-visualization hardware and software.

Costs for acquiring and maintaining these more advanced 

capabilities will not be trivial but are necessary to ensure 

that USGS scientists have access to the scientific-computing 

resources they require to meet future challenges. It should 

also be emphasized that providing a more advanced scientificinformation infrastructure for the Bureau will not just support 

USGS environmental information science research activities, but will be of vital importance for the entire spectrum of 

USGS science activities. Not all of this infrastructure investment would necessarily need to come from the USGS itself. 

The Bureau should actively collaborate with key partners 

(such as NSF, NASA, or NOAA) to leverage their existing 

infrastructures (e.g., national supercomputer facilities) and to 

share costs on new infrastructure investments. 

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. 

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 nonpermanent staff, post-doctoral research fellowships, temporary details and assignments, etc. 

Gaps in noncore 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 developmental 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, 

Scientific Information Infrastructure Requirements 
10 Future Science Direction: Environmental Information Science Plan

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 collaborative 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, collaborative 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 a 

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 NGA, 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 Cooperative Research and Development 

Agreements (CRADAs) with private industry. The USGS 

should facilitate and encourage USGS scientists to work as 

(non-funded) coinvestigators on collaborative environmental 

information science research projects funded solely by nonUSGS sources, such as NSF or NASA. The USGS should also 

collaborate with other agencies and institutions in providing 

“cross-training” opportunities for natural scientists and information scientists to jointly learn about new ways to integrate 

natural and information science techniques to address complex 

environmental science issues. For example, in 2004, several USGS geological scientists and information technology 

specialists participated in a special week-long “geoinformatics 

training institute” offered at the San Diego Supercomputer 

Center as part of the NSF-sponsored GEON research initiative.

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 

Example 4: Mapping the Sea Floor

Improved characterization of the sea floor is a top priority in “A Plan for a Comprehensive National Coastal Program” (USGS Report to Congress, 2002). Acquisition and 

interpretation of data needed to produce accurate sea-floor maps pose unique challenges that USGS scientists are meeting through the use of several, advanced information science techniques. 

During at-sea experiments, geographic information system (GIS) methods are 

used to continually improve data collection and planning by integrating newly acquired 

and previously collected data with real-time navigation systems. This rapid integration 

of old and new data, essentially creating a continually updated “real-time” digital base 

map, is revolutionizing the way field programs are conducted. Scientists also use GISbased visualization to see relationships between different data sets quickly and easily. 

For example, repeated mapping of particular areas can show damage and sediment 

mobility after the passage of a hurricane, or the recovery and recolonization of sites 

where the habitat has been destroyed by bottom fishing. These techniques provide scientists with dynamic, three-dimensional representations of complex sea-floor data sets. 

Advanced visualization techniques are essential to allow scientists to better communicate complex sea-floor science issues to resource managers and other decisionmakers. 

Perhaps the biggest future challenge in sea-floor mapping is managing huge data 

sets efficiently while at sea, where computer resources are typically more limited than 

ashore. Data collection can reach between tens and hundreds of gigabytes per day. 

Ultra-high-resolution digital maps of regional topography that incorporate multibeam 

bathymetry, scanning laser imagery, seismic-reflection profiles, and ancillary data (such 

as bottom photography, bottom video, or sample information) will stretch the limits 

of acquisition and processing systems. The development of autonomous underwater 

vehicles and remotely operated underwater vehicles to collect data in remote or hostile 

environments will challenge acquisition strategies and telemetry systems. Incorporating 

these multiple data sets into predictive numerical models (such as for sediment/ pollutant/ nutrient/ larval transport or habitat characterization) will require not only the latest 

computer technology, but also intelligent choice of parameterization and algorithm 

development. Simultaneously sharing all this information in useful formats among 

Federal, State, and local interested parties will also be a nontrivial task. Each of these 

challenges involves multidisciplinarity and complexity, which are well suited to the 

application of information science principles.
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 and others, 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. Highlight 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. Highlight 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 Webbased database technologies).

