1. Overview
South Asia is a flashpoint for natural disasters with profound societal impacts for the region and globally.
Half the world’s population depends on the region’s great rivers, the Indus, Ganges, and Brahmaputra.
The frequent occurrence of floods, combined with large and rapidly growing populations, ongoing crossborder conflicts, high levels of poverty, and unstable governments, make South Asia highly susceptible to
humanitarian disasters. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion
of the Kosi River in India, and the 2010 flooding of the Indus River in Pakistan exemplify disasters on
scales almost inconceivable elsewhere—disasters devastating local residents and communities while
posing significant threats to the U.S.’s and other nation’s security interests and assets in the region. The
challenges of mitigating such devastating disasters are exacerbated by limited flood forecast capability,
lack of forecast use and sharing in and between countries, and the transboundary nature of the hazard.
At the same time, the South Asia situation poses an appropriate and valuable context for the
interdisciplinary study of how technical and social factors at multiple levels and scales positively or
negatively impact societal vulnerability and resilience to hazardous events. Despite past flood-related
disasters, high risk of more severe disasters, and the increasing availability of forecasting, only limited
advances have been achieved in improving forecasting leading to risk mitigation. Many national
meteorological and hydrological agencies in South Asian countries provide at most a 1-3 day forecast of
streamflow and potential flooding, often with no warning at the upstream border (examples include
Nepal/India and India/Bangladesh). This is in part due to a lack of streamflow data sharing among the
different countries. Studies undertaken using ensemble weather forecasts have begun to address
technological gaps in meeting specific, regional flood vulnerability problems (i.e. data sharing, and
forecast lead time) such as for Bangladesh (Hopson & Webster, 2010; Webster et al., 2010).
Consequently, flood prediction partnerships are suggested as a means to bridge the gap between the
existing, global scale, long lead time weather prediction, and actual implementation and use of the
resulting much-enhanced flood prediction capability (Webster 2013), since it is clear better flood
discharge prediction will not on its own result in effective outcomes (Syvitiski & Brakenridge, 2013).
There are major gaps in understanding how to ensure forecasting and monitoring improvements result in
enhanced mitigation of flood risk and flood damage. In such circumstances, trust is especially important
for successful policy implementation and related behavior changes (e.g., Leach & Sabatier, 2005;
Schlager, 1995; Zafonte & Sabatier, 2004) because individual decision makers and policy makers are
negotiating uncertain contexts compounded by the delivery of probabilistic forecasts, which are
inherently uncertain.
Our interdisciplinary research will increase understanding of these interconnected, technical,
social, and policy-related dynamics. It will take an essential step in developing sustainable
approaches to mitigating hazards in policy contexts where new technologies are being implemented
to advance flood-forecasting science. Specifically, in response to the catastrophic threat posed by
flooding in South Asia and the intellectual challenges they pose to understand and mitigate them, we have
formed the South Asia Flood Prediction Partnership (SAFPP)—an interdisciplinary team of researchers
from the University of Nebraska, U.S. National Center for Atmospheric Research (NCAR), and the
Dartmouth Flood Observatory at the University of Colorado (DFO). Our partnership brings together a
long history of advancing knowledge in water policy and behavioral changes; trust studies (including
research related to water resource and hazards); multilevel and latent variable statistics needed to expand
the capabilities of social science and policy scientists' to simultaneously model multilevel influences; and
geoscience, hydrological modeling, flood forecasting, and remote sensing. Together the SAFPP proposes
linked activities to mitigate the impact of severe floods in the Brahmaputra, Ganges, and Indus basins,
while advancing a new framework and methodologies for examining policy implementation and trust at
multiple levels (e.g., micro, meso, and macro levels). Our work will determine the technical,
psychosocial, and policy impacts of forecast improvements and warning-related societal communications
and responses. Specifically, we hypothesize trust is a key indicator of policy uptake and successful
implementation. We predict trust will affect how forecast information is used, or actively resisted, and in
turn shape what policy options at different levels are feasible and considered worthwhile. Our proposed
work will leverage our interdisciplinary team’s prior NSF-, DOE-, NASA-, and USAID-funded research
on flood forecasting, near real time flood remote sensing, measurement and mapping, social science of
trust, and multilevel, structural equation and latent variable statistics for contextual and multilevel data.
As indicated in our letters of partnership from the Flood Forecasting Division of the Pakistan
Meteorological Department (PMD), the Regional Integrated Multi-Hazard Early Warning System for
1
Africa and Asia (RIMES), and the World Bank, these agencies and organizations will enable access to the
decision-making processes, support, and data we require to conduct our inquiry.
2. Objectives
By accomplishing the following specific objectives we will test our central hypotheses that: a) innovative
data assimilation approaches will advance big river flood forecasting, b) public availability of such
forecasts will alter societal reaction, including national and state water policy responses, and c) successful
policy reform, including needed changes in the use of flood forecasts in decisions affecting tens of
thousands of inhabitants, depends on the presence of specific patterns of trust at multiple-levels.
Objective 1: Implement long-lead, public-access flood forecasting systems for the Brahmaputra,
Ganges, and Indus basins and quantify the benefits of data assimilation of satellite-derived river
discharge estimates on improving forecasting skill. The Climate Forecasting Applications for
Bangladesh (CFAB) river flow forecasting system (Hopson & Webster, 2010), currently operational only
for Bangladesh, will be extended into India and implemented basin-wide for the Indus. We will then
improve existing forecast modeling through assimilation of satellite microwave remote sensing of river
discharge, creating an enhanced lead-time (10-15 day) probabilistic river flow forecasting scheme. This
can provide operational updates of the forecast model’s in-stream flows and soil moisture conditions. We
will test the skill gains provided by each data assimilation component compared to the initial forecast
system as a function of watershed, spatial-scale, and forecast lead-time. We will also investigate the
predictability improvements by using multi-model weather forecasting fields in the hydrologic forecasting
system, allowing us to compare and contrast uncertainty reductions from data assimilation systems to
those of improvements in weather forecasting.
Objective 2: Transform forecast discharge values (flood peak discharges) into inundation extent
maps, as derived from archival analysis of microwave and optical sensor imagery monitoring actual
inundation extent along the rivers. A unique enhancement to river flow forecasting will be to transform
modeled forecast discharge peaks into inundation extent maps captured from historic flood monitoring
imagery. Importantly, this provides accurate geolocation for disaster relief efforts in complicated terrain,
where numerical inundation modeling would normally fail and is in any case computationally challenging
even when data such as channel bathymetry is available. This will be accomplished using analogue
approaches to select pairings of archived, remote sensing mapping of inundation extent matched with
discharges similar to model predictions.
Objective 3: Test a model of trust, expand capacity for contextual/ecological modeling through
the use of multilevel and latent variable statistics, and investigate policy uptake and implementation
as we integrate the forecast information into reservoir regulation, transboundary water
information sharing, and national and regional disaster planning. We will test our multilevel model
of trust in policy uptake and implementation contexts, at the same time as we provide in-country
education, capacity building, and technological advancements to ongoing flood forecasting systems, and
investigate ways in which the project’s forecast information and technological development can be
incorporated into ongoing forecasting systems and decision-making strategies in Bangladesh, India, and
Pakistan. To achieve this objective, we will conduct interviews, focus groups, and surveys over the same
time period as we are providing in-country input and training workshops for hydrologic engineers and
scientists on the forecasting technologies developed during this project. We will analyze the data to test
the utility and feasibility of competing statistical methods for testing multilevel social and policy models.
3. Expected Significance
Intellectual merit. Through this proposal, our South Asia Flood Prediction Partnership (SAFPP) will
significantly advance the technical and social effectiveness capability of long-lead time flood forecasting
for the Indus, Ganges, and Brahmaputra basins. The SAFPP will further develop and deploy real-time
flood-related hazards monitoring and forecasting which will, for the first time, assimilate satellite-based
measurements of river discharge and reservoir storage status into model-based predictions. We also will
assess and reduce potential barriers to policy uptake in the impacted regions that would interfere with
using this vital forecast information to reduce societal vulnerability.
Specifically, the proposed research is transformative in that it combines advances in weather
forecasting and remote sensing, into a cohesive framework for successfully integrating and advancing
flood forecasting capacity and subsequent use. Development of data assimilation approaches to
2
incorporate remotely-sensed discharge into coupled hydrometeorological forecasting systems can advance
river forecasting, in South Asia, the U.S. and globally. Evaluating the most beneficial pathways to
assimilate remotely-sensed discharge into these systems will reduce forecast uncertainty. Doing so places
this work at the forefront of research into river monitoring and forecasting technology development in
data-poor global basins and thus defines future research trajectories in this field.
