Policy Learning and Community Recovery: Analyzing Responses to Colorado's Extreme Flood Events of 2013

Introduction

A stationary precipitation event settled over the Front Range foothills on September 11, 2013, dropping over 16 inches of rain over a 72 hours period in some areas (Henson, 20131). Flash flooding along foothills communities (Boulder, Lyons, Longmont, Estes Park, Loveland, among others) occurred within hours. As the flood moved east, plains communities were inundated (Evans, Milliken, and Greeley). The Colorado Department of Transportation spent over $450 million to perform immediate highway repairs and expects more costs in the years to come as permanent repairs are completed (Whaley, 20132). In addition to transportation infrastructure, Lyons’ water treatment plant was a total loss, while Evans and Milliken saw theirs shut down for extended periods. Residents of these communities lost utility services for different lengths of time. For example, two-thirds of Evans was in a no-flush zone for eight days, while residents of Loveland did not lose sewer services. Over 1,000 residents in Evans lost their single family or mobile homes in the flood, 20 percent of housing in Lyons is damaged or destroyed, and only minor home damage occurred in Greeley. Community parks and open space areas were also affected, with Evans, Loveland, Lyons, and Longmont experiencing the most extensive damage to their parks.

As populations increase in flood prone areas, communities are becoming more vulnerable to floods. The locus of flood management has shifted from the federal to the local level and communities are now more responsible for making decisions about the adoption of flood-related policies (Brody et al., 20093). As a result, local-level processes drive decisions about mitigating future flood risks, such as if, how, and where to rebuild, as well as changes in zoning practices and public outreach programs. Because of their potentially recurring nature, floods offer an opportunity for communities to learn from and adapt to these experiences with the goal of increasing resiliency through reflection, modification of former policies, and adoption of new policies.

By following the response to the September 2013 floods in Colorado communities, this study investigates if, how, and why communities successfully learn from extreme events to increase resilience and decrease vulnerability to future floods. It will do this over a three-year period, although the research presented here and funded by a Quick Response Grant from the Natural Hazards Center provides a snapshot of the flood damages incurred and an overview of the beginning of flood recovery in seven Colorado communities.

Local-Level Flood Recovery and Adaptation to Extreme Events

Successful response to extreme floods may be due to policy learning – changes of beliefs, attitudes, behaviors, and goals – in response to new information and experiences (Sabatier and Jenkins-Smith, 19994; Busenberg, 20015). This learning can lead to adaptation of local policies to increase the resilience of communities faced with risk from extreme events. Past studies point to a number of key factors that explain variation in learning in response to extreme floods, including type and severity of flood damage, beliefs and values about the causes and consequences of floods, the structure and openness of decision-making processes, resource availability during the flood recovery process, and type and extent of stakeholder participation in recovery (Brody, 20036; Johnson et al., 20057; Brody et al., 20093; Albright, 20118; Vulturius, 20139). While the literature points to key variables that encourage or limit learning, the causal pathways and processes that lead to learning are not fully understood. The overall study seeks to identify important factors explaining variation in local level policy learning, by hypothesizing and testing potential links between flood occurrence and policy learning and change. Few studies have taken a longitudinal, cross-community perspective examining flood response (Brody et al., 20093). The first essential step in a longitudinal analysis is to understand where communities begin with regard to their experiences, decisions, processes, and considerations related to flood recovery.

Understanding the pathways that lead (or do not lead) to policy learning and adaptation in local policy contexts may prove critical, since this can mean the difference between ongoing flood vulnerability as a consequence of extreme weather events rather than long-term resilience. Analyzing the variables that increase policy learning and change towards adaptive local flood management will produce policy-relevant knowledge that will contribute to understanding policy learning, and specifically to long-term local-level resilience to natural hazards.

This study uses a cross-case research design focused on seven communities within three counties in Colorado. These communities are located in the three most severely flood-damaged counties in Colorado (Federal Emergency Management Agency, 201310). This sample of Colorado communities affected by the September 2013 flooding includes communities that vary according to population and local government capacity, extent and type of damage, flood-related participatory processes, and political context.

Policy Learning and the Advocacy Coalition Framework as Theoretical Constructs to Understand Responses to Extreme Events

The policy change and crisis literatures posit several potential causal mechanisms that may explain the occurrence of policy change in the aftermath of an extreme event or crisis (Nohrstedt and Weible, 201011; Sabatier and Weible, 200712). One such framework, the Advocacy Coalition Framework (ACF) is based on the concept of advocacy coalitions – groups of individuals that share a common set of beliefs about an issue and demonstrate a minimum level of coordinated activity (Sabatier and Jenkins-Smith, 19994). According to the ACF, whether or not policy change and learning occurs depends, in part, on the constellation of coalitions in a policy arena, the dynamics among coalitions, and resource distribution across coalitions.

