Permeable Pavement Groundwater Recharge Modeling

ISEF Category: Environmental Engineering

Subcategory: Water Resources Management  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Cities pour rainwater onto roads, parking lots, and sidewalks, then rush it away before it can soak in. That can leave aquifers short on recharge. You can model whether permeable pavement changes that balance. Your job is to find out how much the answer depends on one number, hydraulic conductivity.

What Is It?

Groundwater recharge is the water that moves from the surface down into an aquifer. Think of an aquifer like a hidden sponge underground. If pavement blocks water, the sponge gets less refill. If pavement lets water pass through, more water can reach the ground below.

MODFLOW is a computer model that simulates how water moves underground. FloPy is a Python library that helps you build and run MODFLOW models without clicking through every setting by hand. In this project, you use the model to compare an urban area with regular pavement and the same area after a permeable-pavement retrofit. Then you test sensitivity, which means you change the hydraulic conductivity, a measure of how easily water moves through a material, and see how much your recharge estimate changes.

Why This Is a Good Topic

This topic works well for science fair research because you can study a real city problem with real data and clear math. You are not guessing in the abstract, you are testing how much a design choice changes groundwater recharge. The project teaches modeling, sensitivity analysis, and how to judge whether a result depends on a shaky assumption or a solid one.

Research Questions

• How does assumed hydraulic conductivity change modeled groundwater recharge under permeable pavement?
• What is the effect of different pavement layer conductivities on recharge estimates in a MODFLOW model?
• Does a permeable-pavement retrofit increase simulated recharge enough to matter under urban rainfall patterns?
• To what extent do soil type assumptions change the recharge benefit predicted by the model?
• Which hydraulic conductivity range produces the largest change in modeled infiltration to the aquifer?
• How does spatial placement of permeable pavement affect recharge in a simplified urban grid model?

Basic Materials

• Laptop or desktop computer with enough RAM to run Python and MODFLOW.
• Python installed with FloPy.
• MODFLOW executable that matches your FloPy setup.
• Spreadsheet software for organizing input and output tables.
• USGS groundwater or soil data for a study area.
• GIS viewer such as QGIS for inspecting land cover and grid layout.
• External storage or cloud backup for model files and outputs.

Advanced Materials

• University computer access or high-performance laptop for larger model runs.
• Python with FloPy, NumPy, Pandas, and Matplotlib.
• MODFLOW 6 or a version required by your model design.
• QGIS or ArcGIS for building spatial inputs.
• GIS-derived land cover, elevation, soil, and hydrogeology layers.
• Local precipitation and recharge datasets from USGS or NOAA sources.
• Version control software such as Git for tracking model changes.
• Jupyter Notebook for documenting runs and sensitivity tests.

Software & Tools

• FloPy: Builds and edits MODFLOW groundwater models in Python, and helps you automate sensitivity runs.
• MODFLOW 6: Simulates groundwater flow and recharge in the aquifer system.
• QGIS: Lets you map land cover, pavement placement, and model grid cells.
• Jupyter Notebook: Keeps your code, notes, and model outputs together in one place.
• Python: Handles parameter sweeps, output parsing, and basic plotting.

Experiment Steps

1. Define the urban setting you will model and choose a recharge question that matches the data you can access.
2. Build a simple baseline MODFLOW model that represents current pavement and groundwater conditions.
3. Add a permeable-pavement retrofit scenario and decide which layers or zones will change.
4. Plan a sensitivity analysis for hydraulic conductivity so you can test low, middle, and high assumptions.
5. Set controls that keep rainfall, boundary conditions, and grid size consistent across model runs.
6. Compare recharge outputs with plots and summary statistics, then look for the conductivity range where your result changes most.

Common Pitfalls

• Treating hydraulic conductivity as one exact value, which hides how uncertain the recharge estimate really is.
• Using a grid that is too coarse, which can smear out the effect of a small permeable-pavement area.
• Changing rainfall, soil, and pavement assumptions at the same time, which makes the sensitivity results impossible to interpret.
• Ignoring boundary conditions, which can make recharge changes look larger or smaller than they really are.
• Skipping unit checks between GIS inputs, FloPy settings, and MODFLOW outputs, which can break the model without obvious errors.

What Makes This Competitive

A stronger project goes beyond a single before-and-after comparison. You can test multiple conductivity scenarios, compare different pavement placements, and report how uncertainty changes the policy conclusion. Strong entries also explain why one assumption matters more than another and use clear sensitivity metrics, not just pictures of model output. That turns your project from a demo into a decision tool.

Project Variations

• Compare permeable pavement in parking lots versus sidewalks to see which placement gives the bigger recharge gain.
• Swap the hydraulic conductivity source, then compare literature values, local soil survey values, and calibrated estimates.
• Add different storm or climate scenarios to test whether the retrofit still helps under wetter or drier conditions.

Learn More

• USGS MODFLOW documentation: Search the USGS Groundwater Information page for MODFLOW manuals and examples.
• FloPy documentation: Find the official FloPy docs for Python examples, model building, and plotting tools.
• USGS National Water Information System: Use this database to find groundwater levels, streamflow, and related local hydrology data.
• NOAA Climate Data Online: Search for precipitation records to support rainfall and storm scenario inputs.
• MIT OpenCourseWare Earth, Atmospheric, and Planetary Sciences: Look for free groundwater flow and hydrology lecture materials.
• Groundwater journal: Search recent review articles on groundwater modeling, recharge, and urban infiltration in PubMed or the journal site.