Visualization and Eco-hydrologic Models: Opening the black
box
Christina Tague
James Frew
Bren School of Environmental Science and Management
University of California, Santa Barbara, 93106, USA
Keywords: Ecohydrology, model, visualization, communication
Abstract
Earth system models synthesize the science of interactions among
multiple biophysical and, increasingly, human processes across a wide
range of scales. Ecohydrologic models are a subset of earth system
models that focus particularly on the complex interactions between
ecosystem processes and the storage and flux of water. Ecohydrologic
models often focus at scales where direct observations occur: plots,
hillslopes, streams, and watersheds, as well as where land and resource
management decisions are implemented. These models complement
field-based and data-driven science by combining theory and data to
create virtual laboratories. Ecohydrologic models are tools that
managers can use to ask “what if” questions and domain scientists can
use to explore the implications of new theory or measurements. Recent
decades have seen substantial advances in ecohydrologic models, building
on both new domain science and advances in software engineering and data
availability. The increasing sophistication of ecohydrologic models
however, presents a barrier to their widespread use and credibility.
Because they are “black boxes,” what the models actually do is rarely
clear—even to those who design and use them—and this opacity leads
to mistrust and complicates the interpretation of model results. For
models to effectively advance our understanding of how plants and water
interact, we must improve how we visualize not only model outputs, but
also the underlying theories that are encoded within the models. In this
paper, we outline a framework for increasing the usefulness of
ecohydrologic models through better visualization. We outline four
complementary approaches, ranging from simple best practices that
leverage existing technologies, to ideas that would engage novel
software engineering and cutting edge human-computer interface design.
Our goal is to open the ecohydrologic model black box in ways that will
engage multiple audiences, from novices to model developers, and support
learning, new discovery, and environmental problem solving.
Introduction
Earth system models are a broad class of tools that are most commonly
used for estimating “what” is likely to happen “if” a given
condition occurs. Ecohydrologic models are a subset of earth system
models that focus particularly on the complex interactions between
ecosystem processes and the storage and flux of water. These models span
a wide range of scales, from ecosystem models run at meter scale
vegetation plots — to the terrestrial components of global climate
models. In this paper, we focus on process or mechanistic ecohydrologic
models that encode science-based theory in order to explain how
hydrologic and ecologic systems function.
Ecohydrologic models are often used as predictive or scenario generation
tools to support environmental management, policy development, and land
use or climate change impact assessments. For example, an ecohydrologic
model might be used to assess how water quantity and quality may change
with changing vegetation management activities such as fuel treatments,
restoration, or harvesting. Ecohydrologic models are also used as
investigative science tools to enhance understanding of complex
eco-hydrologic systems; for example, explaining the potential mechanisms
that can lead to a decrease in runoff with forest thinning. Whether they
are used for prediction, assessment, or understanding, process-based
ecohydrologic models complement purely empirical approaches by providing
insight into why observation-based patterns occur. Process-based models
(a) often use observational datasets for initialization and
parameterization, but extend those observations by explicitly
representing mechanisms; (b) investigate both how and why a system might
evolve; and (c) can be used for hypothesis testing and to explore the
implications of theories. Often these theories arise from intensive
field experiments. In this case, mechanistic models serve as virtual
laboratories where scientists can explore the implications of
field-experiment-based findings across a wider range of conditions
Both of these uses of models—prediction in data-limited contexts and
exploring the implications of theory to enhance understanding—require
that models encode current scientific theories about how systems work.
Models in many ways are libraries of these theories, and models evolve
as theories evolve. In ecohydrology, this includes evolving theories
about how water is stored and moves through the subsurface. For example,
theory in catchment hydrology has evolved from conceptual models of
subsurface flow as driven by continuous transmissivity profiles, to
conceptual models of hillslopes comprised of areas that connect and
disconnect with water conditions (fill and spill approach) and account
for macropore flow (Beven & Germann, 2013; Bracken et al., 2013).