Policy Change and Learning

A policy subsystem is the central building block of an ACF analysis, within which all decisions regarding an issue are debated and decided. Within this theoretical framework, the broad social, political, legal, and economic context surrounds the policy subsystem. Within a policy subsystem, stakeholders (government officials, interest groups, non-governmental organizations, community groups, researchers/scientists, members of the media, and target groups (Weible, 200813)) work to affect policy decisions. The ACF suggests three distinct pathways to policy change in response to a shock, such as a flood: (1) shifting of resources (e.g., political, financial) in the aftermath of an event; (2) a minority advocacy coalition, a coalition not currently power, successfully pressing for their policy preferences; and (3) a reexamination of policy beliefs by the dominant coalition through a process of learning (Sabatier and Jenkins-Smith, 19994; Sabatier and Weible, 200712; Nohrstedt and Weible, 201011). In this third path, learning may occur through the accumulation of knowledge and/or through new ideas generated in response to an event (Nohrstedt, 201014; Nohrstedt and Weible, 201011; Sabatier and Weible, 200712). This study examines the links between a shock, (i.e., the floods in Colorado), and policy change and learning, by focusing on potential casual factors (e.g., shifts in resources, coalition strategy, participatory processes).

In the wake of extreme flooding, communities will be faced with decisions about how to respond and what future plans to make regarding emergency management, rebuilding, and similar issues. The outcome of these decisions may depend on the extent and type of learning that has occurred within a community, what is learned, and by whom. While the reflection of past experiences and the collection of new information are central to a variety of learning concepts, the content of what is learned varies (Birkland, 200415; Busenberg, 20015; Sabatier and Jenkins-Smith, 199316; May, 199217; Bennett and Howlett, 199218). The occurrence and type of learning may depend on the complexity, severity, and extent of the policy problem, on the level of conflict among advocacy coalitions and the strategies they use, resource availability, and the occurrence of professionalized meetings (fora) or broader community participatory mechanisms (i.e. meetings or workshops) at which the advocacy coalitions meet (Sabatier and Jenkins-Smith, 19994). The accumulation of information and its subsequent exchange among competing coalitions may play a significant role in encouraging learning. Collaborative decision-making processes, if they occur, may provide such fora for learning to occur.

Coalitions/Coalition Strategy

While it is clear that coalitions, their resources, and the political context in which the events occur are important factors in explaining variation in policy change and learning (Albright, 20118; Sabatier and Weible, 200712), our understanding and empirical testing of the causal mechanisms explaining policy change remains inadequate. The timing of such coalition formation might also be important to understanding policy learning. While in the immediate aftermath of floods we do not see coalition formation, it is likely that as recovery and long-term planning takes place that coalitions will form around certain goals and beliefs. Previous studies found coalition formation and active policy involvement in the aftermath of extreme floods (Meijerink, 200519; Albright, 20118; Vulturius, 20139).

Decisions within this process are made within the constraints of the beliefs and values of stakeholders informing, making, and influencing decisions. Sabatier (198820) and Sabatier and Jenkins-Smith (199321; 199922), among others, have argued that beliefs are central to the formation of coalitions of actors, definitions of problems, and selection of policy alternatives. Understanding these beliefs is a key component of analyzing the differences in policy outcomes, community processes, and political conflict. This might especially be true in the case of extreme events such as floods, wherein the perceived causes of such events or the likelihood of similar future events will play a key role in the development of community-level responses (Stone, 198923; Albright, 2012a24).

Political Context, Resources, and Policy Change

Still under-developed in disaster and policy learning research is the role of the political context within which stakeholders operate and decisions are made. The political context surrounding these focusing events may significantly influence how these events are perceived, processed, and acted upon (Albright, 20118). Specifically, the roles that coalitions and resources play in encouraging or discouraging policy learning in response to focusing events is a research area ripe for expansion (Sabatier and Jenkins-Smith, 199316; Busenberg, 20015; Sabatier and Weible, 200712; Nohrstedt and Weible, 201011; Albright, 20118). Birkland acknowledges the centrality of such political context in disasters (199725; 200626), but does not develop it in detail, nor does the policy change literature (Baumgartner and Jones, 200927; Sabatier and Jenkins-Smith 199928; Kingdon, 200329; Sabatier and Weible, 200712; Weible, 200730).

