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J. Jpn. Soc. Soil Phys.
土壌の物理性

Miscellaneous

No. 134, p.51 ∼ 59 (2016)

Improving the impact of soil science by reconsidering theinteractions of data, models, and decisions — An ongoing
discussion during the 2016 Darcy lecture tour
Ty P.A. FERRÉ 1

Abstract: How can we improve the acceptance and inclusion of science in public debate and decision making? In
this article, I argue that scientists have to do a better job of
understanding what drives people to make decisions. By
listening to stakeholders concerns, we can improve our science and its impact.
Key Words : data, models, decision making

1. Introduction
The following is a discussion born out of the 2016 Darcy
Lecture. This lecture series, sponsored by the National
Ground Water Association’s Research and Education Fund
(NGWREF), allows one hydrogeologist per year to travel
around the world to meet with the hydrogeologic community. The lecturer can choose any topic, or set of topics, topresent. Hosts can invite the lecturer, who then constructs
the final lecture schedule.
I was honored to have been asked to provide the 2016
Darcy Lecture. My research has touched on several areas: hydrogeophysics; vadose zone hydrology; and optimal monitoring network design. After some thought, I decided that I wanted to focus my lectures on a topic thatis relatively new to our research group: how we can improve the impact of science in water-related decision making. This brings together many elements of my research.
But, more importantly, I think that it is a topic that is well
suited to promote conversation and exchange among academic researchers, practicing hydrogeologists, and members of the public who need help making difficult waterrelated decisions. With this in mind, I laid out an ambitious schedule of over 120 talks, including leading research universities, education-focused universities and colleges, professional groups, stakeholder groups, and public
1 Department of Hydrology and Water Resources, University of Arizona,

Tucson, AZ 85721-0011, USA.
2016 年 9 月 26 日受稿

2016 年 11 月 18 日受理

schools. The goal of the lecture series was to propose somechanges that we, as scientists, can make in the way that weapproach our work and communicate our findings. Simultaneously, I hoped to provide non-scientists with a usefulbasis for communicating with scientists to ensure that science serves their needs. The combined goal is to ensurethat we do our part to ensure that science will be includedin public debate.
This is an updated version of a paper published in theproceedings of the recent meeting of the Japanese Association of Hydrologeologists. At the time of writing,I have completed 112 talks in North America, Western
Europe, South America, South Africa, Australian, New
Zealand, Israel and Japan (Nagasaki, Tokyo, Mie, and Kyoto). In this paper, I focus on bringing together the ideasthat have developed through these talks and discussions
that followed with special emphasis on topics related tosoil science. I hope that this article will encourage readers
to contact me to continue discussions on the topics presented. Those who are interested can find more detailed
information about my talk, my contact information, a listof previous questions, and related references on my blog:
https://darcylecture2016.wordpress.com/.

2. My background
I consider myself to be, at least in part, a soil physicist.
As a graduate student, I was drawn to soil physics becauseof the nonlinearities that lie at its heart. The dependenceof hydraulic conductivity on water content, and water content on water potential, lead to surprising, unexpected outcomes — even if you already have a solid background inhydrogeology as it applies to the saturated zone.
My specific research interest was in the area of nondestructive measurement of water content, specifically using time domain reflectometry (TDR). Through my PhD
research I learned that many indirect measurement meth-52

土壌の物理性ods, like TDR, include their own nonlinearities that canlead to surprising results. In particular, the spatial distribution of water in the volume surrounding a TDR probe
can have important impacts on what the probe measures.
As a result, a more complete and accurate interpretationof water content measurements with TDR should often include consideration of the sub-sample-volume water content distribution. But, how can we do this if we don’t have
any way to measure this distribution? The answer, is tomodel the processes of interest, use the model to propose
the water content distributions, use these to model what
TDR would measure given this proposed distribution, andthen use the TDR measurements to test the result. If the
TDR measurement does not agree with the predicted value,then the underlying soil physical model must be changed.
This was the genesis of an approach that has come to be
known as coupled hydrogeophysical analysis. The major
lesson that we took from these investigations is that, once
we couple many complex systems, we must consider thatchanges to any of these systems (soil hydraulic property
dependencies, unsaturated flow, TDR spatial sensitivity)
will affect the fit between the model and the data. Conversely, changes to these systems can compensate for one
another, meaning that a good fit between data and model
results does not prove that all of the components of ourmodel are correct.
As I continued in my research career, I expanded
my research to other geophysical methods (gravity, neutron probes, electrical resistivity, electromagnetic methods,
temperature profiling, and nuclear magnetic resonance)
and to processes beyond the root zone. But, the basic
concept of coupled measurement/model analysis has been
consistent. More recently, I have started thinking about
how our analyses, as soil scientists, hydrologists, or scientists in general, are used by decision makers. I have become especially interested in examining why science is,
too often, ignored in public discussions and decision making processes. My conclusion is that we, as scientists, must
do a better job of considering another type of coupled analysis — how decision making and scientific investigations
influence one another. The purpose of my Darcy Lecture
has been to discuss how science can do a better job of providing the kinds of analyses that decision makers can andwill use, while still maintaining the objectivity that is thefoundation of science.

