U.S. Department of Energy

Office of Science

Office of Biological and Environmental Research

Organizers

Complex Systems Science for Subsurface Fate and Transport

Report from the August 2009 Workshop

Convened by

Suggested citation for this report: U.S. DOE. 2010. Complex Systems Science for Subsurface Fate and Transport: Report from the August 

2009 Workshop, DOE/SC-0123, U.S. Department of Energy Office of Science (www.science.doe.gov/ober/BER_workshops.html).

Todd Anderson 

todd.anderson@science.doe.gov

301.903.5549

www.science.doe.gov/ober/BER_workshops.html

David Lesmes

david.lesmes@science.doe.gov

301.903.2977

Acknowledgements

Workshop organizers thank all of the scientists who energetically participated in workshop discussions and 

generously contributed their time and ideas to this important activity for the Department of Energy’s subsurface science research community. The workshop would not have been possible without the scientific vision 

and leadership of the chairs: Susan Hubbard, Frank Loeffler, and John Zachara. The writing team led by the 

workshop chairs did an exceptional job of capturing the stimulating and diverse discussions and presentations. 

The report was significantly improved by the thoughtful and constructive comments provided by Dan Rothman 

(Massachusetts Institute of Technology), Susan Brantley (The Pennsylvania State University), Michael Manga 

(University of California, Berkeley), and David Thomassen and Anna Palmisano (DOE). We are thankful for 

the excellent work of Sonia Enloe (Pacific Northwest National Laboratory) and Holly Haun, Judy Wyrick, 

Shirley Andrews, Kris Christen, and Betty Mansfield (Oak Ridge National Laboratory), who transformed the 

text and figures into this final report.

Paul Bayer 

paul.bayer@science.doe.gov

301.903.5324

Frank Loeffler 

frank.loeffler@ce.gatech.edu

404.894.0279

John Zachara 

john.zachara@pnl.gov

509.371.6355

Workshop Co-Chairs

Susan Hubbard 

sshubbard@lbl.gov

510.486.5266

Sources and credits for cover images are listed on pp. 51–52.
i

Complex Systems Science for Subsurface 

Fate and Transport

Report from the August 2009 Workshop

Workshop Dates: August 3–5, 2009

Publication Date: March 2010

Office of Biological and Environmental Research

This document is available at www.science.doe.gov/ober/BER_workshops.html.

DOE/SC-0123
ii March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 iii

Executive Summary................................................................................................................................................................ v

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

2. Workshop Objectives and Content ..............................................................................................................................5

3. Research Opportunities for Subsurface Environmental Systems Science .....................................................7

4. Summary of Workshop Findings................................................................................................................................ 19

Appendix A: Complex Systems Science and Subsurface Complexity............................................................... 21

Appendix B: Complex Subsurface Systems and Merit of Approach.................................................................. 29

Appendix C: Workshop Program..................................................................................................................................... 43

Appendix D: Workshop Participants and Observers................................................................................................ 45

Appendix E: References...................................................................................................................................................... 47

Sources and Credits for Cover Images.......................................................................................................................... 51

Contents
iv March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 v

Executive Summary

The subsurface environment, which encompasses the vadose and saturated zones, is a 

heterogeneous, geologically complex domain. 

Believed to contain a large percentage of Earth’s biomass in the form of microorganisms, the subsurface 

is a dynamic zone where important biogeochemical 

cycles work to sustain life. Actively linked to the atmosphere and biosphere through the hydrologic and carbon cycles, the subsurface serves as a storage location 

for much of Earth’s fresh water. Coupled hydrological, 

microbiological, and geochemical processes occurring within the subsurface environment cause the 

local and regional natural chemical fluxes that govern 

water quality. These processes play a vital role in the 

formation of soil, economically important fossil fuels, 

mineral deposits, and other natural resources.

Cleaning up Department of Energy (DOE) lands 

impacted by legacy wastes and using the subsurface 

for carbon sequestration or nuclear waste isolation 

require a firm understanding of these processes and 

the documented means to characterize the vertical and 

spatial distribution of subsurface properties directing 

water, nutrient, and contaminant flows. This information, along with credible, predictive models that integrate hydrological, microbiological, and geochemical 

knowledge over a range of scales, is needed to forecast 

the sustainability of subsurface water systems and to 

devise ways to manage and manipulate dynamic in situ

processes for beneficial outcomes.

Predictive models provide the context for knowledge 

integration. They are the primary tools for forecasting 

the evolving geochemistry or microbial ecology of 

groundwater under various scenarios and for assessing 

and optimizing the potential effectiveness of proposed 

approaches to carbon sequestration, waste isolation, 

or environmental remediation. An iterative approach 

of modeling and experimentation can reveal powerful 

insights into the behavior of subsurface systems. Stateof-science understanding codified in models can provide a basis for testing hypotheses, guiding experiment 

design, integrating scientific knowledge on multiple 

environmental systems into a common framework, 

and translating this information to support informed 

decision making and policies.

Subsurface behavior typically has been investigated 

using reductionist, or bottom-up approaches. In these 

approaches, mechanisms of small-scale processes 

are quantified, and key aspects of their behaviors are 

moved up to the prediction scale using scaling laws 

and models. Reductionism has and will continue to 

yield essential and comprehensive understanding of 

the molecular and microscopic underpinnings of component processes. However, system-scale predictions 

cannot always be made with bottom-up approaches 

because the behaviors of subsurface environments 

often simply do not result from the sum of smallerscale process interactions. Systems exhibiting such 

behavior are termed complex and can range from 

the molecular to field scale in size. Complex systems 

contain many interactive parts and display collective 

behavior including emergence, feedback, and adaptive mechanisms. Microorganisms—key moderators 

of subsurface chemical processes—further challenge 

system understanding and prediction because they are 

adaptive life forms existing in an environment difficult 

to observe and measure. A new scientific approach 

termed complex systems science has evolved from the 

critical need to understand and model these systems, 

whose distinguishing features increasingly are found 

to be common in the natural world.

In contrast to reductionist approaches, complexity 

methods often use a top-down approach to identify 

key interactions controlling diagnostic variables at the 

prediction scale; general macroscopic laws controlling system-scale behavior; and essential, simplified 

models of subsystem interactions that enable prediction. This approach is analogous to systems biology, 

which emphasizes the tight coupling between experimentation and modeling and is defined, in the context 

of Biological Systems Science research programs 

under DOE’s Office of Biological and Environmental 

Research (BER), as “the holistic, multidisciplinary 

study of complex interactions that specify the function 

of an entire biological system—whether single cells or 

a multicellular organism—rather than the reductionist 

study of individual components.”

In August 2009, BER held the Subsurface Complex 

System Science Relevant to Contaminant Fate and 
vi March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Executive Summary

Transport workshop to assess the merits and limitations of complex systems science approaches to subsurface systems controlled by coupled hydrological, 

microbiological, and geochemical processes. Important objectives were to

•	 Define complex subsurface systems, identify their 

distinguishing features, and establish why they are 

important to different DOE mission outcomes.

•	 Consider how the coupling of subsurface hydrological, microbiological, and geochemical processes 

defines complex system response and dynamics, 

and identify research challenges that, if resolved, 

would lead to high-impact subsurface science 

advances.

•	 Evaluate the need for new research approaches that 

identify and account for the influence of smallerscale processes and their mechanisms on largerscale system behavior.

•	 Conceptualize models and associated knowledge 

needed to describe and predict complex system 

behavior at different scales.

•	 Identify significant, long-term, interdisciplinary 

research opportunities associated with complex 

subsurface systems.

The workshop was attended by participants from 

universities and DOE national laboratories with broad 

expertise in the environmental, microbiological, 

Earth, and marine sciences. These researchers represented different perspectives spanning mechanistic 

investigations at the molecular level to intermediatescale laboratory studies to field investigations of 

natural environments at scales ranging from meters to 

kilometers. Participants also included practitioners of 

complex systems science. They concluded that effectively managing subsurface systems requires a comprehensive understanding of their controlling processes, 

interactions, and response to change as well as a means 

to predict their integrated effects at multiple spatial 

and temporal scales within a heterogeneous framework. This great challenge can be met only by first 

recognizing these attributes and then looking beyond 

the tenets of any one single research approach (such as 

reductionism and complexity science).

This report lays out the need, identified by workshop 

participants, for a new subsurface science research 

strategy that includes a well-conceived convergence 

of complementary top-down and bottom-up methods to access and integrate the special insights and 

understanding provided by each approach. Melding 

select strengths of reductionism and complexity could 

enable the understanding of key underlying mechanisms and interactions, while simultaneously providing insights on common macroscopic laws governing 

complex system behavior at the prediction scale.

To resolve key research gaps and needs that currently 

limit effective, knowledge-based management of 

complex subsurface systems, workshop participants 

identified three high-impact, interdisciplinary research 

opportunities for advancing subsurface science. These 

three opportunities, associated challenges, and strategies for resolving them are summarized below. They 

emphasize hierarchical subsurface systems dominated 

by coupled processes, the characterization of inter- and 

cross-scale process interactions and feedback mechanisms that lead to emergent or other complex behaviors, and the development of heuristic and simulation 

models for system understanding and prediction.

Understand Fundamental Subsurface Process 

Coupling. Tight coupling occurs between microbiological, geochemical, and transport processes 

at mineral interfaces, within particle fractures and 

pores, and within different sediment types that make 

up the subsurface system as a whole. These complex 

biogeochemical systems are difficult to understand 

because of heterogeneities at different spatial scales; 

highly variable reaction time scales; and nonlinear, 

biologically catalyzed reactions and their associated 

feedbacks. Critical priorities are characterizing and 

measuring biogeochemical dynamics at the mineralmicrobe interface, identifying microbial community 

responses to variable environmental conditions, establishing biogeochemical reaction rates in heterogeneous media, and quantifying and modeling feedbacks 

between biogeochemistry and water flow.

Identify and Quantify Scale Transitions in Hierarchical Subsurface Systems. Knowledge of how 

processes occurring at different levels of a hierarchical 

system link with each other and control higher-level 
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 vii

Executive Summary

behavior is essential to understand complex systems. 

Particularly challenging is the ability to quantify the 

influence of smaller-scale processes on higher-scale 

behavior across three main scale transitions: molecular 

to pore, pore to porous medium, and porous medium 

to field. Required advances include measuring diagnostic variables indicative of complexity, identifying 

interactions that lead to complexity, and developing 

scale transition models.

Understand Integrated System Behavior. The interplay between coupled biogeochemical processes in 

the presence of in situ heterogeneity, complex water 

flow patterns, and flow transients can lead to complex 

subsurface system behavior. This behavior is further 

impacted through linkages with the ecosystem and 

climate. Prerequisites for successful management of 

subsurface resources and problems are understanding 

subsurface complexity associated with the movement 

and reaction of waterborne solutes over scales relevant for many aquifers, plumes, and watersheds and 

identifying the patterns and emergent behavior that are 

shared by many systems and that can be predicted by 

models. Key challenges that must be addressed include 

identifying and modeling emergence and other complex 

behaviors, devising innovative strategies to interrogate 

large-scale system behavior, and developing phenomenological models for understanding and prediction.

Pursuing these research opportunities will enable 

us to understand how complex subsurface systems 

behave, identify the properties and processes controlling them, and predict the various consequential 

aspects of their behaviors. This new understanding 

will provide critically needed transferable scientific 

concepts for managing and manipulating complex 

subsurface systems at all scales, from the kilometer 

and above. These contributions are important to multiple DOE mission areas focused on the subsurface 

environment. Additionally, the proposed scientific 

direction has significant potential to result in highimpact subsurface science advances and the development of a distinguishing BER leadership capability in 

subsurface science.
viii March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 1

The subsurface environment, which encompasses the vadose and saturated zones, is a 

heterogeneous, geologically complex domain. 

Believed to contain a large percentage of Earth’s biomass in the form of microorganisms, the subsurface is a 

dynamic zone where important biogeochemical cycles 

work to sustain life. Actively linked to the atmosphere 

and biosphere through the hydrologic and carbon 

cycles, the subsurface serves as a storage location for 

much of Earth’s fresh water. Coupled hydrological, 

microbiological, and geochemical processes occurring 

within the subsurface environment cause the local and 

regional natural chemical fluxes that govern water quality. These processes play a vital role in the formation 

of soil, economically important fossil fuels, mineral 

deposits, and other natural resources.

Cleaning up Department of Energy (DOE) lands 

impacted by legacy wastes and using the subsurface 

for carbon sequestration or nuclear waste isolation 

require a firm understanding of these processes and 

the documented means to characterize the vertical and 

spatial distribution of subsurface properties directing 

water, nutrient, and contaminant flows. This information, along with credible, predictive models that integrate hydrological, microbiological, and geochemical 

knowledge over a range of scales, is needed to forecast 

the sustainability of subsurface water systems and to 

devise ways to manage and manipulate dynamic in situ

processes for beneficial outcomes.

Prediction is an important goal of subsurface science 

because of its connection to water resource management in support of human needs. Predictive models are 

the primary tools for forecasting the evolving geochemistry or microbial ecology of groundwater under various 

scenarios and for assessing and optimizing the potential 

effectiveness of approaches to carbon sequestration, 

waste isolation, or environmental remediation. These 

models also are the principal basis for integrating scientific knowledge on multiple environmental systems into 

a common framework and for translating this information to support informed decision making and policies. 

Predictive models thus are essential components of 

subsurface research—providing a context for knowledge integration, a vehicle to demonstrate the degree of 

1. Introduction

system understanding and the sensitivities of outcomes 

to parameters and processes, and a tool to forecast 

future system behavior either with or without manipulation. An iterative approach of modeling and experimentation can reveal powerful insights on system behavior. 

State-of-science understanding codified in models can 

provide a framework for testing hypotheses and guiding 

experiment design.

Important disciplinary advances in subsurface science 

over the past decade have allowed the development of 

sophisticated models. These models describe chemical 

migration, microbial population dynamics, and other 

system responses (e.g., CO2

 release or sequestration) 

based on molecular and microscopic understandings of 

geochemical and microbiological processes and their 

coupling with hydrology. Generally reductionistic in 

nature, the models describe how multiple processes 

occurring at the microscopic and mesoscopic scales 

integrate to control field-scale behavior. For even the 

simplest of these state-of-science models, obtaining the 

multiscale characterization data and coupled process 

knowledge needed to adequately and mechanistically represent subsurface systems is challenging. To 

date, success has been varied in using reductionistic 

approaches to predict, with the required accuracy, the 

behavior of hierarchical subsurface systems.

Despite major scientific and technological advances 

in subsurface science, there is growing evidence that 

many environmental systems are not predictable using 

reductionistic or bottom-up approaches alone. This 

situation can occur when numerical models based 

on knowledge of small-scale processes and their 

interactions are inadequate to describe the way in 

which the larger system behaves. While reductionism 

provides foundational understanding of component 

mechanisms and processes that can robustly describe 

behavior at the studied scale, these relationships alone 

may not capture the essence of system function and 

response at the prediction scale. The “system” may be 

a single microorganism, a soil or sediment column, 

a coupled groundwater–surface water domain, or a 

nuclear waste repository, each containing numerous 

interactive parts or phenomena. In such cases, either 

the effects of uncharacterized heterogeneity or the 
2 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

1. Introduction

Fig. 1.1. Examples of Collective Structures in 

Emergent Systems. Self-organizing biological and 

physical structures are observed across a wide range 

of spatial scales. Illustrated are patterns observed in 

association with (a) bacterial cell colonies grown in 

culture [From Ben-Jacob and Levine 2006] and (b)

river system geomorphology [Published in Howard 

2009 courtesy of the Florida Department of Revenue 

(FDOR). Reprinted by permission from FDOR and 

Macmillan Publishers Ltd: Nature Geoscience. See also 

Abrams et al. 2009]

collective responses of linked processes or components 

can yield new, unpredictable features or behaviors 

that distinguish the system at higher scale. Systems 

exhibiting behavior as a result of collective responses 

are considered complex, and a top-down approach 

termed complex systems science has evolved over the 

past 25 years to investigate, conceptualize, characterize, 

and model these system types (Bar-Yam 1997). The 

approach is analogous to systems biology (Bork 2005; 

Kitano 2002), which emphasizes the tight coupling 

between experimentation and modeling and is defined, 

in the context of Biological Systems Science research 

programs within DOE’s Office of Biological and Environmental Research (BER), as “the holistic, multidisciplinary study of complex interactions that specify 

the function of an entire biological system—whether 

single cells or a multicellular organism—rather than 

the reductionist study of individual components.”

Driving the evolution of complex systems science is 

the need to describe and predict the holistic behaviors of natural and biological systems containing a 

large number of interacting or interwoven components. Occurring at virtually any scale, these systems 

are difficult to analyze and understand and exhibit 

higher-scale features or behaviors not readily derived 

from the sum of their parts. Key to this is the concept 

of emergence (Baas and Emmeche 1997; Manson 

2001): generally unpredictable behaviors or features 

arising at higher scale from multiple interactions of 

different types of processes and phenomena. Emergent behaviors might be different in every system, or 

they might be the same in many systems. Complex 

systems science is fundamentally an interdisciplinary 

and integrative approach seeking to understand the 

primary interactions that determine emergence and 

overall system behavior at the prediction scale. Without common disciplinary boundaries, this approach 

has two goals: (1) identify broadly operative macroscopic laws applicable to different systems and (2) 

predict system-scale behaviors using phenomenological rather than mechanistic models.

In emergent systems, interactions between many 

components, which themselves may be complex, 

result in the self-organized formation of distinctive collective structures, patterns, and behaviors 

different from those associated with individual 

components or with systems consisting of only a few 

components (see examples in Science 1999). These 

structures can be manifest across a broad range of 

spatial scales, and the behaviors can be summarized 

by the often quoted statement, “More is different,” 

(see Fig. 1.1. Examples of Collective Structures 

in Emergent Systems, this page, and Appendix A, 

Complex Systems Science and Subsurface Complexity, 

p. 21). The greatest intersection between complexity 

(a)

(b)
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 3

1. Introduction

science and subsurface science is likely to be found 

through exploring the causes for and implications of 

emergence, self-organization, and collective behaviors. While a reductionist approach is applicable 

in theory, it may not be practical when many components act together and with their environment 

to generate distinctive structures and behaviors. 

The differences between complicated and complex 

systems (see Appendix A) are debated often. However, making this distinction may not be necessary or 

productive for subsurface systems that are inherently 

heterogeneous, difficult to characterize and observe, 

and challenging to understand and model.