Political context consists of predictable election and legislative cycles [131] , public opinion, political ideology, and the presence and focus of stakeholder lobbying (Kingdon, 200329). Boin et al. (2009) posit that the time of the crisis relative to an upcoming election may influence policy change. Additionally, the strategies, resources, and expansion of conflict that coalitions attempt to create in the wake of extreme events help comprise the political context within which policy decisions are made (Sabatier and Jenkins-Smith, 19994; Weible, 200730; Nohrstedt and Weible, 201011). In the aftermath of an extreme event, we may see increased resources (e.g., inflow of external funds or emergent leadership) and/or the redistribution of resources including shifts in financial and technical resources, changes in access to decision makers, the mobilization of supporters, and shifts in public opinion (Sabatier and Weible, 200712; Weible and Nohrstedt, 2011; Albright, 20118).

Democracy and Transparency in Policy Learning

Since Schattschneider’s (196032) work, scholars have known that the process of expanding and contracting venues of debate, political discourse, and actors involved in policy decisions is a key variable in understanding policy change. We have not, however, looked at the openness of governance as a central element of disaster response and policy adaptation to extreme events. Scholars have demonstrated that when experts dominate policy decision processes, citizens are typically less involved, and processes are less open (Crow, 201033; Schneider and Teske, 199234). Experts are often granted a higher level of trust by decision makers and have lower barriers to entry into the political system due primarily to their political or professional expertise. This, in addition to the suggestion that expert language is used to marginalize or prevent robust citizen involvement (Schneider and Ingram, 199735), suggests that when experts are granted higher levels of access to decision makers, citizens might less central to decision processes. If processes are less open, new actors and/or new ideas may not be likely to enter into the policy debate.

Coalitions without political authority may have a greater ability to influence political discourse in more open systems, which may lead to policy learning (Albright, 20118). However, the occurrence of this learning might be mediated by several factors including the extent to which stakeholder beliefs and values are elicited prior to development of policy alternatives (Keeney, 199636). We expect to find evidence of more substantial and lasting policy learning and policy change in communities with more open and transparent processes, in which stakeholder values were explicitly elicited prior to policy alternative development.

Research Questions and Hypotheses

Based on the literature outlined above, and the expectations articulated therein, the following research questions and associated hypotheses will be analyzed through this project. [237] Those highlighted **are the focus of this Quick Response report, the data collection for which focused on the initial considerations and processes undertaken in communities as they began recovery.

RQ1: What factors are associated with observed variations in policy change and learning in flood mitigation and prevention at the local level?

H1: Policy change and learning will vary across communities based on extent of flood damage, resource availability, political factors, public opinion, perceptions of flood risk, and degree of openness of decision-making processes.

RQ2: How do political context and available resources within communities influence policy change and successful adaptation to extreme flood events?

H2: We expect to see communities with (1) greater post-flood resource availability (e.g., financial, technical, relationship/network, public support) and (2) more extensive shifts in resources to demonstrate greater levels of policy change and learning.

H3: We expect to see communities with (1) election and legislative cycles that coincide with flood recovery, (2) widespread and severe flood damage, and (3) public support for local government recovery action to demonstrate greater levels of policy change and learning.

RQ3: How and to what extent does public perception of flood risks and preferences towards policy alternatives influence policy change and learning?

H4: In communities where the public is (1) more knowledgeable about community-wide flood damage and government flood recovery efforts, and (2) where recovery processes have been more open to public input, we expect to see higher levels of public support for recovery action and we expect also to see greater levels of policy change and learning.

H5: In communities with more than one active advocacy coalition, we expect to see greater levels of policy change when the public’s risk perception and policy preferences align with those of a pro-change coalition.

RQ4: Do communities with a higher level of democratic governance demonstrate a greater degree of policy learning or policy adaptation to extreme flood events?

H6: Communities with greater openness in collaborative and public involvement processes are expected to display greater levels of policy change and learning in response to the floods.

H7: Communities where greater levels of local knowledge of flood risks and damage are present, along with public input regarding recovery goals being incorporated into planning processes, are expected to see higher levels of public support for policy outcomes and greater policy change and learning.

RQ5: What beliefs do stakeholders have about extreme flooding, including causes, consequences, future risks, and appropriate responses, and how do these beliefs influence policy learning and adaptation?

H8: In communities with an adversarial subsystem, with coalitions holding conflicting policy belief systems about the floods causes, consequences, and future risks, we expect to see a lesser degree of policy change and learning than in those communities with collaborative subsystems (Nohrstedt and Weible, 201011).

H9: In communities where stakeholders hold beliefs consistent with (1) expert causal explanations of the 2013 floods, (2) official community flood damage estimates, and (3) predictions of increased flood vulnerability due to climate change or human development patterns, we expect to see stakeholders more involved in public recovery processes and more supportive of policy change towards community adaptation to flood risk.