At the same time and integrated into the forecasting advances, we will advance knowledge about trust
and its impact on policy uptake and implementation. Within the social science of trust, there is a need to
develop a comprehensive, multilevel framework to guide the study of institutional trust and confidence
(Li, 2012). Nowhere is this need greater than in the context of building resilience to disasters and creating
the preconditions for sustainable development. Thus, in this project, we will advance the understanding of
conceptualization, measurement, and development or emergence of trust within and across levels of
decision making and within society (Kozlowski & Klein, 2000). Such understanding is especially
important in policy contexts involving multiple actors facing a potential set of action options through
which they may collectively produce outcomes (e.g., reduce vulnerability to disaster and increase
sustainability) on behalf of others. Related, our proposed research provides a distinctive opportunity to
explore the conceptual considerations underpinning policy change leading to sustainability by leveraging
our work on trust in institutions. One of the most pressing considerations facing modern society is how
policy makers confront the myriad sources and forms of uncertainty which could derail the effectiveness
of their preferred alternatives (Baumgartner & Jones, 2009). Hauser and Benoit-Barne (2002) suggest
issues of trust often arise out of uncertainty. Consequently, we propose to investigate, a critical yet under
investigated dynamic: How do different levels and dispersion patterns of different forms of trust impact
group processes and outcomes in contexts of high uncertainty? While there is a growing literature on the
public’s trust in government, there is little research on how trust at different levels impacts collective
decisions involving local and state inter-organizational collaborations. In an age in which authority is
divided among agencies and across jurisdictions, members of organizations must engage in collaborative,
cross-agency initiatives to fulfill their own organization’s mandate (Michaels, Goucher, & McCarthy,
2006). Thus, we will study the conceptualization, measurement, and development of trust government
officials have in each other and the collective decision making or advisory groups and processes in which
they participate. Throughout this research, we will advance the statistical methodology and capabilities of
social and policy scientists to assess and model multilevel and latent variable influences.
Broader impacts. On a local level, project results will benefit millions of people exposed to flooding
risk in the study region by significantly improving the warnings (in terms of accuracy, lead time,
accessibility and use) vulnerable residents receive. Indeed, if a facility such as the one operating at NCAR
had been online for the Indus River in July 2010, making discharge/flood forecasts and allowing
community access, it could have made a tremendous difference. The Kosi, in 2008 along the
India/Nepalese border, represents another avoidable catastrophe: The flood flow that broke the levee was
not a very large one. The levee could have been reinforced if the area had the kind of warning that is now
possible and can be readily implemented as proposed in this project. This is what, in part, this proposal is
requesting funding to accomplish.
We know in order for successful implementation of technologies, there needs to be acceptance and
use. The proposal’s social science components are intended to assure successful technical and policy
uptake. We will investigate the extent to which the capacity to couple discharge forecasts to direct
historical observational imagery provides forecasts of the range of possible inundation extent in a form
accessible to decision makers while still conveying realistic, probabilistic estimates. This project will
provide all data in a convenient database accessible to researchers, practitioners and members of the
public, and research results will inform ongoing efforts in the Indus, Brahmaputra, and Ganges to
effectively engage key stakeholders in emergency management, hazard mitigation and economic
development policy and implementation. But we do not presume that increased forecast skill and
accessibility is enough. We expect the necessity of understanding the role of trust in the policy making
and implementation process in which they engage to enhance inter-organizational collaboration in
disaster planning. Achieving the full benefits of scientific and technological breakthroughs driving the
quickly evolving field of flood forecasting requires the buy-in and cooperation of experts in
disaster/emergency planning and response and robust, regional economic planning. Policy outcomes of
our successful work will include developed protocols for manipulating reservoir water levels, for
example, intended for rapid execution in circumstances with little opportunity for reflection. Through
3
participating in this project, local communities could even be empowered to directly access relevant flood
hazard information.
Importantly, we will work with our partners to assure successful transfer of the flood forecasting
technology to vulnerable communities in our target sites. We are cognizant of the challenges in successful
technology transfer, both as a matter of technology as well as politics. We will work closely with our
partners who have extensive expertise in achieving sustainability of technology transfer. For example, one
of our key partners, RIMES, already is working with communities in Bangladesh on improving access to
flood forecasting warnings. The enhanced model we develop will be disseminated to other communities
with which RIMES presently is working. Beyond South Asia, our research will be of value to many
communities struggling to make the transition from vulnerability to resilience and groups engaged in
emergency preparedness and mitigation.
Thus, we will disseminate our research outcomes not only in South Asia, but also to vulnerable
communities elsewhere in Africa, Asia, and so on, working in partnership with the organizations like
RIMES, CARE-Bangladesh, INIH, FFD, and the World Bank until the technologies are fully adopted by
the various Governments in these vulnerable nations. We will seek additional funds (e.g., from the World
Bank, USAID, etc.) to support these efforts, as they go beyond the scope of science and technology
development, but they are critical for long-term impact and sustainability.
The project also provides extensive interaction and knowledge sharing between U.S. and South Asian
researchers and engineers, and student training in multi-disciplinary, hands-on operational forecasting
systems, data collection techniques, data analysis, and presentations. Two postdoctoral fellows and GRAs
and URAs will assist with the entire project. We also will request an REU so additional undergraduates
can participate in varied research activities, such as conducting literature reviews, setting up surveys,
coding, and analysis.
4. Research Plan
Our ambitious research objectives
require four years to complete.
Nonetheless, we are planning to
achieve substantial progress and
produce tangible accomplishments
beginning in the first year. Arranged
by objective, the detailed methods are
provided below..
4.1. Implement long-lead flood
forecasting systems.
Climate Forecasting Applications
for Bangladesh (CFAB) has been
producing operational flood forecasts
for the Ganges and Brahmaputra
Rivers in Bangladesh since 2003, on
timescales from days to months
(Hopson & Webster, 2010; Webster et
al., 2010). The proposed methodology
builds upon this effort and integrates
adaptable hydrological streamflow
multi-model, probabilistic
meteorological/climate forecasts, and
satellite and in situ data.
We will adapt the ensemble forecasting scheme developed by Hopson and Webster (2010) shown in
Figure 1 for the major river basins in Bangladesh. This will make use of the recently available
THORPEX Interactive Grand Global Ensemble (TIGGE1) multi-center ensemble weather data, with focus
on extreme precipitation2 in designing a fully-automated scheme for 1-15 day predications of river
1
2
See: http://tigge.ecmwf.int/
See: http://tparc.mri-jma.go.jp/TIGGE/tigge_extreme _prob.html
4
discharge forecasts for the South Asia region. The hydrological forecast model is a hydrologic multimodeling system initialized by NASA and NOAA precipitation products (e.g., TRMM 3B42, Huffman et
al., 2005, 2007; CMORPH, Joyce et al., 2004; NOAA HydroEstimator) whose states and fluxes are
forecasted forward using TIGGE data products and conditionally post-processed to produce calibrated
probabilistic forecasts of river discharge for key river reach locations. Already operational over the
Brahmaputra and Ganges river basins, this system will be extended and calibrated for the Indus basin.
Provide for Public Access. Although this is the last task represented in Figure 1, providing the public
with truly beneficial flood warning information is a complex endeavor, and is the subject of much of this
proposal’s research. However, our team can build from ongoing regional efforts. To enhance and evaluate
the benefits of advanced warnings to vulnerable Bangladeshi communities living in flood-prone regions,
RIMES, in early
collaboration with the
Center for Environmental
and Geographic
Information Services
(CEGIS) and the
Forecasting and Warning
Centre (FFWC) of
Bangladesh, created a
direct dissemination
network to local
communities and
individuals living in fifteen
pilot unions (Fig. 2, left
panel showing original six
pilot sites in 2006) in
Bangladesh (CEGIS,
Figure 2. Collaborative partner RIMES-CEGIS selected pilot unions of Bangladesh in 2007,
2006). Participants receive
selected to receive CFAB long lead time flood warnings; CFAB 2007 10-day lead-time
forecasts (right panel) showing strong likelihood of severe flooding: ensemble forecasts flood hazard warnings
colored lines; observed discharge – solid black; danger level threshold – black dashed.
based on CFAB model
forecasts. The warnings are disseminated to the pilot unions during the monsoon season, and are tailored
to be understandable to affected communities. For example, in July and September 2007, severe flooding
occurred in the Brahmaputra basin (Fig. 2, right panel), and warnings were disseminated to the pilot sites
days in advance of the flooding. This contributed to mitigating the disastrous consequences of the flood
wave on lives and livelihoods. Post flood assessments are also carried out to assess the effectiveness and
drawbacks of the flood forecast and dissemination system at the community level and individual level.
This pilot dissemination and surveying program is now being managed and expanded by our collaborative
partner, the Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES).
These established pilot sites will be locations in which the University of Nebraska social science team
conducts interviews and focus groups.