The subsurface is characterized by multiple processes 

operating in a heterogeneous physical framework (see 

Fig. 1.2. Commonplace Heterogeneities in Physical 

Properties Resulting from System Geological Processes, this page, and Fig. 3.1. Three Research Opportunities and Selected Challenges, p. 9). These processes 

function and interact over a hierarchy of scales ranging 

from the sub-nanometer to kilometer, and their 

outcomes are spatially variable because of heterogeneity. Even one of these processes alone can 

exhibit complex behavior. For example, 

fluid-fluid displacement processes are well 

known for their ability to generate fractal 

patterns in homogeneous porous media. 

These complex fluid-fluid displacement patterns and their three-dimensional counterparts can be simulated using simple models 

such as the diffusion-limited aggregation 

model and the invasion percolation model 

(Witten and Sander 1981; Wilkinson and 

Willemsen 1983). While these models predate modern complexity approaches, their 

strategies in describing the higher-level 

aspects of behavior are similar to those now 

used in complex systems science (Boccara 

2004; Shalizi 2006). Subsurface microorganisms, in particular, possess attributes 

of complex systems, including (1) properties (i.e., phenotype) not fully explained 

by component parts (i.e., genotype), (2) 

dynamic biochemical processes that adapt 

when subject to evolutionary forces, and 

(3) robustness arising from adaptation 

to change that may occur through redundant components able to compensate if 

primary components fail. The widespread 

coupling of microbiological, hydrological, 

and geochemical processes in the subsurface may yield daunting complexity, such 

as feedback mechanisms, memory effects, 

and nonlinear behavior at multiple scales. 

Consequently, collective behavior and selforganization are expected to be common 

in the subsurface, with emergent features 

evolving at different scales.

Fig. 1.2. Commonplace Heterogeneities in Physical 

Properties Resulting from System Geological Processes. 

Subsurface physical heterogeneity, such as the variations in 

grain size and sedimentary structure shown here, influences 

the distribution of fluids, geochemical reactivity, and microbiological activity. Such heterogeneity, coupled with the 

observational constraints associated with subsurface systems, 

poses challenges for developing a predictive understanding of 

emergent behaviors at larger scales. [Courtesy Andy Ward]
4 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

1. Introduction

In applying the concepts and methodology developed in complex systems science to the subsurface, 

the interplay between heterogeneity and collective 

(emergent) behavior is expected to be important and 

challenging. New research approaches and sensing 

technologies will be necessary for acquiring a comprehensive understanding, and the development of new 

experimental, analytical, and modeling methods will 

be required to detect, quantify, and interpret self-organized spatial and temporal patterns in the subsurface. 

Also needed are new ways to recognize and quantify 

emergent behavior to enable us to focus on important 

systems for which understanding and prediction can 

be obtained without relying completely on a bottomup strategy. Addressing these needs will allow identification of systems-level patterns or behaviors that are 

repeatedly observed in spite of differences in systemspecific environmental variables or characteristics. 

These common behavioral patterns are key findings 

because they represent a reality that our models must 

reproduce. Although the exact positions or magnitude 

of subsurface components may not be predictable, 

there often will be patterns of important properties 

or behaviors that are. Understanding the controls on 

these patterns and building models that describe and 

predict them are important long-term research goals.

Complementary top-down and bottom-up approaches 

that include iterative experimentation and modeling 

are required to evaluate hypotheses on key interactions controlling system behavior. Also needed are 

subsystem-level concepts and understandings that are 

based on knowledge of molecular mechanisms but do 

not require their explicit upscaling for systems-level 

predictions. New concepts on the function of hierarchical, multiprocess systems that comprise the subsurface must be developed along with ways to effectively 

characterize and describe such systems in the context 

of heterogeneity. The anticipated scientific impacts of 

resolving these research needs are sizeable and include 

the identification of underlying interactions, principles, 

and natural laws governing the behaviors of complex 

subsurface systems of different types (e.g., Murray 

2003; Murray 2007). Beyond this, the expected development of new, pragmatic modeling approaches will 

enable more-robust and defensible systems-scale predictions at the spatial and temporal scales necessary to 

effectively and sustainably manage subsurface systems 

for human needs and to protect human health.

This document reports on the DOE BER workshop, 

Subsurface Complex System Science Relevant to Contaminant Fate and Transport, held August 3–5, 2009, 

in Gaithersburg, Maryland. The workshop evaluated 

complex systems science as a potential new approach 

for subsurface science research. Although comparable 

approaches have been described as a new direction 

in environmental biology (Raes and Bork 2008), 

subsurface science as a discipline exhibits significant 

differences from this area of biology. These differences 

include the nature of key science questions and issues, 

system scale range and framework, types and diversity 

of interactive processes, accessibility to observation, 

and need for prediction. Thus, workshop goals were 

to (1) broadly assess with the subsurface research 

community the merits and limitations of the complex 

systems science approach; (2) determine how its 

concepts might be implemented in subsurface science 

research; and (3) identify new subsurface research 

gaps, needs, and opportunities.

In this report, we summarize proceedings of the 

workshop then present its primary findings. The findings include (1) research needs to integrate concepts 

of complex systems science into subsurface science 

research and (2) new scientific opportunities and 

challenges whose solutions significantly could advance 

the predictive capability and state of science for such 

research. We conclude with a description of potential 

implementation strategies and anticipated benefits. 

The report is supported by appendices that provide 

a technical basis for the research needs and opportunities described in the report core. In addition, the 

appendices describe the scope and goals of complex 

systems science and characteristics of complex systems 

(see Appendix A, p. 21). Also discussed are examples 

of complex subsurface systems and an evaluation of the 

merit of the complex systems science approach for the 

subsurface environment (see Appendix B, Complex 

Subsurface Systems and Merit of Approach, p. 29).
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 5

2. Workshop Objectives and Content

The BER workshop on Subsurface Complex 

System Science Relevant to Contaminant Fate 

and Transport was motivated by recognition 

that subsurface systems at all scales display great complexity and often are difficult to predict. Participants 

explored the question of whether the tenets of complex 

systems science are applicable to and could advance 

scientific understanding and prediction of subsurface 

systems. Overall workshop objectives were to

•	 Define complex subsurface systems, identify their 

distinguishing features, and establish why they are 

important to different DOE mission outcomes.

•	 Consider how the coupling of subsurface geochemical, microbiological, and hydrological processes 

defines complex system response and dynamics, 

and identify research needs and gaps to advance 

understanding of important disciplinary and interdisciplinary science issues associated with subsurface complexity.

•	 Evaluate the need for new research approaches that 

identify and account for the influence of smallerscale processes and their mechanisms on largerscale system behavior.

•	 Conceptualize models and associated knowledge 

needed to describe and predict complex system 

behavior at different scales.

•	 Identify significant long-term interdisciplinary 

research opportunities associated with complex 

subsurface systems.

The workshop consisted of a plenary session with lectures on various aspects of and perspectives on complex 

systems science followed by three breakout sessions 

(see Appendix C, Workshop Program, 43). Specific 

topics were discussed during each breakout session, 

including (1) the identification of relevant complex 

subsurface systems and their key functional components; (2) major disciplinary research needs that could 

enable the characterization, monitoring, understanding, and prediction of such systems and components; 

and (3) the research required to integrate knowledge 

and models of these systems and their interactive components across scales for improved field-scale predictions. An important outcome for the workshop was to 

identify new interdisciplinary approaches, concepts, 

and research needs for subsurface science that address 

the high degree of process coupling, complexity, and 

heterogeneity existing in the subsurface.

Workshop participants included scientists from universities and DOE national laboratories with a broad 

range of expertise in the environmental, microbiological, Earth, and marine sciences as well as in 

complex systems science. Researcher experience 

and perspectives spanned mechanistic investigations 

at the molecular level to intermediate-scale laboratory studies to field studies of natural environments 

ranging in scale from meters to kilometers. Experts 

in modeling and experimental science were well 

represented, and all participants generally were proponents of the merits of interdisciplinary research. 

BER-invited observers from other DOE offices and 

government agencies were important members of 

the audience (see Appendix D, Workshop Participants and Observers, 45).

This report represents the primary product of the 

workshop. It is not intended to be a research plan for 

BER’s Subsurface Biogeochemical Research activity but rather speaks only to how complex systems 

science concepts may be implemented within the 

overall program. The writing team attempted to capture the spirit and content of workshop deliberations 

focused on the complex systems science approach 

and to consistently and logically summarize often 

diverse perspectives and recommendations about 

scientific need. The topic of complexity and complex 

systems science does have semantic and conceptual 

ambiguities discussed in Appendix A, Complex 

Systems Science and Subsurface Complexity, p. 21. 

Attempts were made in report preparation to meld 

the concepts and vernacular of complex systems 

science with those of subsurface science in Appendix B, Complex Subsurface Systems and Merit of 

Approach, p. 29, since this issue was debated during 

the workshop. Technical results of the workshop—

supported by citation and post-workshop conceptual analysis—are summarized in Appendix B, with 

emphasis on the merits of the complexity approach 

for subsurface systems.
6 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

2. Workshop Objectives and Content

The need for a new interdisciplinary scientific 

approach and the research gaps, requirements, 

and opportunities identified as primary workshop 

outcomes recognize the subsurface as a complex system. This new perspective for subsurface science will 

face technical challenges in implementation. However, potentially large scientific payoffs may result—

for BER’s Subsurface Biogeochemical Research 

activity and for subsurface science in general.
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 7

3. Research Opportunities for Subsurface 

Environmental Systems Science

S

cientific study often relies on either bottom-up 

or top-down approaches and methods. These 

differ in philosophy and strategy and impact the 

types of data collected and the nature of conceptual 

and numerical models developed (see Appendix B, 

Complex Subsurface Systems and Merit of Approach, 

Table B.2, Characteristics of Reductionistic and 

Complexity Approaches, p. 41). The primary goal of 

the bottom-up, reductionistic approach is to quantify 

lower-scale fundamental mechanisms, processes, and 

their interactions. Working upward, reductionistic 

approaches conceptualize and model system behavior 

as the result of its lower-scale components and their 

interactions. In contrast, top-down, complex systems 

approaches strive to identify diagnostic variables defining high-level system behavior. Working downward, 

complexity approaches conceptualize and model critical subscale interactions and environmental variables 

that control system behavior, with an objective to 

identify transferable macroscopic laws and relationships. Both approaches have value and merit, and each 

provides unique scientific insights on different aspects 

of complex system behavior. Given the complexities of 

the subsurface, it seems prudent to explore the intersection of the two approaches and ways that they can 

be integrated to provide a more holistic understanding 

and prediction of system behaviors.

Several Earth science disciplines have explored complex systems approaches for describing emergent and 

self-organizing behavior, including ecology (Harte 

2002; Grimm et al. 2005), landscape geomorphology 

(Murray and Fonstad 2007), and watershed surface 

hydrology (Sivapalan 2005; McDonnell et al. 2007). 

Relative to subsurface science, these fields benefit 

from data accessibility, such as the ease of collecting 

samples from or making measurements on Earth’s 

surface or retrieving data collected from satellites. 

The rich datasets facilitate, for example, recognizing 

patterns or classifications, exploring the connection 

between patterns and processes, and testing models 

that describe complex system behavior.

Perhaps because of inaccessibility, subsurface investigations have been predominantly reductionistic in 

nature. This approach has led to improved understandings of fundamental microbiological, geochemical, 

and hydrological processes. However, the reductionistic approach has met with limitations when used to 

study the integrated behavior of subsurface systems, 

which are characterized by multiple hydrological, 

geochemical, and microbiological processes occurring at different scales; significant heterogeneity; and 

measurement and observational constraints. Although 

complexity science offers potential for improving our 

systems-level understanding, unclear is how measurement networks might be developed to identify complex behavior or how models common to complexity 

would be formulated for reactive subsurface systems.

However, now evident is that subsurface science 

encompasses systems-level and specialized disciplinary (e.g., microbiology, geochemistry, and transport) 

challenges requiring broad and diverse knowledge of 

processes couplings, hierarchies, and controlling properties and features. Consequently, applying multidisciplinary expertise and multiple scientific approaches is 

now necessary to investigate the high level of complexity commonplace in the subsurface. Workshop 

discussions supported the need for a hybrid approach 

in which complexity and reductionistic approaches 

would be integrated to achieve major advances in the 

understanding and prediction of subsurface systems. 

A hybrid approach melding select strengths of both 

approaches is needed to enable the understanding 

of key underlying mechanisms and their interactions 

while simultaneously providing insights on scale 

transitions and macroscopic rules that govern system 

behavior at a larger scale. Needed elements of the 

hybrid approach are

•	 Integrative, multidisciplinary investigations of 

subsurface systems to identify common behavioral 

patterns observed at different scales, regardless 

of exact system characteristics, and the variables 

controlling these patterns. This includes identifying 

features that are diagnostic of complexity and components that control system behavior, determining 

key process and component interactions that cause 

emergence or other complex behaviors, and developing and evaluating top-down system models and 

their predictive power.
8 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

3. Research Opportunities for Subsurface Environmental Systems Science

•	 Complementary experimental, theoretical, or field 

studies of key components or controlling variables 

of subsurface systems in which important process 

interactions and their controlling mechanisms are 

quantified and modeled at lower scales for comprehensive understanding. Upscaling research 

is performed to learn about scale transitions in 

hierarchical systems and to discover how to deal 

with them in the absence of emergence. Simplified 

abstractions of processes and their interactions are 

derived to constrain or enrich top-down models.

Three interdisciplinary Research Opportunities (see 

Table 3.1, this page) were identified at the workshop 

to improve scientific understanding of complex 

subsurface systems and their key component interactions (see Fig. 3.1. Three Research Opportunities and Selected Challenges, p. 9). Each Research 

Opportunity has associated challenges (or research 

gaps) whose solutions hold potential for high-impact 

subsurface science. Implicit to all three is a hybrid 

research approach integrating complex systems 

concepts with reductionism through iterative modeling and experimentation. These Research Opportunities emphasize hierarchical subsurface systems; 

the quantification of inter- and cross-scale process 

interactions (from the molecular to field scale); and 

integrated, systems-level behavior. Crucial overall 

research needs are to identify, quantify, and understand emergent behavior and variables diagnostic of 

such behavior using both top-down and bottom-up 

approaches. Briefly presented in the sections below are 

the rationales for selecting each Research Opportunity 

and a discussion of their key challenges and potential 

research approaches.

Research Opportunity 1: 

Fundamental Process Coupling

The coupling of microbiological, geochemical, and 

hydrological processes exerts a primary control on 

the chemistry of subsurface systems. This coupling 

is fundamental to all DOE mission areas concerning the subsurface, and the intimacy of it is great and 

multidimensional, involving feedback mechanisms at 

different scales. Water movement delivers nutrients 

that drive subsurface microbiological activity. This, in 

turn, influences system geochemistry through oxygen 

fugacity, pH, mineral dissolution, and other effects. 

Physical heterogeneities influence water flow paths 

and travel times and the distributions of microorganisms and geochemical (solid, solute) reactants, thereby 

controlling the rates and directions of biogeochemical 

reactions. Microorganisms adapt to their environment but also change it in multiple ways, leading to 

pathway-specific memory effects.

Research Opportunity 1 identifies challenges associated with four specific coupled processes playing 

significant roles in complex subsurface system behavior. The first two challenges involve microbiologygeochemistry interactions occurring on scales ranging 

from the suborganism to the microbial community on 

mineral surfaces and within the pores and fractures of 

sediment particles. Challenges three and four focus on 

the scale where marked variations in subsurface hydroTable 3.1. Improving Scientific Understanding of Complex Subsurface Systems

Research Opportunity Challenge

1. Fundamental Process Coupling 

A. Coupled Mineral-Microbe Interfacial Processes

B. Microbial Community Responses to Dynamic Subsurface Conditions

C. Biogeochemical Reaction Rates in Heterogeneous Media

D. Feedbacks Between Biogeochemical Transformations and Flow

2. Scale Transitions in Hierarchical 

Systems

A. Measurement Approaches for Key Variables and Diagnostic Signatures

B. Identification of Smaller-Scale Controlling Variables

C. Scale Transition Models

3. Integrated System Behavior

A. Identification of Field-Scale Emergent Phenomena Using Numerical Modeling

B. Strategies for Interrogating Large-Scale Behavior

C. Phenomenological Models for Prediction
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 9

3. Research Opportunities for Subsurface Environmental Systems Science

Fig. 3.1. Three Research Opportunities and Selected Challenges. The subsurface is 

characterized by multiple processes occurring on different spatial scales and operating 

within a heterogeneous physical framework. Investigation of the identified Research 

Opportunities (shown in bold) and their associated challenges using a hybrid approach 

is expected to improve scientific understanding of complex subsurface systems. 

[Graphic courtesy Susan Hubbard]
10 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

3. Research Opportunities for Subsurface Environmental Systems Science

geological and mineralogical properties occur over 

distances of centimeters to meters (e.g., see Fig. 1.2. 

Commonplace Heterogeneities in Physical Properties 

Resulting from System Geological Processes, p. 3), 

causing zones of reduced accessibility, poor mixing, or 

differential reactivity. Fundamental process coupling 

related to these cases is emphasized as it occurs in 

the presence of water flow or geochemical transients 

where the interplay between biogeochemistry, in situ

heterogeneity, and advective and diffusive processes 

can lead to emergent subsurface system behavior. 

The four challenges for Research Opportunity 1 are 

described briefly below.

A. Coupled Mineral-Microbe Interfacial 

Processes

Mineral-microbe interactions are not easily predicted 

from knowledge of individual cellular components 

and mineral properties, and process interactions often 

change dramatically in response to evolving system 

characteristics (see Appendix B, p. 29). The bottomup characterization of the interfacial region between 

microorganisms and reactive solids is a current 

topic of investigation in Subsurface Biogeochemical 

Research. New complexity concepts are needed to 

extend the current research scope to include dynamic 

and complex interfacial processes involving multiple 

interactive microorganism types and the bio-organic 

structures they create. These new concepts should 

encompass issues like physiological adaptation, 

evolution, biofilm formation and function, bioweathering, and biomineralization. One significant need is 

determining how microscale biological, chemical, and 

physical interactions control macroscale processes, 

such as the formation and dissolution of minerals 

(see Fig. 3.2. Microorganisms Involved in the Formation of Minerals, this page).