RQ6: How do the study findings support or refute established policy process theories and advance the theoretical understanding of how and to what extent floods affect policy learning and change?

Multi-Method Comparative Case Study Research Design

The research questions and hypotheses presented above were studied in a comparative in-depth case study (Yin, 200338) of seven Colorado communities, located in the three Colorado counties most severely impacted by the September 2013 floods as measured by the Federal Emergency Management Agency (FEMA) assistance estimates in the month after the disaster (FEMA, 201339). Case study communities vary based on county, size, demographics, extent and type of flood damage, and level of public participation in planning processes in order to analyze the hypotheses related to policy learning, political context, openness of governance, and stakeholder beliefs (see Table 1). Data collected are detailed below, along with analytic methods used to address each research question.

Data Collection and Analysis

From this Quick Response Grant initial phase of the study, we gathered data from semi-structured in-depth interviews (n=24) and publicly available documents.

Data Collection: Interview and Document Data Collection

In-depth semi-structured interviews (Rubin and Rubin, 200540) were conducted within each community. Initial interviews were conducted in November and December 2013 as each community transitioned from ‘response’ to ‘recovery’ phases (for example, Lyons did not move into recovery until December 18, 2013). The majority of the initial interviews were conducted in person and were digitally recorded. Interviews included city elected officials, city emergency management and planning staff, city water and utility managers, economic development officers, and city managers. As each community is studied in future interviews and surveys, additional stakeholders will be identified as appropriate to include in the future rounds of interviews and surveys. As of December 2013, each community had begun its recovery planning.

All documents related to flood management planning, emergency response, evaluation of policies, and community responses to the floods are being gathered and analyzed. This includes all web content and outreach, city council minutes and memos, planning session documents, and other documents as appropriate to each community. Finally, demographic data from each community are being gathered from U.S. Census data and those data available through the Colorado State Demographer. These demographic data will provide political affiliation, economic base, and basic age/racial/gender information for each community and county.

Document and Interview Coding and Analysis

For this Quick Response report, informal coding of interviews was conducted to identify major concepts and patterns across cases. As this research progresses, however, and findings are presented at conferences and in journal publications, a more formal qualitative analysis will be conducted. Both documents and in-depth interviews will be coded using a systematic coding scheme to analyze the qualitative data focusing on the study variables. Qualitative analysis of the interview and document data will be conducted using NVivo software to maximize consistency of coding and analysis, and to allow for examination of the variations and similarities among interview subjects, variables, and cases (Miles and Huberman, 201341). Codebooks for both document analysis and interview coding will be created based upon the variables developed from extant literature (Weston et al., 200142).

Two complementary analytical processes are used here to analyze the qualitative interview data. First, a within-case analysis will be conducted to create a case narrative to explain the policy learning process within each case community (Eisenhardt, 198943; Miles and Huberman, 201341). For each case, an in-depth narrative summary was prepared for each community from which broad patterns across cases were analyzed. Second, and most importantly, a cross-case search for patterns along the variables identified above was conducted. This cross-case pattern analysis forms the basis of the findings presented below (Eisenhardt, 198943; Miles and Huberman, 201341).

Research Findings: Early Flood Recovery Responses in Colorado

The analysis below will focus on the initial findings related to four of the research questions presented above. The process of recovery, including public outreach, project-process, and stakeholder involvement is important to understand in order to determine if these variables matter to policy learning over the long-term recovery period:

RQ1: What factors are associated with observed variations in policy change and learning in flood mitigation and prevention at the local level?

In the context of answering this research question, we expect that multiple factors will influence eventual policy learning within flooded communities. Hypothesis 1 articulates these expected relationships.

H1: Policy change and learning will vary across communities based on extent of flood damage, resource availability, political factors, public opinion, perceptions of flood risk, and degree of openness of decision-making processes.

This research question and hypothesis will be addressed by first conducting initial qualitative analysis related to each of the other questions below. In the discussion section we will then work to tie these variables together to begin to understand the connections and potential influences that may occur over the long-term recovery period.

We are working to understand factors such as political context and availability of resources. While political context will emerge and evolve over the coming months and years to form a complete picture, this initial analysis began to understand the resource availability and needs of the case study communities:

RQ2: How do political context and available resources within communities influence policy change and successful adaptation to extreme flood events?