Similarly, new and sometimes competing theories in ecology are
represented in different sub-models of ecohydrologic models. These
theories address how plants use water and how they respond to water
availability; for example, ecologic theory has considered both hydraulic
failure as well as carbon starvation to explain drought-driven tree
mortality, and both can be represented in ecohydrologic models
(Mencuccini, Manzoni, & Christoffersen, 2019). Similarly, there are
competing theories and sub-model representations to address how plants
change their allocation of carbon (to stems, leaves, and roots) in
response to drought (Franklin et al., 2012).
The mechanistic process-based orientation of ecohydrologic models allows
them to be tools for prediction and scenario generation. They can also
serve as virtual laboratories and knowledge-based libraries of evolving
theories about how ecohydrology works, but this application remains more
limited. Recent papers advocate using process-based models for
hypothesis testing and behavioral understanding (Clark et al., 2017;
Schaefli, Harman, Sivapalan, & Schymanski, 2011); however, this
requires that theories embedded in model processes and their
interactions be visible to the user. The inherent complexity of
ecohydrologic models makes this a key challenge. Indeed, the complexity
of modeling tools is often cited as a barrier to their effectiveness for
informing decision-making, education, and science-based discovery (Coon,
Moulton, & Painter, 2016; Fatichi et al., 2016).
Advancing Visualization: Ways
Forward
We propose a range of possible ways to more effectively communicate the
embedded knowledge in ecohydrologic models through better visualization
of model structure, dynamics, and output. Many of these approaches will
be applicable to earth system models in general. We focus here on
terrestrial eco-hydrologic models because this domain combines visible
features such as forest structure and streamflow with invisible (to the
unaided human observer) stores and fluxes such as evapotranspiration and
non-structural carbohydrates. Further, much of ecohydrology is done at
the plot to small watershed scale, where visualization can be readily
tied to human-scale observations: what you can see when you go for a
walk. Thus, in ecohydrology we can take advantage of what is familiar to
most people in developing model visualizations.
We present four different ways forward. These are not necessarily
alternative approaches, but rather options that can be combined in
various ways for different audiences, models, and applications. Our
proposed options differ in the extent to which they can utilize existing
technologies, and in the users and communication goals that they target.
Before presenting these different approaches, we discuss the
significance of the end user.
The Audience
Consideration of the intended audience is always a central part of
designing visualizations. To enhance the credibility and comprehension
of ecohydrologic models and their output, we need to account for the
level of understanding and the existing conceptual and mental models of
the audience (Rapp, 2005). We consider five types of possible users;
although there is overlap between each category.
- The general public may be relatively unfamiliar with some
basic conceptual models of ecohydrology, or even the basics of the
water cycle. For this group, ecohydrologic model visualizations may
need to build these basic conceptual models and communicate why
ecohydrology is interesting or relevant for solving societal problems,
and motivate the user to engage with the material.
- Students who are actively engaged in learning the science of
ecology or hydrology may be more motivated than the general public
(even if it is simple motivation to pass a course), but may still need
introduction to basic conceptual models, albeit with latitude for
additional sophistication.
- Field and other domain scientists will have sophisticated
understanding of particular components of ecohydrology. A key goal of
this group is to use eco-hydrology models to place their
domain-specific theory or field research findings into a broader
context. For example, consider a field-based study that quantifies
plant species differences in drought response by measuring the soil
water potential that initiates stomatal closure. The ecohydrology
model might facilitate field scientists by estimating the implications
of these differences for plant water use given different
meteorological forcing conditions or different locations within a
landscape. This audience understands ecohydrology but may be
unfamiliar with the ecohydrologic model and how it represents the
mechanisms that they are interested in and the range of possibilities
for model output.
- Well-educated managers seek to use the model for scenario
development. Here the audience needs to understand how to think about
what is included and not included in the model to ensure that it is
appropriate for their decision-making context.
- Ecohydrologic modelers may want to compare different models,
or try to understand why the model produces the patterns that it does.
They may be interested in model sensitivity both to parameters and to
different sub-model structures.