In systems biology, advances have been made in collecting global datasets of gene activity and protein 

expression and applying these data to predict cell 

gene regulatory and metabolic networks. However, 

such measurements have been taken on populations 

of cells and assume homogeneity. The ability to make 

biological, physical, and chemical measurements at 

very small scales is critical because the interfacial 

region between living cells and mineral surfaces can 

Fig. 3.2. Microorganisms Involved in the Formation of Minerals. These interactions occur through poorly 

understood microenvironment effects at the microbe-mineral interface. Often the mineral forms demonstrate 

complex morphology and highly organized structures. Colonies of microorganisms can extract insoluble iron 

oxide forms from physically inaccessible internal domains of porous silica (a) and precipitate them in highly 

ordered iron phosphate forms [the colony and bioprecipitate (a) is shown in cross-section in (b)]. The 40-μm 

crystal shown in (b) grows upward into the aqueous phase and downward into the porous mineral substrate 

from the active colony of microorganisms. [From Peretyazhko et al. 2010, in review. Copyright Elsevier]

(a) (b)
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 11

3. Research Opportunities for Subsurface Environmental Systems Science

Sidebar 3A

BER’s Rifle Integrated Field Research 

Challenge Site

The Rifle project has been manipulating the biogeochemical conditions of a shallow, uranium-contaminated aquifer along the Colorado River using local 

acetate injection into groundwater. Acetate functions 

as an electron donor for specific organism types (iron 

and sulfate reducers) and stimulates their activity to 

reduce and immobilize dissolved contaminant uranium. Stimulation causes major changes to the subsurface microbiological community. These changes have 

been monitored during and after injection to provide 

insights on the community profile and its members’ 

relationships to terminal electron acceptor transitions, 

sediment properties, and groundwater chemistry. 

Related patterns of simulated microbially mediated 

acetate consumption and precipitate evolution within 

the Rifle aquifer are shown in Fig. B.4. Plan View, 

Model-Computed Map of Acetate and Bromide Transport, p. 35, and Fig. B.5, p. 36. Remote monitoring of 

the biogeochemical transformations using geophysical 

methods is shown in Fig. 3.4. Example Illustrating Use 

of Time-Lapse Geophysical Data to Quantify Microbially Mediated Precipitate Evolution and Permeability 

Reduction, p. 15, and in Fig. B.6, p. 37. Although the 

overall scientific strategy has similarities to the hybrid 

approach (and includes aspects of biological systems 

science), new methods are needed that permit interrogating and predicting the impact of subsurface community dynamics on overall system behavior manifest at 

larger spatial scales (see Appendix B, p. 29). 

[Image courtesy Phil Long]

be on the order of tens of nanometers and concentration gradients can be steep. New techniques are 

required to (1) characterize component distributions and interactions (such as co-visualization of 

cells, key proteins, and mineral solids) and their 

spatial and temporal variations in the context of local 

hydrochemical environments; (2) place molecularlevel information (e.g., gene expression at the singlecell level) into a spatial and functional context; and 

(3) measure in situ reaction rates. Another recognized research need is the development of statistical 

methods for analyzing highly multivariate data from 

multiple sources with differing levels of technical 

error and spatial resolution.

B. Microbial Community Responses 

to Dynamic Subsurface Conditions

Subsurface microbial communities exhibit complex 

behavior resulting from the collective interactions 

between phylogenetically and metabolically diverse 

microorganisms and their local microscopic and 

macroscopic environments (see sidebar 3A, BER’s 

Rifle Integrated Field Research Challenge Site, this 

page). An important scientific need is to understand 

how microbial communities respond to dynamic 

physical and geochemical perturbations commonly 

occurring in shallow subsurface environments. 

Systems-level laboratory and field experimentation 

should be tightly coupled with new top-down modeling strategies to address the following questions:

•	 Which environmental variables control microbial 

community structure and function?

•	 What are the relationships between structural 

and functional heterogeneity in microbial communities, and how do these relate to the changing properties of the physical and geochemical 

environment?

•	 Which forces drive or limit adaptation of individual populations and the microbial community 

to changing environmental conditions?

Tackling such research needs will allow the prediction and manipulation of specific microbiological 

activities to achieve desired outcomes in a broad 

range of subsurface environments.
12 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

3. Research Opportunities for Subsurface Environmental Systems Science

Fig. 3.3. Tracer Experiment Performed 

at the Hanford IFRC Field Site. Further 

described in Appendix B, p. 29, the experiment involved injection of nonreactive tracer 

(red) into the saturated zone near the upper 

left corner of the 60-m triangular field, which 

is instrumented with 37 wells. The color 

contour maps indicate the tracer movement 

through the well field from 1000 minutes (top 

left) to 10,000 minutes (bottom right) after 

injection. Groundwater flow moves from the 

upper left to the lower center of the triangle. 

Evident at the later periods of the experiment 

is a mass transfer–limited domain (lower right 

panel) that retains the tracer for long periods. 

Although ongoing submicron- (not shown) to 

decameter-scale process investigations at the 

Hanford IFRC are illuminating the hierarchy 

of existing mass-transfer domains (see Appendix B), a systems approach could lead to an 

understanding of emergent behavior that may 

be critical for understanding long-term plume mobility.

High-throughput technologies now permit the gathering of large biological datasets of target microorganisms 

(typically in laboratory-controlled systems), and significant achievements allow in situ genome-level measurements of microbial communities. However, interpreting 

these data poses challenges, and often only a fraction 

of the collected data is used. Improving systems-level 

understanding thus requires new or enhanced data analysis methods for pattern recognition, statistical analyses, 

data mining, data integration, and gene annotation to 

integrate geochemical and physical information with 

“omics” information (e.g., metagenomic, metatranscriptomic, metaproteomic, and metabolomic data). 

The development of learning models also could lead to 

important improvements in data interpretation. Placing 

metaomics measurements into a spatial and temporal 

context at appropriate scales is key to investigations of 

subsurface microbial communities and ecosystem biology in general.

Several other research requirements involving 

microbial community dynamics were identified as 

potentially valuable. Model communities need to 

be established to facilitate experimental manipulation and hypothesis testing under well-defined and 

controlled environmental conditions. Also needed 

are methods to improve quantification of the rates of 

in situ microbial activities and approaches to connect 

the various biological components provided via global 

measurements (such as metaomics approaches). 

Prioritizing isolation and characterization activities of 

microbial populations using metaomics approaches is 

required to identify the subset populations affecting 

larger-scale behavior. Another high research priority is moving beyond a descriptive understanding of 

microbes to models that can accurately predict microbial community–wide interactions and responses to 

environmental perturbations at a systems level.

C. Biogeochemical Reaction Rates 

in Heterogeneous Media

Subsurface heterogeneity strongly influences systemwide distribution of chemical components and 

microbial catalysts, the time scales of reactions, and 

biogeochemical reaction rates (see Fig. 3.3. Tracer 

Experiment Performed at the Hanford IFRC Field 

Site, this page). Heterogeneity causes gradients in 

concentration and rates; it leads to incomplete mixing 

of reactants at both the pore scale (due to diffusion 

Elapsed = 5000 (min) Elapsed = 10,000 (min)

Elapsed = 1000 (min) Elapsed = 3000 (min)

IFRC March 2009 Tracer Test

IFRC March 2009 Tracer Test

IFRC March 2009 Tracer Test

IFRC March 2009 Tracer Test
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 13

3. Research Opportunities for Subsurface Environmental Systems Science

limitations) and the field scale (due to preferential 

flow paths). Complex kinetic processes in heterogeneous subsurface systems depend on the in situ rate 

of coupled reaction and transport and on the rates 

of mass transfer to and from points of reaction. Mass 

transfer rates are dynamic, scale dependent, and time 

variable. Overall reaction rates observed in these 

poorly mixed, heterogeneous media often are slower 

than those observed in the laboratory, leading to large 

uncertainty in field-scale numerical simulations.

Research is needed to measure, understand, and 

model biogeochemical reaction rates in heterogeneous subsurface environments having different 

physical and geochemical character and different 

microbial communities. Controlled experiments in 

laboratory-constructed heterogeneous systems and in 

natural systems are needed to quantify scale-specific 

and averaged transport and reaction rates in the presence of heterogeneity. Related requirements are the 

development of measurement approaches that can 

assess in situ reaction rates and a better understanding 

of measurement volume averaging or proxy signatures 

associated with indirect measurement approaches.

D. Feedbacks Between Biogeochemical 

Transformations and Flow

Biogeochemical processes can lead to mineralogical 

changes (e.g., precipitation or dissolution) and biological transformations (e.g., biofilm formation or the generation of gas or extracellular polymeric substances) 

that impact pore space and fluid flow. These transformations occur at the pore scale, but their impacts are 

strongly manifest at the mesoscopic and field scales as 

discussed in Appendix A, Complex Systems Science 

and Subsurface Complexity, p. 21 (Englert et al. 2009; 

Li et al. 2009). The interplay between biogeochemical 

transformations and water advection exhibits intermittent and threshold-governed feedbacks characteristic 

of complexity.

Important research gaps are quantifying and modeling 

the process coupling between nonlinear biogeochemical reactions and transport that leads to complex system 

behavior. Experimentation is needed in which the 

biogeochemical system is perturbed in ways resulting 

in mineral precipitation or dissolution and microbial 

growth across scales. Feedback mechanisms with water 

advection must be better understood, and new models 

explaining and describing noted behaviors are required. 

Resolving these research needs will provide new 

insights on the dynamic relationships between biophase 

and biomineral structure and distribution, biogeochemical reaction kinetics, heterogeneity structures, porosity, permeability, tortuosity, and reactive surface area 

relevant for pore- to field-scale system understanding.

Research Opportunity 2: Scale 

Transitions in Hierarchical Systems

Knowledge of how processes occurring at different 

levels of a hierarchical system link with each other and 

control higher-level behavior is key to understanding 

complex systems. A critical gap is defining how the 

effects of process interactions are translated within 

and modulated across scales. Goals involve identifying and quantifying lower-scale processes that control 

higher-scale behavior and determining which information about them is necessary to understand emergent 

system features. For example, experiments (based on 

our current understandings) are required to identify 

diagnostic, measurable signatures of effective system 

response. Drawing on statistical approaches or complex systems analytical tools (see sidebar 3B, Applying 

Complexity Science Tools, p. 14), new model types are 

needed that explicitly consider the process, parameter, 

and scale hierarchies leading to complex behavior.

Transitions within and across three scale ranges need 

to be considered. The molecular to pore scale is the traditional realm of continuum and statistical mechanics. 

For many well-defined systems, continuum descriptions can be obtained that accurately represent the 

behavior of complex molecular systems. Although this 

is perhaps the best understood scale transition of the 

three, major scientific challenges remain for fundamental biological and geochemical process-coupling 

research. The pore to porous medium scale transition 

moves from the scale of discrete grains and pores—

where continuum mechanics of separate phases 

can be applied—to the scale of an effective porous 

medium, where explicit pore geometry is disregarded. 

In traditional numerical models of subsurface systems, the properties and processes are homogenized 
14 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

3. Research Opportunities for Subsurface Environmental Systems Science

or averaged over a representative elementary volume 

(REV), within which variations no longer are considered explicitly. One example issue, therefore, is how to 

handle incomplete mixing of reactants and phases at 

sub-REV scales, particularly for cases in which reaction 

rates are nonlinear with respect to local concentration. 

The final transition is that from porous medium to field 

scales, where recent research has focused on resolution (or stochastic representation) of spatiotemporal 

heterogeneity and its impact on long-term contaminant behavior. Three challenges are identified within 

Research Opportunity 2.

A. Measurement Approaches for Key Variables 

and Diagnostic Signatures

Approaches are needed to measure the features and 

variables indicative of integrated or macroscopic 

system behavior at the prediction scale. Emphasis 

is placed on developing experimental, analytical, or 

model-based methods that identify and quantify 

complexity measures such as diagnostic signatures of 

emergent system behavior. Example research in this 

challenge might investigate the energy and material 

balance of representative biofilms to understand their 

function as dynamic sources and sinks for subsurface 

chemical agents and nutrients.

Other needed research is the development of new 

imaging or interpretation methods for exploring the 

response of a microbial community to a transition 

in geochemical conditions across scales. Examples 

include quantifying geophysical (see Fig. 3.4. Example 

Illustrating Use of Time-Lapse Geophysical Data to 

Quantify Microbially Mediated Precipitate Evolution 

and Permeability Reduction, p. 15) or isotopic signatures diagnostic of critical complex system controls, 

transitions, and behaviors.

B. Identification of Smaller-Scale 

Controlling Variables

The robust determination of the integrated or macroscopic behavior of a complex hierarchical system 

naturally motivates and guides more-detailed hypotheses on processes and variables responsible for system 

response. These processes and variables may occur at 

the prediction scale or originate at much smaller scales, 

with their effects propagated upward through multiple 

Sidebar 3B

Applying Complexity Science Tools

Complex systems science methods (e.g., Shalizi 

2006) can be broadly defined in two categories: methods for analyzing high-dimensional, 

multivariate datasets and methods for modeling complex systems.

In the first category, statistical learning and 

data mining methods seek to infer reliable 

predictive models from large datasets with 

strongly dependent variables by identifying 

data patterns. Specific model architectures 

include neural networks, splines, classification 

trees, and support vector machines. Methods 

such as regularization, cross-validation, and 

structural risk minimization are used to determine the appropriate level of model fit and 

parameter tuning. Time-series analysis methods include those that envision time series 

as random samples of underlying stochastic 

processes (e.g., auto-regressive moving average 

models), as well as newer methods of nonlinear 

dynamics that conceptualize time-series data as 

being generated by a deterministic process with 

added noise.

Cellular automata (CA) and agent-based 

models are the most well known in the second 

category of complexity science tools. CA methods simulate changes in discrete states at each 

grid cell on discrete time intervals by applying 

simple transition rules to each cell based on 

the current states of a limited set of neighboring cells. By specifying rules appropriately, 

emergent behavior at macroscopic scales can 

be efficiently simulated. Agent-based models 

can incorporate the behavior of individual 

discrete entities and their interactions, where 

an entity might be an organism or a molecule, 

as opposed to aggregated (population-level) 

simulation. These models are particularly useful when individual history or heterogeneity in 

individual states or behaviors is important to 

overall system outcomes.
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 15

3. Research Opportunities for Subsurface Environmental Systems Science

Fig. 3.4. Example Illustrating Use of Time-Lapse Geophysical Data to Quantify Microbially Mediated 

Precipitate Evolution and Permeability Reduction. (a) Spectral Induced Polarization (SIP) geophysical 

data were collected synchronously with geochemical data during a Rifle-based column-scale biostimulation 

experiment. The geophysical data were used within an estimation framework to 

quantify: (b) the volume fraction evolution of microbially mediated FeS precipitates, (c) the dynamics of precipitation aggregation, and (d) the associated 

reduction in permeability caused by the biostimulation. Figures (b) and (d)

compare the geophysically obtained estimates with measured geochemical and 

hydrological measurements, respectively. This study illustrates how diagnostic geophysical signatures potentially can be used to quantify biogeochemical 

transformations and feedbacks on flow characteristics in subsurface domains 

where these direct measurements are difficult to obtain. Examples of time-lapse 

SIP data collected during field-scale environmental remediation at the Rifle site 

are given in Appendix B, p. 29. [Figures b–d modified from Chen et al. 2009. 

Reprinted with permission from the American Geophysical Union]

scale transitions. Many biogeochemical reactions follow this pattern. For example, adsorption or desorption 

and oxidation or reduction are molecular-level processes, yet their effects on chemical migration can be 

clearly visible at the field scale through measurement 

of the aqueous concentrations of diagnostic variables 

(e.g., dissolved uranium). This challenge builds on the 

preceding one to identify (1) microscopic components 

and processes exerting primary control on observed 

overall system behavior and (2) process, parameter, or 

hierarchy interactions causing emergence.

For this challenge, research is needed to identify—

within and across process hierarchies or scale 

transitions—the signatures of process interactions 

or effective system response in DOE-relevant subsurface systems. Associated research might include, 

for example, quantifying which critical properties or 

environmental conditions control biofilm or precipitate evolution at the pore scale or redox zonation or 

contaminant fluxes at the aquifer scale. Iterative comparison of experimental results with process modeling 

is needed to identify the components and processes 

that most critically impact larger-scale behavior and 

the most effective ways to describe them and their 

interactions in a systems context.

C. Scale Transition Models

Needed developments are models describing how 

processes and phenomena at different scales influence 

and couple with each other in hierarchical systems. The 

models and approaches might be mechanistic, mathematical, statistical, or phenomenological in nature. 

New variants of volume averaging, hybrid mixture 

theory, statistical and continuum mechanisms, and 

decomposition methods are necessary for addressing 

scale translations from the pore to the porous medium. 

These advances also are needed to integrate molecular 

biogeochemical process information into pore-scale 

(a)

(b) (c) (d)
16 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

3. Research Opportunities for Subsurface Environmental Systems Science

descriptions of reactivity. Because subsurface systems 

may not exhibit distinct scale separations but rather 

gradations, new methods or scaling approaches are 

required to account for a continuum of evolving heterogeneity within or across scales. A variety of complex 

systems science tools and methods holds promise for 

this research (see sidebar 3B, Applying Complexity Science Tools, p. 14).

Research Opportunity 3: 

Integrated System Behavior

Research Opportunities 1 and 2 focus on scales ranging from submicrons to the multimeter-field scale, 

the latter of which generally represents a small fraction of the aerial extent of many plumes, aquifers, or 

watersheds of concern (e.g., compare Fig. 3.3, p. 12, 

and Appendix B, Fig. B.1. The 300 A Uranium Plume 

at the Hanford Site, p. 32). Within DOE energy and 

environmental mission areas, an acute need involves 

understanding and predicting the temporal and 

spatial dynamics of larger-scale subsurface systems 

as a whole and describing, with reasonable accuracy, 

their interactions with other linked environmental 

systems (e.g., surface waters and uncontaminated 

zones), ecosystems, climate, and human populations (see Appendix B, p. 29). These systems pose 

great challenges for understanding and prediction 

because of large, up-front data and monitoring 

demands requiring both time and resources to 

acquire. Complex systems science methods may 

provide an effective means for system conceptualization to optimize characterization and data 

collection efforts, thereby maximizing scientific and 

predictive impact.

Research Opportunity 3 seeks to move beyond our 

current mode of subsurface investigation and prediction to explicitly characterize and simulate subsurface 

hydrological, geochemical, and microbiological properties and processes with as much detail as possible. 

New strategies are needed for studying the integrated 

behavior of poorly described but extremely important large-scale systems, such as dynamic and static 

groundwater plumes. This Research Opportunity 

focuses on the three challenges described below.

A. Identification of Field-Scale Emergent 

Phenomena Using Numerical Modeling

Both bottom-up and top-down strategies exist for 

modeling the integrated response of complex, hierarchical systems (Murray 2007). Modelers using 

bottom-up numerical approaches strive to mechanistically represent processes on scales as small as practical. 

Reactive transport models integrating geochemical 

and microbiologically driven reactions as well as 

advection, dispersion, and diffusion (e.g., Bethke 

2008; Regnier et al. 2003; Steefel et al. 2005) are now 

essential components of many subsurface flow and 

transport investigations. These models can vary markedly in sophistication, but all calculate temporal and 

spatial concentrations of solid and aqueous chemical 

components resulting from equilibrium or kinetic 

reactions as well as from microbial growth and decay 

occurring within a representative elementary volume 

of the modeled system (e.g., Fang et al. 2009; Yabusaki 

et al. 2007). Most reactive transport simulations are 

performed at a single scale of prediction, and relationships are invoked to compensate for the effects of 

heterogeneity and process uncertainties across scales. 