Results indicate that the cost of recovery will vary significantly among communities. In all cases, the standard FEMA cost-share of 75% over the amount covered by insurers, along with 12.5% from the State of Colorado are important recovery resources. This leaves the communities to cover 12.5% of FEMA-approved costs related to flood recovery. In most cases, this does not include river corridor repair and restoration except for that which is directly tied to flood hazard mitigation. This can be a significant expense, especially for communities reliant on river recreation such as Lyons, which must also restore the river corridor to restore the local economy. The flood-related resource needs of communities vary from the low end in Greeley, to the high end in Lyons and Evans, relative to the size of the municipal budgets. The costs will also significantly influence the decision processes made by local governments (discussed below). See Table 2 for a breakdown of the resource needs and availability for the case study communities.

In addition to fiscal resources, community managers and staff frequently mentioned the importance of an array of resources that have aided or will assist in immediate flood response and long-term recovery. Of these resources, relationships with non-governmental organizations, faith-based organizations, state and federal agencies, counties, other communities, and community members were seen as most important. Several interviewees also mentioned the importance of technical capacity, such as the ability to GPS map the high water line during the flood (Loveland) and public information outreach using digital media (e.g., Twitter, Facebook, websites, etc.).

To understand the processes that communities undertake to recover from extreme floods, and whether those processes influence eventual policy learning and the possibility of increased resilience to extreme events, we are working to understand the degree to which democratic and transparent processes related to recovery efforts might influence outcomes:

RQ4: Do communities with a higher level of democratic governance demonstrate a greater degree of policy learning or policy adaptation to extreme flood events?

We already see variation in the community-level participatory approaches being developed for flood recovery in Colorado. Lyons is holding extensive and deliberative meetings on every aspect of recovery and asking citizens to volunteer for a committee on which they will serve (housing, parks and recreation, arts and culture, roads and bridges, infrastructure, public facilities, stream recovery, and individual assistance, businesses), while Loveland plans to treat recovery processes akin to traditional capital projects with design workshops but limited policy deliberation among stakeholders. The town council of Evans will approve a recovery task force comprised of community members with specific expertise and involve the public in specific areas, such as park redevelopment. Greeley, with its very limited flood damage did not speak of community-level involvement in flood recovery. We expect to see variation across this participatory dimension as the recovery process continues, potentially leading to differences in preferences towards policy alternatives.

Initial findings suggest that the extent of flood damage, resource availability, and resource needs all may interact to help determine the extent to which the public is involved in recovery processes. Lyons may, in part, be driven to develop extensive participatory processes due to the limited capacity of its Town Government. This, however, is not likely the complete picture. For example, in Boulder, where resource needs and availability are manageable, the City of Boulder has conducted neighborhood meetings to understand local knowledge about the flood event and solicit resident input. As this project moves forward, it will be important to understand local political context (RQ2) and beliefs of residents in order to gain a complete understanding of what drives decisions with regard to the processes undertaken in flood recovery.

It is important to note that in multiple communities the resources, timeline, and requirements mandated by FEMA drive many of the immediate decisions about recovery and planning. These timelines can prevent communities from taking more time to consider options and planning decisions in order to qualify for the full FEMA reimbursement. See Table 3 for a breakdown of the processes within each community.

While we cannot yet analyze public opinion and beliefs about flood risk and causes [344] (RQ3), we can begin to understand the beliefs that elites in among our interview subjects hold with regard to the risk of future flooding in their community and the causes of the 2013 floods:

RQ5: What beliefs do stakeholders have about extreme flooding, including causes, consequences, future risks, and appropriate responses, and how do these beliefs influence policy learning and adaptation?

The initial interviews indicate that perceived future flood risk is much higher in some communities than others. This may be, in part, due to past flood experiences or due to the availability of local information resources. The communities vary greatly in the timing of the most recent updates to floodplain maps. Evans’ most recent update was in the 1970s, whereas Loveland updated floodplain maps twice in the past decade. Variation in community-level resources and in previous flood experiences may drive these differences. For example, a flood in 1997 in Fort Collins, Larimer County motivated Loveland to update the floodplain maps in conjunction with the National Flood Insurance Program despite the fact that the flood did not affect Loveland. Table 4 examines some of the community-specific differences. An important issue to note is that in a community like Lyons, which was devastated by the flood, there is a sense of helplessness and futility regarding the ability of any town to prepare for a flood of the magnitude that they experienced.

Discussion

The goal of this quick response study was twofold: (1) to get out quickly in the field to capture the status of flood response and recovery in a diversity of communities affected by the September 2013 flood; and (2) to start to identify and understand the array of factors that may prove important in explaining policy change and learning, and by extension, local-level adaptation and resilience to future floods. The 24 interviews across seven communities in Colorado indicate that the devastating floods impacted each community differently.