Approaches for Improving Visualization of Ecohydrologic
Models
Our first two approaches focus on model output. Here we assume that by
helping users explore and play with model outputs, we can help them to
understand what the model does and enhance their learning.
Traditionally, investigating model output has occurred within the user
interfaces provided by the model’s native environment, or by ingesting
model output into generic data analysis software such as Excel, R,
MATLAB, etc. While these tools readily support complex data analysis,
they do not necessarily help guide learning about the model from its
output. Particularly, more novice users, such as the general public and
student audiences, may simply not know where to start.
We propose two potential ways to improve the visualization of model
output. We then turn to approaches that focus on revealing the structure
of the model (and its parameters). Visualization in this case presents
the basic assumptions, conceptual models, and ultimately actual
equations and parameters that are used in the model. The goal here is to
radically change how model documentation is presented and ultimately
generated.
Interactive Output
Animation
Augmented reality (AR) uses an increasingly available technology to
engage users with model output. Augmented reality may be particularly
valuable for engaging the public and students. The use of AR for STEM
education is a maturing field (see review (Ibáñez & Delgado-Kloos,
2018)) and many review papers focus specifically on earth system and
ecological science (e.g. Kamarainen, Reilly, Metcalf, Grotzer, & Dede,
2018; Klippel et al., 2019). Interactive virtual reality, games, and
virtual laboratories have shown promise as science education and
outreach tools (Castruccio, Genton, & Sun, 2019; Lv et al., 2013;
Potkonjak et al., 2016). While evaluating the impact of AR on learning
remains an area of ongoing research, there is evidence that the
immersive, play-oriented, experiential characteristics of AR tools
contribute to inquiry-based learning, and enhance student motivation and
spatial abilities (Akçayır & Akçayır, 2017). A key question is whether
AR-assisted learning actually leads to the construction of knowledge
(rather than simply memory and information).
The innovation of AR is linking “real world” objects with
“information about those objects.” Linking environmental model output
with observable features in a familiar landscape may help audiences who
are mistrustful, overwhelmed, or have other barriers to understanding
environmental science that are related to a lack of familiarity.
Ecohydrologic model output can be linked to a particular place (e.g.
“the” meadow beside a stream), or a particular type of object (e.g. a
tree). Mapping model output onto a more “real world” representation
allows the user to relate model output to actual landscapes or familiar
features within landscapes. At the same time, AR allows the user go
beyond what is normally visible. For example, AR could allow users to
“see” model estimates of evaporation from an actual tree in a
botanical garden.
Recent examples of AR for STEM education occur not only in the classroom
but in informal settings: museums, botanical gardens (Ibáñez &
Delgado-Kloos, 2018), even shopping malls (BBC’s AR version of Frozen
Planet). Virtual learning environments (VLEs) are ARs specifically
designed to support public and undergraduate education. A recent example
used a VLE to facilitate learning about environmental monitoring data in
the Online Watershed Learning System (OWLS) (Smith & Lohani, 2019).
Presenting model output in AR can also be combined with games. Reviews
of the use of simulation games and virtual labs for education generally
show modest gains in meeting learning objectives, including improved
understanding of core science concepts, and non-cognitive or conceptual
change, including changing attitudes about science and increased
motivation and engagement with learning. For example, Boyle et al.
(2016) reviewed literature that documented the effectiveness of games,
including simulations, for STEM learning—although results varied with
type of game, type of evaluation method, etc. Many of these reviews
target K-12, public, and undergraduate education. Recent games, such as
WWF’s Free Rivers, Biome Viewer or iBiome-Wetland, and DIY Lake Science,
have a hydrologic/ecologic focus, and some include simple models. If we
link more sophisticated ecohydrologic models with ARs and VLEs, the
biggest gains may be for both upper division undergraduate and graduate
students, and field domain scientists.