C. Provide 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.

E. Establish an interagency 

environmental information science coordination group (or use 

or expand on an existing group) 

that could meet periodically as 

Example 5: Understanding Earthquake Fault Zone Structure

Understanding the structure of fault zones is a key element of the Earthquake Hazards 

Future Science Direction. USGS seismologists are currently using genetic algorithm 

techniques to determine the structure of fault zones in California and other regions using 

seismic waves that become trapped inside fault zones. Such waves can provide a great 

deal of information about the internal composition of fault zones; however, inverting these 

waveforms for fault zone structure is a very nonlinear process that makes it difficult to 

solve with traditional, calculus-based optimization techniques.

Genetic algorithms are a form of machine learning that uses the building blocks of 

natural genetics and evolution to solve complex, multidimensional optimization problems. 

By encoding the problem as a character string and then manipulating the bits or characters, 

the individuals in the “population” can then be made to go through a simulated “evolution” 

by evaluating the fitness of the initial population against the data and then iteratively creating new (“fitter”) populations (successive generations) through mutations, crossbreeding, 

etc. The new generations tend to contain better and better models because the best (most 

fit) models have a higher probability of parenting new models.

Use of genetic algorithms also holds great potential for many other nonlinear 

problems in earthquake science, including determining fault slip patterns from geodetic 

data, crustal structure from gravity and magnetic fields, and the characteristics of buildings from vibrational data. Currently, genetic algorithms are carried out on individual 

personal computers. However, USGS seismologists and computer scientists are working 

to convert algorithms to run on high-performance computer clusters which speed up the 

calculations proportionally to the number of processors. In the case of fault zone trapped 

wave analysis, this will allow us to simultaneously analyze multiple waveforms in order to 

improve the accuracy of our results and better constrain the models to be used for hazards 

assessment.

Opportunities for Collaboration 11
12  Future Science Direction: Environmental Information Science Plan

a more formal way for agencies (such as USGS, NSF, 

NASA, DOE, USFS, etc.) to share information on their 

activities and identify new collaborative opportunities.

It should also be recognized that development of a strong 

and well-designed collaborative 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 program of research partnerships and 

other 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, 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. 

The Bureau should also proactively support disciplineand regional-related information activities that facilitate 

interdisciplinary environmental information science research 

(e.g., the Geologic Discipline Mendenhall post-doctoral 

program, which for the first time in FY 2006, includes a 

geoinformatics research position). It is also recommended 

that the Bureau consider making some existing funding (e.g., 

$500,000–$1 million) 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, 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. 

Summary

This plan has described an important interdisciplinary scientific direction for the USGS for the coming decades. By 

forging a connection between natural environmental science 

and computer/information science, environmental information 

science can help USGS scientists answer some of the most 

complex and challenging problems facing them in the 21st century. Three major environmental information science research 

challenges have been identified: 

• Improve our capabilities to acquire and extract information from environmental systems and processes. 

• Advance our capabilities to synthesize, interpret, and 

model this information. 

• Increase our ability to communicate and represent the 

information. 

The specific research questions underlying these major 

challenges will require natural scientists and information 

scientists to work together in applying cutting-edge information science principles and methodologies to understanding, 

predicting, and communicating about the complex systems and 

processes which make up our environment.

The USGS is uniquely positioned to become a national 

(and international) leader in this domain by virtue of the 

environmental science research challenges which comprise our 

scientific mission, our multidisciplinary scientific workforce, 

and our long-term investment in scientific data-collection 

networks, data sets, and information delivery systems. At the 

same time, environmental information science research potentially offers a wealth of opportunities for meaningful scientific 

collaboration with academia, government agencies, and private 

industry.

Several important near-term actions are recommended 

to begin to develop this new USGS research direction. These 

include:

Current USGS EIS Research Activities: A survey 

should be conducted to identify current USGS research activities that are investigating the use of specific advanced information science techniques or methods to help address specific 

natural science questions. Identify ways these current activities 

can be more effectively integrated or leveraged to contribute 

to addressing EIS-related research questions of interest to 

multiple science programs. 

