Background: Effective porosity determination in groundwater systems relies on traditional methods with significant limitations. Conventional approaches include standard relationships for unconfined aquifers such as:

n=Tt²/2.3rs,

 where n is effective porosity, T is transmissivity, t is time, r is distance from the pumping well, and s is the drawdown. Also, laboratory testing of sediment samples, which are expensive, time-consuming, and prone to substantial error margins. Laboratory methods risk sample disturbance, while driller's logs often contain inaccuracies.

Objectives: Effective porosity is essential for particle tracking models determining groundwater travel time, particularly in three-dimensional models with thousands of cells. Current practices assign textbook values assuming homogeneity, potentially compromising model accuracy. This study addresses the knowledge gap regarding relationships between effective porosity, pumping rate, and drawdown during well operations.

Methods: We conducted iterative analysis of pumping test data to investigate how water flow through porous sediments creates measurable drawdown constrained by effective porosity. Multiple pumping tests were analyzed to establish empirical relationships between field-measurable parameters and effective porosity.

Results: A novel empirical relationship was developed:

n=0.15108(Q/s)^0.0826

where n represents effective porosity, Q is pumping rate (m³/day), and s is drawdown (m). This demonstrates that effective porosity is a mathematical function of specific capacity (Q/s), providing in-situ measurements independent of aquifer type (confined/unconfined) and lithological characteristics.

Conclusions: This method provides enhanced accuracy by measuring effective porosity under actual field conditions, improving groundwater modeling while reducing time and costs compared to traditional methods. The approach delivers field-scale measurements applicable to diverse aquifer types. However, practitioners should carefully evaluate data quality and temporal variability when implementing this methodology.