Modelling the dynamics of human and estuarine systems with regulatory feedbacks
We will create a modelling system for the Chesapeake Bay and Watershed that represents human activities such as transportation, land use and land cover change, and their impacts on water quality, including the feedback from impaired water quality that triggers regulatory systems.
Mission of the project
Policies for reducing nutrient loading have had limited success, largely because of political disconnects preventing implementation of measures to reduce water pollution. However, we know that environmental governance can improve as people learn about and experience negative effects of pollution. We aim to quantify the potential for improved restoration governance in the Chesapeake Bay watershed by coupling models of the built (human influenced landscape) environment and the natural system with a multi-tiered agent-based model of the policy-making process.
Human behavior drives change in transportation, land use, water quality, and ultimately living resources (e.g., fish habitat and seagrass growth), which, ultimately, impacts the quality of human life. Models allow us to predict how human behavior impacts the environment, but we do not yet have the ability to predict how human-induced changes in the environment feedback to impact humans. As a result, we know much more about how the environment responds to human decisions than we do about how humans respond to a changing environment and how these responses drive decision-making. This project will develop a coupled modeling system that represents the complex interrelationships among socio-economic activity, transportation, land use, land cover, and water quality with two-way interactions between humans and the environment. We will use this modeling system to predict how socio-economic changes and policy decisions in the Chesapeake Bay watershed impact water quality. And, further, how changes in water quality (e.g., changes in the size of the anoxic “dead zone”) in Chesapeake Bay influence human behavior and decision-making. These predictions will include several plausible future scenarios that project different trajectories of land uses in the context of climate change (e.g., smart growth versus business as usual). The different future development scenarios, which will include trajectories that lead to more severe nutrient pollution, will allow us to learn how environmental degradation impacts different communities and how they respond by driving different policies and actions around land use, transportation, and land cover. All of these scenarios will include climate change to account for its impacts on future efforts to restore Chesapeake Bay.
Project Outcomes
Our modelling of human behaviour and decision-making will allow us to better understand how coupled human and natural systems in the Chesapeake Bay and its watershed work and how they will respond under different future development trajectories and climate change. Human have impacted water quality and living resources throughout the country and thus the lessons learned about the dynamics of coupled human and natural systems in the Chesapeake Bay and its watershed will be applicable to other regions. This project will also produce a model that will help state and local officials make informed decisions related to planning for population growth and climate change impacts, and for making decisions about how to restore Chesapeake Bay.
Schematic of model coupling and linkages. WTM = Chesapeake Bay Watershed Transportation Model, SILO = Simple Integrated Land use Orchestrator, LCM = Chesapeake Bay Land Cover Model, CWM = Chesapeake Bay Community Watershed Model, WQM = Chesapeake bay CH3D physical model implementation with ICM biogeochemistry, PABM=Policy Agent Based Model Ensemble. Red arrows are the novel proposed linkages.
Research Questions
Impacts of different future trajectories and climate change on the environment and humans.
1. How do different future trajectories impact the environment? Specifically, how will they impact:
a. forest and farmland conversion?
b. land use patterns?
c. water quality?
d. the likelihood that TMDL allocations will be met/violated?
2. How do different future trajectories impact humans? Specifically, how will they impact:
a. human population growth and distribution?
b. household relocation?
c. traffic patterns and congestion?
d. policy development and implementation?
3. What is the added influence of climate change on water quality and the likelihood that TMDL allocations will be met under climate change for each future trajectory?
4. How will climate change impact human policy development and implementation under different future trajectories, and if future scenarios are included in planning will it influence policymaking?
5. Will implementation of BMPs be sufficient to meet TMDL allocations under all future trajectories or, in some cases, will more substantial measures, such as changes in land development and transportation policies, be necessary?
Impacts of policy implementation on the environment and humans and vice versa.
6. How does policy-making and degree of implementation affect changes in water quality relative to other drivers like economic development, population growth, and climate change?
7. Are there feedbacks between policy and other drivers that would amplify or dampen their effects on water quality? Specifically, does a change in:
a. land-use alter the likelihood that a TMDL allocation will be met?
b. economic circumstances alter the likelihood that a TMDL allocation will be met?
c. the severity of the environmental quality problem (e.g., due to climate change or bad environmental policy) alter the likelihood that a TMDL allocation will be met?
8. How do mismatches in spatial scale between water quality drivers and groups benefiting from improved water quality affect policy implementation and water quality in the Bay watershed? How does:
a. proximity to the Bay affect policy implementation?
b. voluntary vs. involuntary participation affect policy implementation?
c. institutional collaboration affect policy implementation (e.g., do past collaborations predict similar mitigative actions, do adjacent regions adopt policies similar to their neighbors)?
9. How do mismatches in temporal scale of policymaking compared with environmental remediation affect
policy implementation and water quality in the Bay watershed?
a. How does slow change in the environment (e.g., climate change) or the environmental response (e.g., slow recovery) vs. fast change (e.g., a crisis event or tipping point) affect policy implementation?
10. Where are the best leverage points to improve future implementation of the TMDL or other policies?
Questions?
Contact rhood at umces dot edu to get more information on the project