3. Science and decision making
Humans are decision-making machines. We make thousands of decisions, large and small, every day. But, we
often fail to consider how we make decisions, leaving us
open to common failures in decision making. Recent re-

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search has led to greatly improved insights into the processes of decision making. In the popular literature, therange of these findings are well described in two books:
Incognito, The Secret Lives of the Brain (Eagleman, 2012)
and Thinking Fast and Slow (Kahneman, 2011). These authors point to the different impacts of detailed versus structural uncertainty in decision making. Specifically, Eagleman suggests that our brains have evolved to make decisions under considerable detailed uncertainty because oursenses are imperfect sensors of the world around us. Kahneman suggests that we resort to shortcuts and unconscious
biases when confronted with more fundamental, structural
uncertainties. That is, when we don’t fully understand asystem, we make simplifications to systems that we canunderstand, and then we fall into habitual approaches tomaking decisions in ways that ‘feel’ justifiable.
It behooves us, as scientists, to recognize that the information and insights that we provide will be viewed in thecontext of flawed decision making processes. In particular, we must consider that structural uncertainty, which
we often face when representing natural systems, will betreated with some degree of illogical thinking. Further,that we all tend to hold to the stories that we have constructed to make sense of complex systems and we resist arguments to change our ways of thinking. Anotherpopular book, Nudge, Improving Decisions about Health,
Wealth, and Happiness (Thaler and Sunstein, 2009) is relevant to this point. This work has been applied widely inadvertising and is based on the following premise: people
are more likely to change their opinions (for example, tobe informed by scientific results) when you make it easy
for them to do so. My Darcy Lecture has evolved into an
examination of ways that scientists can conduct their investigations in ways that are more likely to nudge decision
makers to more scientifically sound decisions while maintaining the objectivity and rigor that marks science as aunique way of seeing the world.
In working with students of all ages, from pre-school tograduate school, I have also come to believe that humans
are also scientists by nature. That is, we naturally employ
the scientific process of proposing a hypothesis, seeking
data to challenge the hypothesis, and revising the hypothesis based on observations. We seek underlying and generaltruths and find security in the knowledge of how the world
works.
Major difficulties arise when we must make decisions
for problems that are not addressed easily by science. In
general, these problems are marked by a high degree ofcomplexity in the underlying systems: psychological problems and the function of the human brain; economic problems and the function of groups of individuals; climatological problems and the interactions of many entangled資料：Improving the impact of soil science by reconsidering the interactions of data, models, and decisions

systems; and geologic problems that rely on processes that
occur at multiple temporal and spatial scales that are often difficult or impossible to observe. Despite how muchtime and effort humans have devoted to understanding soil,
the interactions of physics, chemistry, and biology in soil
make this one of the most complex systems that we canstudy. How can we apply simple, fundamental scientificunderstanding to a question as complex as the cyclingof nutrients in soil under different proposed management
schemes? How can we describe our scientific findings ina way that correctly identifies what we know and what we
do not, yet, know so that decision makers can rely on the
results and so that the public will understand the need forcontinued scientific study?