5
Forecast
dissemination will
also occur at
national and
international levels.
Since the
probabilistic
(ensemble) forecasts
can be presented in
different ways,
during the
dissemination and
input workshops, we
will work with endusers and
prospective end
users to determine
the most useful
ways to present
valuable and
uncertain flood
forecast information. The Dartmouth Flood Observatory (DFO) River Discharge Measurements web site
is a model format on which we can build.3 “River Watch 2” is an existing automated processor supported
by NASA Earth Science Research and Applications Programs. In support of our proposed work, the
University of Colorado is making available extensive space on River Watch 2 for South Asia-focused
displays to provide a prototype portal for both satellite-based present status information and the modelbased discharge prediction and flood warning information. Thus, Figs. 3 and 4 indicate what has been
entirely missing but is now feasible for South Asia. It provides a subscene of the present DFO River
Watch 2 map view (automated, updated daily) of current river flow severity status of monitored sites (Fig.
3), and also a sample time-series of the existing present and historic status output at one site (Fig. 4). We
can adapt these displays to incorporate forecast and
present-status information (as in Figs. 3 and 4) and
create more appropriate map displays publishable at
larger scale for the three river basins of interest on
the DFO and partner organization websites.
Examine the benefits of data assimilation of
satellite-derived river discharge estimates on
improving forecasting skill. Bangladesh is a classic
example of flooding issues exacerbated by
international boundaries and lack of upstream river
flow information. Consequently, members of our
research team, in collaboration with the Global
Disaster Alert and Coordination facility in Europe,
have used satellite-based daily measurements of
stream widths at multiple locations upstream of
Figure 5 Daily time series of observed river discharge (solid)
Bangladesh’s borders (similar to those shown in
and model nowcast (dash) based on the satellite-based river
discharge estimates for Ganges River at Hardinge Bridge
Figs. 3 and 4, see also Fig. 7) to remove this
ground station in Bangladesh. Satellite-derived information
limitation. At certain microwave wavelengths, there
at locations with distance ranging from 63 KM to 1828KM
upstream Hardinge Bridge station were used. From (Hirpa et
is very little interference from cloud cover (floods
al., 2013).
can be measured even when the ground surface is
obscured from optical sensors such as MODIS).
Using a strategy first developed for wide-area optical sensors (Brakenridge, Anderson, Nghiem, & Chien,
2005), such data can be used to measure river discharge changes (Brakenridge, Nghiem, Anderson, &
Mic, 2007). As rivers rise and discharge increases, floodplain water surface area increases. Microwave
3
See: http://floodobservatory.colorado.edu/CriticalAreas/DischargeAccess.html
6
emission over river measurement sites, observed from space, can monitor such changes. Our work has: 1)
examined the capability of using these data to track the downstream propagation of flood waves through
India, and 2) evaluated their use in producing river flow nowcasts (Fig. 5), and forecasts at 1-15 days lead
time (Hirpa et al., 2013). The results have demonstrated the propagation of a flood wave along both river
channels can be tracked reliably in near real time, and therefore could be incorporated into river flow
predictive modeling.
One of the main sources of hydrologic prediction error is due to uncertainty in the model parameters,
as well as routing errors due to lack of river cross-section information. These problems are often linked to
the lack of reliable ground discharge observation used for model calibration. This problem can be
mitigated by using discharge estimates derived from satellite-based river measurements at locations where
there are limited ground observations. Additionally, another large source of hydrologic predictive
uncertainty is lack of knowledge of current in-stream flows and catchment soil moisture states. Data
assimilation can be used to improve both of these hydrologic uncertainty problems through optimally
combining satellite-based river discharge estimates with hydrologic model flow states, as well as using
the causal link between in-stream flows and catchment soil moisture. This approach then modifies a priori
state of the model by taking into account the relative errors in the model simulations and the estimates. In
the past few years, hydrologic data assimilation has become popular mechanism for reducing forecast
uncertainty, in part due to the availability of a wide range of satellite-based soil moisture and river
information. This proposal’s research is at the forefront ofexploring the use of satellite-based river flow
measurements in this context.. Equally relevant for our hydrologic application are assimilation of in-situ
discharge (e.g., Seo et al., 2009; Moradkhani et al., 2005; Weerts and El Serafi, 2006; Clark at al., 2008;
and Lee at al., 2012), and satellite-based water elevation (e.g., Montanari et al., 2009).
In this project we propose to improve and extend the lead time of the near-real time riverflow
prediction of the CFAB hydrologic model using data assimilation of upstream flow information provided
by the remote sensing at several upstream locations. We intend to use sequential Monte Carlo data
assimilation techniques (Arulampalam et al., 2002) for the assimilation at each model run time steps. Our
recent work demonstrated the value of the remote sensing data assimilation for operational prediction
(Hirpa et al., 2013) in basins with no ground upstream river discharge observation, strictly by tracking
flood waves. By also assimilating this information into physical hydrologic models, however, we
anticipate the overall forecast skill of the CFAB scheme could be improved significantly.
4.2. Transform forecast discharge values (flood peak discharges) into inundation extent maps.
NASA’s orbital technology has been used at DFO extensively, since the launch of the twin MODIS
sensors in early 2000 and 2002, to map flooding in South Asia. Unlike other remote sensing-based
organizations active in flood
response, DFO maintains a large and
growing archive of such map data, in
digital (GIS) format, and for use in
making comprehensive regional
displays indicating the history of
inundation as well as on-going
flooding (Fig. 6). The archival flood
information is exceptionally valuable,
providing as it does a view of flood
hazard.
Figure 6. The DFO record of flooding in this portion of the Ganges Basin is
shown as light blue (2000 to 20111), light red was flooding in the 10 days prior to
map update date, and red was current flooding. The numbers indicate River
Watch discharge measurement sites. This is a small subscene of the complete
Surface Water Record display for this region.
7
This large archive of such
mapped inundation resident at
DFO will allow production of
an innovative flood prediction
product. As illustrated in Fig.
7, past inundation extent can
be matched to the
corresponding remote
sensing-derived discharge
values (the same approach
can be used for any ground
station sites for which data
output is available publicly).
Linkage to the appropriate
inundation map can be
provided at the individual site
Figure 7. Left: MODIS imaging and mapping of 2003 flooding along the Ganges River
displays: when a particular
between river measurement sites 200 and 201. At site 200 (uncalibrated) peak discharge
discharge and flood threshold
was 8500 m3/sec. Right: mapping of 2004 flooding. The uncalibrated peak discharge here
is only ~3500 m3/sec.
is predicted, the user can call
up the inundation that
resulted, historically, from the same values. Mapping inundation maps to the ensemble of river forecasts
produced by the CFAB model could then produce a range of possible inundation extent scenarios. Such
capability highlights the importance of understanding how and why end users process this uncertain
information. We hypothesize trust is an important consideration at the individual, group and intergroup
level of interpretive understanding.
4.3. Test a model of trust, investigate policy uptake and implementation, and expand the capacity for
contextual/ecological modeling through the use of multilevel and latent variable statistics.
Trust has long been considered a key variable in understanding cooperation in policy and
international relation contexts (e.g., Hoffman, 2002), as well as in natural resource management contexts
(e.g., Earle, 2010; Leahy & Anderson, 2008; Poortinga & Pidgeon, 2006; Stern, 2008; Tennberg, 2007;
Winter & Cvetkovich, 2010). Kingdon (1984), Sabatier and Jenkins-Smith (1993), and Dye (1995) each
propose a conceptualization of policy making involving the notion of policy communities to probe how
the policy process is shaped by events and actors. Within the policy community are a core cadre of
participants who make most of the policy domain’s routine decisions (Baumgartner & Jones, 2009;
Birkland, 1997). These participants function in the policy process as individuals, members of groups, and
members of networks or systems. In light of this, we hypothesize success in making policy decisions is
based, in part, on patterns of trust that emerge at individual, group, and systems levels and the interactions
between trust at these levels. Trust likely impacts the diversity of ideas and the different contributions
individuals make, which then impacts the organizational capacity upon which policy-community
members draw (Innes & Booher, 2003). At the same time, interpersonal relations are increasingly
recognized as contributing to explaining human behavior in policy contexts (Sabatier & Weible, 2007).
The social component of generating and expanding knowledge, including trust in institutions, is a
function of collective reasoning (Fleck, 1979): Individuals assume and develop “changed patterns of
collective action” (Heclo, 1974, p. 306) through exchanges with others. Potentially, these social practices
are examined continuously and reformed as participants develop insights into the practices in which they
are engaged (Giddens, 1990).