Prior to the floods, each community had a unique set of resources and relationships that they may bring to the recovery process. Laid on top of this diversity in resources (e.g., fiscal or budgetary, staff, technical expertise, political factors, public opinion), lays a great variation in the extent and type of damage incurred on each community – the amount of damage to public infrastructure, private residences, and businesses, or a mix of all three sectors. The damage costs incurred as a percentage of annual budgets vary widely. In response to these floods, the cities have experienced resources shifts, whether due to an influx of money from FEMA, state agencies or insurance payments, additional staffing and volunteer participation, or outside technical expertise from contractors and consultants. Stemming in part from all of these factors, as well as past practices, community culture, and federal and state processes (e.g., National Incident Management System), each city has adopted its own process for incorporating the broader community into decisions surrounding recovery and future planning. While it is too early in the recovery process to know how each of these factors will motivate (or not) changes in policy, plans, and programs, the experiences of these seven communities and this list of potential causal factors should be useful in understanding the causal links that explain policy change and learning.

In our first hypotheses that will guide the overall direction of this study, grounded in the literature of ACF and focusing events, we posit that the extent and depth of policy change and learning will depend on extent of damage, resource availability, political factors, public opinion, perceptions of flood risk and degree of openness of decision-making processes.

Extent of Damage

As we have discussed above, the seven communities, while all impacted by the flood to varying degrees, experienced unique distributions of damages across public infrastructure, residences, and commercial entities. Further, public infrastructure damage varied across communities in terms of what was hardest hit (e.g., stormwater systems, power, transportation systems, drinking water, parks and open space, etc.). While some communities were forced to use port-a-lets for extended periods, other communities’ parks and open space were most heavily damaged causing park closures, economic consequences, and public complaints. The type of damage experienced by a community, along with residents’ preferences and resource needs, may motivate different community responses. For example, in a city with greatest impacts on transportation systems, city officials and residents may push for change in this sector, while communities with damaged parks may reconceptualize how parks should be redeveloped in the future. Our initial results suggest that it may be important to not only assess the total damage experienced by a community, but how these damages vary across these sectors.

Resources

It remains to be seen if shifts financial resources such as increased flows in federal funds (through FEMA and other sources) will motivate greater extent of policy change or alternatively limit municipal action in the recovery process. A few of the interviewees suggested that the FEMA process of procurement and reimbursements dictates, to a large extent, the recovery process. How FEMA and other federal and state funds incentivize or discourage actions will be analyzed as we follow the communities’ actions over the three-year recovery period.

Openness of Decision Making Processes

Research suggests that when a new venue opens encouraging an increase in diversity of voices and opinions, changes in policies might ensue. This suggests that those communities with more open and deliberative processes, like those forming in Lyons, may motivate greater change and learning. Some cities have a more engrained culture in openness of city planning processes (e.g., Boulder), while other communities do not have a long history of these practices. The adoption of new modes and methods of community involvement in decision-making processes, such as seen in Lyons, for example, may lead to changes in municipal officials’ understanding about flood risks and perceptions about policy alternatives.

Conclusion

We believe that this study and the subsequent longitudinal study will provide important knowledge for both scholars and practitioners who work in hazards mitigation and recovery. Through the integration of multiple research methods, new theoretical frameworks, and the measurement of previously unspecified variables (political context, openness of governance, etc.) this study will provide new knowledge and push the field of disaster recovery and planning research forward. The results of this study will be presented in workshops, reports, and website content. The goal of this work is not only to understand the important causal links between hazards such as floods and resulting community-level policy learning, but to apply this knowledge to produce recommendations for governments to increase the likelihood that their recovery processes will lead to adaptive policies. We expect that our recommendations to practitioners will focus on several key concepts, but will likely expand as this study progresses:

  1. Tools for increasing openness used in recovery processes, and the degree to which stakeholders are deliberately engaged in recovery decisions.

  2. Mechanisms by which governments can increase their own capacity (i.e. engaging in citizen deliberative processes or working with faith-based groups) to respond to hazards.

  3. Lessons for collaboration among neighboring communities, government entities, and stakeholder groups to more effectively recover and plan for future floods.

As discussed above, the findings presented here are a snapshot in the initial weeks of flood recovery. Each community will spend many months, if not years, in recovery and planning. Our future work will follow these recovery processes and expand on the findings presented here. Importantly, the long-term project will analyze final recovery results to analyze policy learning along the way. Also, residents will be surveyed to understand perceptions, beliefs about risk, and support for local policy action and recovery decisions. These additions to the project will enhance both the longitudinal strength of the findings, but also the ability to speak not only about decision-maker opinions, but also those of residents of flood-affected communities.