4. Pure and applied science
Conversations that I have had over this year have led me
to believe that there are two kinds of science and that we
too often confuse the two. I will use the term ‘pure’ science to refer to the search for general truths. I will callthe application of the findings of pure science to complex
systems as ‘applied science’. Generally, when we conduct
pure science, we are careful to control as many variables
and conditions as possible to allow us to isolate the effectsof the processes of interest to us. Some realms of science
are particularly able to do this, ranging from chemistry,
to physics, to astronomy. Others sciences do not allow
for controlled experiments on ethical grounds: economics;
psychology; environmental sciences. Most sciences simply involve too many coupled systems to admit highly controlled experiments — soil science and hydrology fall inthis category.
My claim that soil science is not a ‘pure’ science
is not intended to say that we cannot conduct controlled experiments, such as infiltration tests, or variable
irrigation/fertilization tests. But, we must always recognize that the interpretations that we use to interpret thesetests may not be appropriate if the conditions do not match
those for which the analyses were developed. The clearest
example is the effect of heterogeneity on the interpretationof infiltration tests. Most of the analyses that we use to interpret the test results are based on an assumption of homogeneity and then return one ‘average’ property value. But,
we know that if we were to relocate the test, often even by ameter or less, we may get very different results. Given thisuncertainty, how can we build scientific models? As I see
it, there are three approaches: detailed modeling with distributed measurement; upscaling with large-scale measurements; and correlative modeling using all measurements
available.
It is natural to assume that the problem of heterogene-53

ity is reduced if we can measure at a scale that is smaller
than the spatial scale of the heterogeneity. This suggeststhat if we could collect many measurements, we could develop small scale, pure-science-based models for each location and link them together to simulate a larger system.
In some cases this is true, and the development of smaller,
lower power, more easily networked sensors is supporting
this approach. But, in other cases, the required scale ofmeasurement to support a pure-science representation is
simply impractically small. Consider, for example, discussion related to the appropriate scale at which to consider
reactions in porous media, or the transport of nanoparticles
through soil, or the function of microbial communities insoils and on plant roots.
If we conclude that it is impossible to measure at a scaleat which conditions are sufficiently homogeneous to apply
pure-science models, then we must consider some version
of upscaling. In general, all of these approaches can be
distilled into some version of the following. We will assume that the pure-science models that we have formulated
for homogeneous conditions apply for heterogeneous media if we can identify the appropriate averaging to provide‘effective’ parameter values. Of course, this cannot be expected to apply when considering processes that involve
many coupled systems (consider microbial growth that depends on solute flux that depends on water flow in a variably saturated medium averaged even on a 1 cm3 scale).
But, even for those systems that may allow for upscaling,
it is naı̈ve to believe that the manner in which our measurement methods and their associated interpretations average
properties or states is the correct average for the processof interest to us. Therefore, even our most strictly purescience-based models must be seen as some approximationof a real system.
Given the limitations of pure-science based models forreal systems, there is increasing interest in the development of correlative modeling. This includes approaches
such as statistical inference, neural network modeling, andmany approaches collectively referred to as big data or machine learning. At the risk of oversimplifying, these approaches abandon the development of pure-science models, assuming that all of the limitations stated above makethem impractical for real, complex systems. Rather, theseapproaches search for mathematical or statistical relationships between data and outcomes of interest. Generally,
many simple models are proposed and selected primarilyon the basis of their ability to predict past observations.
The danger inherent in these approaches is that they can
produce models that have no basis in truth — they can mistake correlation for causation. This can lead to incorrect
projections and can lead to incorrect conclusions about underlying scientific concepts.

54

土壌の物理性Based on the preceding discussion, it would be easy toconclude that science is doomed. If we cannot rely on anyof the three general approaches to building models of complex systems, then what can we do? How can we hope tomake a contribution to the important decisions that society
must make and that should be informed by science? What
I have learned over the course of this year is that there are
two important elements that we, as scientists, must remember: the power of storytelling; and the value of developing
multiple competing hypotheses.