Although much work remains, progress is being made in conceptualizing, defining, and measuring
trust in institutions, defined most commonly as some combination of positive expectations and – or
leading to – trust-relevant actions, such as willingness to be vulnerable to, comply with, and/or otherwise
support the institution (e.g., as reviewed in McEvily & Tortoriello, 2011; Schoorman, Mayer, & Davis,
2007). The recent emphasis on advancing the theoretical and methodological foundations of trust in
institutions offered by our interdisciplinary trust research team (Bornstein, Tomkins, Neeley, Herian, &
Hamm, in press; Hamm et al., in press; Hamm, PytlikZillig, Herian, Tomkins, & Dietrich, 2012; Hamm et
al., 2011; PytlikZillig, Tomkins, Herian, Hamm, & Abdel-Monem, 2012; Tomkins, PytlikZillig, Herian,
Abdel-Monem, & Hamm, 2010) and by other researchers (e.g., Mayer, Davis, & Schoorman, 2006;
Nannestad, 2008; Rousseau, Sitkin, Burt, & Camerer, 1998) has identified critical gaps in understanding
8
how institutional trust develops and changes over time. As a key component of our contributions, we
posit a sophistication model of trust development (see Fig. 8) that hypothesizes as an individual gains
experiences with and knowledge about an institution (i.e., develops sophistication), the individual will
shift from relying on general, trust dispositions, to more institution-particiularized and then commitmentrelevant trust-constructs (Herian, Hamm, Tomkins, & PytlikZillig, 2012).
We have been
researching our
sophistication
model in the
contexts of courts,
city government,
and water
regulatory agencies
in the United
States. We find the
predictive ability of
Figure 8. Conceptual diagram of our sophistication model of trust in institutions
trust-related
constructs has varied across samples (Hamm et al., in press; Hamm et al., 2011), potentially as a function
of differing levels of sophistication. We are currently examining our sophistication hypothesis more
rigorously in a multi-year longitudinal study of students who, at initial testing, had very little experience
and knowledge of water regulation institutions. We have randomly assigned some students to read (over
many months) and learn about water regulation institutions, and others to a control condition (reading
about health-related regulatory activities). We expect sophistication to change most in the experimental
students, and along with those changes, the bases of trust in water regulatory institutions to also change.
An important gap in our research and in the field of trust research at large concerns the potential
influence and interaction of trust constructs as stakeholder sophistication increases and trust develops and
emerges at different ecological or contextual levels (e.g., micro, meso, macro levels). The vast majority of
trust research, including ours, generally examines trust at a single level (e.g., the individual, micro, or
between-group, meso level). There is little, if any, research, however, investigating how trust at different
levels might interact or jointly influence important outcomes (e.g., vulnerability, resilience, policy
outcomes), though it has been argued that a multilevel approach is critical to understanding how trust
impacts policy and other political behaviors (e.g., Hutchinson & Johnson, 2011). Filling this gap is
important for more fully understanding trust in institutions, especially given that governance institutions
operate in an inherently multilevel, policy context, in general, and especially when natural resource
decisions cross national borders (e.g., Hoffman, 2002; Tennberg, 2007).
Moving toward a multilevel model of trust and sophistication. Examination of trust constructs within
a multi-organization and multilevel context is an ideal place to test, refine, and expand our sophistication
model because amounts and types of knowledge and experience with an institution (i.e., type of
sophistication) are likely to vary across different levels of the institution and to be dependent on one’s
roles and relationships with that institution. For example, persons involved in the development of flood
forecasts may be quite sophisticated regarding the science and information provided by the forecasts, but
less sophisticated regarding the politics occurring between stakeholders that might use or be affected by
the use (or non-use) of the forecast information. Similarly, the public could vary widely in their
knowledge, opinions and appreciation for the complexities involved in decision making related to a
developing forecasting system. Meanwhile, decision makers may have an added layer of interpersonal
and potentially emotional experience (e.g., frustration, felt responsibility, discomfort with uncertainty)
with the emerging forecast system. Although trust research has been conducted at levels ranging from the
individual micro to the societal macro levels (e.g., for macro-level research see La Porta, Lopez-deSilanes, Shleifer, & Vishny, 1997; You, 2012), research on trust constructs and processes that span
multiple levels is rare. Thus, little is known about whether and how trust at one level (among forecast
developers) might affect trust at another level (decision makers or the public), or the extent to which
emergent processes (Kozlowski & Chao, 2012; Kozlowski & Klein, 2000) impacts trust at higher-levels.
9
Our guiding theoretical model (illustrated by Fig. 9) predicts that different forms and patterns of trust
across multiple contextual levels will impact intra- and intergroup processes, and thereby affect the
outputs (products) of that performance. At the highest level, we expect trust in the system as a whole to be
impacted via emergent processes for insiders (e.g., emerging patterns of relationships with other insiders
from within and outside of their identity groups), and via more direct (e.g., performance outcomes such as
useful and accessible forecasts) processes for the public. Although the model is comprised of multiple
parts, in the present research we will focus primarily on the central hypothesis that positive group
processes, within and between “insider” groups (groups most directly involved in forecast development
and use of forecasts in policy decisions), will be facilitated by optimal (not high or low) levels of and
variability in trust and result in more positive outcomes and higher levels of overall “public” trust (likely
mediated by perceptions of performance outcomes). The rationale for this hypothesis stems from research
in the social sciences pertaining to group functioning and team performance. Within-group or intra-team
trust refers to the levels and patterns of trust (or trust constructs) that occur among members of the same
group. Average within-group (intra-team) trust refers to the average extent to which each member of the
group trusts each other member (cf., intraentity trust, Fang, Palmatier, Scheer, & Li, 2008). Prior research
has found that higher levels of within group trust may facilitate collaboration (e.g., resulting in high
reciprocity and coordination between group members), but also to less adaptability to change (e.g.,
undermining monitoring, promoting features of group think) (Fang et al., 2008). Thus, we predict that that
too high or low within-group trust will be detrimental to overall group functioning, especially in contexts
that are uncertain, changing, and unpredictable, such as warning and emergency communication contexts.
Variance in own group trust refers to the overall variability around the average level of trust in a group to
which someone is a member. We hypothesize that different levels of individual trust within a group trust
may serve different purposes (with the high-trustors facilitating cooperation and optimism, and low
trustors facilitating monitoring of processes and tasks), and thus, that there may be an optimal amount of
variance of trust in groups of which one is a part. Without enough variance, different roles and purposes
will not be fulfilled, but too much variance could cause conflict and undermine group processes. We also
will explore emergent processes and their relationships to variance in own group trust, such as whether
the variance in extent to which people trust their whole group arises out of variance in dyadic trust within
the group, and/or from differences in dispositional trust between individual members, and whether one
source of variance is more beneficial than another over time. Between group trust refers to the levels and
patterns of trust that occur between groups (cf. interorganizational trust, Fang et al., 2008). For example,
between group trust could refer to the trust between two organizations, two working groups, a working
group and a governing council, a larger group and a sub-group, and so on. Meanwhile, variance in
between group trust refers to the variability in the extent to which trust exists between groups. Although
variables at different levels (e.g., within vs. between groups) may not necessarily operate the same, it
seems reasonable that, at least in highly interactive and overlapping groups (such as we propose to study),
too much or too little trust or variance in trust may impact the extent to which groups effectively
collaborate and monitor one another’s efforts.
Study 1. Initial “insider” measure development and context description. The objective of Study 1,
which will be conducted during months 1-6
of the project, is to develop measures and
establish a foundational understanding of the
context in which our model testing will take
place. This study will be part 1 of an
exploratory, sequential design (Creswell &
Plano Clark, 2007; Small, 2011), and will be
primarily qualitative in nature. Data sources
will include structured notes from focus
groups, and interviews (which will also be
audiotaped in case of the need for
clarification). Participants in the focus
groups and interviews will include engineers
and scientists currently producing weather
and flood forecasts, those directly involved
in the ongoing efforts to put the
sophisticated flood forecasts to use in
Figure 9. Our guiding multilevel trust conceptual model
planning for disasters and regional economic
10
development, including World Bank planners, NGO specialists working in the field on mitigation
strategies, government policy makers, and residents.