  1. Henson, Bob. (2013). Inside the Colorado Deluge: How Much Rain Fell on the Front Range, and How Historic Was It? Retrieved March 10, 2014, from http://www2.ucar.edu/atmosnews/opinion/10250/inside-colorado-deluge 

  2. Whaley, Monte. (Nov. 20, 2013). “All flood damaged roads to open before Dec. 1 repair deadline,” Denver Post. 

  3. Brody, Samuel, D., Zahran, Sammy, Highfield, Wesley, E., Bernhardt, Sarah P., & Vedlitz, Arnold. (2009). Policy Learning From Flood Mitigation: A Longitudinal Assessment of the Community Rating System in Florida. Risk Analysis, 29(6), 912-929. 

  4. Sabatier, Paul & Jenkins-Smith, Hank. (1999). The Advocacy Coaltion Framework: An Assessment. In Paul Sabatier (Ed.), Theories of the Policy Process (117-166). Boulder, Colorado: Westview Press. 

  5. Busenberg, George J. (2001). Learning in Organizations and Public Policy. Journal of Public Policy, 21(2), 173-189. 

  6. Brody, Samuel, D. (2003). Are We Learning to Make Better Plans? A Longitudinal Analysis of Plan Quality Associated with Natural Hazards. Journal of Planning Education and Research, 23(2), 191-201. 

  7. Johnson, Claire L., Tunstall, Sylvia M., & Penning-Rowswell, Edmund C. (2005). Floods as Catalysts for Policy Change: Historical Lessons from England and Wales. International Journal of Water Resources Development, 21(4), 561-575. 

  8. Albright, Elizabeth, A. (2011). Policy Change and Learning in Hungary: An Advocacy Coalition Approach. Policy Studies Journal, 39(3), 485-511. 

  9. Vulturius, Gregor. (2013). Policy Change and Policy Learning for Enhanced Flood Policies at Local, Subnational and EU Level. In E. Carina H. Keskitalo (Ed.), Climate Change and Flood Risk Management: Adaptation and Extreme Events at the Local Level. Northhampton, Massachusetts: Edward Elgar Publishing. 

  10. Federal Emergency Management Agency. (2013). Colorado Flooding One Month Later: Positive Signs of Recovery. Retrieved January 29, 2014, from http://www.fema.gov/news-release/2013/10/11/colorado-flooding-one-month-later-positive-signs-recovery . 

  11. Nohrstedt, Daniel & Weible, Christopher M. (2010). The Logic of Policy Change after Crisis: Proximity and Subsystem Interaction. Risk, Hazards, & Crisis, 1(2), 1-32. 

  12. Sabatier, Paul & Weible, Christopher M. (2007). The Advocacy Coalition: Innovations and Clarifictions. In Paul Sabatier (Ed.), Theories of the Policy Process (189-220), Second edition. Boulder, Colorado: Westview Press. 

  13. Weible, Christopher, M. (2008). Expert-Based Information and Policy Subsystems: A Review and Synthesis. Policy Studies Journal, 36(4), 615-635. 

  14. Nohrstedt, Daniel. (2010). Do Advocacy Coalitions Matter? Crisis and Change in Swedish Nuclear Energy Policy. Journal of Public Administration Research and Theory, 20(2), 309-313. 

  15. Birkland, Thomas A. (2004). Learning and Policy Improvement After Disaster. American Behavioral Scientist, 48(3), 341-364. 

  16. Sabatier, Paul & Jenkins-Smith, Hank (Eds). (1993). Policy Change and Learning: An Advocacy Coalitions Approach. Boulder, Colorado: Westview Press. 

  17. May, Peter J. (1992). Policy Learning and Failure. Journal of Public Policy, 12(4), 331-54. 

  18. Bennett, Colin J., & Howlett, Michael. (1992). The Lessons of Learning: Reconciling Theories of Policy Learning and Policy Change. Policy Sciences, 25(3), 275-294. 

  19. Meijerink, Sander. (2005). Understanding Policy Stability and Change: The Interplay of Advocacy Coalitions, Epistemic Communities, Windows of Opportunity, and Dutch Coastal Flooding Policy 1945-2003. Journal of European Policy, 12(6), 1060-1077. 

  20. Sabatier, Paul A. (1988). An Advocacy Coalition Framework of Policy Change and the Role of Policy Oriented Learning Therein. Policy Sciences, 21, 129-168. 

  21. Sabatier, Paul & Jenkins-Smith, Hank (Eds). (1993). Policy Change and Learning: An Advocacy Coalitions Approach. Boulder, Colorado: Westview Press. 