5. Harnessing the power of storytelling
Every culture has developed a form of storytelling. Thisis a fundamental approach that humans use to collect disparate facts, put them into a consistent context, and weave
a coherent explanation of the world. While this is a useful,even vital, ability, it also has potential limitations. Specifically, most of us are highly resistant to changing our internal narrative once it is formed. We fall in love with thestory in our head and will require compelling evidence tochange it.
Consider a scientist who is presenting their results basedon their current best representation of a complex system. If
their results strongly suggest a change in the way that the
public should understand the system, and if this change inconceptualization requires a change in behavior that is difficult or costly, the public will look for reasons to discredit
the science. On the other hand, if a scientist first listens
to the concerns of a stakeholder group and then translates
their story into a scientific model of the system, they havean opportunity to demonstrate weaknesses of that model(if they exist) and to introduce more scientifically supportable concepts. This requires a change in the way that we
do science — it requires that we actually embrace one of
the basic tenets of science. That is, we must view all of
our work as an effort to challenge hypotheses. This canonly be done by developing multiple competing hypotheses. Unfortunately, as I stated above, this runs counter toour natural tendency to prefer one unchanging internal narrative.
Through discussions across many diverse groups, thetheme of the power of storytelling has emerged as a potentially strong model for explaining science. In particular, I
have come to believe that we can view many multi-party
water issues as multiple groups that believe different stories based on a common set of facts. There are times thata group’s concerns run counter to basic science. But, more
often, they are based on incomplete or inaccurate understanding of science. This is to be expected when systems
are complex, involving many interacting processes. In fact,
this is one reason that we, as scientists, build models — to

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integrate knowledge in ways that are difficult to integrate‘in our heads’.
Following on the concept of nudging, I think that it could
be very useful for us to view the model building process as
an attempt to construct as many scientifically plausible stories about a system as possible. In this way, we should
act like detectives — simultaneously using all available
information to propose many different possible suspects,
then building a story that would implicate each suspect,then finding the key pieces of evidence to assess the likely
guilt or innocence of each suspect. Our current approach
would not be acceptable in a police investigator — decideupon the best representation of a system that we can currently conceive (choose a suspect) and then examine how
we have to modify parameter values (massage the circumstantial evidence) to fit the model to the data (to prove their
guilt).
In an applied context, this proposed approach would require us to make every attempt to construct plausible models that could manifest stakeholders’ specific concerns.
Based on the discussion of the benefits and weaknesses of
our three general approaches to modeling, I would contend
that we should consider all modeling approaches: detailed
and spatially discrete; upscaled; and correlative. For mostapplied cases, the ultimate ‘truth’ of the models is not the
primary aim. Rather, we are interested in using models toeither: a) draw a consensus from multiple perspectives to
identify reliable courses of action; or b) identify importantdifferences in projections based on different viewpoints,
thereby prioritizing further scientific efforts.
The proposed modeling effort would thus require moreconsideration of multiple conceptualizations, perhaps requiring simpler models that are less rigorously calibrated
to data. The approach will require that we spend moretime in the creative elements of model building, and lesson model calibration. It may require that we have a morebalanced view of the value of pure-science and applied science models. It will also require that we develop tools, likethose that have been developed to assist with parameter
estimation and uncertainty analysis, to make model construction more efficient and more universal. Ultimately,
we need to view an ensemble of models like a population— the success of the species lies in the ability of the groupto adapt to new conditions, rather than in the ability of thecurrently most-fit animal to excel. As conditions change,usually gradually, there will be differential breeding success within the population. Equivalently, as we continuallygather new data, we should expect to see relatively smallchanges in the weight that we place on individual models in
our ensemble for decision support. But, at times we will be
faced with major ‘surprises’ (Bredehoeft, 2005). At thesetimes, we will be forced to rethink our model ensemble,

資料：Improving the impact of soil science by reconsidering the interactions of data, models, and decisions

possibly adding new processes or structures. This process
of model building should come to resemble the punctuatedequilibrium attributed to biological systems as explained in
popular literature (Gould, 1980).
The most direct benefit of this approach is that it provides testable representations that serve as proxies forstakeholders’ general concerns. The more general advantage is that in forming multiple competing models weare actually following the tenets of the scientific method:
generating hypotheses that can then be tested objectively,
through observation, against counter hypotheses.

