From "Actionable Information" to High-Level Decision Support
"Actionable Information:" Developing a Dynamic Information Framework
for Decision Support.
Key to better decision making in the face of complex challenges
is "actionable information" - " the synthesis and "bringing to life" of the key
information that integrates the end-to-end knowledge required to provide the
high-level decision support to make the most informed decisions. The objective of the Dynamic Information
Framework (DIF) is to proived such information, as the foundation for a high-level
decision support, providing (quantitative) analyses of complex, interdependent
environmental problems. The strategy is via a "geospatial" gateway for dynamic
understanding, management, and planning of the landscape. The functional
components of DIF of include data modules (base and directed data layers) focused
on synthetic objectives, provided by computation engines (geospatially-explicit,
process-based, cross-sector simulation models) facilitated input/output
(including visualizations), and decision support system and scenario testing
capabilities. The objective is to provide a "time machine," for
The DIF is based on how water and the landscape (topography,
soils, vegetation, biodiversity) converge, in space and time. The central
thread is that water provides spatial, time-based, and operational connectivity
among the multitude of DIF layers, because everyone understands water (one has it or not, it is of adequate quality or not, it is available in the
right place at the right time or not). Most important it is observable,
measureable, subject to being modeled, as
a function of known drivers and spatial-temporal relationships.
To meet these challenging criteria, the modeling effort draws on
the emergence of a new generation of Earth
System Science, based on the rapidly evolving capabilities for addressing
global change issues. Earth System
Science involves use of satellites, new generations of dynamic computer
"models," field measurements focused by
model requirements covering wide areas, and, especially, a thinking and
practice of "integrated systems." Fundamental to these is a new class of open
and publically accessible hydrology models, which can be regarded not only as
hydrology models, but also as overall landscape models, because of the
processes (and data layers) they represent. The requirements of the model
dictate what data modules must be assembled, and the structure of the model
allows the production of the output variables, which ultimately provide the DSS
with the information to make decisions.
Establishing the process to actually execute such
models is not a trivial process, for several reasons. The information required comes from multiple
sources, from individual rain gauges to statistics on hydropower and grain
yields, to glacier melting to rock types. The information required comes from
multiple disciplines, which presents problems with even communication between
specialists. Existing data holdings are not always readily obtainable,
sometimes for institutional reasons, or have to be purchased. New field
measurements, especially holistic and cross-boundary, are challenging. Handling such diverse data and
executing models is not straightforward. There are very real problems in
converting data streams into useful information that go beyond a database.
Perhaps most challenging is how to not only create such information, but how to
get it into the hands of users of different levels, from the specialist to the
local and regional decision makers to the local farmer or fisherman. Hence it is necessary to be clear and explicit about
exactly what information is required by each stakeholder.
The Computation Engine, and
Earth System model, as the core of the computation engine, could be provided by
different geospatial hydrology models. For example, the Variable Infiltration
Capacity (VIC), is a so-called semi-distributed grid-based mesoscale to macroscale
hydrologic model (Liang et al., 1994, 1996, Nijssen et al., 1997, 2001a, b),
which represents explicitly the effects of vegetation, topography, and soils on
the exchange of moisture and solar energy between land and atmosphere. The core VIC model can then be coupled to
other models, and compared to independent data sources, to ultimately provide
the basis for management-focused applications in the DSS.
information required to support modeling must, of necessity, be derived from
multiple sources. Even in very remote, data-sparse regions, global coverages
can provide at least first-order estimates (think Google Earth). It is
convenient to first think in terms of input data; i.e., those data required for
running the model. The first type of input data are essentially static (don't
change over the course of the model run). This includes the basic structure of
the river basin (topography, river networks), soil properties (how deep are the
soils, what is their texture), vegetation properties (how deep are the roots,
how tall is it, leaf area index).
second type of input data is climate forcing, which includes the daily average
precipitation, minimum and maximum temperature, and winds. These data are more
dynamic, as they change over the course of a model run, and essentially "drive"
the model. These data can be derived from meteorological observation networks,
climate weather models, or directly from satellite observations. Changing the
climate forcing data, while maintaining the more structural data constant,
allows testing of different climate scenarios.
third type of data required for the modeling effort is actual observed
discharge data. These data are used for model calibration (to adjust the model to observed values at
several points, as input data are rarely good enough to specify exactly how the
basin is structured) and validation (to test the calibrated model, against
observed data from a different time period than used for calibration).
The results of complex, multi-layer, 4-dimensional (including
space, time) analyses of landscapes and their resources is difficult enough for
the specialist to understand, never mind the non-specialist. Visualizations as
the medium are part of the message. Hence substantial effort has gone into how
to present spatial and temporal information to a broader audience, from
students to decision makers to the general public. Examples of this philosophy
represent the underpining of this website.
The Information Environment
To keep track of all of these elements, it is necessary to
establish a "Dynamic Information Framework" (DIF, Figure 4), with the objective
of providing a consistent theoretical basis, overall capability of integrating
across sectors, and providing information using recent advances in
cyberinformatics (including delivery of advanced visualizations, to enable a
viewer to more readily assimilate the message being delivered). The DIF then
provides the core for a DSS.
The process of creating the DIF provides
- An integration of data from multiple sources (of interest to all
- Provides a
means for interpolation of sparse data
quantitative baselines and an instrument for analysis of interdependent
the basis for cross- scale/ upscaling analyses
- Provides a
foundation for "scenarios"
- Perhaps most importantly, the construction of a DIF promotes
cooperation and communication between individuals and sectors that rarely, if
Lessons Learned and Future
Developing a fundamental
understanding of how all sectors of the landscape are interdependent, then
creating a decision-evaluation framework and institutional support structure
that can be accessed by all stakeholders is a critical step in the process of
developing viable strategies for adaptation to global changes for specific
regions and countries. The DIF concepts represent advances towards providing
such a framework that has become practical in today's world. It is not a
shrink-wrapped, commercial application; rather, it is a process and framework
that must be jointly developed and evolved for specific applications. The
"environmental cyber informatics" driving it is not trivial; there is a
tremendous amount of "computer" detail that goes into making a DIF functional.