In summary, recent applications of AR and VLE suggest that games can
help the user engage with AR tools and guide them in exploring key
relationships. Since ecohydrologic models are frequently used to
generate “what if” scenarios, these games can be designed to provide
roadmaps to both scenario design and the ecohydrologic mechanisms that
lead to different model output under different scenarios. Consider for
example a game where the player scores points for determining whether
forest thinning impacts of streamflow are increased or decreased under a
climate-warming scenario.
Modern Data Mining Techniques for Output
Exploration
For more science-savvy audiences (more senior students, researchers from
other fields, and modelers themselves), facilitating rapid, structured
exploration of model output may be key for using ecohydrologic models
for hypothesis development and testing. One of the strengths of
ecohydrologic models is their ability to explore multiple interactions
between variables, accounting for co-variation across space and time.
Ecohydrologic models have been widely used to evaluate core
relationships among climate, hydrology and the biosphere. Classic
hydrology models explored the relationship between precipitation and
streamflow, and vegetation cover and streamflow; ecohydrology models
have been used to explore more complex multi-variable interactions, such
as between solar radiation (as mediated by aspect), soil water, and
plant productivity (Fatichi et al., 2016; Mencuccini et al., 2019).
In many of these papers, model experts selected from hundreds or
thousands of different model outputs and even greater numbers of
possible relationships in order to develop scientifically interesting
conclusions. Experts familiar with model assumptions, parameters, and
embedded ecohydrologic theory usually are typically the people who do
model output analysis. The experts’ backgrounds allowed them to choose
which relationships between model outputs to focus on, and to make
strategic choices about appropriate time and space scales. However, for
users less familiar with the model (such as field scientists) or
students with limited background in ecohydrologic theory, the sheer
range of model outputs often limits one’s ability to discover meaningful
relationships. In a recent seminar, we presented output from an
ecohydrologic model to computer science students. In their application
of deep learning techniques to this model output, they made mistakes
related to their lack of domain knowledge, such as expecting high
correlations between hourly streamflow and precipitation, without
accounting for the lag between precipitation and streamflow responses.
Making sense of model output can also be a barrier for domain
scientists. In addition to commonly available outputs such as
evapotranspiration or net primary productivity, model outputs could also
include intermediate variables (such as stem and stomatal conductance.)
Many models compute these but do not necessarily output them in order to
reduce data volumes. Model output may also be aggregated in space and
time in multiple ways (e.g. estimates of hourly evapotranspiration for
each point in a grid, versus aggregated annual evapotranspiration over a
watershed). Domain experts may be interested in exploring relationships
with intermediate or disaggregated variables, but may not even be aware
that they are available, particularly for multi-dimensional, complex
ecohydrologic models.
Improved user interfaces for exploring model output could be designed to
guide both novice and more experienced ecohydrologists in their
exploration of model output. A simple approach would be on-screen menus
that meaningfully organize core model outputs around particular topics
(e.g. precipitation – streamflow, vegetation - streamflow
relationships, soil moisture, topographic controls on biogeochemical
cycling, etc.). Emerging toolkits such as Shiny (for the R programming
environment) allow these types of interfaces to be easily created. More
involved approaches for efficient exploration of model output space
might use formal semantic indexing and other architectures for
organizing information. Studies have shown that these semantic tools can
facilitate user searching in geospatial databases (Janowicz & Hitzler,
2017; Jiang et al., 2018). Interfaces that facilitate searching,
however, may not help novice users who will simply be overwhelmed by the
number and diversity of output options. For these users, an interface
that links outputs to conceptual models may be more useful than
interfaces designed for searching.
Finding possible outputs is a first step; however, exploring
relationships among model outputs is where learning from models occurs.
Providing tools for rapidly finding salient relationships in
multi-dimensional data may be critical. A wide range of data mining and
machine learning techniques are increasingly being used for earth system
science data (Bui, 2016; Liu, et al., 2018; Shen, 2018,). Many of these
techniques, while developed for observational data, apply equally well
to exploring ecohydrology model output. Machine learning techniques that
account for temporal lags and can deal with spatial data are
particularly relevant (Shen, 2018; Papacharalampous, Tyralis, &
Koutsoyiannis, 2019).