6. The role of science in decision
supportThrough conversations with a wide audience, I havecome to another realization: in most cases, science doesn’t
answer questions, it only helps to answer questions. This
may seem like a distinction without a difference, but it has
important implications for the way that we choose to pursue applied scientific problems. Essentially, two things areimportant for us to remember. First, the scientific elementof a question is often only a part, sometimes a small part, ofthe discussion. Second, as physical scientists, we may beable to predict outcomes of actions on physical systems,
but we are not equipped to translate these outcomes intomeaning for stakeholders. The latter point requires that wework with social scientists and economists to develop relations between our predictions and stakeholder utilities.
This concept is shown schematically in Fig. 1. Here,a hydrologist may be able to predict drawdown beneatha stream (with uncertainty) due to pumping for a nearby
proposed mine (Fig. 1a). But, these predictions must betranslated to perceived value, or utility, to determine howeach group views each predicted outcome (Fig. 1b). Theseutility functions are at once critical to understand decision
making and extremely challenging to create. Entire fieldsof study are devoted to this process, which I cannot review here. Rather, I will point out that the key element ofthese curves for our purposes are that they define the relative importance of different possible predicted outcomesfor different interested parties.
My students I have been working on ways to bring together the curves presented in Fig. 1 to define a new approach to science for decision support. We refer to the approach as DIRECT — the Discrimination Inference to Reduce Expected Cost Technique. DI refers to an approach
to identify additional data that are most likely to reduce
prediction uncertainties in ways that are useful for decision
makers. RECT refers to different approaches to making decisions under uncertainty. DIRECT is based on combining
the curves presented in Fig. 1, recognizing that they share55

a common x-axis. This allows us to form utility probability
density functions, as shown in Fig. 2.
The curves shown in Fig. 2 can be used to support
three very different decision styles. Simple decision making only considers the most likely perceived value that agroup will have and is based on the maximum likelihood
value of each curve (the peak). As scientists, we are often
guilty (although unaware) of assuming that this describes
the decision making process — stakeholders will logicallyprocess scientific results, accepting uncertainty, but being
willing to make decisions on the current best representation of scientific understanding. There is increasing realization that decisions are more robust if they consider
lower probability outcomes, rather than focusing solely on
the most likely prediction. (Again, Eagleman suggests that
we have evolved to integrate such ‘what if’ strategies into
our decision making even at the level of brain function.)
This is the next level of abstraction of the decision making process — quantitative risk assessment based on theFig. 1 a) Multi-model prediction of a drawdown of interest
made by hydrogeologists; b) valuation of drawdown for two
stakeholders groups (a mine and a local community) made bysocial scientists.

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土壌の物理性Fig. 2 a) The probability density function of utility for eachstakeholder group formed by combining the two curves inFig. 1. The dashed ellipses identify relatively low probability,
but high risk outcomes that often drive risk-averse decisions.

likelihood-weighted (or expected) utility. This is also afully logical approach and it is based on a strong assumption that the uncertainties that we have defined represent
the actual uncertainties that are most relevant for decision
makers. But, much of Kahneman’s work focuses on theidea that humans rarely make logical decisions when facedwith structural uncertainty. This leads to the third decisionstyle, which can be described as threshold decisions or risk
averse decision making. Regardless of our desires to represent decision making as scientifically informed and logical,
this may be the most common approach in practice. One
key element of risk averse decision making is that, whether
consciously or not, decisions are often driven by relatively
low probability outcomes that carry very high risks (lowutilities) if they occur. These outcomes are highlighted bydashed ovals in Fig. 2. With this in mind, I would contend
that science can have greater impact on decision making
if it is addressed at testing these outcomes objectively andopenly. That is, we will be more effective if we take anudge approach to decision support by trying to develop
models that predict outcomes of concern to decision makers and then make efforts to test these associated models
against alternative explanations that lead to different outcomes.

7. DIRECT as an approach to
implementing nudging
At base, DIRECT attempts to reverse our usual approachto scientific analysis. As natural scientists, we tend to startwith efforts to understand a system in the most generalterms. Then we condense our understanding into a model
representation of the system. Finally, we use our models
to make predictions that can form the basis of decision
making. We are proposing that efficient use of scienceshould start with consideration of the decision being sup-第 134 号