The capability can't be represented not only as research at major universities,
but operationally at Ministries and universities and field camps and in the
media in the developing world. The expectation is that the "cost-benefit" ratio
of executing a DIF pays off many times over, given what is at stake.
Developing a fundamental understanding of (1) how all sectors of
the landscape (from water movement to species production and distribution), are
interdependent, then (2) creating a decision-making framework and institutional
support structure that can be accessed by all stakeholders reflecting that
understanding, is a critical step in the process of developing viable
strategies for adaptation to global changes. The DIF concepts represent advances
towards providing such a framework that has become practical in today's world.
The capabilities now being provided though "Earth System Sciences,"
with its utilization of geospatial information from satellites combined with
ground measurements, internet-accessible databases, and dynamic process-based
models provides tools that are simply of a new generation. The capabilities for
advanced visualization not only make it easier for the advanced practitioner to
understand his/her own results, but to convey them to a much broader audience,
including decision makers.
Highly promising prototypes have been developed. But they need
to be taken to the next level, of sophistication and application. What is
needed to take DIF to next level and how can this is
The environmental cyber informatics" driving the DIF is
pushing the limit. There is a tremendous amount of "computer"
detail that goes into making a DIF functional. A critical component of this
endeavor is that the capability is represented not only as research at major
universities of the U.S. and Europe, but operationally at Ministries and
universities and field camps and in the media in the developing world. This
presents important challenges.
To address the issues on the table, the component models are
being pushed to the limits of what they were designed for. Streaming
information from multiple information sources (satellites, weather records and
operational climate models, soil profiles, stream gauges, species lists and attributes),
then ingesting that information into the models is hard work. Producing
compelling visualizations and interactive scenario generation capabilities to
the non-specialist, and to make that available through a web-portal, is
The ability to convey the DIF distributed data, simulation
platforms, and products through the "Cloud" could greatly enable the
The most sophisticated, yet usable, models must be deployed. Multiple earth-system models being developed around the world today, each with
its own attributes (and liabilities). The objective of the DIF isn't to a
priori develop component models, but to provide a framework for and to harness
the power of the most suitable models for the tasks at hand. Of particular
relevance is being able to improve and couple models
Daily to seasonal weather (especially for operational models). If
Hydromet and Druk Green Power Corporation of Bhutan can do a better of job of
predicting the weather of the next few days, they can do a better job of
predicting how much power they can commit to sell to India, while keeping track
of flooding risks. At planting time, a farmer along the Zambezi would love to
have an idea of what the weather might be in three weeks, so he would know whether
it is safe to plant the higher risk/higher value crop. But today's ability to
do that is limited.
and climate impact, scenarios. This is obviously an area of major focus,
throughout the world, for the decades out time horizons (which is on the time
scale for major infrastructure investments). But to be especially relevant to a
particular region, how to downscale, and to ingest the results of model
ensembles for those regions is necessary. On shorter time scales, if the tea
farmer of Rwanda has an idea of climate shifts three to four years out, she can
produce cultivars responsive to those
Regional water balances and hydropower. The
degree to which the water resources of many regions are not well-defined is
stunning. The partitioning between surface and groundwater, and groundwater
depletion through irrigation, is not even a blackbox; it is a virtual unknown.
The Aral Sea has shrunk so much because the original "planners"
had little idea of how much water (aka precipitation) was available (the region
still has little idea). Appropriation of surface waters between domestic use,
commercial use, agriculture, and hydropower is one of the most profound of
immediate environmental challenges. Current regional-scale hydropower analysis
models (capable of computing the cumulative effects of, say, the upper and
lower Mekong dams) are surprisingly primitive. The current era of
"Hydropower Renaissance" has taken on new meaning, in the post-Japan
tsunami world. In an even broader context, the ability to predict water and
temperature distributions impacts human health
Terrestrial carbon and agricultural productivity. A
fundamental issue is, of course, how much carbon is sequestered and released
across the landscape, as function of what activities. Agriculture in much of
the developing world is still quite primitive. Models that can relate
productivity of specific crops to soil fertility and soil infiltration, the
relative merits of different fertilizers, and to (changing) water availability
and temperature would enable adaptation including in the short
The (physical) structure of the landscape and biodiversity. An
important issue underlying biodiversity is, why are the species where they are?
History? Current conditions? An interesting aspect brought by the DIF construct
is to be able to "map" the biophysical world, from topography to
soils to water, at a higher level than is traditional done, in many
regions. It might be provocative to explore the "biophysical basis of
The application of the DIF construct
to a range of data-rich environments would advance the development of how to
analyze complex regions. The range of pilot applications to date has been quite
limited, but has certainly developed the capability to move more rapidly to new
regions. Potential target areas might include the Amazon basin (including
linking the Andes-Amazon and the tropical river-to-ocean continuum projects)
and the Pacific Northwest, building from the Puget Sound and salmon
initiatives). Southeast Asia, mainland and insular, is ripe with possibilities
- and needs.
Essentially, a DIF is a numeric and quantitative "Commons,"
or meeting place, which builds on the legacy of knowledge from experience, with
the goal of "harmonizing" region function for multiple users. The
best way to develop something is to actually do it.