An Integrated Approach to Evaluating the Economic and Environmental Impacts of Drought Through Probabilistic Modeling of a Multi-reservoir River Basin

(This project is the subject of Joe Lobuglio's doctoral research, click here to see an abstract of his paper in Water Resources Research)

Water supply challenges are increasing worldwide and demand on U.S. water resources continue to increase although our water supply does not. Consequences of increased demand are evident even in the traditionally water-rich region of the south-eastern United States where hydropower production competes with municipal supply and other resource users, especially in times of drought. In such an environment of increasing water scarcity, understanding the economic value of competing uses and the environmental consequence of various water allocations strategies becomes critical.

The objective of this research is the creation of a probability-based model for a multi-reservoir system in North Carolina (Catawba River System) to develop strategies that maximize hydropower generation and municipal supply reliability subject to maintaining a healthy freshwater ecosystem. In this probability framework, ecological and economic models will be added to water resource estimates so that a broader set of outcome variables (e.g. chlorophyll a concentrations, total net economic benefit) can be considered.

A probability network is used to reduce the complexity of the system being modeled. Such a model sacrifices some temporal and spatial resolution in favor of greater accuracy of aggregate-level results. The probability model also accounts for uncertainty, making uncertainty estimates for the outcome variables explicit and transparent while allowing decisionmakers to understand the probability of adverse extreme events in addition to the likely outcomes.
Runoff quantity and quality will be estimated using the Soil and Water Assessment Tool with the GLUE package to evaluate model parameter uncertainty. An existing water balance model of this system will be recast in a probability network framework to obtain predictions of reservoir releases and the frequency and magnitude of water supply conflicts that include uncertainty in estimates of future water and hydropower demand as well as that from the SWAT/GLUE model. Empirical relationships between nutrient loading and water quality in reservoirs (e.g. Vollenweider (1976)) will be developed using ten years of reservoir monitoring data acquired from Duke Energy and data from the USGS and EPA. An economic model will also be added to identify scenarios that minimize economic damages during drought conditions.
The proposed work is unique in that it encompasses primary inputs, such as rainfall and water demand, and predicts economic and environmental outcomes that are directly relevant to decisionmakers along with estimates of uncertainty for these outcomes. The model will allow the exploration of management approaches that will minimize the economic and environmental impact of meeting water resource objective.