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ported, including the outcomes of concern that are driving
the decision making process. Models should then be constructed to describe as wide a range of plausible descriptions of the system as possible, with special efforts taken
to identify models that could predict outcomes of concern
to each stakeholder group. As discussed above, these models should encompass different approaches to modeling(distributed and discrete, upscaled, and correlative). They
should also consider all of the simplifying assumptions that
we make in developing each of these models. In particular,
we should examine those assumptions that are hardest to
defend and that, in our professional judgement, have thegreatest likelihood to affect predictions of interest. Thesemodels, which can be viewed as competing hypotheses,
can be used to identify discriminatory data — data that are
best able to test one subset of models against other competing descriptions of the system (models). In practice,
DIRECT achieves this as follows:
1. A diverse ensemble of models is proposed. Thesemodels must be plausible based on current scientific knowledge and provide an acceptably good fit
to existing data. But, only limited efforts are madeto fit the models to the data through extensive calibration.
2. The models are used to make predictions of interest.
The result (as in Fig. 1a) provides a measure of thelikelihood of predictions of concern. Then, the ensemble is divided into contrasting groups, generally
based on whether they make predictions of concern
to a given stakeholder group (see Fig. 1b). Models
that produce very low utility outcomes with sufficiently high probability (the predictions within thedashed ellipses on Fig. 2) are identified as modelsof concern.
3. A future measurement is proposed. The expected
measured value is predicted using all of the models. A discriminatory index (Kikuchi et al., 2015)
is defined based on how different the observation ispredicted to be between models of concern and allother models in the ensemble. This is repeated formany possible measurements to identify measurements that are most likely to improve our understanding of the likelihoods of outcomes of concern
by testing their associated models.
4. Combinations of these promising observations are
examined to define measurement sets that minimize
redundant information.
5. The data are collected and the likelihoods of allmodels in the ensemble are revised. The likelihoodof the outcomes of concern are updated and the decision maker determines if further investigations arenecessary.

資料：Improving the impact of soil science by reconsidering the interactions of data, models, and decisions

8. A soils-related example
The case study that I highlighted in my Darcy talk waspractical and related to contaminant hydrogeology — how
can we best advise a client who may have to design, build,and operate a groundwater treatment facility? But, theideas put forward in DIRECT are equally applicable toa range of practical and more purely scientific questions.
Here, I will discuss briefly a practical and a scientific example that relate more directly to soil science.
As an illustrative example, we can consider groundwater protection related to the flushing of N through the rootzone of an agricultural field. To understand and manage theproblem, we need to develop a model. We may propose an
upper boundary condition — periodic drip irrigation with
infrequent high intensity rainfall, a crop based model ofET, and diurnally fluctuating evaporation. Then we propose soil hydraulic properties — single porosity, two layers (each homogeneous and isotropic), no preferential flow,
the Kosugi hydraulic function. Then chemical properties— no degradation or transformation, linear reversible sorption, no internal sources or sinks. Finally, we propose root
conditions – root water uptake can be distributed functionally by depth, no root growth, and roots do not affect soil
hydraulic or chemical properties.
Any one of these many assumptions that underlie ourmodel could be the subject of one or more PhD dissertations. That is, we cannot make a clear and confident decision regarding whether the assumptions that we made arecorrect for the system under study. Our usual approach,driven by the need to make progress in a timely fashion, is
to make a set of assumptions (build a model) and move forward (calibrate the model to our data). We will generally
only change one or more of our assumptions if we cannot
make our model fit our data ‘acceptably well’ using ‘reasonable’ parameter values. That is, unless we are forced
to do so by clear and compelling data, we will not revisit
our initial assumptions. Unfortunately, this is not really ascientific approach. Essentially, we are working very hard
to confirm our hypothesis (encoded as a model). As scientists, of course, our job is to try to test our hypotheses —the opposite of our usual practice. The discussions that I
have had as part of my Darcy Lecture focus on how we canestablish an approach to modeling that takes advantage ofcontinually testing our assumptions while still allowing foruse in solving practical problems.
The key step to our proposed approach is to force ourselves to formulate multiple competing hypotheses (models). To do this, we need to identify those assumptions that
we cannot defend solidly and that, if we were to change
them, may still lead to plausible models given the data that57