Modelers using a top-down approach typically ignore 

the mechanistic details of lower-scale processes and 

are interested most in describing the effects of smallerscale processes on larger-scale system behavior. Recent 

theoretical and numerical studies have illustrated that 

watersheds indeed exhibit complex behavior (e.g., 

Bloomfield et al. 2008; Kirchner et al. 2001; Kollet and 

Maxwell 2008a). However, explicit consideration of 

complexity in reactive subsurface transport modeling 

has not been attempted yet.

Innovative modeling approaches are needed for 

numerically identifying emergent system behavior 

at the field scale. A needed first step is the explicit 

coupling of bottom-up, mechanistic submodels across 

scales. This might include loose coupling of microbial 

community dynamic and pore-scale flow models 

(Scheibe et al. 2008) with subsurface reactive transport models or linking subsurface reactive transport 

with land-surface and climate simulators. To determine where mechanistic model predictions deviate 

from observations, evaluation is needed of the nested 

and coupled model predictions relative to systemslevel measurements (e.g., changes in the composition 
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 17

3. Research Opportunities for Subsurface Environmental Systems Science

and functioning of a microbial community in response 

to field-scale perturbation or a time-series of solution chemistry at receiving waters). Rather than the 

common practice of refining or further calibrating the 

mechanistic model to improve the match between 

simulations and observations, this research challenge 

will emphasize using deviation points to identify and 

quantify emergent or other complex system behaviors. 

Numerical sensitivity analysis is needed to identify 

which variables and couplings have the most influence 

on integrated, complex subsurface system behavior. 

Once identified, these key variables and couplings can 

be investigated more thoroughly and models can be 

developed that seek to identify, explore, and predict 

the behavioral patterns that they control.

B. Strategies for Interrogating 

Large-Scale Behavior

New research strategies are necessary for interrogating large-scale properties and quantifying diagnostic 

measures of system function and response. This 

approach contrasts with the common practice of 

collecting subsurface data at increasingly higher 

resolution and precision. Important needs include 

developing new sensors, acquiring different data 

forms and types, and deploying observational 

networks—all specifically designed to characterize critical aspects of system behavior or large-scale 

system control. For example, rather than a detailed 

characterization of potentially influential variations 

in subsurface hydraulic conductivity, emphasis is 

needed on assessing the degree of flow connectivity 

or on measuring the responses of subsurface reactive constituents to natural forcings (e.g., hydrological and storm events) over various diagnostic 

time scales. Research is needed to identify response 

patterns that are repeatedly observed for one system 

and generalized patterns common to many systems. 

Such patterns should become predictable if an 

adequate number of natural complex systems are 

understood sufficiently.

Large-scale field manipulations are needed to perturb 

key system components (e.g., microbial community 

structure or redox state), as illustrated at the smaller 

field scale in sidebar 3A, p. 11. The response to intentional perturbation will provide a basis for understanding the linkages and function of internal processes and 

their roles in overall system regulation and control. 

New techniques, including different types of innovative research monitoring, are required to discover and 

quantify space-time patterns that might indicate integrated, emergent, or self-organizational behavior within 

a particular system or between similar systems. Critical 

to advancing subsurface complex systems science are 

systematic learning and model development that must 

progress concurrently with system interrogation. This 

topic forms the basis of the following challenge.

C. Phenomenological Models for Prediction

Phenomenological models are needed to understand 

and eventually describe large-scale system dynamics 

through systematic learning. Such learning is promoted by integrating collected data, using complexity 

methods for analysis, and establishing and testing 

falsifiable models (e.g., Harte 2002) developed to 

explain large-scale behavior relevant to subsurface 

flow and transport. Envisioning the form of these 

models is difficult. They may invoke accepted complex 

system modeling formalisms, such as cellular automata, network models, or agent-based models (see 

sidebar 3B, p. 14). They also could entail the development of hybrid approaches conforming to general 

principles observed at the larger scale while honoring 

mechanistic processes characterized at smaller scales. 

Numerous described models offer potential starting 

points for this effort (e.g., Boccara 2004). Research 

needs include developing models that predict key 

parameters of interest (i.e., not just those of expedience), testing model predictive power (e.g., accuracy 

and uncertainty) at relevant DOE sites, and identifying the organizing principles that might underlie the 

complexity and heterogeneity of complex subsurface 

systems sharing common characteristics.
18 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 19

4. Summary of Workshop Findings

Field-scale subsurface systems and their smallerscale component processes can be extremely 

complicated. In many cases, they demonstrate 

accepted characteristics of complex systems (see 

Appendix A, Complex Systems Science and Subsurface Complexity, p. 21), such as emergence, nonlinearity and feedback, and memory (see Appendix B, 

Complex Subsurface Systems and Merit of Approach, 

p. 29). Other characteristics of subsurface systems that 

have long challenged the scientific community include 

inaccessibility, widespread heterogeneity at multiple 

scales, and hierarchical coupled processes. These difficult characteristics, when combined with society’s 

critical need to understand and predict subsurface 

environments, mandate that all reasonable scientific approaches to investigate and understand such 

systems be carefully considered and applied where 

and when appropriate. To date, the complex systems 

science approach has not been explicitly incorporated 

into BER’s Subsurface Biogeochemical Research 

but appears to have significant merit, given insights 

provided by this approach to other areas of the Earth 

and biological sciences (see Appendices A and B). 

Moreover, the approach has high potential to advance 

subsurface science regardless of whether the particular 

systems studied are formally considered complicated 

or complex.

Complexity science tells us the holistic behavior 

of a complex system cannot be fully understood or 

predicted from knowledge of its smaller-scale features or processes. This does not mean that studies of 

component processes are unnecessary or useless or 

that upscaling research on how smaller-scale processes 

influence or control aspects of higher-level behavior is 

futile. Given the current state of science, each activity 

promises major future scientific advances for subsurface science. Indeed, both are needed as components 

of complexity-based research to document emergence 

when it occurs and to understand the nature and outcomes of fundamental, multiscale process interactions 

leading to complex behavior.

The overall research objectives of both reductionism 

and complexity science may be similar (e.g., systemslevel predictions), albeit with distinctions related to 

the intermediate scientific outcomes and impacts 

sought. Variants of both approaches intersect and 

preserve select strengths of each, as desired for the 

scientific application of interest or to meet program 

science goals. In some cases, a combination of reductionistic and complexity science approaches may 

provide critical insight into the workings of systems 

that are intractable using either approach alone. The 

most beneficial overall approach will be one that 

maximizes program goals for scientific understanding 

of system-scale behavior while providing valuable and 

transferrable scientifically based tools for prediction of 

meaningful and useful variables.

The terms understanding and prediction are nuanced 

and relate to a research approach through the types 

of models employed (i.e., the “toolkit” associated 

with the approach). Understanding involves the 

mechanisms, processes, and interactions within a 

system that collectively define dynamic behavior 

in response to environmental variables. Models 

are fundamental to understanding because they 

integrate different forms of data and knowledge 

and encapsulate our belief about the way a system 

operates. As such, they are essential for hypothesis 

definition and evaluation. Prediction involves many 

levels ranging from qualitative assessments of system 

behavior (based on conceptual models and heuristic principles) to quantitative forecasts of dynamic 

system response. Clearly, the need to improve our 

predictive understanding of subsurface systems is 

great. Reductionistic, bottom-up approaches have 

recognized limitations, and simplified top-down 

models characteristic of complex systems science 

are unlikely to independently predict the spatial and 

temporal distributions of chemical components in 

groundwaters. As each of these approaches reveals 

and quantifies different but important aspects of the 

system, a worthy avenue of pursuit lies in pragmatically applying a combination of top-down and 

bottom-up approaches through iterative experimentation and modeling. Systems-level understanding 

can be iteratively tested and advanced by comparing 

qualitative and quantitative model predictions to 

carefully designed experiments.
20 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

4. Summary of Workshop Findings

The following research gaps and needs were identified 

after evaluating the current state of understanding and 

predictive capability for complex subsurface systems.

1. A hybrid research approach combining complementary bottom-up reductionism with top-down 

complexity concepts is needed to reveal the inner 

workings of complex hierarchical subsurface systems. This approach will help identify and quantify 

emergent properties and behaviors when they 

occur and provide new means for robust and verifiable system-scale predictions.

The melding of reductionism and complexity 

approaches will identify mechanisms and interactions defining system behavior at different scales 

and will support development of new types of 

models (both theory based and pragmatic) with 

the predictive power needed to effectively manage subsurface systems. Groundbreaking scientific 

outcomes with a new perspective are expected.

2. Three interdisciplinary Research Opportunities 

and their associated challenges have been identified as major gaps in understanding and predicting 

complex subsurface systems: (1) fundamental process coupling, (2) scale transitions in hierarchical 

systems, and (3) integrated system behavior. Their 

resolution holds high potential for the impact and 

advancement of subsurface science.

Well-conceived, hybrid research efforts are needed 

on relevant field study sites and representative laboratory model systems to develop an understanding 

of fundamental interactions between microbiological, geochemical, and hydrological processes 

and their impact on overall system behavior. The 

linkage of iterative experimental and modeling 

activities is required at all scales to accomplish both 

heuristic and simulation goals.

3. Complex systems science approaches are required 

to provide the scientific basis for effective DOE 

management of Earth systems involving interactions between water, solutes, microorganisms, 

and mineral solids in heterogeneous subsurface 

environments.

Interdisciplinary, collaborative research is needed 

to transfer new BER-developed system characterization, observation, and modeling strategies to key 

DOE mission sites for broader impact on critical 

national needs.

Pursuing the three Research Opportunities and 

resolving their scientific gaps will improve the understanding of complex subsurface systems at different 

scales and the process interactions controlling or 

distinguishing them. By melding reductionism and 

complexity concepts, the merits of both bottom-up 

and top-down modeling concepts will become clear, 

revealing essential insights on the best approaches 

for achieving predictive capability. Emphasizing the 

quantitative understanding of fundamental process 

interactions as a primary control on system complexity will produce transferable scientific concepts applicable to different subsurface systems. The research will 

provide (1) new concepts for transitioning essential 

information between scales; (2) appreciation for 

systems in which complex system concepts will and 

will not work; (3) higher-level knowledge of natural 

laws governing the behaviors of complex systems; and 

(4) new modeling approaches to address such systems 

at all scales, from the kilometer and above. This fourth 

contribution will give important scientific support to 

multiple DOE mission areas focused on the subsurface environment.
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 21

A. Complex Systems Science

Complex systems science has evolved from the need to 

describe and predict the holistic behaviors of natural 

and human systems containing myriad interacting or 

interwoven parts. Occurring at virtually any scale, these 

systems are difficult to analyze and understand, and all 

exhibit higher-scale features or behaviors not readily derived from the sum of their parts (Murray and 

Fonstad 2007; Watkins and Freeman 2008). Key to 

this is the concept of emergence—behavior or features 

that do not clearly or cleanly derive from the attributes 

or function of internal components but do arise at 

higher scale from their multiple interactions (Baas and 

Emmeche 1997; Manson 2001). The field of complex 

systems science is well described in recent Wikipedia 

texts (e.g., Bar-Yam 1997) and on websites of academic 

research centers espousing this approach (e.g., Santa Fe 

Institute, CSIRO Centre for Complex Systems Science, 

and the University of Michigan Center for the Study 

of Complex Systems). Fundamentally an interdisciplinary and integrative science, the complex systems 

approach seeks to understand the primary interactions 

that determine emergence and overall system behavior at the prediction scale. The goals of this approach 

are (1) identifying broadly operative macroscopic 

laws applicable to different systems and (2) predicting systems-scale behaviors using phenomenological 

rather than mechanistic models.

Complexity and complex systems have become terms 

of our time, partly because of recognized global climate 

change issues; the extreme complexity of Earth-scale 

systems; and the need to forecast future conditions for 

effective resource management, mitigation, and societal protection. Complex systems science has distinct 

and evolving analytical (Shalizi 2006) and modeling 

approaches (Boccara 2004; Grimm et al. 2005; Murray 2007) and theoretical concepts (Manson 2001). 

Modeling often uses a top-down approach based on 

empirical descriptions of emergent variables, features, 

patterns, or behaviors rather than mechanistic descriptions of smaller-scale processes (e.g., Murray 2007; 

Werner 1999). However, approaches to explicitly deal 

with complexity from the bottom up also exist (Grimm 

et al. 2005). Various modeling strategies continue to 

be debated for different applications. The complexity 

approach is increasingly applied in ecology (Bousquet 

and Le Page 2004; Kreft et al. 2001; Wooton 2001), 

geomorphology (Fonstad 2006; Murray 2003; Murray 

2007; Werner 1999; Werner 2003), and catchment 

hydrology (Kirchner et al. 2000; Kirchner et al. 2001; 

McDonnell et al. 2007; Sivapalan 2005). To date, 

application of this approach to subsurface systems has 

been only limited (Bloomfield et al. 2008). Complex 

systems science is recognized as a potential integrative 

strategy in environmental science, a tool for pragmatic systems-level explanation and prediction, and a 

functional means for linkage with the social sciences. 

As such, it is a recommended component of high-level 

nuclear waste assessment (Ewing 2004) and future 

research by national science agencies from the United 

States and abroad (NERC 2007a; NERC 2007b), even 

while the merit of such approaches is debated (Sardar 

and Ravetz 1994).

B. Characteristics of Complex 

Systems

There are no widely accepted formal definitions for 

complexity or complex system (see, for example, Science 1999). However, characteristics associated with 

both terms include complex behavior (deterministic 

chaos, intermittency, and oscillation) in simple systems, 

particularly nonlinear ones. Other attributes include 

extreme sensitivity to initial conditions, perturbations, 

or ambient conditions (experimental control parameters or theoretical model parameters) and the formation of spatial and temporal patterns by the collective 

behavior of many components, also referred to as 

emergent behavior. Complexity often is associated with 

nonlinear and far-from-equilibrium systems, but neither 

is an essential characteristic of complex system behaviors. Formation of spatial and temporal fractals and the 

processes associated with them generally are considered 

part of complexity science, as is the study of networks, 

their properties, and processes taking place within them.

In emergent systems, interactions between many 

components—which themselves may be simple or 

complex—result in the self-organized formation of 

distinctive collective structures and behaviors much 

Appendix A

Complex Systems Science and Subsurface Complexity
22 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

ing the temperature difference across the fluid layer, 

a variety of more-complex behaviors is observed, 

including intermittent and chaotic behaviors. These 

patterns could, in principle, be simulated using 

molecular dynamics. However, simulating so many 

molecules would be cost prohibitive (each convection 

cell in Fig. A.1 consists of about 1015 molecules, and 

the number of molecules in the field of view is on the 

order of 1018). Instead, the simulation of RayleighBénard convection patterns is based on continuum 

computational fluid dynamics (see Fig. A.2. Simulation of Spiral Defect Chaos in Rayleigh-Bénard 

Convection, p. 23) or lattice Boltzmann simulations, 

which do not include molecular details.

Cooperative, self-organized pattern formation is common in biological systems. For example, Fig. A.3. SelfOrganized Bacterial Cell Colonies, p. 24, shows 

dendritic colonies of the bacterium Paenibacillus 

dendritiformis grown on a nutrient substrate in a Petri 

dish. In this case, many millions of bacteria act cooperatively to form a structure beneficial to the whole 

different than those associated with the individual 

components or with systems consisting of only a 

few components. Summarized by the often quoted 

statement, “More is different” (Anderson 1972), 

this characteristic is where the greatest intersection 

between complexity science and subsurface science is 

likely to be found. While applicable in theory, a reductionistic approach may be impractical when numerous 

components act together and with their environment 

to generate distinctive structures and behaviors. In 

this context, understanding the difference between 

complex and complicated systems is important. Like 

complex systems, complicated systems consist of many 

interacting parts that often are quite different, yet these 

systems lack self-organization and emergent behavior. 

Compared to complex systems, complicated systems 

contain relatively few parts, and fully understanding them is possible, at least in principle, based on a 

bottom-up approach. An automobile usually would be 

considered a complicated system (however, the turbulent combustion process in cylinders is an example of 

complexity, as is the dynamic behavior associated with 

coupled vibrations). In contrast, the intermittent and 

quasi-periodic behavior of heavy traffic on a freeway is 

an example of complexity and can be understood and 

modeled without understanding the internal workings of an automobile or driver. Importantly, there are 

no uniform criteria to distinguish complicated and 

complex systems. Some people further argue that complicated systems are so specialized, few general insights 

can be learned from studying them.

A simple example of collective behavior is the formation of hexagonal Rayleigh-Bénard convection cells 

(see Fig. A.1. Hexagonal Convection Cells in SF6

near Its Critical Point, this page). As the temperature 

gradient is increased across a thin horizontal layer of 

fluid heated from below, a simple convection pattern, like that shown in Fig. A.1, emerges spontaneously because of the collective behavior of myriad 

fluid molecules when a critical Rayleigh number1 

is exceeded. In Rayleigh-Bénard convection experiments, as the Rayleigh number is raised by increasAppendix A: Complex Systems Science and Subsurface Complexity

Fig. A.1. Hexagonal Convection Cells in SF6 near 

Its Critical Point. A thin layer of compressed SF6, 

confined between two parallel transparent sheets, is 

heated from below. The collective behavior of myriad 

fluid molecules forms a simple convection pattern 

when the Raleigh number is exceeded. [From G. Ahlers, “Experiments with Rayleigh-Bénard Convection” 

(www.nls.physics.ucsb.edu/papers/Ah_Benard_03.

pdf) and from J. Oh and G. Ahlers, unpublished]

1The Rayleigh number is defined as Ra = (αgΔtb3ρ)/(DT η), where α

is the thermal expansion coefficient, g is the acceleration due to gravity, Δt is the temperature difference imposed across the horizontal 

cell (Δt = TL − TU , where TL is the temperature of the lower wall and 

TU is the temperature of the upper wall), b is the width of the fluidfilled gap, DT is the thermal diffusivity, and η is the fluid viscosity.
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 23

colony. The individual microorganisms are complex, 

self-organized systems interacting with each other 

(via chemotaxis) and with their substrate. In this 

example, the bacteria collectively secrete a lubricant 

enabling them to move on hard surfaces. The internal 

chemical “machinery” in each bacterial cell is not well 

understood. Nevertheless, high-level models based on 

simple concepts about the ways individual bacterial 

cells interact with each other can be used to simulate 

patterns remarkably similar to those formed in a Petri 

dish (see Fig. A.3, p. 24). This provides an understanding of how the cell colonies work and “adapt” to 

changes in their environment. This understanding is 

achieved despite the fact that the number of cells in 

the model system is much smaller than the number of 

cells in the experimental system (by a factor of almost 

a million) and that only a few simplified interaction 

mechanisms are included in the model. The hierarchical complexity in bacterial cell colonies is found in 

many other complex systems. In mammals, for example, small molecules and macromolecules interact to 

form products such as membranes, vesicles, chromosomes, and a nucleus (or nuclei in some cases). These 

products in turn form cells, organs, individuals, and 

groups (e.g., packs and herds) that interact with each 

other and their environment. At each level in the 

hierarchy, the behavior can be described and at least 

partially understood in terms of the properties of and 

interactions between the components constituting the 

immediate underlying level in the hierarchy.