Our procedures will involve traveling to the study areas early in Year 1, and conducting
approximately 3 focus groups (one per country), and up to 15 semi-structured interviews of different
stakeholders (5 per country) in order to sample a range of participants from each country and watershed,
to explore the elements of our model (Fig. 9) and initial drafts of our measures of model elements. We
will recruit relevant stakeholders for interviews and focus groups with assistance from CAREBangladesh, INIH, FFD, World Bank, and RIMES. Initial drafts of measures of the trust and process
variables will be adapted from prior work (e.g., De Jong & Dirks, 2012; Fang et al., 2008; Hamm et al.,
2011), while measures of the “products” (individual and team performance, performance outcomes, such
as related to the accessibility and utility of the forecasts) and “system sustainability” will be developed as
part of this research, with input from stakeholders. We expect interviews to last about 1 hour, and focus
groups to last 2 hours. During the workshops, focus groups and interviews, in the tradition of Glaser and
Strauss’s (1967) grounded theory, we will use generative questions (Trochim, 2005) to discuss each of the
model components (intra and intergroup trust, group processes, products/performance, and system
sustainability) and subcomponents (e.g., the subcomponents of trust, the importance of expertise and
competence assessments, the impact of uncertainty), as they relate to key aspects of the forecasts and
events related to flood forecasts (e.g., actual flooding). The in-depth, semi-structured nature of our
protocols will give respondents the opportunity to provide detailed explanation and clarification (Lewis,
2003), and allow us to probe a consistent set of issues while hearing a range of perspectives (Berg, 1998;
Hughes, 2002), thereby revealing strengths and weaknesses in our emerging theoretical model in this
specific context. Note that the strategy of developing new variables dependent on what we learn from the
focus groups and interviews, as well as our observations of dissemination workshops, makes protocol
development an ongoing feature of the research rather than an activity we complete at the outset. Study 1
will begin this process, which will be continued in Studies 3 and 4. During the interviews we will also use
cognitive interview techniques (Davison, Vogel, & Coffman, 1997) to better understand participant
thoughts and reactions to the survey measures we develop, and for validation and fine tuning. A trained
researcher will be present at each focus group to take notes. S/he also will listen to and take structured
notes from the audio recordings of the interviews, and these notes, along with the notes from the flood
forecast use dissemination workshops, will form the qualitative data base.
Analyses of data from the Study 1 focus group and interview notes will be conducted throughout Year
1, using Atlas.ti or other qualitative coding software. Two coders will use a draft coding protocol to
analyze a subset of the notes for themes relating to the model components as well as additional themes
that may become apparent during analyses. Coders will then meet with other research team members to
expand, revise, and refine the codes as needed. Each document in the corpus will be coded by at least one
coder, with a subset of the documents coded by two coders to estimate intercoder reliability.
Study 2. Testing and refinement of “insider” measures. Study 2 will be conducted at the end of Year
1 of the project (at about the time of the dissemination workshops) and is part 2 of the exploratory
sequential design. The objective will be to gather data pertaining to the statistical/psychometric and
construct validity of the measures (developed as part of Study 1) and to provide baseline data for Study 4
(Specific Aim 5). Participants will include all participants in the 3-day dissemination workshops (Type A
participants, n ≈ 30, about 10 persons per country) which will include engineers and scientists directly
involved in the ongoing efforts to put the sophisticated flood forecasts to use in planning for disasters and
regional economic development; and other members of involved institutions who are both knowledgeable
and not about the use of sophisticated flood forecasting but not directly involved in the workshop
activities (Type B participants, n ≈ 120, approximately 40 per country), including RIMES experts,
World Bank specialists, scientists and engineers currently producing weather and flood forecasts but not
attending the workshops, and social scientists and NGO specialists working in the field on mitigation
strategies. Participants will be recruited using multiple strategies with the assistance of our in-country
collaborators (i.e. CARE-Bangladesh, etc.) and, if relevant, snowball sampling where those more
centrally involved invite colleagues who are interested and have varied levels of knowledge about the use
of sophisticated flood forecasting in developing disaster preparedness and regional economic plans).
To ensure efficient and appropriate measurement, surveys will be customized depending on the
participant’s role or relationship to using sophisticated flood forecasting in developing disaster
preparedness and regional economic plans (e.g., specific questions for workshop participants or relevant
group members will differ from questions for more marginally involved participants). The survey will
11
include measures of multilevel trust, process perceptions, and perceptions of team performance outcomes
and sustainability of the system as whole and designed to take approximately 10-15 minutes. Initial
quantitative analyses will include item analyses, scale reliabilities, exploratory factor analyses,
correlation, and multiple regression analyses designed to examine both scale reliability/validity and
hypothesized relationships between variables. Power analyses indicate that approximately 150
participants will be sufficient in our model to find effects as small as .22 at a power level of .8 and p < .05
(Soper, 2013). In addition, these data will be used in the more sophisticated investigations in Study 6.
Study 3. Development of “public” measures. During Years 1 and 2, measures of public trust in the
flood forecast and warning systems will also be developed based on our prior measures. Participants in
this study will be students from area universities who will serve as proxies for the general public (N ≈ 300
total, 100 per country). Procedures will be primarily survey-based. After a draft of the survey is
developed and translated into the appropriate languages, at the end of Year 1 (around the time of the first
workshops) we will conduct 3 cognitive interviews per country with students to ensure the
understandability and appropriateness of the questions. The students will be recruited through our incountry collaborators and chosen for their interest in flood and forecasting topics. Because they may not
know much about our team’s efforts, we will prepare background information about the ongoing activities
and flood forecasting in general for students to read before they complete the survey (which will assess
trust and perceptions of team performance outcomes and of the system as whole, and will be designed to
take about 10-15 minutes) and then engage with a researcher in a cognitive interview for approximately
30-45 minutes. They will be paid a $25 incentive for their time and effort. After refining the questions and
background materials based on the student interviews, the reading and questions will finalized into both a
paper and web-based survey. During Year 2, additional students (N ≈ 300; 100 per country) will be
recruited from courses related to weather, climate and forecasting. These students will read background
material, varying in detail and depth, about our project prior to completing the measures. The varied
background given to the students, as well as natural variation in student knowledge and experience with
the topics, will simulate differences in sophistication that we expect to find in the general public. We will
conduct confirmatory factor analyses on the items to examine their dimensionality and to determine
replication of prior findings. Initial analyses will assess scale reliability, using model-based reliability
estimates (omega), and construct relationships, using structural equation modeling. Power analyses
indicate that 300 participants will be more than sufficient to detect effects as small as .15 in our model
(Soper, 2013). In addition, these data will be used in the more sophisticated investigations in Study 6.
Study 4. Simultaneous, explanatory, mixed-method, longitudinal study. This study uses an
explanatory, mixed-method design in which qualitative study of the model conceptually outlined in
Figure 1 is designed to help explain and understand (as well as triangulate) quantitative data pertaining to
the model (Small, 2011). In addition, the study will continue the work started in Study 1 to construct our
ultimate outcome variables (e.g., final assessments of perceptions of “key events” that occur during the
project time period). This study will examine our previously-described optimal trust working hypothesis.
Participants will include the same target groups (i.e., Types A and B) as described for Study 2. Our
procedures will include the use of surveys developed during Studies 1 and 2 administered at the end of
Year 1 (as part of Study 2), and again at the end or Years 2 and 3 (in conjunction with the Year 3
workshops, which will include some of the same participants as involved at the end of Year 1). Initial
analyses of surveys will include longitudinal multilevel modeling procedures that take into account the
highly nested nature of the data (time points nested within individuals nested within groups and
organizations); but once again, these data will also be investigated in Study 6.
To obtain the explanatory qualitative data we will conduct twelve focus groups (one with each Type
A/B participants in each country at the end of Year 1 (six focus groups) and likewise at the end of Year 3
(six more groups), with approximately 8-10 participants per group) as well as approximately 15
interviews (5 per country) conducted and spaced out between the two sets of focus groups. Focus groups
will be conducted as part of the workshops; interviews will be conducted using distance methods (e.g.,
with the assistance of an in-country partner trained to conduct the interview, and a research co-interviewer
in attendance via Skype or conference call). For the focus groups and interviews (which also will be
audiotaped), we will use semi-structured protocols to examine how decision process outcomes result from
initial conditions. This will provide important context for understanding changes in trust in the process of
development and use of the forecasts. As was the case in Study 1, a trained research assistant will use a
structured note taking protocol to gather data while attending the focus groups and from the audio tapes of
the focus groups and interviews. The coding protocol for the qualitative data will build upon findings
12
from Study 1 but also will be adapted as needed to take into account events taking place during the study.
Study 5. Public survey study. A large-scale survey will investigate public perceptions of and trust in
flood forecast systems, science, and forecast information during Years 2 and 4 of the study. This survey,
initially developed as part of Study 3, will take approximately 10-15 minutes. Participants will be a
sample of the general public (100 per country, across 3 sites per country or 9 sites total) who will be
asked their opinions about utility of flood forecasting in mitigating devastation from floods (as in Study 3,
N = 300+ will allow us to achieve the power we need for our initial analyses). Survey procedures will be
used, and measures will include perceptions of team performance outcomes and of the system as whole.
Survey participants will have the opportunity to (but will not be required to) read or hear background
information about the project previously developed for and tested with students. Initial analyses of the
survey data will aim to replicate those used with Study 3 data, but now with data from the general public.
Once again, however, we expect to also use these data in our Study 6, discussed next.