we have. This list of assumptions can guide us to develop afamily, or tree, of related models that cover our fundamental, structural uncertainty. In practical cases, and when weare working as part of a multi-disciplinary team that must
coordinate our investigations, we need to prioritize consideration of those assumptions that, if changed, may lead tooutcomes of concern. That is, in practical cases, we should
focus on building models that, if they are true, would have
major impacts on decision making. If we believe that wehave conditions that are best suited to one of the three general approaches to modeling described above, we can focus
on building multiple models of that type. But, more generally, we should be open to considering multiple modelingapproaches as well.
For scientific studies, we need to focus on the models
that make predictions that would have larger or more important impacts on other scientific studies. (For example,
fish biologists may be interested in stream temperature at
specific locations and times. To feed into their work, wewould want to focus on multiple models that may predict
different groundwater fluxes through time, even if this focus reduces our ability to answer other questions about thehydrologic system.) In this way, ‘pure’ and ‘applied’ problems can be seen as highly comparable. The only real difference is the basis used for determining the outcomes ofgreatest interest.
Once we have formed a diverse model ensemble, we cansubdivide our models into two or more groups. This canbe based on a prediction or predictions of interest. For our
example, this may be some threshold or regulatory limit
concentration of N reaching the water table. Or, for purely
scientific studies, subdivision can be based on underlying
hypotheses. For example, we may want to know if it is possible to infer whether sorption is reversible given all otheruncertainties that we have about our system. As describedby Kikuchi et al. (2015), discriminatory data are those thatare predicted to be most distinctly different based on theimposed division of the models. That is, we can formulate
many models, differing in their treatment of sorption andin many other aspects, and predict what we expect to measure at any proposed measurement location and time. If thepredicted values that we would observe are for the models
in different model divisions, then the observation is informative and should be collected. Measurements that do notcluster in accordance with the model subdivisions are not
likely to test one subset of models against the other set or
sets: those data are non-discriminatory and should not beprioritized. For the example problem described, a practical investigation may be most interested in the seasonalaverage N loading to an aquifer. A scientific investigation
may be interested in the role of macropores in delivering
N to the aquifer. It is likely that different measurements

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土壌の物理性(type, locations, spatial and temporal resolutions) will be
optimal for these different objectives.

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Our proposed approach has practical benefits for both
applied and purely scientific studies. In both cases, thenumber of measurements that we can collect is limited.
Some experiments cannot allow for the disruption of taking many samples, others are too remote to allow for extensive sampling, still others are budget- or time-limited.
We propose that it is our responsibility to propose multiple competing hypotheses that are plausible given existing
data. This encourages us to think more critically about ourmodels, allows us to define data that are likely to test ourmodels once collected, and avoids the collection of nondiscriminatory data. In the end, we predict that this will
cost less than our current approach and will lead to betterand more useful science.

the most difficult challenge in implementing DIRECT is
to fight our natural human preference for holding a single,
unambiguous internal narrative. The ability to formulatecompeting hypotheses has been praised highly in science— but, we still seem to prefer studies that end with a single, unambiguous conclusion. Of course, the greatest challenges lie beyond the realm of science. We are, at best,advisors to decision makers. We can do a better job ofidentifying scientific advances that are most likely to havea positive impact on decision making. We can also commit
to focusing our science in ways that provide useful descriptions of scientific uncertainty. But, ultimately, the impactof science will depend on the willingness of decision makers to act on the best information that science can provide.
In this area, scientists must act as all other citizens to encourage responsible action.

9. Challenges to implementing DIRECT

10. Conclusion to date

To date, the underlying ideas of DIRECT seem to resonate with both pure and applied scientist, regulators, andmembers of the general public. But, there are several questions that arise consistently. In general, concerns are focused on the practicalities of developing diverse model ensembles. I think that these concerns are based on the factthat DIRECT requires us to think differently about the purpose and process of model development. We are accustomed to viewing a model as the current best descriptionof a system. Our model building culture has developed tomake firm decisions about model structure, often despite
considerable uncertainty. Then, we take great pains to adjust model parameters to fit our model to existing data. It
can be argued that this is a clear example of confirmation
bias — we have a hypothesis (our model, a suspect) and
we actively seek to make it agree with our data (calibration, framing). This runs counter to the idea that scientists
should act as the most ardent critics of their own ideas byseeking the data that are most able to disprove them. We
contend that it is more scientifically valid, in addition to
being more useful, to intentionally develop an ensemble ofdivergent plausible models. Thus, as suggested in Team of
Rivals: The Political Genius of Abraham Lincoln (Goodwin, 2012), as Lincoln did when forming his cabinet, we
would form a set of rival models that provide greater confidence in their areas of agreement and important insights
in their areas of disagreement. Affecting this change in
our approach to modeling will require changes in our viewof the purpose of models. It will also require significant
changes in our approach to model building. In particular,
it will require that we develop tools that mimic currently
available automated parameter estimation algorithms thatare aimed at exploration of model structure. For scientists,