Data mining techniques typically require domain knowledge to facilitate
selection of appropriate variables (and their time and space scales).
While blind application can lead to unexpected discoveries, many argue
that domain or expert knowledge is needed to fruitfully apply data
mining techniques (Gibert, Horsburgh, Athanasiadis, & Holmes, 2018).
When the user is a field scientist or other domain expert, their
knowledge of ecohydrologic principles can guide their selection of model
outputs. For these users, who may not be familiar with data mining
options, visual interfaces could direct them to potential data mining
tools and their application to model outputs. For example, an interface
could provide a menu of structural equation models that represent some
commonly explored relationships between model outputs.
Applying machine learning techniques to model outputs can also help
model developers with parameter calibration and evaluation, and with
sensitivity and uncertainty analysis. Hybrid approaches that combine the
strengths of machine learning for improving the parameterization of
physically based approaches have been shown to improve the reliability
and accuracy of model predictions (Booker & Woods, 2014; Bui, 2016;
Clark et al., 2017). Here again model interfaces could be designed to
facilitate the application of machine learning techniques to model
output. A key first step is to develop tools that streamline the
ingesting of model output into available machine learning software.
Post-hoc visualization of conceptual
submodels
For many audiences, particularly domain scientists who know something
about the processes being modeled, examining model output may not be
enough. These users want to know something about the underlying
assumptions, the processes that are included or excluded, the
mechanistic detail with which processes are represented, and how these
process representations are parameterized. The traditional source for
this information has been model documentation, ideally in peer-reviewed
journals. Frequently these papers contain cartoon figures that, to
varying degrees, capture what the model represents. Often, however,
these figures are not detailed enough, providing only a superficial
high-level representation of the model. More detailed figures can also
be problematic when their level of detail makes them difficult to parse.
Many modelers figure out what is in a process-based ecohydrology model
by reading the source code. If the code is accessible (e.g. open-source)
and is well-structured and well-documented, then this approach can work
for an experienced model developer, but not for many domain scientists.
Interactive visualizations of conceptual hierarchical models might offer
a more user-friendly way to communicate underlying model structures.
Similar visualization techniques have already been used to support the
coupling of disparate models (or submodels) within flexible modeling
systems (Coon et al., 2016). In addition to supporting the software
engineering task of coupling different models, these visual tools can be
used post-hoc to support model documentation. These types of approaches
for visualizing model structure are particularly valuable when there is
consistency in how model structure is described. For example, CDMS, a
model coupling system, provides a GUI to support model coupling
(Peckham, Hutton, & Norris, 2013). As part of this tool, model
developers provide model descriptions (as an HTML help document) in a
consistent format that is easily accessible as part of the coupling
framework. These documents are standardized to include an extended model
description, references, the main equations of the model, sample input
and output, and acknowledgment of the model developer(s). Similarly,
model development frameworks that use tools like dependency graphs are
examples of how these structured descriptions of models can be generated
(Coon et al., 2016). These model description tools are often text-based,
as opposed to graphical, but they are a step in the right direction and
could be expanded upon to provide visual representations.
Ultimately the presentation of model structural information needs to
address multiple audiences. The complexity of underlying model
structures means that a single conceptual figure is unlikely to satisfy
the needs of diverse audiences, or even a single user who seeks to
understand multiple components of the model. To address these, visual
representations will need to be hierarchical, so users can explore
details as needed. Hierarchical structures also reflect the underlying
best practices in software engineering that build complexity through
modularity.
The hydrologic community has already had some success with improving
metadata and documentation standards in order to improve data
accessibility and usability. Hutton et al. (2016) argue that a similar
effort to define metadata standards for model documentation is needed.
We concur but also emphasize that a visual hierarchical approach that
can support different audiences is needed. Community databases for
sharing models and data such as Hydroshare (Horsburgh et al., 2016)
provide platforms and metadata standards that contribute to sharing.
Improved visualization tools would complement and extend these efforts.