Detailed USGS Research Agenda: Work with USGS 

science programs in all disciplines to develop a detailed, long 

term environmental information science research agenda. 

Funding: Provide seed funding through an interdisciplinary research prospectus to initiate new projects focused on 

high-priority research questions facing the Bureau. Develop an 

out-year budget increase initiative to provide continuing funding for USGS environmental information science research and 

for building and maintaining an advanced scientific information infrastructure for the entire Bureau.
Science Workforce Development: Develop a list of 

specific skills and capabilities needed to address the USGS 

environmental information science research agenda. 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.

Collaboration: Initiate specific measures to make the 

broader (non-USGS) information science research community 

more aware of the USGS’ unique environmental information 

science research challenges and of specific opportunities for 

research collaboration with USGS scientists. These include: 

highlighting availability of special USGS “challenge problems” that would be attractive research topics for non-USGS 

information science researchers; highlighting availability of 

USGS data sets for use as research test beds; 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; establishing an “Information Scientist in 

Residence” program to bring computer/information science 

researchers into the USGS to work on specific research problems; and establishing an interagency environmental information science coordination group.

References

Atkins, D., and others, 2003, Revolutionizing Science and 

Engineering Through Cyberinfrastructure: Arlington, Va., 

Report of the Advisory Committee for Cyberinfrastructure, 

National Science Foundation, Arlington, Va., 84 p.

Bohlen, Steven R., and others, 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. Singapore, Springer-Verlag, 148 p. 

Gahegan, Mark, 2001, Data Mining and Knowledge Discovery 

in the Graphical Domain: Arlington, Va., in preprint for 

Workshop on Intersections Between Geospatial Information and Information Technology, The National Academies, 

Computer Science and Telecommunications Board, October 

1–2, 2001.

Maier, David, and others, 2001, Research Directions in Biodiversity and Ecosystem Informatics, Greenbelt, Md., Report 

of an NSF, USGS, NASA Workshop, NASA Goddard Space 

Flight Center, June 22–23, 2000.

National Research Council, 1999, Science for Decisionmaking: Coastal and Marine Geology at the U.S. Geological 

Survey: Washington, D.C, National Academy Press.

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

National Academy Press.

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

Press. 

Pfirman, Stephanie, and others, 2003, Complex Environmental 

Systems: Synthesis for Earth, Life and Society in the 21st

Century: Arlington, Va., Report of the Advisory Committee 

for Environmental Research and Education, National Science Foundation, 68 p.

Sarewitz, Daniel, Byerly, Radford, Jr., and Pielke Jr., R.A., 

eds., 2000, Prediction, Science, Decision Making, and the 

Future of Nature: Washington, D.C., Island Press, 405 p.

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: U.S. Geological Survey Open-File Report 99–456, 13 p.

References 13
14  Future Science Direction: Environmental Information Science Plan

Appendix I: Framework for a LongTerm Environmental Information 

Science Research Agenda

An initial framework for a long-term environmental information science research agenda for the USGS is below. 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 of environmental processes, 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 four-dimensional models.

• Examine data-handling techniques for extremely large 

and complex environmental 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, and 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 USGS data and 

information (e.g., visualization, and digital cartographic techniques).

Adaptive systems

• Evaluate innovative remote data-acquisition platforms 

that would monitor real-time natural phenomena (e.g., 

floods) for continual input to and modification of 

models and to automatically enrich new real-time data 

streams with existing/previously collected data.

• Study computer-based field acquisition platforms that 

are consistent with a centralized multidimensional, 

object-oriented natural science database 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.

• 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 fourdimensional 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 codependence and covariance specifically?

• How can we map geographic problems to the hypothesis space used by tools, whether they are 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 nonexpert?

• What kinds of synergy are possible between the computer itself and the domain expert, and how can they 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?

Appendix II: Examples of Scientific Questions 15