An important note is that if Rayleigh-Bénard experiments were to be repeated under conditions that 

generate patterns similar to the one shown in Fig. A.2, 

this page, the results could be completely different 

because of sensitivity to control parameters. Many 

complex systems have numerous distinct “states,” and 

in simple numerical models, the number of states often 

is infinite. In practice, it often is impossible to control 

experimental complex systems precisely enough to 

prevent switching between different states during 

an experiment or to guarantee the system will be in 

the same state if an experiment is repeated. This has 

a profound impact on quantifying the uncertainty of 

numerical simulations of complex systems because 

standard statistical approaches cannot be used. Even if 

experimental conditions did not change, sensitivity to 

initial conditions and perturbations (exponential amplification of perturbations in nonlinear systems) could 

lead to different results. For example, the cell colonies 

illustrated in Fig. A.3, p. 24, are not subject to large 

transitions when conditions change slightly. However, if 

one of the experiments were to be repeated, some patterns would be quite different, such as the locations of 

the branches and their side branches. Nevertheless, the 

human eye and brain instantly would recognize the pattern and associate it with the appropriate one shown on 

the right-hand side of Fig. A.3. In addition, all statistical 

measures associated with the two patterns would be the 

same. This is similar to the problem of weather forecasting. Because the weather system’s dominant Lyapunov 

time (the time constant associated with the exponential 

growth of perturbations) is on the order of days, longterm weather prediction (beyond one to two weeks) is 

fundamentally impossible. However, predicting climate 

(weather statistics) is possible, at least in principle.

Although using first-principles molecular models to 

simulate typical complex systems is futile, higher-level 

modeling and simulation approaches can provide 

important understanding. They also can enable prediction of expected behavior and the quantitative generic 

characteristics (often expressed in terms of statistical measures) associated with this behavior (Boccara 

Appendix A: Complex Systems Science and Subsurface Complexity

Fig. A.2. Simulation of Spiral Defect Chaos in Rayleigh-Bénard Convection. [From Egolf et al. 2000. 

Reprinted by permission from Macmillan Publishers 

Ltd: Nature]
24 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix A: Complex Systems Science and Subsurface Complexity

(a)

(c)

(d)

(b)

(e) (f)

Fig. A.3. Self-Organized Bacterial Cell Colonies. Parts a, c, and e show colonies 

of the bacterium Paenibacillus dendritiformis grown under medium (a), high (c), and 

low (e) nutrient level conditions. Parts b, d, and f show corresponding patterns generated by a simple computer model. For the experiments, color is added according to 

the density of bacterial cells; for the simulations, colors indicate growth time. [From 

Ben-Jacob and Levine 2006]
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 25

Appendix A: Complex Systems Science and Subsurface Complexity

2004). Examples of such models include those developed to simulate the spread of disease (Grais et al. 

2003), animal and plant territories (Grimm et al. 2005; 

Letcher et al. 1998), and the behavior of a school of 

fish or flock of birds (Reynolds 1987). In most cases, 

complex behavior emerges from simple interactions 

between otherwise independent agents. This often 

requires the numerical development and implementation of systems-level models (Shalizi 2006) that may be 

phenomenological and of largely empirical origin. In the 

case of Rayleigh-Bénard convection, a direct numerical 

simulation approach based on continuum fluid dynamics and thermal physics can be used, and model parameters can be accurately measured or obtained from 

Fig. A.4. Complex Patterns Created by Fluvial Systems. (a) Meandering river in Alaska. [© AccentAlaska.com]

(b) Braided pattern of the Sunwapta River in Alberta, Canada. [Reproduced with permission of Natural Resources 

Canada 2010, courtesy of the Geological Survey of Canada (Photo 2002-597 by R. Couture and G. B. Fasani)]

(c) River delta, Ganges River, Bangladesh. [Courtesy of USGS National Center for EROS and NASA Landsat Project Science Office] (d) Branched channels in Florida formed primarily by groundwater outflow (Abrams et al. 2009; 

Howard 2009). [Published in Howard 2009 courtesy of the Florida Department of Revenue (FDOR). Reprinted 

by permission from FDOR and Macmillan Publishers Ltd: Nature Geoscience] Patterns shown in a–c are highly 

dynamic; the one in d may develop further by headward growth, but the channels change slowly once established.

(a)

(c) (d)

(b)

molecular simulations. However, similar reductionistic 

approaches are not applicable to most complex systems. 

In many cases, the level of reduction is minimal. For 

example, in simulations of the behavior of a school of 

fish, individual fish are the interacting components or 

agents, and further reduction (such as the mechanisms 

allowing one fish to detect and respond to its neighbors) 

is not sought.

In the Earth sciences, geomorphology and landscapes 

of different types have been evaluated from the perspective of complexity (Murray and Fonstad 2007; Werner 

1999). Rivers and streams form a variety of spectacular self-organized patterns (see Fig. A.4. Complex Patterns Created by Fluvial Systems, this page), including 
26 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix A: Complex Systems Science and Subsurface Complexity

meandering and braided channels, deltas, branched 

networks, and anastomotic channels (branched and 

interconnecting rivers and networks). However, 

morphology varies widely within these general classes, 

and “transitional” patterns also occur. This variability 

arises from several sources: the nature of sediment load, 

slope of the land surface, erodibility, the rate at which 

sediment is transported into a river, tectonic effects, 

climate, large-scale geological heterogeneity, vegetation, 

and other influences. Transitions, for example between 

meandering and braided and between straight (low sinuosity) and meandering (high sinuosity) patterns, also 

may result (Schumm 1985) from changing conditions. 

In the United States, rapid transitions between various 

river patterns have occurred due to water diversion and 

farming that influence sediment load and type (Hickin 

1985; Schumm 1985).

Simple models developed to simulate these and other 

complex fluvial patterns and features provide important insight into how rivers transport sediment and 

solutes (e.g., Abrams et al. 2009; Murray and Paola 

1994; Rinaldo et al. 1993; Sun et al. 1996; Sun et al. 

2002). In the case of branched rivers, braided rivers, 

and the basin boundaries of branched rivers, several 

general power-law (fractal) relationships have been 

observed and are consistent with complex behavior. 

Simple models mimicking these complex systems 

have been created (e.g., Dodds and Rothman 1999; 

Rinaldo et al. 1993; Tarboton 1999).

C. Complexity and Subsurface 

Science

In the subsurface, heterogeneity will complicate 

research efforts. Recognizing and measuring selforganization in subsurface geological media will be 

much more difficult than in bacterial colonies grown 

on a homogeneous substrate (e.g., see Fig. A.3, p. 24). 

Moreover, unlike the land systems shown in Fig. A.4, 

25, that are readily observed at almost all scales and 

easily by air, the subsurface poses tremendous observational and characterization challenges for both spatial 

and temporal features and behaviors. The subsurface 

is fundamentally inaccessible, and in situ observations 

often are made by boreholes and wells. With limited 

vertical and spatial resolution, these techniques do not 

easily capture lateral continuity and three-dimensional 

distributions and may not allow resolution of emergent 

behavior even if it exists. For many subsurface systems, 

devising monitoring systems or characterization strategies that successfully define the statistical character 

of key or determining properties or behaviors may be 

difficult. This situation differs somewhat for watershed 

studies in which stream-flow monitoring provides 

access to integrated system response. Owing in part to 

these characterization and monitoring challenges, subsurface science still is at the stage of process discovery 

for its key workings.

However, the same mechanisms controlling formation of the microbiological patterns shown in Fig. A.3 

(e.g., cell motion, growth, and division in response 

to the concentrations and concentration gradients of 

nutrients, metabolic products, and chemical signaling 

agents) still can be expected to operate in the subsurface. Yet they operate in this environment for different reasons and in response to different local drivers. 

Vastly more complicated, the subsurface contains a 

great diversity of organisms with different functions 

and activities residing in varied and distributed physical settings including biofilms and colonies. These 

organisms interact synergistically and antagonistically, 

yielding a community with collective behaviors that 

reflect past and current environmental conditions 

and that dynamically respond to perturbations. This 

system has all the hallmarks of complexity.

The subsurface is characterized by coupled microbial, 

geochemical, and hydrological processes operating 

in a heterogeneous physical framework. Even one of 

these processes alone can exhibit complex behavior. 

For example, fluid-fluid displacement processes are 

well known for their ability to generate fractal patterns in homogeneous porous media under some 

circumstances (see Fig. A.5. Fractal Patterns in Fluid 

Displacement, p. 27). These complex fluid-fluid 

displacement patterns and their three-dimensional 

counterparts can be simulated using simple models, 

such as the diffusion-limited aggregation model (Witten and Sander 1981) and the invasion percolation 

model (Wilkinson and Willemsen 1983).

While these models pre-date modern complexity 

approaches, their strategies in describing the higher-
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 27

Appendix A: Complex Systems Science and Subsurface Complexity

level aspects of behavior are similar to those now 

used in complex systems science (Shalizi 2006). The 

coupling of multiple processes can lead to complex 

feedback mechanisms and phenomena, different 

types of memory effects (e.g., biomineral phases and 

organism microniches), and nonlinear behavior at 

multiple scales. Consequently, collective behavior 

and self-organization are expected to be common in 

the subsurface, with emergent features evolving at 

different scales.

At a much larger scale, the coupling between 

groundwater and surface water systems is a challenging domain in hydrology (Cardenas 2007; 

Freeze and Harlan 1969; Kollet and Maxwell 

2008a; Sudicky et al. 2008) that plays a key role in 

the dissemination of contaminants to ecosystems 

(Ford 2005). Using time-series data analysis techniques, Kirchner et al. (2000) observed power-law 

distributions of travel times for nonreactive tracer 

(Cl–

) elution from multiple watersheds (see Fig. A.6. 

Analysis of Long-Term Stream Chemistry Data, this 

page). These distributions indicated “memory” of 

(a)

(c) (d)

(b)

Fig. A.5. Fractal Patterns in Fluid Displacement. 

These develop during slow (capillary- and gravity 

force–dominated) displacement of a wetting fluid by 

a nonwetting fluid in an inclined quasi-two-dimensional porous medium. Top row: Experiments in 

which air displaces a water-glycerol mixture. Bottom 

row: Computer simulations using a gradient invasion percolation model. On the left-hand side, the 

Bond number (Bo is the ratio between gravitational 

and capillary forces, which can be varied by changing the inclination) is 0.005, and on the right-hand 

side, Bo = 0.018. In the Bo→ 0 limit, the pattern is 

a self-similar fractal. In the non-zero Bond number 

case, illustrated here, the structure is fractal on short 

length scales and uniform on long length scales, 

with a Bond number–dependent crossover length. 

[From Birovljev et al. 1991. Copyright (1991) by 

The American Physical Society and used with permission from APS and authors]

Fig. A.6. Analysis of Long-Term Stream Chemistry 

Data. The observed catchment response (modeled by a 

gamma function) is distinctly different from what would 

be expected with the use of an exponential distribution 

(commonly used to model travel times in catchments 

considered to be well-mixed reservoirs) or the advectiondispersion equation (which assumes that solutes reach 

the stream by transport and dispersion along a single 

flow path) for prediction. [From Kirchner, Feng, and 

Neal 2000. Reprinted by permission from Macmillan 

Publishers Ltd: Nature.] This and other studies illustrate 

the potential for complex systems science approaches 

to identify hydrological system behaviors that may be 

similar across different catchments.
28 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

with Variably Saturated Subsurface Flow, this page). 

Model results also indicated long-range correlations. 

These studies point to commonality in watershed 

elution behavior over a range of watershed types and 

phenomenological behavior differing from current 

concepts and models.

These and other related observations have led to a new 

view of watershed hydrology based on concepts of 

complexity science (Sivapalan 2005). Such studies of 

natural environments from the perspective of complexity science are in their infancy. An increase in their 

number is expected to reveal a high frequency of complex systems at different scales in human-significant 

settings and insights on general principles and laws 

governing system behavior.

Appendix A: Complex Systems Science and Subsurface Complexity

past events, a characteristic of complex systems. More 

importantly, generalized catchment-scale phenomenological relationships were developed for these 

emergent phenomena and later were used to provide 

constraints for robust hypotheses testing of systemsscale interactions controlling stream tracer concentrations at multiple locations (Kirchner et al. 2001). 

Similarly, the flow paths and travel time distribution 

obtained from a three-dimensional simulation of 

Oklahoma’s Little Washita watershed (with an area 

of ~ 1000 km2

) were well described with a particletracking model constrained by a power-law (fractal) 

distribution (Kollet and Maxwell 2008b; see Fig. A.7. 

Flow Paths and Travel Times Obtained from a 

Three-Dimensional Model that Couples Surface Flow 

Fig. A.7. Flow Paths and Travel Times Obtained 

from a Three-Dimensional Model that Couples Surface Flow with Variably Saturated Subsurface Flow. 

The travel times were obtained using a backward-intime Lagrangian particle-tracking algorithm after the 

three-dimensional velocity field was calculated. The 

color scale indicates the travel times in years, and the 

solid black line denotes the watershed boundary. The 

flow simulation was based on the Little Washita watershed in central Oklahoma. [From Kollet and Maxwell 

2008b. Used with permission from the American 

Geophysical Union]
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 29

Appendix B

Complex Subsurface Systems and Merit of Approach

A. The Subsurface Environment 

as a Complex System

Subsurface systems usually are not characterized or 

evaluated from the perspective of complex systems 

science. The subsurface includes various types of 

soil-sediment-water systems, such as the vadose zone, 

aquifers, coupled groundwater-river systems, and the 

hyporheic and riparian zones. To a certain extent, 

their scale is arbitrary—generally exceeding a meter 

but ranging up to hundreds of kilometers in the case 

of a regional groundwater aquifer. All exhibit water 

flux; are populated by microorganisms; and display 

chemical reactions between water, mineral phases, 

microbes, and contaminants. Most hydrobiogeochemical systems display significant physical and 

geochemical heterogeneity resulting from geological 

depositional events, inherited geological structures, or 

in situ biogeochemical processes. Heterogeneity may 

be random, ordered, or fractal. In turn, heterogeneous 

physical and chemical features as well as water and 

nutrient fluxes propagate microbiological heterogeneity through the physicochemical environment.

A hierarchy of different interactive subcomponents 

and subsystems is present over a wide range of scales in 

subsurface systems. For microbiology, different features, 

behaviors, and processes occur when moving downscale from the ecosystem, to community, to population, 

to individual organism, and finally to the suborganism 

scale. A similar geochemical hierarchy exists in which 

reaction type, kinetic nature and rate, and reactive 

components change across scales. This hierarchy is 

evident when moving downscale from an assemblage of 

hydrogeological facies (e.g., a sediment layer with common macroscopic properties), to the pore scale within a 

particular facies, to a specific mineral surface, and finally 

to nanometer internal pores or specific crystal faces 

on a reactive mineral phase. Likewise, for hydrology, 

dominant transport pathways change from meter-scale 

or larger preferential flow channels (at the longest 

scale), to meter-scale interfacies advective and diffusive 

transport, to pore-scale preferential advection pathways 

between rock fragments and mineral grains, to intragrain diffusion in particle coatings and internal pore and 

fracture space. Different interactions affecting overall 

system behavior occur at each scale. Some components 

are more significant at one scale than another, yet others 

important at smaller scales may propagate to or even be 

magnified in the field. Each subsystem itself also may 

exhibit complex behavior, producing emergent behaviors at different scales. Subcomponent interactions 

may lead to fully predictable behavior at higher scales 

(emergent simplicity) or even more complex behavior 

(emergent complexity; see Bar-Yam 1997).

Workshop participants evaluated several subsurface 

systems and their important subcomponents (both 

microbiological and geochemical) from the perspective 

of systems complexity using criteria noted by Bloomfield et al. (2008) and others (see Table B.1. Criteria 

Used in Judging Whether a Subsurface System or Its 

Subcomponents Exhibit Complexity, p. 30). This evaluation was not exhaustive. Some systems met the criteria 

of complexity; others did not. With additional emphasis and experience, other criteria diagnostic of complex 

subsurface systems might be found in the future.

Subcomponents of subsurface systems can exhibit 

complex behavior. In biological systems, interactions 

between parts as well as influences from the environment are well recognized as often giving rise to new 

or emergent behavior (Alm and Arkin 2003; Morange 

2001; Van Regenmortel 2004). Biological systems 

also can exhibit the complex systems attribute of 

robustness (Kitano 2002), a trait which may enable 

adaptation to changes in environmental conditions 

to occur through the presence of redundant components that compensate if primary components fail. In 

particular, microorganisms possess many characteristics of complex systems. These include (1) system 

properties (i.e., phenotype) not fully explained by an 

understanding of component parts (i.e., genotype), 

(2) coupled processes resulting from myriad interacting components (e.g., genetic regulatory elements, 

protein-protein interactions, allelopathic and quorumsensing compounds, and syntrophic and parasitic 

relationships), and (3) dynamic interacting components and processes capable of adapting as they are 

subject to evolutionary forces (e.g., via lateral gene 

transfer, spontaneous mutation, and natural selection). 
30 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Table B.1. Criteria Used in Judging Whether a Subsurface System 

or Its Subcomponents Exhibit Complexity*

Feature Description

System is open

Complex systems exist in a thermodynamic gradient, dissipate energy, and typically are 

far from an energetic equilibrium. Despite this, they may show dynamically stable local 

patterns or phenomena.

System boundaries

Defining or locating the boundaries of a complex system is difficult and may require 

relatively arbitrary decisions by the observer. Complex systems often are nested, a trait 

which also may lead to difficulties in defining boundaries. Components of complex 

systems may themselves exhibit complex characteristics.

Interactions between objects

Interactions between many linked objects or agents lead to a network that can share 

information. The rules of interaction are important and can lead to phenomena such as 

system memory and emergent behavior.

Feedback and adaptation

Dynamics of interactions at small scales shape macroscopic system dynamics that in turn 

feed back to influence the smaller scale. Potential exists for multiple outcomes or regime 

shifts due to exogenous or endogenous influences. 