Automating visual Representation of model
structure
The previous section makes the case for visual representations of model
structure within a user interface that facilitates exploration from the
diverse perspective of different audience types—from a public that may
benefit from a pictorial representation of a carbon cycle, to a field
scientist who may want to know the details of the submodel used to
estimate photosynthesis.
However, such visual representations can be difficult to create and
deploy in conjunction with existing models. Incremental development by
generations of researchers more trained in science than software
engineering can lead to models whose code is difficult to understand.
Model documentation to support replication of research results is
increasingly required for peer-reviewed publications, but this may be
limited to documenting workflows, data, and model structures, rather
than multiple visual representations to support understanding of model
outputs and/or structure.
The challenge in this case is to reduce the time and effort required to
visualize model structure. Tools that at least semi-automate model
documentation are increasingly available and recommended for earth
system and biological science model development (Karimzadeh & Hoffman,
2018). Adding visual elements to code that could then be used to
generate visualizations on the fly might be a next step. Efforts to
standardize metadata for model description move in this direction (Gil,
Ratnakar, & Garijo, 2015), but they do not necessarily provide easy
access to model structural components, nor are they searchable and
visual.
Graphical/visual programming environments, or model building tools that
translate visual elements into code, have been used for decades to help
students learn to develop models (STELLA is a well-known example, but
there are many others (Navarro-Prieto & Cañas, 2001), as well as domain
specific visual programming environments tailored to hydrology (e.g.
GeoVISTA (Takatsuka & Gahegan, 2002)) and biology (e.g. BioUML
(Kolpakov, Puzanov, & Koshukov, 2006)). While these tools have
strengths within an educational context, more complex ecohydrology and
earth system science models do not readily fit into these
environments—because they require computational efficiency, code
architectures, and memory resources that these tools generally do not
support.
In their relatively recent review of virtual worlds, Potkonjak et al.
(2016) did not find examples where the complex system dynamics of
individual components (e.g. a complex model of photosynthesis within an
ecohydrology model) were incorporated into these worlds. In other words,
there remains a gap between highly visual model interfaces for
education, and the complex ecohydrology models that support developing
new science and science applications. Addressing this gap may require a
community, similar to the multi-institution large-group development of
virtual laboratories, that support shared innovation in areas such as
physics (Potkonjak et al., 2016).
What is needed is essentially a reverse engineering of these approaches,
where visual or graphical elements are linked with the text of the
source code comprising ecohydrologic models. New advances in
visualization languages and tools that provide such graphical notation
may be a way forward (Holm-Peterson et al., 2014). The ideal interface
would be multi-level, adaptable, searchable, and provide information on
inputs, outputs, model structure, and submodels.
What’s next
In this section we propose three areas where we believe progress is most
needed and can beneficially advance by “opening the black box” of
ecohydrologic and earth system models.
Software engineering
Software engineering best practices applied to current and future models
can go a long way towards facilitating visibility into model structure
and operation. In addition to the obvious benefits of readable code (the
software is its own documentation) and component reuse (portability of
the human reader’s knowledge), there are two structural benefits that
can specifically impact model visualization:
- Component graph: To the extent that the model’s software
modules directly reflect the model’s conceptual components, the flow
of data and control during the model’s execution can be automatically
constructed from a combination of function call graphs (static) and
execution profiling (dynamic). The resulting graph can serve as a
scaffold for model visualization, as well as important documentation
in its own right. This strongly argues for extra engineering effort to
preserve the structural relationship between the model’s conceptual
and software components.
- Rendering hints: Given a component graph, it is
straightforward to visualize a model as “boxes and arrows”; i.e., as
component processes and the flows of data and control between them. It
is more challenging to visualize the operation of components or data
using visual metaphors appropriate for the specific content. We
suspect that semantic tagging of model components and data will prove
useful here. For example, a submodel representing photosynthesis in
conifers could be tagged such that the light wavelengths and leaf
structures involved could be rendered by a generic visualization
environment that had no specific knowledge of the biophysics of
photosynthesis or vegetation.