Study 6. Evaluation of methods for statistical modeling of multilevel trust. Multilevel research
efforts are often hindered by an inability to simultaneously and statistically consider multiple levels of
ecological and/or environmental influence (Bovaird, 2007). Ecological “models” such as the posed model
of trust are often theoretically unified, but tested in a piecemeal fashion. Innovative modeling methods
that overcome these limitations and are applicable to studying such multilevel determinants are needed.
Multilevel structural equation modeling (MSEM) presents a statistical modeling framework that
synthesizes multilevel modeling (MLM) appropriate for considering the effects of complex sampling,
with broader techniques appropriate for integrating latent variables and measurement assumptions into
multivariate linear models, thus enabling simultaneous evaluation of comprehensive ecological or
contextual systems (Bovaird, 2007). The goals of this study are to: 1) continue to expand the capacity of
current MLM and SEM frameworks to allow designation of complex levels of influence; 2) determine the
sufficient data conditions necessary for implementing both complex univariate MLM models and
complex MSEM; and 3) evaluate recently developed commercial and research software to enable
implementation of the methodologies evaluated in this study. The first goal of this study will be achieved
by implementing a statistical modeling framework that allows for consideration of more than two levels
of an ecological MSEM for the multilevel theory of trust developed and evaluated in Studies 1-5. The
data obtained in Studies 1-5 will be integrated to provide substantively valid estimates of population
parameters based on relationships posed in the model of multilevel trust. In pursuit of the second goal,
Monte Carlo simulation methods will be used to validate the statistical modeling framework with
simulated data. Based on parameter estimates from empirical data collected in studies 1-5, Monte Carlo
simulation methods will then be used to determine the data conditions necessary to confidently apply
substantively relevant MLM and MSEM models. Finally, the third goal will be achieved by evaluating
accessible software for implementing the expanded MSEM in the Mplus, SAS, WinBUGS, and R
computing environments. This study will provide a significant expansion of the MSEM framework to
allow consideration of complex realistic multilevel ecological models. The methods developed and
evaluated in this study will be applicable to all research areas in social sciences that consider ecological or
contextual models of behavior or development. This work is essential for answering the call for multilevel
rather than single level approaches to understanding how trust impacts policy and other political
behaviors (Hutchinson & Johnson, 2011).
Both the empirical and simulation phases of this study will compare model estimates from the following
softwares: the recently released Mplus version 7 (Muthén & Muthén, 1998-2012) which now allows
complex hierarchical nesting; a two-step approach using a combination of Mplus version 7 and
procedures/packages from the SAS 9.3 and R software environments; and WinBUGS for implementing
Bayesian methods through Markov Chain Monte Carlo (MCMC). Of particular interest is the newlyreleased Mplus version 7 which has the expanded ability to model three-or-more nested hierarchical
levels or cross-classified non-nested levels with multivariate latent variable models. Mplus 7 utilizes a
Bayesian approach through MCMC, as does WinBUGS, to implement such complex multilevel models;
however, the software has only been available for a limited time (as of November 2012) and has not been
thoroughly evaluated. Prior to the release of this version of Mplus, there were limited options (i.e. only
two levels) for estimating complex multivariate latent variable model such as required in simultaneous
estimation of complex multilevel ecological models. Thus, the proposed work is timely and has the
potential for broad impacts across the social and behavioral sciences.
Also of particular focus in this study—particularly goal 2— are the methodological limitations posed
by multilevel ecological research pertaining to sample size and geographical nesting. Generally speaking,
13
a “large” sample size (often j = 50-100 macro level units) is required for multilevel modeling and
particularly MSEM primarily because of the model complexity and estimation methods used to obtain
parameter estimates. Obtaining a large sample size at the micro/individual level is often not a problem,
but obtaining a sufficiently large sample size at the macro level is. In the current study, the micro-level
population is relatively large and accessible, while the macro-level populations (regions and countries) are
finite and small. Song and Lee (2004) have shown that Bayesian methods similar to those implemented in
Mplus and WinBUGS can be particularly effective for estimating structural models with small samples.
In addition, geographically-based macro-level units vary greatly in population size, making it difficult if
not impossible to have a balanced design.
5. Relation to Results of Prior NSF Research, Other Research, & Long-Term Goals
The proposed research brings the Colorado team’s (NCAR and DFO) prior research on flood
forecasting together with the Nebraska team’s prior NSF-funded research on trust and stakeholder
engagement. In doing so we are able to advance the fields of flood forecasting and trust in the context of
technical and policy acceptance and implementation. The inclusion of the sophisticated statistical
modeling and latent variable analysis components of this project address the challenge of assessing trust
and policy uptake at different scales. Thus, as a team, we will work towards our long-term, shared goals
of reducing flood vulnerability through advancing flood forecasting and its use by agencies and groups,
on behalf of vulnerable individuals.
We will leverage our successful NASA-funded research feasibility project designed to better define
the pathway to be taken to full and sustainable implementation of a flood mapping processor, a merger
between an automated, near real time (NRT), MODIS sensor-based flood map product and a
complementary, radar frequency, Envisat ASARbased global flood mapping processor (Kleuskens, et al.,
2011; Westerhoff et al., 2010). Also, in collaboration with end users, the research team is implementing,
on a trial basis, coupled flood discharge and inundation forecasting for Bangladesh. During our
dissemination and input workshops with end users we will determine, for example, which forecast
locations would likely provide the earliest demonstration of specific enhancements to end-user
capabilities and operations. This will contribute to increasing local capacity to plan for the flood threat.
Building on the advances in flood forecasting by NCAR and DFO, we are developing a valuable product
to provide decision makers, within and outside of government, with maps indicating anticipated extent of
flood plain inundation. Such work and our comparative examination of trust in multiple organizations and
among individuals integral to the successful use of flood forecasts is made possible by leveraging our
exceptional access to and positive relationships with them made possible through our relationships with
CARE-Bangladesh, INIH, PMD, World Bank, and the World Food Programme.
As key parts of each of our NSF-funded projects relating to trust (Testing a Three-Stage Model of
Institutional Confidence across Branches of Government (SES-1061635); Law and Social Science
Postdoctoral Fellowship: Trust and Confidence (SES-1228559); Understanding the Role of Trust in
Cooperating with Natural Resource Institutions (SES-1154855); SBES: Medium: Investigating the Role of
Distrust in Unauthorized Online Activities Using an Integrated Sociotechnical Approach; IGERT:
Resilience and Adaptive Governance in Stressed Watersheds (DGE-0903469)), we have been developing
reliable and valid measures of trust-related constructs and testing and refining an interdisciplinary theory
of the development of institutional trust. In the cybersecurity project, we are examining the relevance of
the construct of distrust and its impact on hacking behaviors. The theory-testing and measures
development of the trust measures is currently ongoing in all the projects. Relevant publications include:
Bornstein et al. (in press); Hamm et al. (in press); Hamm et al. (2011); PytlikZillig et al. (2012); Tomkins
et al. (2010). Relevant papers include: Hamm et al. (2012); Tomkins, PytlikZillig, Herian, and Hoppe
(2012). The proposed project will build on this prior work, allowing us to assess and refine our
sophistication model in a dynamic, real-world, societally-important, policy context. It also provides us
with the opportunity to go beyond the individual or micro level and test the potential influence and
interaction of trust constructs at the meso and macro levels. At the same time, the proposed research
allows us to explore further the role of time, as we study trust constructs as they evolve, thus providing us
another opportunity to assess sophistication—not simply as a static, point-in-time construct measured
cross-sectionally—but also as a longitudinal and developmental phenomenon.
The present work also builds on our NSF-funded work in public engagement (Developing a SocialCognitive, Multilevel, Empirically-Based Model of Public Engagement for the Shaping of Science and
Innovation Policy (SBE-0965465)) which uses U.S. students as research participants and experimentally
14
investigates methods for enhancing public engagement and the public’s use of complex science-related
information in their policy recommendations concerning nanotechnology development and regulation.
This research has included examination of trust in science and scientists and has involved about 1,000
science majors in considering ethical, legal and social issues related to nanotechnology (PytlikZillig &
Tomkins, 2011; PytlikZillig et al., 2011; PytlikZillig et al., 2013). The current proposal will allow us to
build on our experiemental work in real-world, international contexts. We have also conducted NSFFunded work in water contexts (Knowledge Discovery and Information Fusion Tools for Collaborative
Systems to Adaptively Manage Uncertain Hydrological Resources (IIS-0535255). This project studied
complex hydrologic data and decision making in the U.S., and provided the basis for studying water
resource institutions, along with considering aspects of trust and social justice in this policy arena
(Bornstein et al. (2009)). The proposed research takes that research into international contexts.