Memory and learning

Regularly occurring external relationships reinforce growth of the same set of 

components and subsystems in a complex system. This reinforcement can cause the 

system to appear to have a memory through the persistence of internal structures.

Nonlinear behavior and relationships

Defining a linear sum of independent components to solve for a nonlinear variable 

is impossible for a complex system. Complex nonlinear systems are inherently 

unpredictable, in that small perturbations in the system may cause large effects, a 

proportional effect, or no effect at all (Phillips 2006).

Emergence Emergent phenomena arise out of nonlinear behavior and simple interactions between 

numerous agents or objects.

Self-organization

Some complex systems evolve toward a dynamically stable condition known as selforganized criticality (Bak 1996; Frigg 2003) that permits the system to more effectively 

interact with the environment.

Broad distribution of events or scales Diagnostic system variables, features, or behaviors can show spatial and temporal scale 

invariance represented by a power law.

*Modified from Bloomfield et al. 2008. Copyright British Hydrological Society.

Appendix B: Complex Subsurface Systems and Merit of Approach

Also demonstrating complex behavior are various 

biogeochemical subcomponents, such as the mineralmicrobe interface (emergence), coupled abiotic and 

biotic kinetic reaction networks with multiple components (nonlinearity and feedbacks), and certain types 

of biomineralization processes (memory and selforganization). Subcomponents often are studied in 

laboratory model systems under controlled, frequently 

homogeneous conditions in which robust spatial and 

temporal measurements and system characterization 

can be performed. Consequently, emergent behavior 

and other complex system characteristics are easier to 

recognize when they occur.

Workshop participants concluded that many field-scale 

subsurface systems display characteristics of a complex 

system. These characteristics include nonlinearities 

and feedbacks from process coupling and geochemical 

gradients; numerous process and property interactions 

at different scales; and the presence of memory effects 

resulting from past contamination and waste disposal 

(e.g., high-low pH excursions), hydrological events 

(rising and falling water tables), or seasonal biogeochemical regimes (e.g., redox stratification). For some 

subsurface systems, emergent or collective behavior 

may be identified through data analysis for watershed 

studies (as described in Appendix A. Complex Systems 

Science and Subsurface Complexity, p. 21). However, 

demonstrating such criteria for other cases could prove 

more difficult because of two fundamental problems. 

First, few subsurface systems have been studied with 

sufficient detail to make these evaluations. Second, the 
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 31

Appendix B: Complex Subsurface Systems and Merit of Approach

criteria themselves are ambiguous and thus challenge 

the development of a transparent definition for subsurface systems. Moreover, documenting emergence—

behavior not readily predictable from its parts—in 

some cases requires that the parts (e.g., processes, 

subcomponents, or subsystems) be understood and 

described and that their controlling properties be sufficiently characterized. Accomplishing this is especially 

problematic for the subsurface, where multiscale processes and their interactions only now are being studied 

in detail and where collective behavior (if it exists) may 

be obscured because of difficulties in observing subsurface phenomena. Consequently, upscaling lower-level 

process information or models to document or evaluate 

emergence is a research challenge in its own right with 

significant data and knowledge requirements.

Given the characterization challenges posed by many 

subsurface systems, considering certain field sites as 

de facto complex systems may be useful. For these sites, 

significant heterogeneity and difficulties in sampling 

and characterization may prevent clear identification of 

emergence, self-organization, or other key complex system attributes. Such system types also greatly challenge 

a bottom-up modeling strategy because properties 

controlling the rate and extent of small-scale processes 

are virtually indeterminate over the spatial scale of 

interest and show large variability or high uncertainty. 

Still others in this category may have many interactive 

processes defying accurate quantification and description. The upscaling of small-scale process information 

or models for de facto complex systems is unlikely to 

satisfactorily describe the system as a whole, regardless 

of whether it is complicated or complex. An appropriate descriptor for such systems is reductionistic intractability. For these, a top-down analysis and modeling 

strategy may be the only option for adequate system 

description and prediction.

B. Examples of Complex Subsurface 

Systems and Components

Recognizing the hierarchies of scales and associated 

processes discussed previously, the following cases 

provide examples of subsurface complexity at three 

distinct scales discussed at the workshop. Complexity may exist from the kilometer to the molecular 

scale in subsurface systems, and many field-scale 

systems contain embedded levels of complexity in 

which lower-level systems exert strong influence on 

higher-level behaviors. These examples are presented 

because of the unresolved discussions that ensued at 

the workshop on the distinctions between complex 

and complicated subsurface systems. Evaluating these 

particular cases from the perspective of complex systems is enabled by the relatively large knowledge and 

research base existing for each.

1. A Complex Field System: The 300 A 

Uranium Plume

Contaminated field-scale systems are complicated 

because of source-term history and conditions, 

variable geological structures and facies, multiple 

processes and interactions, heterogeneity at different scales, and impacts of weather and seasonality. 

These characteristics challenge the understanding and 

prediction of contaminant behavior for all but the 

simplest field systems. But the question is, do these 

systems conform to the criteria of complexity, and is 

our understanding of them likely to be improved by 

a complexity approach? Can a general understanding 

of complex systems behavior and the identification 

of common governing laws be derived from studying 

field sites with often unique, site-specific attributes?

The 300 A uranium (U) plume at the Hanford site is 

a subsurface system representative of the Columbia 

River corridor where groundwaters strongly interact 

with surface waters. The 2-km2

 groundwater plume 

containing dilute dissolved U (< 100 μg/L) as the 

primary contaminant has been monitored for over 

40 years (Peterson et al. 2005; Peterson et al. 2008). 

The plume resides within a coarse-textured, 8-m aquifer zone containing preferential flow paths and metersized, silt-textured clasts. It discharges discontinuously 

to the Columbia River along its eastern boundary 

(see Fig. B.1. The 300 A Uranium (U) Plume at the 

Hanford Site, p. 32) through the hyporheic zone and a 

series of seeps. Plume hydrology including water level, 

flow direction, and velocity is influenced strongly by 

Columbia River stage, which varies both seasonally 

and daily (see Fig. B.2. Columbia River Discharge by 

Month and Year, p. 32). Uranium concentrations in the 

plume (see Fig. B.1) and fluxes to the Columbia River 

also vary seasonally in a complicated manner that has 
32 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix B: Complex Subsurface Systems and Merit of Approach

at low concentration in the aquifer (Arai et al. 2007; 

Catalano et al. 2006; Singer et al. 2009; Stubbs et al. 

2009). Rising and falling water table events driven 

by river stage fluctuations desorb U from the lower 

vadose zone and resupply the plume. The desorption behavior of sorbed contaminant U shows strong 

kinetic behavior at all scales studied (see Fig. B.3. 

Desorption Profile of Contaminant U from a Core 

of Field-Textured, Deep Sediment in the Vadose 

Zone p. 33), with release rates approximated by 

power-law functions with scale-dependent statistical 

parameters (Bond et al. 2008; Liu et al. 2009; Liu et 

al. 2008; Qafoku et al. 2005). These behaviors result 

from the coupling of geochemical and transport 

not been well described with any modeling approach. 

The plume has not dissipated due to groundwater 

flushing as projected by earlier modeling. The contours 

shown in Fig. B.1 are average saturated-zone concentrations that mask the true and significant heterogeneity 

in vertical and spatial U concentrations resulting from 

incomplete mixing and complex hydrogeology.

The geochemistry of the site has been studied 

extensively with samples from select locations, 

emphasizing the microscale to macroscale transition. Precipitated phases exist in the upper vadose 

zone, and adsorbed chemical species are present at 

moderate concentration in the deep vadose zone and 

Fig. B.1. The 300 A Uranium (U) Plume at the Hanford Site. Depicted are U concentration contours (in 

parts per billion, μg/L) measured in December 2007 

(gold) and June 2007 (green). These seasonal variations are different each year and are not predictable 

with existing models. The regulated concentration is 

30 parts per billion. The DOE BER Hanford Integrated 

Field Research Challenge site is shown as the magenta 

triangle. Other locations represent excavations and 

other boreholes studied previously by DOE’s Subsurface Biogeochemical Research activity. [From Um et 

al. 2010, in press. Copyright Elsevier]

Fig. B.2. Columbia River Discharge by Month and 

Year. Large seasonal variations exist and are dominated 

by spring snowmelt. Short-term fluctuations represent 

dam manipulations for fish passage and power generation. The near-shore groundwater system and the 

Columbia River are in close hydrological communication. The groundwater water table rises in early spring 

and falls 

during the 

summer, 

strongly 

influencing 

dissolved 

uranium 

concentrations in the 

plume.

Discharge (cubic feet per second)
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 33

Appendix B: Complex Subsurface Systems and Merit of Approach

(diffusion and advection) processes at a hierarchy 

of scales (intragrain, pore, and facies). Seasonally 

varying time scales of water residence in the aquifer 

effect power function shapes. Reaction reversals 

from desorption to adsorption, and back again, 

occur as groundwater flow vectors and water compositions change in response to river stage variations 

(Yabusaki et al. 2008).

Larger-scale geological features influence the overall behavior of the plume at different scales. Buried 

channels of coarse sediments cut across the plume in 

several locations, providing conduits for rapid water 

exchange between the groundwater and river. These 

channels strongly influence reaction time scales in 

nearby sediments and cause mass transfer and other 

physical effects at multiple scales.

The 300 A uranium plume conforms to many criteria 

of complex systems. The current distribution of U is 

influenced strongly by the “memory” of past disposal 

operations, historical events of high water table that 

redistributed chemical species, and yearly water table 

rise and fall cycles. The geochemical behavior of U 

is kinetically controlled by different processes and 

phenomena at each hierarchical scale. The presence 

of domains with highly variable reaction time scales 

and directions controlled by river stage fluctuations promotes nonlinear chemical interactions and 

responses. The seasonal and spatial distribution of U 

concentrations within the groundwater plume and the 

consequent flux of U to the Columbia River appear 

as emergent variables. Though currently unpredictable, these variables are determined by geological 

structures, multiple process interactions, memory 

effects, distributions of unknown properties, and 

seasonal details that vary by year. Extended periods 

of high river stage increase U inventory in the plume 

through vadose zone solubilization, which then is 

subject to forward geochemical reaction (adsorption). In contrast, extended periods of low river 

stage decrease U plume inventories by discharge to 

the river, which in turn promotes backward reaction 

(desorption). The number of interactive and coupled 

multiscale processes—combined with the large spatial 

range, presence of geological structures, significant 

heterogeneity, and complex patterns of controlling 

properties—makes using a reductionistic approach to 

study plume-scale behavior expensive and impractical 

and thus intractable.

The site has a long history of inadequate predictions. 

To understand multiscale coupled processes operating 

in the plume and to develop simplified but robust fieldscale process models, research currently is under way at 

a 1300-m2

 site within the plume footprint [see Hanford 

Integrated Field Research Challenge (IFRC), magenta 

triangle, in Fig. B.1, p. 32]. The overall approach entails 

calibrating simple, linked surface complexation and 

mass transfer models in the laboratory and upscaling 

parameters to the 1300-m2

 study domain. Predictions 

from the resulting upscaled models that incorporate 

three-dimensional geostatistical distributions of reaction and transport properties will be compared to 

outcomes of field experiments assessing the rates and 

extent of in situ adsorption and desorption.

Fig. B.3. Desorption Profile of Contaminant U 

from a Core of Field-Textured, Deep Sediment in 

the Vadose Zone. Concentration spikes result from 

stop-flow events that were performed to evaluate in 

situ diffusion rates. Significant kinetic behavior was 

observed and is approximated by a power-law function. The scaling of reaction parameters from smallerscale studies to this one is difficult. The desorption 

behavior is best described by a distributed rate (DR)–

dual porosity (DP)–surface complexation (SC) model 

with microscopic transport parameters derived from 

pentafluorobenzoic acid (PFBA) tracer. [From Liu et 

al. 2008. Reprinted with permission from the American Geophysical Union]
34 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix B: Complex Subsurface Systems and Merit of Approach

However, the only viable approach for forecasting 

plume-scale (e.g., 106

 m2) behavior without and with 

engineered remediation may be to conceptualize the 

entire plume and its associated groundwater-river 

hydrological domain as a complex system. This is 

an important Hanford site need with great financial 

implications. A higher-level approach to identify 

key factors controlling overall plume behavior and 

emergent features would involve comprehensive 

monitoring activities designed with the consideration 

of complex system characteristics. Taking such an 

approach might assure that the collective effects of 

smaller-scale processes are accurately and efficiently 

captured and properly represented in a systems-scale 

predictive model. Moreover, a top-down predictive 

model based on scales not much smaller than that 

of interest might avoid the pitfalls of a bottom-up 

approach in which unavoidable inaccuracies in smallscale process models can add up to predicted largescale behaviors that are unrealistic (Murray 2007).

2. A Complex Intermediate-Scale Subsurface 

System with Coupled Biogeochemical Processes

In the previous field-scale example, complex behavior 

is caused by a hierarchy of interactive geochemical and 

transport processes operating largely above the meter 

scale in a hydrologically dynamic and heterogeneous 

environment. Emergence in the form of unpredictable 

groundwater U concentrations and fluxes was observed 

at this level of observation. Complex behavior also can 

occur at the intermediate scale (millimeters to meters), 

where interdependent and dynamic physical, geochemical, and biological processes interact to control 

system chemistry and contaminant mobility.

The ability of subsurface microbial communities to 

respond to changing environmental variables is an 

inherent but unpredictable property of natural and 

engineered systems. For example, natural attenuation 

and environmental remediation strategies depend 

on specialized microorganisms and the supporting 

microbial community to catalyze contaminant transformation reactions of interest. However, numerous 

environmental parameters can impact the activity, 

stability, resilience, and propensity for adaptation of 

the key players and microbial community as a whole. 

Current tools and knowledge fall short of being able 

to predict microbial responses to natural and engineered perturbations. Heterogeneous fluctuations in 

environmental variables—such as pH, temperature, 

electron donor and acceptor availability, and presence of inhibitors or predators—induce changes 

in microbial behavior and microbial community 

composition that prove difficult to mechanistically 

describe and predict in the subsurface. Fluctuating 

redox conditions (e.g., aerobic versus anaerobic) can 

alter substantially the structural (mineral dissolution 

and precipitation), geochemical (redox poising), and 

microbiological (community composition and activity) character of the local environment. These changes 

directly can affect flow conditions and contaminant 

mobility in desirable or deleterious ways. Microbial 

community responses, including their consequences 

on the local physical and chemical environment, meet 

all the requirements of a complex system and exhibit 

emergent character.

As an example of an intermediate-scale system exhibiting complex behavior, consider the response of a subsurface system to the introduction of an electron donor 

in the form of a degradable substrate, either via natural 

nutrient input or an environmental remediation strategy. Field studies of substrate addition have been performed at DOE BER’s Oak Ridge and Rifle IFRC sites 

to enhance U remediation. These field experiments 

have documented the capability to lower dissolved U 

concentrations to acceptable values through biostimulation. However, the two studies were largely empirical, 

and important questions cannot be answered a priori, 

including where, when, and for how long microbial 

activity will be stimulated in the subsurface.

The pattern of stimulated growth and activity induced 

through environmental remediation will be heterogeneous because spatial variations in hydraulic and 

geochemical properties (e.g., solid-phase electron 

acceptors) impact the distribution of injected amendments. Figure B.4. Plan View, Model-Computed Map 

of Acetate and Bromide Transport, p. 35, is a model 

simulation of acetate (an electron donor) and bromide 

(a conservative tracer) transport from an injection well 

gallery (on the left side of each panel) through a 20-m 

monitoring array at the Rifle IFRC. The simulation 

demonstrates the effects of subsurface heterogeneity 

(e.g., hydraulic conductivity and bioavailable Fe(III) 
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 35

Appendix B: Complex Subsurface Systems and Merit of Approach

oxide distribution) on bromide and acetate dispersal 

and transport. Microbial activity, stimulated in areas 

where acetate is available, modulates local chemical and 

physical environments that affect pore-scale permeability and contaminant transport. Substrate utilization and 

depletion by microorganisms give rise to biogeochemical reaction products that evolve heterogeneously over 

space and time, as shown by the simulation in Fig. B.5. 

Plan View Map of Simulated, Heterogeneous Patterns of 

Mineral Distribution and Biomass, p. 36. The evolution 

of precipitates and biofilms can, in turn, impact aquifer 

permeability (e.g., Fig. 3.4, p. 15), which influences the 

effectiveness and sustainability of the treatment.

Understanding these feedbacks between biogeochemical transformations and flow in the presence 

of heterogeneity requires hierarchical studies of 

controlling processes at multiple observation scales. 

Detailed metabolic studies of environmentally relevant 

microbes and simplified communities, combined with 

geochemical studies, are beginning to unravel the complex mechanistic couplings involved in biogeochemical 

processes impacting contaminant transport and fate. 

Integral to these studies is the iteration between observation, experiment, and modeling. Genome-enabled 

simulations at the cellular and population levels are 

beginning to provide mechanistic information on cell 

metabolism, growth, and activity in a form that can be 

coupled to reactive transport codes. These codes then 

can be used to predict the impact of microbial activity on local geochemical and hydrological parameters 

(Scheibe et al. 2009). Such models ultimately will be 

5 10 15 20

5

10

15

20

Y (meters)

X (meters)

>3.79E -03

3.38E -03

2.97E -03

2.56E -03

2.15E -03

1.74E -03

1.33E -03

9.21E -04

5.11E -04

1.00E -04

Acetate (M)

>5.69E -04

5.08E -04

4.46E -04

3.84E -04

3.23E -04

2.61E -04

2.00E -04

1.38E -04

7.66E -05

1.50E -05

Br (M) -

5

10

15

20

Y (meters)

5 10 15 20

X (meters)

A. Bromide at day 25 in 2002 

C. Acetate at day 25 in 2002 

B. Bromide at the end of 2002 

D. Acetate at the end of 2002 

Fig. B.4. Plan View, Model-Computed Map of Acetate and Bromide Transport. The solutes were injected 

into a heterogeneous 20-m domain of the uranium-contaminated Rifle aquifer. Computed patterns of solute 

migration are shown at early (left) and later (right) times. Flow is from left to right. Concentration decreases 

from red to green to blue. [Adapted and reprinted from Li et al. 2010 with permission from Elsevier]
36 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix B: Complex Subsurface Systems and Merit of Approach

able to incorporate new information on regulatory processes governing cell metabolism that will provide crucial insight into how environmental variables impact 

metabolic processes that direct cell growth and activity. 

