Postdoctoral Research at UVM

I joined the Basin Resilience to Extreme Events (BREE) project within the Vermont EPSCoR program at UVM after finishing my PhD in July of 2017. My role with BREE is to analyze the multi-scale, multiplex water governance networks in the Lake Champlain Basin - and to link this complex governance system to land use (dis)incentives and water quality outcomes. One of the reasons I chose to join BREE was the opportunity to study the multi-scale nature of the incentives and constraints that affect land use in the Lake Champlain Basin (LCB). This research program has followed nicely from my graduate work. Farmers in the LCB, for example, are certainly affected by field and farm-scale processes. However, rules and regulations - as well as financial incentives - are designed at regional, state, and federal scales as well. The project provides an opportunity for me to further develop my modeling framework in a new setting/system with strong regulatory influences. In addition, UVM and VT EPSCoR are great, and Vermont is an amazing place to live.

Below, I briefly describe aspects of my research at UVM. Please feel free to contact me with any questions.


Agent-based modeling of water governance in the Lake Champlain Basin (LCB)

My primary task with BREE is the development of an agent-based model (and modeling framework, more generally) that captures interactions among governance actors and institutions to land use and other environmental change (e.g., climate, water quality) in the LCB. The ABM, written in MASON, simulates:

  1. the regionalization of water quality funds and statutory requirements managed by the state government (e.g., block grants), including coordination among municipalities and regional actors

  2. different levels of implementation and planning capacity, and their distribution across the governance network

  3. alternative prioritization schemes for leveraging capacity and funds to address load reductions mandated by an EPA TMDL.

The governance ABM is coupled to climate, hydrological, and land use models, and allows for the investigation of alternative scenarios under different assumptions of climate, policies, and environmental lags. Our initial work was presented at AAG 2018 in New Orleans, and we are currently preparing our findings for publication while also working to expand model functionality.


Social (and social-ecological) network analysis

As part of our research on LCB governance, we have surveyed institutions and municipalities in the Basin to better understand how they share resources, distribute information, and coordinate activities. This complex social network spans scales and domains, and its structure can help us better understand where to target policy interventions. I am currently developing an analysis of the social networks focusing on the structure of actors’ ego networks to characterize differences among local network structures. We’re also working to link social network morphology to the hydrological network in the system to identify social-ecological mismatch and areas of need relative to estimates of phosphorus loads. Finally, we’re developing another wave of the survey, to be carried out in the summer of 2019. This survey will link governance actors to particular policy forums or “arenas” and facilitate the analysis of bimodal networks in the system.


Focus group-based research

In addition to developing model scenarios in consultation with state agencies and regional planning commissions, we’re also engaged with groups of citizens to build local capacity in decision-making. For example, we are working with the Clean Water Advisory Committee (CWAC) of the Northwest Regional Planning Commission to develop a participatory GIS-based decision support tool. The decision tool is being constructed in R Shiny, and incorporates various spatial data layers and collects data on user priorities. Once completed, this software will help the CWAC to identify and weight co-benefits of clean water projects in their jurisdiction and prioritize the allocation of funding.