  22. Sabatier, Paul & Jenkins-Smith, Hank. (1999). The Advocacy Coaltion Framework: An Assessment. In Paul Sabatier (Ed.), Theories of the Policy Process (117-166). Boulder, Colorado: Westview Press. 

  23. Stone, Deborah. (1989). Causal Stories and the Formation of Policy Agendas. Political Science Quarterly, 104(2), 281-300. 

  24. Albright, Elizabeth, A. (2012a). A Comparative Analysis of Local-Level Causal Understanding of and Policy Response to Extreme Flood Events: The Upper Mississippi River (U.S.) and the Tisza River (Hungary) Basins. Presented at the International Conference on Culture, Politics and Climate Change, Boulder, Colorado, 13 September 2012. 

  25. Birkland, Thomas A. (1997). After Disaster: Agenda Setting, Public Policy and Focusing Events. Washington, D.C.: Georgetown University Press. 

  26. Birkland, Thomas A. (2006). Lessons of Disaster: Policy Change After Catastrophic Events. Washington, D.C.: Georgetown University Press. 

  27. Baumgartner, Frank R. & Jones, Bryan D. (2009). Agendas and Instability in American Politics. Chicago: University of Chicago Press. 

  28. Sabatier, Paul & Jenkins-Smith, Hank. (1999). The Advocacy Coaltion Framework: An Assessment. In Paul Sabatier (Ed.), Theories of the Policy Process (117-166). Boulder, Colorado: Westview Press. 

  29. Kingdon, John. (1995). Agendas, Alternatives, and Public Policies. Amsterdam: Longman. 

  30. Weible, Christopher, M. (2007). An Advocacy Coalition Framework Approach to Stakeholder Analysis: Understanding the Political Context of California Marine Protected Area Policies. Journal of Public Administration Research and Theory, 17(1), 95-117. 

  31. Legislative cycles that are external to the community-level decisions might be important to this research and to community resources, and learning. For example, the Colorado General Assembly leadership stated that flood recovery legislation was a top priority as the session began on January 8, 2014. 

  32. Schattschneider, Elmer E. (1960). The Semi-Sovereign People. New York: Holt, Rinehart and Winston. 

  33. Crow, Deserai A. (2010). Local Media and Experts: Sources of Environmental Policy Initiation? Policy Studies Journal, 38(1), 143-164. 

  34. Schneider, Mark, & Teske, Paul. (1992). Toward a Theory of the Political Entrepreneur: Evidence from Local Government. The American Political Science Review, 86(3), 737-747. 

  35. Schneider, Anne Larason, & Ingram, Helen. (1997). Policy Design for Democracy. Lawrence, KS: University Press of Kansas. 

  36. Keeney, Ralph. (1996). Value-Focused Thinking: A Path to Creative Decision Making. Boston: Harvard University Press. 

  37. One of the outcomes of this initial Quick Response study was to develop a full proposal to the National Science Foundation to engage in a three-year longitudinal study involving these cases, along with expanded emphasis on the counties as well. The research questions and hypotheses presented here include those that were developed for this full NSF proposal. Those analyzed for this report are highlighted using **. 

  38. Yin, Robert K. (2003). Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications. 

  39. Federal Emergency Management Agency. (2013). Colorado Flooding One Month Later: Positive Signs of Recovery. Retrieved January 29, 2014, from http://www.fema.gov/news-release/2013/10/11/colorado-flooding-one-month-later-positive-signs-recovery . 

  40. Rubin, Herbert J. & Rubin, Irene S. (2005). Qualitative Interviewing: The Art of Hearing Data (2nd). Thousand Oaks, CA: Sage Publications. 

  41. Miles, Matthew, B. & Huberman, A. Michael. (2013). Qualitative Data Analysis: A Methods Sourcebook. Thousand Oaks, CA: Sage Publications. 

  42. Weston, Cynthia, Terry Gandell, Jacinthe Beauchamp, Lynn McAlpine, Carol Wiseman, & Beauchamp, Cathy. (2001). Analyzing Interview Data: The Development and Evolution of a Coding System. Qualitative Sociology, 24(3), 381-400. 

  43. Eisenhardt, Kathleen M. (1989). Building Theories from Case Study Research. The Academy of Management Review, 14(4), 532-550. 

  44. This will be done through a survey of resident opinions, beliefs about flood risk, support for ongoing flood recovery efforts and new policies in each community. This survey will be conducted in summer 2014. 

Suggested Citation: Crow, Deserai A. and Elizabeth A. Albright. Policy Learning and Community Recovery: Analyzing Responses to Colorado’s Extreme Flood Events of 2013. Quick Response Grant Report series; 248. 2014.