Finally, the proposed project will build on the Department of Education-funded and related
research that has advanced statistical research on multilevel modeling (Bovaird, 2007; Bovaird &
Embretson, 2008; Bovaird & Koziol, 2012; Locker, Hoffman, & Bovaird, 2007) and latent variables
(Little, Bovaird, & Widaman, 2006) as well as modeling contextual effects in longitudinal studies (Little,
Bovaird, & Card, 2007).
15
REFERENCES
Adler, R.F. et al, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly
Precipitation Analysis (1979-Present), J. of Hydromet., 4 (6).
Arulampalam, M.S., S., Maskell, N., Gordon, and T., Clapp, 2002: A tutorial on particle filters for online
nonlinear/non-Gaussian Bayesian Tracking. IEEE Tran. Signal Proc. 50, 174-88.
Aubert, D., C. Loumagne, and L. Oudin, 2003: Sequential assimilation of soil moisture and streamflow
data in a conceptual rainfall-runoff model. J. Hydrol. 280, 145-161
Brakenridge, G. R., S. V. Nghiem, E. Anderson, and R. Mic (2007), Orbital microwave measurement of
river discharge and ice status, Water Resources Research, 43(W04405,
doi:10.1029/2006WR005238).
Brakenridge, G., R., Cohen, S., Kettner, A.J., De Groeve, T., Nghiem, S.V., Syvitski, J.P.M., Fekete,
B.M., Calibration of satellite measurements of river discharge using a global hydrology model, J.
Hydrol. (2012), doi: http://dx.doi.org/10.1016/j.jhydrol.2012.09.035.
Brocca, L., F. Melone, T. Moramarco, W. Wagner, V. Naeimi, Z. Bartalis, and S. Hasenauer, 2010:
Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Hydrol.
Earth. Sys.. Sci. 14, 1881-1893, doi:10.5194/hess-14-1881-2010
Brocca, L., T. Moramarco, F. Melone, W. Wagner, S. Hasenauer, and S. Hahn, 2012: Assimilation of
surface and root zone ASCAT soil moisture products into rainfall-runoff modeling. IEEE T. Geosci.
Remote Sens. 50, 2534-2555. Doi:10.1109/TGRS.2011.2177468
CEGIS (2006), Sustainable end-to-end climate/flood forecast application through pilot projects showing
measurable improvements, CEGIS Base Line Rep, 78.
Clark, M.P., D.E., Rupp, R.A., Woods, X., Zheng, R.P., Ibbitt, A.G., Slater, J. Schmidt and M.J,
Uddstrom, 2008: Hydrological data assimilation with the ensemble Kalman filter: Use of
streamflow observations to update states in a distributed hydrological model. Adv. Water Resour.
31, 1309-24.
Cohen, S., A. Kettner, J. Syvitski, and F. B.M. (2011), WBMsed: a distributed global-scale daily riverine
sediment flux model - model description and validation, Computers & Geosciences, In Review.
Crow, W.T., and D. Ryu, 2009: A new data assimilation approach for improving runoff prediction using
remotely-sensed soil moisture retrievals. Hydrol. Earth. Sys.. Sci. 13, 1-16 doi:10.5194/hess-13-12009
De Groeve, T., and P. Riva (2009), Early flood detection and mapping for humanitarian response,
Proceedings of the 6th International ISCRAM Conference – Gothenburg, Sweden, May 2009, J.
Landgren and S. Jul, eds.
De Groeve, T., Z. Kugler, and G. R. Brakenridge (2006), Near Real Time Flood Alerting for the Global
Disaster Alert and Coordination System, Proceedings ISCRAM2007 (B. Van de Walle, P.
Burghardt and C.Nieuwenhuis, eds.), 33-39.
Draper, C., J.F. Mahfouf, J.C. Calvet, E. Martin, and W. Wagner, 2011: Assimilation of ASCAT nearsurface soil moisture into the SIM hydrological model over France. Hydrol. Earth Syst. Sci. 15,
3829-3841. doi:10.5194/hess-15-3829-2011
Hain, C.R., W.T. Crow, J.R. Mecikalski, M.C. Anderson, and T. Holmes, 2011:An intercomparison of
available soil moisture estimates from thermal infrared and passive microwave remote sensing and
land surface modeling. J. Geophys. Res. Let. 116, D15107. doi:10.1029/2011JD015633
Hirpa, F.A, T. Hopson,T. De Groeve, G.R. Brakenridge, M. Gebremichael and P. J. Restrepo, 2012:
Upstream Satellite Remote Sensing for River Discharge Prediction: Application to Major Rivers in
South Asia. Remote Sen. of the Environt. DOI:10.1016/j.rse.2012.11.013
Hopson, T.M. and P.J. Webster, 2010: A 1-10-Day Ensemble Forecasting Scheme for the Major River
Basins of Bangladesh: Forecasting Severe Floods of 2003-07, J. of Hydro., 11, 618-641.
Huffman, G. J., R. F. Adler, S. Curtis, D. T. Bolvin, and E. J.Nelkin (2005). Global rainfall analyses at
monthly and 3-hr time scales. Measuring Precipitation from Space: EURAINSAT and the Future,
V. Levizzani, P. Bauer, and J. F. Turk, Eds., Springer, 722 pp.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., . . . Stocker, E. F. (2007).
The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combinedsensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1), 38-55.
Kleuskens, M., R. S. Westerhoff, and J. Huizinga (2011), Operational Flood Mapping: A Pilot Study in
the Mekong Area, paper presented at Proceedings of the International Symposium of Remote
Sensing of the Environment, Sydney, Australia, April 10-15 2011.
16
Lee, H.S., D.J. Seo, Y. Lui, V. Koren, P. McKee, and R. Corby, 2012: Variational assimilation of
streamflow into operational distributed hydrologic models: effect of spatiotemporal scale
adjustment. Hydrol. Earth Syst. Sci. 16, 2233-2251. doi:10.5194/hess-16-2233-2012
Montanari, M., R. Hostache, P. Matgen, G. Schumann, L. Pfister, and L. Hoffman, 2009: Calibration and
sequential updating of a coupled hydrologic-hydraulic model using remote sensing-derived water
stages. Hydrol. Earth Sys. Sci. 13, 367-380. Doi:10.5194/hess-13-367-2009
Moradkhani, H., K. Hsu, H.V. Gupta, and S. Sorooshian, 2005: Uncertainty assessment of hydrologic
model states and parameters: sequential data assimilation using particle filter. Water Resour.
Resear. 41, W05012, doi:10.1029/2004WR003604.
Parajka, J., V. Naeimi, G. Bloschl, W. Wagner, R. Merz, and K. Scipal, 2006: Assimilating scatterometer
soil moisture data into conceptual hydrologic models at the regional scale. Hydrol. Earth Sys. Sci.
10, 353-368. Doi:10.5194/hess-10-353-2006
Pauwels, V.R., R. Hoebeb, N.E. Verhoest, and F.P. De Troch, 2001: The importance of the spatial
patterns of the remotely sensed soil moisture in the improvement of discharge prediction for small
scale basins through data assimilation. J. Hydrol. 251, 88-102
Reichle, R.H, W.T. Crow, C.L. Keppenne, 2008: An adaptive ensemble Kalman filter for soil moisture
data assimilation. Water Resour. Resear., 44, W03423, doi:10.1029/2007WR006357.
Seo, D.J., L., Cajina, R. Corby, and T. Howieson, 2009: Automatic State updating for operational
streamflow forecasting via variational data assimilation. J. Hydrol. 367, 255-275.
Syvitski, J. P. M., and G. R. Brakenridge (2013), Causation and avoidance of catastrophic flooding along
the Indus River, Pakistan, GSA Today, 23 (1), doi: 10.1130/GSATG1165A.1131.
Verdin, J., K. Verdin, H. Gadain, and M. Mathis (2011), Development and Implementation of Globally
Applicable Methods for Characterization of Flood Hazards: Project Completion Report, in Project
performed by the U.S. Geological Survey, in partnership with UNEP, and with the funding support
of the USAID Office of U.S. Foreign Disaster Assistance (OFDA). edited, p. 28.
Webster, P. J., and e. a. T. M. Hopson (2004), Regional application of Monsoon Dynamics:
Implementation of a three-tier flood and precipitation forecasting scheme for Bangladesh and
surrounding regions, CLIVAR Contribution to Exchanges, 30.
Webster, P.J., J. Jian, T.M. Hopson, C.D. Hoyos, P. Agudelo, H-R. Chang, J.A. Curry, R.L. Grossman,
A.R. Subbiah, 2010: Extended-Range Probabilistic Forecasts of Ganges and Brahmaputra Floods in
Bangladesh,” Bulletin of the American Meteorological Society, 91 (11), 1493-1514.
Webster, P. J., V. E., Toma and H. M. Kim, 2011: Were the 2010 Pakistan floods predictable? Geophys.
