Probabilistic Modeling of Rainwater Harvesting and Artificial Aquifer Recharge Efficiency

Over the past decade, our client, a global products company, has supported hundreds of rainwater harvesting (RWH) and artificial aquifer recharge (AAR) projects throughout the world.  The majority of these projects were implemented to address community needs and recharge aquifers.  For most, it was simply assumed to have a positive efficiency ratio and that actual measured values were not necessary nor were they a core objective.  Given the meteoric rise in awareness and scrutiny of global water resource management issues, our client has increasingly explored methodologies to better gauge the effectiveness and socioeconomic impact of community water projects. 

 

Over the past year, we have worked collaboratively on developing a global methodology and probabilistic model for assessing the quality and quantity of water captured and recharged to aquifers by individual projects.  We applied probabilistic modeling due to the fact that the actual impacts and efficiencies of these projects are dependent upon a myriad of uncertainties and site-specific conditions ranging from physical conditions (i.e., soil type and infiltration, rainfall intensity, and geology) to socioeconomic conditions (i.e., land and water rights, water end use, and economic viability). 

 

The model incorporates site-specific considerations (i.e., precipitation, design characteristics, soil types, etc…) and referenced coefficients (i.e., catchment coefficients, rates of recharge, infiltration, etc…).  The model is Microsoft Excel-based using Crystal Ball® software to run the Monte Carlo simulation.   Our client is open to sharing our research and exploring other applications of the general methodology (e.g., stormwater management).