I have greatly enjoyed the hundreds of conversations that
I have gotten to have with hydrologists and decision makers during my 2016 Darcy Lecture series. I think that theDarcy Lecture is a rare opportunity to have a conversation around the world and throughout a discipline. What’smore, I think that these opportunities are increasingly important as science is called upon to help inform ever moresocietally important questions. It is not a task for everyone— there are costs to family and career for taking a year
out of your life. But, if you are interested in expanding
your thinking, promoting broader conversation, and simply seeing the world, there is no better experience that theDarcy Lecture or its cousins in other fields. This opportunity would not have been possible for me without the generous support of the many hosts who have made my visits
possible. Most of all, my year has been possible due to theselfless support of my wife (Leslie) and my children (Benand Luke) and the very generous contributions of time andgood will by my fellow faculty members in the Departmentof Hydrology and Atmospheric Sciences at the Universityof Arizona.

11. Audience questions — JapanI have maintained a blog throughout my tour. This
provides access to supporting material, photos of generalinterest, and a record of all of the questions that I was
asked (and my answers) throughout the year. I learned agreat deal from these questions and discussions that followed from them. I would encourage you to visit thesite (https://darcylecture2016.wordpress.com/ — the page
‘Topics for Discussion’ includes the questions) for more
information. But, to give a flavor of the types of questions

資料：Improving the impact of soil science by reconsidering the interactions of data, models, and decisions

59

that I was asked, I am including the questions that I was
asked during my three stops in Japan. My answers can befound in this article and on the blog!

10/24/16 — Satoshi Izumoto — Tokyo University, Japan

10/28/16 — audience member — Kyoto, Japan

10/24/16 — Toshiko Komatsu — Saitama University,
Japan— How do we form a model ensemble in a way that avoids
bias?
10/28/16 — Masaru Mizoguchi — Tokyo University,Japan— Following on your element of storytelling — I havespent a lot of time working on the problems related to
Fukushima these five years. In this case, there are manyfacts and many people telling different stories. The problem is — even if we make a good, scientifically informed
decision, many non-scientific people don’t believe the scientific basis of the decision. Maybe this is most serious problem in science and technology caused by the
Fukushima Daiichi nuclear disaster in Japan. So, I thinkthat it is also important that we find better ways to communicate our science to the public to support decisions thathave been made using science.

— When can you use statistical models and when do you
need a physical model?

— It is not clear to me how your results for the contamination example relate to a specific decision that the client
had to make. Can you clarify?
10/21/16 — Mitsuyoshi Ikeda — Nagasaki, Japan —Japanese Association of Hydrogeologists— I appreciate your use of poetry in a scientific talk. I will
return this with a question inspired by Shakespeare. I haveover 30 years of experience in geologic modeling. I havefound that the relationship between cost and improved understanding is not linear — there are times of very steep
learning for relatively little cost and other times of little
learning at high cost. So, how can we find the informative conditions? To measure or not to measure, that is thequestion!

References
10/28/16 — Audience Member — Kyoto, Japan— How did you choose the locations for the nine observations in your example?
10/26/16 — Ken Kawamoto — Saitama University, Japan— Sometimes it can be unclear who the actual decision
maker is. What do we do in these cases?

Bredehoeft, J. (2005): The Conceptualization Model Problem —Surprise. Hydrogeology Journal, 13: 37–46.
Eagleman, D. (2012): Incognito: The Secret Lives of the Brain.
First Vintage Books edition. Vintage Books, New York.
Goodwin, D.K. (2012): Team of Rivals: The Political Genius of
Abraham Lincoln. Simon & Schuster, New York.
Gould, S.J. (1980): The Panda’s Thumb: More Reflections in
Natural History. Norton, New York.

10/26/16 — Yosuke Matsuda — Mie University, Japan

Kahneman, D. (2011): Thinking, Fast and Slow. Farrar, Straus— How does your approach relate to global climate change
discussions?

Kikuchi, C.P., Ferré, T.P.A. and Vrugt, J.A. (2015): On the opti-

and Giroux, New York.
mal design of experiments for conceptual and predictive dis-

10/24/16 — audience member — Tokyo, Japan— How do you make a decision if you have multiple models that make different predictions? In particular, what doyou do if you have multiple different groups who are involved in the decision making process?

crimination of hydrologic system models, Water Resour. Res.,
51: 4454–4481, DOI:10.1002/2014WR016795.
Thaler, R.H. and Sunstein, C.R. (2009): Nudge: Improving Decisions About Health, Wealth, and Happiness. Penguin Books,
New York.