In both of these cases the goal is to automate to maximum extent
possible the generation of visualizations of model structures and
operation.
Visualization
effectiveness
Noteworthy in reviews of the effectiveness of AR and gaming or STEM
education are calls for additional “metacognitive scaffolding and
experimental support” (Ibáñez & Delgado-Kloos, 2018). In other words,
users (especially novice ones) often need guidance in the use of such
tools. Guidance may be part of the interface, such as games with
increasing levels of complexity. These recommendations are likely to be
applicable in the design of more sophisticated tools for using model
output to understand the underlying science in the model.
Critiques of AR frequently emphasize issues with navigation and
usability; specifically, the problem of dealing with “too much
information.” Similarly, reviews of virtual laboratories and games for
science education often find that the complexity of the interface is a
barrier to its ease of use and effectiveness (Boyle et al., 2016;
Potkonjak et al., 2016). Not surprisingly “Ease of use” and
“perceived usefulness” are commonly cited attributes for the
effectiveness of games and other technology for STEM education (Šumak,
Hericko, & Pušnik, 2011). Further, some studies comment on the
challenge of differentiating between “games” as entertainment and
games for more serious learning (Boyle et al., 2016).
Reviews also consistently advocate for more evaluation, and it is clear
that more work is needed in designing games and visual interfaces in
general to best achieve objectives, whether these are knowledge
acquisition, or behavioral, attitudinal, or motivational change. Success
in interface design for improving the use of ecohydrologic models will
depend on how well tools are designed, and on using iterative feedback
to evolve the design. Formal techniques for design and assessment can be
used for this evaluation (e.g. Technology Acceptance Model (Šumak et
al., 2011), User Engagement (O’Brien & Toms, 2008), ARCS (attention,
relevance, confidence, satisfaction) design principles for effective
learning (Keller, 2008), and measures that formally test knowledge
acquisition).
Community support
In this paper, we argue that to make more effective use of ecohydrologic
models for science discovery, education, and environmental problem
solving will require transforming how we visualize not only model
results, but also the knowledge embedded in these digital laboratories.
In our review we have identified several promising directions that
include new technologies and new approaches, and we acknowledge that
there are likely many examples that we have missed. At this time, these
technologies and practices are not widely used within the ecohydrologic
modeling community and further existing tools simply do not go far
enough. We need new strategies that can be tailored to address multiple
visualizations objectives and multiple audiences.
To do more, however, will require the engagement of the ecohydrologic
community and beyond. This engagement needs to be supported on multiple
fronts. Improving model visualization requires time and effort, so there
must be incentives to support this. Within the academic and science
funding communities, there are increasing efforts to provide credit for
software products as well as publications, and to support new
cyberinfrastructure (Howison and Bullard, 2015, Stall et al., 2018).
This is a step in the right direction. But significant advances will
require funding that supports extended interdisciplinary collaborations
such as working groups and centers. It is only through interdisciplinary
collaboration among model developers and users, field data scientists,
communication experts, and computer scientists that we can build a new
generation of environmental model visualizations. Without engaging
experts in those fields, ecohydrologic modelers
will miss the rich and
evolving body of work on the technologies, their best practices, and
perhaps most importantly—the art of effective visualization design for
communication and learning. In the end “opening the black box” of
environmental models will require opening the box of how we architect
environmental models and present their output. This will take real work,
but results could greatly accelerate the contributions of ecohydrologic
models for evolving science and bringing science findings to the many
communities that can benefit from this knowledge.
Acknowledgements
NSF-SERI fire (Grant 1520847); Crossroads program at UCSB, and members
of the Crossroads working group (Hollerer, Wendy Meiring, and graduate
students in the Opening the Black Box of Environmental Models seminar);
Janet Choate for her help with figures and editing. Ethan Turpin for
insights into model visualization from an artistic perspective.
*Data sharing not applicable to this article as no datasets were
generated or analysed
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Figure 1: An example of a hierarchical structure to visualize
sub-components of ecohydrologic models