Authors: Jay Diffendorfer*, USGS, Monica Dorning, Western Carolina University, Steve Garman, USGS/BLM-retired, Darius Semmens, USGS, Seth Haines, USGS, Karen Jenni, USGS
Topics: Energy, Spatial Analysis & Modeling, Coupled Human and Natural Systems
Keywords: energy, simulation, integrated model, shale, oil, human impacts, ecosystem services
Session Type: Paper
Presentation File: No File Uploaded
To make informed management decisions, regional planners need information about how different energy development alternatives may affect ecosystems and the people and wildlife relying on them. Integrating probabilistic models of natural resources, environmental change, and ecosystem responses associated with possible future energy development can provide unique insights and information for weighing alternatives while considering uncertainty. We are developing probabilistic models to assess the impacts of energy development on landscapes, ecosystems, and people, beginning with two complementary approaches to modeling change associated with oil and gas wells. Both of these approaches utilize spatially explicit, stochastic energy footprint models to simulate well and well pad development and the accompanying expansion of road networks based on USGS assessments of recoverable energy resources. The first approach employs a computationally efficient algorithm designed to integrate with USGS resource assessments and to provide aspatial output for an assessment area. The second approach incorporates additional land-use policies and constraints to create scenarios of potential energy development outcomes. We present these approaches applied in southwest Wyoming and western Colorado to wildlife habitat and ecosystem services. We also discuss an approach for fully connecting the framework to human impacts. Appropriate data and empirical analyses of the relationships among energy development, ecosystem impacts, and human costs or benefits are often lacking at the landscape scale making it difficult to determine the full range of potential effects. We aim to improve integration of probabilistic models of energy development to enable deeper investigation of potential effects on humans and wildlife.