Metaomics studies integrating genomic, transcriptomic, and proteomic information expand findings 

made at the single-cell and population levels to those 

of consortia (defined mixed communities) and natural 

subsurface communities. Information generated by 

these tools will provide the foundation for understanding the key couplings between microbial, geochemical, 

and hydrological processes at the pore scale that drive 

complex and emergent behavior at larger scales.

The patterns of microbial activity and the effects on 

local geochemical and hydraulic conditions illustrated 

in Fig. B.5, this page, provide a visual example of the 

complex nature of coupled biogeochemical processes 

in the subsurface. While the simulation models used 

embody the current state of the science in understanding the coupling of biogeochemical processes and how 

these processes may change and evolve in the subsurface over time, significant improvements are both 

necessary and possible. Measurements at the pore and 

intermediate scales are critical data inputs to generate 

systems-level understanding. However, the ultimate 

goal of predicting landscape-scale system behavior 

requires methods that measure the integrated effects 

of hierarchically structured subsystems at the larger 

scale. Steps in this direction have been taken. Several 

geophysical and sensing techniques are being developed to track—over field-relevant scales—the systems-level changes in subsurface biogeochemistry (see 

Fig. B.6. Remediation-Induced, Time-Lapse Changes 

in Electrical Induced Polarization Phase Responses, 

p. 37, and Fig. 3.4, p. 15). These techniques also could 

track other system properties resulting from the 

remediation-induced activity of microbial communities. (See, for example, Druhan et al. 2008; Hubbard et 

al. 2008; Williams et al. 2009.)

These and other imaging techniques offer opportunities to link pore-scale properties and processes with 

intermediate- and larger-scale diagnostic signatures of 

systems-level behavior. Obtaining and integrating information from multiple scales are expected to advance 

our ability to predict the functioning of subsurface 

systems over meaningful temporal and spatial domains.

Fig. B.5. Plan View Map of Simulated, Heterogeneous Patterns of Mineral Distribution and Biomass. 

The simulated patterns of mineral distribution were a consequence of acetate injection into the subsurface 

to immobilize dissolved uranium as uraninite (UO2) through microbiological action. Simulation results are 

given for only one time point after injection. The complex patterns result from heterogeneities in hydraulic 

conductivity and Fe(III) oxide distribution and their attendant coupled effects on system biogeochemistry. 

[Adapted and reprinted from Li et al. 2010 with permission from Elsevier]
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 37

Appendix B: Complex Subsurface Systems and Merit of Approach

3. A Microscale Biogeochemical Subcomponent 

as a Complex System: The Mineral-Microbe 

Interface

Moving down to the molecular and microscopic scale, 

this final example discusses an important microscopic reactive domain in many subsurface systems: 

the mineral-microbe interface. The Rifle IFRC is 

investigating field-scale bacterial metal reduction as 

a means of immobilizing contaminant uranium (see 

sidebar 3A, BER’s Rifle IFRC Site, p. 11, and previous 

intermediate-scale example, Section 2, p. 34). The 

mineral-microbe interface is a crucial subcomponent 

of this overall system where microorganisms attach to 

existing mineral surfaces or facilitate the formation of 

new ones. The interface is where subsequent interfacial 

Fig. B.6. Remediation-Induced, Time-Lapse Changes in Electrical Induced Polarization Phase 

Responses (at 0.25 Hz). Surface Spectral Induced Polarization datasets were collected over time along 

surface transects oriented orthogonal (A) and parallel (B) to groundwater flow direction during a biostimulation experiment conducted at the Rifle IFRC site. In agreement with behavior elucidated through 

laboratory biostimulation experiments (Williams et al. 2005), these figures show the development of a 

phase response that corresponded with changes in groundwater geochemistry accompanying stimulated 

iron and sulfate reduction and sulfide mineral precipitation. The accumulation of mineral precipitates and 

electroactive ions are interpreted to have altered the ability of the pore fluids to conduct electric charges, 

thus leading to the development of the phase response shown here. This example indicates the potential 

of geophysical methods for tracking biogeochemical transformations over field-relevant scales. [Adapted 

with permission from Williams et al. 2009. Copyright 2009 American Chemical Society]. Note that related 

research (see Fig. 3.4., p. 15) has shown the utility of using this type of geophysical information for quantifying the volume of evolved precipitates and associated permeability reduction associated with the same 

remediation treatment.
38 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix B: Complex Subsurface Systems and Merit of Approach

electron transfer is mediated between microorganisms, 

solid-phase electron acceptors, and polyvalent solutes 

(e.g., uranium) when appropriate electron donors (e.g., 

acetate) are present. This microscopic biogeochemical 

process ultimately determines whether contaminant 

uranium is immobilized at the groundwater plume 

scale. Although many other factors and processes also 

are important, reactions at the mineral-microbe interface define the Rifle groundwater system, as they do at 

numerous locations nationwide. Moreover, mineralmicrobe interactions are fundamental to the fate and 

migration of many contaminant types, to carbon 

cycling and sequestration, and to bioenergy.

The interplay between microorganisms and their 

surrounding environment is a dynamic process 

that has impacted the planet for nearly its entire 

history. Because of their small size and high ratio 

of surface area to volume, microorganisms exert a 

tremendous influence on the geosphere. As agents 

of weathering for rocks and minerals, microbes 

also are catalysts for their formation and influence 

subsurface water quality through such interactions. 

The interfacial region between microorganisms 

and reactive solids is dynamic, with chemistry and 

structure determined by adaptive interplay, response, 

and feedback. Microenvironments are generated 

with unique chemical and structural attributes that 

enhance microbe survivability. The mineral-microbe 

interface can be considered a complex system for 

many reasons but primarily because the behavior of 

the assembled biochemical system is dramatically 

distinct from the sum of its components.

The mineral-cell interface is characterized by steep 

concentration gradients that may not reflect bulk 

properties in either the solid or solution phase. With 

even the most sophisticated modern instrumentation, 

investigating it is difficult. The properties of the mineral-microbe interface are dynamic, reflecting timevariable biosynthetic and biogeochemical reactions 

occurring at small spatial scales (i.e., < 1 nm). Our 

understanding of the complexity of microbial cells is 

improving. This progress includes insights into how 

microbes sense and respond to each other and to their 

environment and how they modify their immediate 

surroundings via secretion of extracellular polymers 

to form biofilms, proteins for catalyzing reactions with 

solids and substrates not accessible to intracellular 

compartments, and signaling molecules for communicating with other cells. In a colloquium sponsored by 

the American Academy of Microbiology (Nealson and 

Ghiorse 2001), participants concluded that significant 

and critical events in geobiology happen at the level of 

individual cells or groups of cells and that the details 

of such processes are best revealed by observations 

and measurements made at small scales.

Complex systems behavior also can be found in 

molecular-scale aspects of microbe-mineral interfaces. 

An important example being studied is operation of 

the transmembrane electron-transport chain in the 

cell envelope of metal-reducing bacteria (Fredrickson 

and Zachara 2008). This chain is composed of a series 

of structurally complex mono- and multiheme redox 

proteins (i.e., c-type cytochromes; Shi et al. 2007) that 

transport electrons more than 8 nm from the inner 

membrane, across the periplasmic space, through the 

outer membrane, and to bioavailable forms of Fe(III), 

Mn(III,IV), or contaminant metals such as U(VI) 

and Tc(VII) (see Fig. B.7. Roles of MtrC and OmcA 

in Shewanella oneidensis MR-1-Mediated Extracellular 

Reduction of Fe(III) Oxides, p. 39). The architecture 

of the chain entails several linkages between watersoluble and membrane-bound proteins in which 

structured multiheme cytochromes interface in a 

specific molecular fashion to create a functional wire 

of heme cofactors capable of electron transfer over 

nm distances (Hartshorne et al. 2007; Hartshorne et 

al. 2009; Shi et al. 2009). The rate of electron transfer 

from cofactor to cofactor within a protein is strongly 

coupled to local and tertiary protein structures and 

their fluctuation dynamics (Pascher et al. 1996; Smith 

et al. 2006); these component molecules also display 

complex behavior.

Occurring within the mineral-microbe interface 

are hierarchies of biochemical and biogeochemical 

interactions that determine overall system behavior. 

The efficiencies of the interfacial protein-protein 

electron transfer and the terminal protein-metal electron transfer to the mineral interface depend on the 

lifetimes of protein-protein and protein-metal interactions (Kerisit et al. 2007; Rosso and Dupuis 2006). 

Dynamics in the cofactor structure, solvation, and 

distance of separation at the molecular scale involve 
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 39

Appendix B: Complex Subsurface Systems and Merit of Approach

picosecond to nanosecond fluctuations (Kerisit et al. 

2007; Wang et al. 2008). At the biomolecule scale, 

tertiary fluctuations operate at micro- to millisecond 

time scales. Many key protein structures, proteinprotein super-complex structures, and interaction 

lifetimes are unknown, complicating the understanding of key component interactions. Furthermore, 

the structure and dynamics at the protein-metal or 

protein-mineral interface are elusive and difficult to 

probe directly. At a higher level, the transmembrane 

electron-transport system is dynamically coupled to 

other metabolic systems in a living cell that itself is 

responsive and adaptive to environmental conditions. 

The collective behavior of the electron-transport 

system cannot be quantitatively examined by separating individual biomolecular components, nor can the 

boundaries of the system (e.g., any ancillary components that constrain interaction distances to optimal 

values) be easily identified. Consequently, the system 

behavior is unpredictable, with the overall cellular 

electron-transfer rate an emergent property. This 

system therefore is complex, requiring a systems-level 

approach to understand and model key interactions 

determining electron-transfer rate.

This is just one of many reaction- and process-specific 

examples that could be chosen because the mineralmicrobe interface is a dynamic region fundamental 

to biogeochemistry. An important aspect of these 

small-scale complex systems is that they are one of 

Fig. B.7. Roles of MtrC and OmcA in Shewanella oneidensis MR-1-Mediated Extracellular Reduction of 

Fe(III) Oxides. Proteins known to be directly involved in the reduction include (1) the inner membrane (IM) 

tetrahaem c-Cyt CymA that is a homologue of NapC/NirT family of quinol dehydrogenases, (2) the periplasmic 

decahaem c-Cyt MtrA, (3) the outer membrane (OM) protein MtrB, and (4) the OM decahaem c-Cyts MtrC 

and OmcA. (A) Together, they form a pathway for transferring electrons from the quinone and quinol pool in 

the IM to the periplasm (PS) and then to the OM where MtrC and OmcA can transfer electrons directly to the 

surface of solid Fe(III) oxides. (B) MtrC and OmcA also might reduce Fe(III) oxides indirectly by transferring 

electrons to either flavin-chelated Fe(III) or (C) oxidized flavins. [From Shi et al. 2009]
40 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix B: Complex Subsurface Systems and Merit of Approach

several primary controls on field-scale behavior. The 

mineral-microbe interface also is a domain suitable 

to complex systems science approaches and models 

(Kreft et al. 2001; Pennisi 1999; Weng et al. 1999) in 

which extremely complex molecular interactions may 

be described using more simple rules. Consequently, 

integrating systems biology–based models of microbial metabolism and gene regulation into complexity 

models of the mineral-microbe interface is envisioned. 

Their subsequent incorporation into higher-level models (e.g., macroscopic or field scale) of biogeochemical 

systems would eliminate the need for complex upscaling relationships. Such an approach could yield new 

insights on common microscopic functional relationships occurring at all mineral-microbe interfaces.

C. Application of Complexity 

Approaches and Merit of Concept

Despite uncertainties in categorizing representative 

subsurface systems as complex in terms of accepted 

criteria, many workshop participants believe there is 

merit in conceptualizing the subsurface as such and 

applying tools of complex systems science as part 

of a comprehensive research strategy to understand 

and predict subsurface behavior. Understanding how 

processes (e.g., system subcomponents) acting on 

different scales and hierarchal levels are bound to 

one another is an important goal of interdisciplinary 

subsurface research, regardless of whether a reductionistic or systems approach is taken. However, 

there are major differences in carrying out these two 

approaches, with consequent implications for the 

types of data collected and the nature of conceptual 

and numerical models developed.

In the reductionistic approach, ad hoc decisions often 

are made about which components or processes of 

the real system to include or ignore based on preconceived beliefs, biases, and observations at limited 

or convenient scales. Sometimes these choices are 

representative of important processes controlling 

system behavior at the higher scale, and sometimes 

they are not. Often, reactive transport models with 

associated lower-scale parameters are developed and 

upscaled in an attempt to describe larger-scale system 

behavior. These “mechanistic” models integrate 

geochemical and microbiologically driven reactions 

along with advection, dispersion, and diffusion (e.g., 

Bethke 2008; Regnier et al. 2003; Steefel et al. 2005). 

Varying markedly in sophistication, reactive transport 

models calculate temporal and spatial concentrations 

of solid and aqueous chemical components resulting 

from equilibrium or kinetic reactions and diffusive 

or advective transport. They also calculate microbial 

growth and decay occurring within a representative 

elementary volume of the modeled system (see, for 

example, Fang et al. 2009 and Yabusaki et al. 2007). 

Considerable current research is focused on how 

reaction parameters and models upscale and on which 

higher-level system interactions, characteristics, or 

attributes (often related to undescribed effects of 

heterogeneity, complex hydrological flow fields, or 

microbial ecology) to include for adequate system 

understanding and description.

In a systems approach, the system is looked at holistically to identify (1) patterns characterizing it and its 

dynamic behavior (Kirchner et al. 2000) and (2) the 

key variables, processes, and their interactions that 

appear to define emergent or otherwise complex 

behavior (e.g., Kirchner et al. 2001; Kollet and Maxwell 

2008b). Emphasis may be placed on longer-term monitoring of the studied system to provide time-series data 

for observational constraints on models (Kirchner et 

al. 2000) or to identify hierarchal interactions controlling the system-scale features to be predicted and their 

description by phenomenological means (e.g., Werner 

1999; Werner 2003). The resulting model is tied to the 

internal, functional organization of the real system as it 

controls behavior at some targeted scale (Grimm et al. 

2005) and thus can be used for explanation and generally coarse-scale prediction (Kreft et al. 2001; Murray 

2007; Werner 1999). However, since the complexity approach has not yet been applied to subsurface 

systems, several things are unclear, including how 

models common to complexity [e.g., particle dynamics, cellular automata, agent based, or pattern oriented 

(Grimm et al. 2005; Shalizi 2006)] would be applied 

or formulated for the subsurface or their subcomponent processes. Also unclear are which system variables 

these models are likely to describe and to what level 

of uncertainty and spatial specificity. Thus, the true 

promise (or lack thereof) of the complex systems sci-
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 41

Appendix B: Complex Subsurface Systems and Merit of Approach

ence approach as currently practiced cannot be easily 

ascertained. Beyond that, applying this approach to 

subsurface science likely will require new perspectives 

and modeling strategies, given that systems have different characteristics, constraints, and prediction needs.

The philosophy and approaches for investigating 

and modeling subsurface systems vary depending 

on whether a reductionistic or complex systems 

approach is adopted (see Table B.2. Characteristics 

of Reductionistic and Complexity Approaches, this 

page). A reductionistic approach provides motivation to study previously chosen system subcomponents in great detail and to drive programmatic 

scientific accomplishments at more mechanistic and 

fundamental levels. Because of various uncertainties, this approach typically places less emphasis 

on upscaling and on documenting that the chosen 

subprocesses and characteristic properties describe 

real system behavior. In contrast, a complex systems 

approach identifies and characterizes (presumably 

without bias) the subcomponent interactions and 

drivers responsible for emergent behaviors and 

yields the integrative, higher-scale scientific findings better suited for predicting systems behavior. 

As research and understanding progress, both 

approaches support the development of sequential 

scientific hypotheses on the interactions of subcomponents or subprocesses controlling system-scale 

behavior. However, the nature of hypotheses, the 

scientific footprint, and the resulting models describing these interactions may be quite different.

In the past, most scientific investigations have relied 

almost entirely on either bottom-up or top-down 

approaches and methods. Employing a broader 

Table B.2. Characteristics of Reductionistic and Complexity Approaches

Reductionism Complexity

Philosophy

Most systems are too complex to study in their 

natural state. They must be deconstructed into 

more-fundamental units or smaller-scale parts 

for comprehension. Understanding the detailed 

specificity of a given problem is key. Fundamental 

process understanding is a desired objective because 

it is transferrable between different systems. 

Higher-scale behavior results from interactions 

of smaller-scale processes and is predictable from 

detailed models of them.

A complex natural system should be studied as a whole 

because its behavior does not result from the sum of 

its smaller-scale parts. Higher-scale behavior exhibits 

emergence resulting from unexpected interactions of 

processes or the effects of other properties at different 

scales. Complex systems exhibit common, underlying 

macroscale laws that are applicable elsewhere. 

Understanding pervasive commonalities between 

systems and their relationships to more simple systems 

is key.

Strategy

Quantify lower-scale fundamental mechanisms, 

processes, and their interactions in detail as a primary 

goal. Working upward, understand and model system 

behavior as some permutation of the sum of its lowerscale parts (e.g., processes) and their interactions. 

Blame heterogeneity for shortcomings.

Identify diagnostic variables and common patterns 

of emergence defining high-level system behavior. 

Working downward, identify, understand, and model 

critical subscale interactions and environmental 

tensors that control diagnostic variable dynamics and 

behavioral patterns. Identify transferrable macroscale 

laws as a primary goal.

Research 

Approach

Investigate underlying mechanisms at multiple 

scales below the prediction scale, often using model 

or abstract systems with hypothesis-based research. 

Develop robust generalized models at the subscale 

for simulating process interactions and detailed 

responses. Ascertain how heterogeneity in properties 

controls process manifestations at the prediction scale.

Collect significant databases on target system(s) over 

time, or with perturbation, to define characteristic 

behavior. Apply data analysis tools to identify diagnostic 

emergent system variables, controlling properties, 

and macroscopic laws to constrain models. Develop 

transparent models that explain system operation and 

simulate emergent variable responses.

Modeling

Mechanistic details of lower-scale processes are 

preserved but streamlined in upscaling. Relationships 

are developed that link fundamental process models 

and their parameters to the prediction scale. Lowerscale models are calibrated at the prediction scale 

because of characterization uncertainties and the 

effects of heterogeneities.

Mechanistic details of lower-scale processes are ignored. 

Phenomenological models are used to explain and 

describe key process contributions, interactions, and 

properties controlling characteristic system behavior 

at the prediction scale. Add more-complete theory as 

needed for improved explanation and prediction.
42 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix B: Complex Subsurface Systems and Merit of Approach

arsenal of techniques and approaches is now necessary 

because the focus of scientific research is shifting from 

narrowly defined topics requiring in-depth specialized 

knowledge and understanding to more-complex, multicomponent applications requiring a much broader 

multidisciplinary approach (without losing sight of 

the importance of in-depth understanding). Society 

faces challenges at the nexus between sufficient energy 

to support a thriving economy, adequate high-quality 

water to provide food for a growing population and for 

industry, and the need for a high-quality environment 

so that all can enjoy a healthy and rewarding lifestyle. 

