M&A identifies and quantitatively assesses the potential impacts of wellfield pumping on a critical habitat area using an innovative statistical model approach.
Impacts to surface water from pumping — particularly impacts to a critical habitat area — can have environmental and legal implications. In the case of this industrial facility, long-term monitoring indicated that pumping could have caused small water-level declines observed adjacent to the stream. Since these declines could also be partially or entirely related to drought conditions and/or pumping by other entities in the area, M&A proposed using a statistical approach to evaluate various factors.
M&A applied various tools to assess climate and/or pumping impacts on streamflow and to attempt to distinguish these impacts. The evaluation involved statistically analyzing environmental datasets and modeling potential groundwater impacts using a null-space Monte Carlo approach.
- Analyzed precipitation data and quantified the departure from the mean to identify drought periods
- Analyzed streamflow data and applied methodology to isolate baseflow and seasonal runoff components
- Conducted statistical analysis to evaluate precipitation-baseflow relationships for stream reaches with critical habitat area; compared the correlation statistics to those for other area streams
- Analyzed data to statistically separate climatic and pumping factors affecting streamflow
- Adapted an existing groundwater flow model for null-space Monte Carlo analysis to evaluate the likelihood and potential magnitude of pumping-related streamflow impacts
- Developed code to run and compile the results of numerous simulations to assess the effects of uncertainties in parameters and other assumptions on model projections
- Developed simulations to quantify the impacts of pumping by the client and by other entities in the study area, to distinguish these impacts, and to project the results of planned reductions in pumping
- Recommended additional analyses and data collection to decrease uncertainty and improve the model projections