Uncertainty in groundwater recharge estimation using groundwater level fluctuation and aquifer test

For sustainable groundwater management the rate of groundwater recharge and specific yield are both of the most important elements in the analysis and management of groundwater resources, and, sometimes, estimation of these parameters remains a challenge. This research presents a combining approach of the water-table fluctuation method (WTF) with an aquifer test to estimate both and quantify their uncertainty. The methodology requires at least three wells: two instrumented observation wells with a level sensor for long-term monitoring and a pump well located nearby for aquifer testing. The test interpretation was supported by the Aqtsolv Demo software obtaining the best fit with the method proposed by Tartakovsky-Neuman, with a specific yield varying, in 2a, between 9.4% and 10.6%. Recharge was estimated with WTF, and the uncertainty in recharge is obtained by propagating the uncertainties about the specific yield (Bayesian inference) and the groundwater recession dynamics to the WTF. The uncertainty about recharge stems from uncertainty about the specific yield. The approach was applied on the campus of the Federal University of Para, Belem, Brazil. Recharge was estimated at 1078.9 mm, from 03/sep/2020 to 30/sep/2021, with an associated uncertainty of 129.5 mm in 2a, which equates to a range between 33.9 and 39.8% in terms of precipitation. Through the use of cost-effective instrumentation and interpretation methodology, replication of that approach can be encouraged to provide reliable estimates of recharge and specific yield in a site specific. Such condition can be useful to reduce the predictive uncertainty of groundwater management.

Automated summary

For sustainable groundwater management, the rate of groundwater recharge and specific yield are crucial elements, but their estimation remains a challenge. This research combines the water-table fluctuation method (WTF) with an aquifer test to estimate and quantify their uncertainty. The approach was successfully applied in Brazil, with an estimated recharge of 1078.9 mm and an associated uncertainty of 129.5 mm in 2a. By reducing predictive uncertainty, this approach can contribute to improved groundwater management.