These challenges can be met only through a holistic 

approach that takes advantage of the full spectrum of 

scientific methods and capabilities. In this context, 

the relative weight placed on reductionistic or complexity approaches, and the ways in which they are 

combined to solve important problems, will be driven 

by programmatic science and modeling goals. The 

choice ultimately depends on the particular aspects of 

a system for which understanding and simulation are 

desired and what degrees of uncertainty are acceptable 

(Beven 2001; Murray 2003).

Workshop discussions supported the need for a new 

research strategy to investigate subsurface systems. 

In this strategy, complexity and reductionistic 

approaches could be melded to (1) identify common 

behavioral patterns for different types of complex 

subsurface systems, (2) understand and describe 

the workings of representative complex systems at 

relevant scales, and (3) yield new modeling strategies 

for improved system understanding and prediction. 

Complex systems selected for research could include 

those at the field scale and the key subcomponents 

and subprocesses at lower scales known to control 

field-scale behavior and that have potential for high 

scientific impact. Critical overall needs are research to 

emphasize hierarchical subsurface systems containing 

coupled processes and the characterization of interand cross-scale process interactions and feedback 

mechanisms that can lead to emergent behavior or 

other complex system characteristics.

A hybrid approach is needed for integrating complementary top-down and bottom-up research. 

Combined with robust system characterization and 

modeling, this approach would allow the comprehensive understanding and prediction of complex 

subsurface system behavior. Two primary elements are 

needed in this approach:

•	 Multidisciplinary investigations of important 

subsurface systems to identify common behavioral 

patterns observed at different scales, regardless 

of exact system characteristics, and the variables 

controlling these patterns. This includes identifying features diagnostic of complexity, determining 

emergent behaviors and controlling interactions 

that constrain models, and developing and evaluating systems models and their predictive power.

•	 Experimental, theoretical, or field studies of key 

components, interactions, controlling variables, 

and hierarchies of subsurface systems. In these 

studies, (1) interactions and key processes and 

mechanisms are quantified and modeled at lower 

scales, (2) simplified process abstractions are 

developed and their transitions to larger scales and 

their roles in emergent and collective behavior 

investigated, and (3)systems-scale predictions are 

made using upscaled models of primary processes 

believed to be responsible for system response.

Such melding of perspectives is necessary to simultaneously develop robust understandings of key underlying 

mechanisms and interactions and the importance of 

scale transitions and to provide insights into common 

macroscopic laws governing complex system behavior 

at the prediction scale. Changes to traditional research 

strategies are required to enable comprehensive 

investigations of complex subsurface systems with their 

characteristic multitude of processes at different scales, 

significant heterogeneity, and ever-present measurement and observational constraints. Integrating fundamental information derived from multiple perspectives 

is essential to achieve the level of in-depth understanding and quantitative system predictability needed for 

complex subsurface systems.
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 43

Appendix C

Workshop Program

BER Workshop on Subsurface Complex System Science Relevant 

to Contaminant Fate and Transport

Workshop Co-Chairs: Susan Hubbard, Frank Loeffler, and John Zachara

Sunday, August 2, 2009 – EVENING

Pre-meeting: Workshop organizers and panel leads

6:00 p.m. – 6:15 p.m. Welcome and purpose of meeting

6:15 p.m. – 6:30 p.m. Overview of agenda

6:30 p.m. – 7:00 p.m. Discussion of breakout sessions

7:00 p.m. – 8:00 p.m. Working dinner: Workshop report and discussion

Monday, August 3, 2009 – MORNING

7:00 a.m. – 8:00 a.m. Registration and continental breakfast

Plenary Opening Session

Session 1: Introduction

8:00 a.m. – 8:10 a.m. Introductory remarks Anna Palmisano

8:10 a.m. – 8:25 a.m. ERSP strategic planning David Lesmes

8:25 a.m. – 8:50 a.m. Workshop structure and agenda Co-Chairs

8:50 a.m. – 9:20 a.m. Summary of DOE-BES Workshop: Donald DePaolo

Basic Research Needs in the Geosciences

9:20 a.m. – 9:45 a.m. Break

Session 2: Complex Systems Science – Perspectives from the Field

9:45 a.m. – 10:30 a.m. Scaling, Universality and Geomorphology Peter Dodds

10:30 a.m. – 11:15 a.m. Mercury Cycling in the Environment Scott Brooks

11:15 a.m. – 12:00 p.m. Microbial Oceanography Mick Follows

Monday, August 3, 2009 – AFTERNOON

Breakout Session 1: Complex Subsurface Subsystems at Different Scales

12:00 p.m. – 1:00 p.m. Working lunch – describe breakout agenda/format

1:00 p.m. – 3:30 p.m. Series of presentations by panelists: My Favorite Things...

3:30 p.m. – 4:00 p.m. Break

4:00 p.m. – 6:00 p.m. Discuss, identify, and prioritize key complex systems at different scales for breakout session

Monday, August 3, 2009 – EVENING

6:00 p.m. – 7:30 p.m. Working dinner

7:30 p.m. – 9:00 p.m. Draft slides describing system attributes, scientific challenges, scientific impacts, and interdisciplinary research approaches for 3 to 5 “high-priority” complex systems for breakout session
44 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Appendix C: Workshop Program

Tuesday, August 4, 2009 – MORNING and AFTERNOON

7:30 a.m. – 8:30 a.m. Continental breakfast

Plenary Session

8:30 a.m. – 10:00 a.m. Presentation of Complex Subsurface Subsystems at Different Scales (three 30-minute blocks of time 

for presentations and discussion).

Breakout Session 2: Disciplinary Research to Understand Complex Systems

10:00 a.m. – 11:00 a.m. Review the key complex systems at different scales and suggest refinements to be presented

at the plenary session.

11:00 a.m. – 12:00 p.m. Determine major disciplinary research goals that advance our ability to characterize, monitor, 

understand, and predict the dynamic behaviors of key complex subsurface systems and their 

functional components. Develop a Logic Model to summarize the near-term, mid-term and 

long-term research goals and the potential scientific impacts of these advances.

12:00 p.m. – 1:00 p.m. Working lunch

1:00 p.m. – 2:30 p.m. Continue working in disciplinary groups and preparing Logic Models for presentation 

at the plenary session.

Plenary Session

2:30 p.m. – 4:00 p.m. Feedback by disciplinary groups about proposed key complex systems at different scales.

Presentation of Logic Models by Transport, Geochemistry, and Microbiology breakout groups 

for Disciplinary Research to Understand Complex Systems.

Breakout Session 3: Complex Systems Across Scales

4:00 p.m. – 6:00 p.m. The objective of this exercise is to identify research needed to connect and integrate knowledge 

and models of complex systems across scales as is needed to make accurate predictions of 

contaminant fate and transport in naturally complex subsurface systems. A hierarchical approach, 

involving interdisciplinary groups, will be used to develop two alternate (“competing”) 

Integrated Logic Models for Complex Systems across Scales.

Tuesday, August 4, 2009 – EVENING

7:00 p.m. Dinner with your assigned group at a local restaurant 

(6 small interdisciplinary groups with 5 to 6 people/group)

Wednesday, August 5, 2009 – MORNING

7:30 a.m. – 8:30 a.m. Continental breakfast

Breakout Session 3: Complex Systems Across Scales (continued)

8:30 a.m. – 12:00 p.m. Group 1 and Group 2 each merge inputs from three scale-based and interdisciplinary subgroups to 

independently develop alternate (“competing”) Integrated Logic Models for Complex Systems Across Scales

Wednesday, August 5, 2009 – AFTERNOON

Plenary Closing Session

12:00 p.m. – 2:00 p.m. Working lunch

12:00 p.m. – 12:30 p.m. Presentation of Complex Subsurface Subsystems at Different Scales

12:30 p.m. – 1:00 p.m. Disciplinary Research to Understand Complex Systems – Presentation of Logic Models by 

Transport, Geochemistry, and Microbiology breakout panels

1:00 p.m. – 1:15 p.m. Complex Systems Across Scales – Presentation of Integrated Logic Model by Group1

1:15 p.m. – 1:30 p.m. Complex Systems Across Scales – Presentation of Integrated Logic Model by Group 2

1:30 p.m. – 1:45 p.m. Closing Remarks

Begin Writing Workshop Report

2:00 p.m. – 6:00 p.m. Workshop co-chairs and writing team begin organizing and writing the first draft of the workshop report.
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 45

Appendix D

Workshop Participants and Observers

Participants

Banfield, Jill

University of California, Berkeley

Bargar, John

SLAC National Accelerator Laboratory

Beller, Harry

Lawrence Berkeley National Laboratory

Brooks, Scott

Oak Ridge National Laboratory

DePaolo, Donald

Lawrence Berkeley National Laboratory

Dodds, Peter

University of Vermont

Edwards, Elizabeth

University of Toronto

Follows, Mick

Massachusetts Institute of Technology

Fredrickson, Jim

Pacific Northwest National Laboratory

Giammar, Dan

Washington University

Ginn, Tim

University of California, Davis

Hansel, Colleen

Harvard University

Hubbard, Susan

Lawrence Berkeley National Laboratory

Konopka, Alan

Pacific Northwest National Laboratory

Lichtner, Peter

Los Alamos National Laboratory

Loeffler, Frank

Georgia Institute of Technology

Lovley, Derek

University of Massachusetts

Maxwell, Reed

Colorado School of Mines

Meakin, Paul

Idaho National Laboratory

Redden, George

Idaho National Laboratory

Roden, Eric

University of Wisconsin, Madison

Rosso, Kevin

Pacific Northwest National Laboratory

Saiers, James

Yale University

Scheibe, Tim

Pacific Northwest National Laboratory

Slater, Lee

Rutgers, The State University of New Jersey

Spormann, Alfred

Stanford University

Steefel, Carl

Lawrence Berkeley National Laboratory

Templeton, Alexis

University of Colorado, Boulder

Tokunaga, Tetsu

Lawrence Berkeley National Laboratory

Tringe, Susannah

Lawrence Berkeley National Laboratory

Van Cappellen, Philippe

Georgia Institute of Technology

Wildenschild, Dorthe

Oregon State University

Williams, Ken

Lawrence Berkeley National Laboratory

Yee, Nathan

Rutgers, The State University of New Jersey

Zachara, John

Pacific Northwest National Laboratory
46 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Nicholson, Tom

Nuclear Regulatory Commission

Palmisano, Anna

U.S. Department of Energy

Ramirez, Rosa

U.S. Department of Energy

Sayler, Gary

University of Tennessee

Skubal, Karen

U.S. Department of Energy

Srivastava, Prem

U.S. Department of Energy

Thomassen, David

U.S. Department of Energy

Van Luik, Abe

U.S. Department of Energy

Weatherwax, Sharlene

U.S. Department of Energy

Wellman, Dawn

U.S. Department of Energy

Woodward, Nick

U.S. Department of Energy

Zhu, Ming

U.S. Department of Energy

Appendix D: Workshop Participants and Observers

Observers

Anderson, Todd

U.S. Department of Energy

Bayer, Paul

U.S. Department of Energy

Chatterjee, Lali

U.S. Department of Energy

Christen, Kris

Oak Ridge National Laboratory

Cole, Dean

U.S. Department of Energy

Graber, Joe

U.S. Department of Energy

Hirsch, Roland

U.S. Department of Energy

Joseph, Renu

U.S. Department of Energy

Katz, Arthur

U.S. Department of Energy

Kuperberg, Mike

U.S. Department of Energy

Lesmes, David

U.S. Department of Energy

Mansfield, Betty

Oak Ridge National Laboratory

Morss, Lester

U.S. Department of Energy
Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 47

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Department of Energy Office of Science · Complex Systems Science for Subsurface Fate and Transport · March 2010 51

Column 1

Framboidal pyrite from a naturally 

bioreduced alluvial sediment, Rifle, 

Colorado. [From Qafoku, N. P., 

R. K. Kukkadapu, J. P. McKinley, 

B. W. Arey, S. D. Kelly, C. T. Resch, 

C. M. Wang, and P. E. Long. 2009. “Mineralogical Control on 

U(VI) Attenuation in the Contaminated Sediments from Rifle, 

Colorado.” American Chemical Society Spring Annual Meeting, Salt Lake City, Utah. March 24, 2009. See also Qafoku, 

N. P., R. K. Kukkadapu, J. P. McKinley, B. W. Arey, S. D. Kelly, 

C. Wang, C. T. Resch, and P. E. Long. 2009. “Uranium in Framboidal Pyrite from a Naturally Bioreduced Alluvial Sediment,” 

Environmental Science & Technology 43(22), 8528–34]

Shewanella oneidensis MR-1 with 

associated biogenic uraninite (UO2

). 

[Modified from supporting materials of Marshall, M. J., A. E. Plymale, 

D. W. Kennedy, L. Shi, Z. Wang, 

S. B. Reed. A. C. Dohnalkova, C. J. Simonson, C. Liu, D. A. Saffarini, M. F. Romine, J. M. Zachara, A. S. Beliaev, and J. K. Fredrickson. 2008. “Hydrogenase- and Outer Membrane C-Type 

Cytochrome–Facilitated Reduction of Technetium(VII) 

by Shewanella oneidensis MR-1,” Environmental Microbiology 

10(1), 125–36. Copyright 2008 John Wiley and Sons]

Column 2

[Image courtesy Alexis Templeton, 

University of Colorado, Boulder]

Column 3

Colony of metal-reducing bacteria 

and iron phosphate bioprecipitate.

[From Peretyazhko, T. S., J. M. Zachara, 

D. W. Kennedy, J. K. Fredrickson, 

B. W. Arey, J. P. McKinley, C. M. Wang, 

A. C. Dohnalkova, and Y. Xia. 2010. “Ferrous Phosphate 

Surface Precipitates Resulting from the Reduction of Intragrain 

6-Line Ferrihydrite by Shewanella oneidensis MR-1,” Geochimica

et Cosmochimica Acta, in review. Copyright Elsevier]

Column 4

Scanning electron microscopy (SEM) 

backscattered image of framboidal 

pyrite from a naturally bioreduced 

alluvial sediment, Rifle, Colorado.

[Reprinted with permission from 

Qafoku et al. 2009 Environmental Science & Technology paper. 

Copyright 2009 American Chemical Society. Also from 

Qafoku et al. March 2009 presentation at the ACS Spring 

Annual Meeting. See related image in Column 1]

Iron phosphate bioprecipitate in 

cross-section with bottom extending 

into internal pores of silica. [From 

Peretyazhko et al. 2010. Copyright 

Elsevier. See related image in Column 3]

Column 5

Cryo-SEM image of a bioprecipitate formed during incubation of 

S. oneidensis MR-1 with ferrihydrite 

(magnification = 1500 ×) [From Reardon, C. L., A. C. Dohnalkova, P. Nachimuthu, D. W. Kennedy, D. A. Saffarini, B. W. Arey, L. Shi, 

Z. Wang, D. Moore, J. S. McLean, D. Moyles, M. J. Marshall, 

J. M. Zachara, J. K. Fredrickson, and A. S. Beliaev. 2009. “Role of 

Outer-Membrane Cytochromes MtrC and OmcA in the Biomineralization of Ferrihydrite by Shewanella oneidensis MR-1,” 

Geobiology 8(1), 56–68. Copyright 2009 John Wiley and Sons]

Column 6

Fractured rock. [Reprinted with 

permission. Copyright 2005 Southwest 

Research Institute®. All rights reserved]

Bacterial dissolution of ferrous phosphate (magnification = 20,000 ×). 

[Courtesy C. Reardon, J. Fredrickson, 

A. Dohnalkova, and J. Zachara; Pacific 

Northwest National Laboratory]

Sources and Credits for Cover Images
52 March 2010 · Complex Systems Science for Subsurface Fate and Transport · Department of Energy Office of Science

Column 7

Tc(IV)O2 precipitates on the surface 

of S. oneidensis MR-1 mutant, with 

MR-1 shown in cross-section. [From 

Marshall et al. 2008. Copyright 2008 

John Wiley and Sons. See related 

image in Column 1]

Physically heterogeneous sediment 

layers. [Courtesy Andy Ward, Pacific 

Northwest National Laboratory]

Column 8

Metal-reducing bacteria on a ferrous phosphate growth plane; 

bioprecipitation or biodissolution?

[Courtesy C. Reardon, J. Fredrickson, 

A. Dohnalkova, and J. Zachara; Pacific 

Northwest National Laboratory]

Branched river channels in Florida

[From Howard, A. D. 2009. “Hydrology: Forming Valleys from Below,” 

Nature Geoscience 2, 165–66. Published 

in Howard 2009 courtesy of the Florida 

Department of Revenue (FDOR). Reprinted by permission 

from FDOR and Macmillan Publishers Ltd: Nature Geoscience]

SEM backscattered image of 

framboidal pyrite from a naturally 

bioreduced alluvial sediment, Rifle, 

Colorado. [Reprinted with permission 

from Qafoku et al. 2009 Environmental 

Science & Technology paper. Copyright 2009 American Chemical Society. Also from Qafoku et al. March 2009 presentation 

at the ACS Spring Annual Meeting. See related images in 

Columns 1, 4]

Column 9

Framboidal pyrite from a naturally 

bioreduced alluvial sediment, Rifle, 

Colorado. [From Qafoku et al. March 

2009 presentation at the ACS Spring 

Annual Meeting. See also Qafoku et al. 

2009 Environmental Science & Technology paper (Copyright 

2009 American Chemical Society) and related images in Columns 1, 4, and 8]

Column 10

Two morphologically distinct phases 

of ferrous phosphate (50–100 μm 

florets and plates) formed during 

incubation of S. oneidensis MR-1 with 

ferrihydrite. [From Reardon et al. 

2009. Copyright 2009 John Wiley and Sons. See related image 

in Column 5]

Shewanella oneidensis MR-1 cells 

engulfed in goethite (FeOOH) resulting from the biologically induced 

recrystallization of ferrihydrite. 

[Courtesy C. Reardon, J. Fredrickson, 

A. Dohnalkova, and J. Zachara; Pacific Northwest National 

Laboratory]

SEM image of a sulfate-reducing biofilm obtained from a borehole used 

for long-term (100+ days) acetate 

injection during biostimulation 

activities at DOE’s Integrated Field Research Challenge 

site near Rifle, Colorado. The outer surface of the biofilm was 

stabilized with ruthenium red prior to SEM analysis. [Image 

courtesy A. Dohnalkova, Pacific Northwest National Laboratory. Biofilm material courtesy K. H. Williams, Lawrence 

Berkeley National Laboratory]
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