Res. Lett. 38, L04806.
Webster, P. J., 2013: Improve weather forecasts for the developing world. Nature 493, 17-19.
Weerts, A.H., and G.Y.H., El Serafy, 2006: Particle filtering and ensemble Kalman filtering for state
updating with hydrological conceptual rainfall-runoff models. Water Resour. Resear. 42, W09403,
doi:10.1029/2005WR004093.
Westerhoff, R., J. Huizinga, M. Kleuskens, R. Burren, and S. Casey (2010), Operational Satellitebased
Flood Mapping using the Delft-FEWS System, paper presented at Proceedings of the ESA Living
Planet Symposium, Bergen, Norway, June 28-July 2, 2010.
Baumgartner, F. R., & Jones, B. D. (2009). Agendas and instability in American politics (2nd
ed.). Chicago: University of Chicago Press.
Berg, B. L. (Ed.). (1998). Qualitative research methods for social science (3rd ed.). Toronto,
Ontario: Allyn and Bacon.
Birkland, T. A. (1997). After disaster: Agenda setting, public policy, and focusing events.
Washington, DC: Georgetown University Press.
Bornstein, B. H., Tomkins, A. J., Michaels, S., Samal, A., Nam, Y., Zellmer, S., . . . Olson, D.
(2009). Social justice and water sustainability and management. In A. Kakanowski & M.
Narusevich (Eds.), Handbook of social justice (pp. 271-282). Hauppauge, NY: Nova
Science.
Bornstein, B. H., Tomkins, A. J., Neeley, E. M., Herian, M. N., & Hamm, J. A. (in press).
Reducing courts’ failure-to-appear rate by written reminders. Psychology, Public Policy,
and the Law, 18. doi: 10.1037/a0026293
17
Brakenridge, G. R., Anderson, E., Nghiem, S. V., & Chien, S. (2005). Space-based measurement
of river runoff. EOS, Transactions of the American Geophysical Union, 86.
Brakenridge, G. R., Nghiem, S. V., Anderson, E., & Mic, R. (2007). Orbital microwave
measurement of river discharge and ice status. Water Resources Research, 43(W04405,
doi:10.1029/2006WR005238).
Creswell, J. W., & Plano Clark, V. L. (2007). Mixed methods research. London: Sage.
Davison, G. C., Vogel, R. S., & Coffman, S. G. (1997). Think-aloud approaches to cognitive
assessment and the articulated thoughts in simulated situations paradigm. Journal of
Consulting and Clinical Psychology, 65(6), 950-958.
De Jong, B. A., & Dirks, K. T. (2012). Beyond shared perceptions of trust and monitoring in
teams: Implications of asymmetry and dissensus. Journal of Applied Psychology, 97(2),
391-406.
Dye, T. (1995). Who is running America? The Clinton years. Englewood Cliffs, NJ: Prentice
Hall.
Fang, E., Palmatier, R. W., Scheer, L. K., & Li, N. (2008). Trust at different organizational
levels. Journal of Marketing, 72(2), 80-98.
Fleck, L. (1979). Genesis and development of a scientific fact. Chicago: University of Chicago.
Giddens, A. (1990). The consequences of modernity. Cambridge, MA: Polity Press.
Glaser, B. G., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative
research. Chicago, Illinois: Aldine De Gruyther.
Hamm, J. A., PytlikZillig, L., Herian, M. N., Bornstein, B. H., Tomkins, A. J., & Hoffman, L. (in
press). Deconstructing public confidence in state courts. Journal of Trust Research.
Hamm, J. A., PytlikZillig, L. M., Herian, M. N., Tomkins, A. J., & Dietrich, H. (2012). Trust and
intention to comply with a water allocation decision: The moderating roles of knowledge
and consistency. Manuscript submitted for publication.
Hamm, J. A., PytlikZillig, L. M., Tomkins, A. J., Herian, M. N., Bornstein, B. H., & Neeley, E.
(2011). Exploring separable components of institutional confidence. Behavioral Sciences
and the Law, 29, 95-115.
Hauser, G. A., & Benoit-Barne, C. (2002). Reflections in rhetoric, deliberative democracy, civil
society, and trust. Rhetoric & Public Affairs, 5(2), 261-275.
Heclo, H. (1974). Modern social politics in Britain and Sweden: From relief to income
maintenance. New Haven, Connecticut: Yale University Press.
Herian, M. N., Hamm, J. A., Tomkins, A. J., & PytlikZillig, L. M. (2012). Public participation,
procedural fairness, and evaluations of local governance: The moderating role of
uncertainty. Journal of Public Administration Research and Theory, 22, 815-840.
Hughes, M. (2002). Interviewing. In T. Greenfield (Ed.), Research methods for postgraduates
(2nd ed., pp. 209-217 ). New York: Oxford University Press.
Innes, J. E., & Booher, D. E. (2003). Collaborative policymaking: Governance through dialogue.
In M. A. Hajer & H. Wagennar (Eds.), Deliberative policy analysis: Understanding
governance in the network society (pp. 33-59). Cambridge, UK: Cambridge University
Press.
Kingdon, J. W. (1984). Agendas, alternatives, and public policies. Boston: Little Brown.
Kozlowski, S. W. J., & Chao, G. T. (2012). The dynamics of emergence: cognition and cohesion
in work teams. Managerial and Decision Economics, 33, 335–354.
Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to theory and research in
organizations: contextual, temporal, and emergent processes. In K. J. Klein & S. W. J.
Kozlowski (Eds.), Multilevel theory, research and methods in organizations:
foundations, extensions, and new directions (pp. 3-90). San Francisco, CA: Jossey-Bass.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1997). Trust in Large
Organizations. The American Economic Review, 87(2, Papers and Proceedings of
theHundred and Fourth Annual Meeting of the American Economic Association (May,
1997)), 333-338.
18
Lewis, J. (2003). Design issues. In J. Ritchie & J. Lewis (Eds.), Qualitative research practice: A
guide for social science students and researchers (pp. 47-76). Thousand Oaks, CA: Sage.
Li, P. P. (2012). When trust matters the most: The imperatives for contextualising trust research.
Journal of Trust Research, 2(2), 101-106. doi: 10.1080/21515581.2012.708494
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (2006). An integrative model of organizational
trust. In R. M. Kramer (Ed.), Organizational trust: A reader (pp. 82-108). Oxford:
Oxford University Press.
McEvily, B., & Tortoriello, M. (2011). Measuring trust in organisational research: Review and
recommendations. Journal of Trust Research, 1(1), 23-63.
Michaels, S., Goucher, N., & McCarthy, D. (2006). Considering knowledge uptake within a
cycle of transforming data, information and knowledge. Review of Policy Research,
23(1), 267-279.
Nannestad, P. (2008). What have we learned about generalized trust, if anything? Annual Review
of Political Science, 11, 413-436.
PytlikZillig, L. M., Tomkins, A. J., Herian, M. N., Hamm, J. A., & Abdel-Monem, T. (2012).
Public input methods and confidence in government. Transforming Government: People,
Process and Policy, 6, 92-111.
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A
cross-discipline view of trust. Academy of Management Review, 23, 393-404.
Sabatier, P. A., & Jenkins-Smith, H. (1993). Policy change and learning: An advocacy coalition
approach. Boulder, CO: Westview Press.
Sabatier, P. A., & Weible, C. M. (2007). The advocacy coalition framework: Innovations and
clarifications. In P. A. Sabatier (Ed.), Theories of the policy process (2nd ed., pp. 189220). Boulder, CO: Westview Press.
Schoorman, F. D., Mayer, R. C., & Davis, J. H. (2007). An integrative model of organizational
trust: Past, present, and future. Academy of Management Review, 32(2), 344-354.
Small, M. L. (2011). How to conduct a mixed methods study: Recent trends in a rapidly growing
literature. Annual Review of Sociology, 37, 57-86.
Soper, D. S. (2013). The free statistics calculators website, Retrieved Jan 10, 2013, from
http://www.danielsoper.com/statcalc/
Tomkins, A. J., PytlikZillig, L. M., Herian, M. N., Abdel-Monem, T., & Hamm, J. A. (2010,
May 17-20). Public input for municipal policymaking: engagement methods and their
impact on trust and confidence. Paper presented at the 11th Annual Conference on
Digital Government Research, Public Administration Online: Challenges and
Opportunities, Puebla, Mexico.
Tomkins, A. J., PytlikZillig, L. M., Herian, M. N., & Hoppe, R. (2012). Trust in government:
Findings from public budgeting consultations. Manuscript submitted for publication.
Trochim, W. M. K. (2005). Research methods: The concise knowledge base. Cincinnati, Ohio:
Atomic Dog.
You, J. (2012). Social trust: Fairness matters more than homogeneity. Political Psychology, 33,
701-721.
19