Comparison of Water Budgets in Urban, Suburban, and Rural Schoolyards in order to Identify the Water Quality of Schoolyards By Team Water Zeny Paul Roger Tim

Part of BES and LTER research is to determine the impact of urban areas on water quality in watersheds. We wanted to look at how storm runoff due to impervious surfaces impacted water quality in schoolyards.

As the percentage of impervious surface increases, the pecentage of water lost as run-off increases and water quality decreases. Impervious Pervious Water Quality Run Off

We wanted to choose three sites with different ecosystems so we chose a rural site, a suburban site, and an urban site. Site Ecosystem Hereford High School Rural Owings Mills High School Suburban Digital Harbor High School Urban

Typical conceptualization of the water cycle with various inputs and outputs.

By creating a model with various inputs and outputs, and assigning values to the inputs and outputs to create a water budget for an ecosystem.

We used a more simplistic model to calculate our water budget only looking at environmental inputs and outputs. run off infiltration

1.Identify the area to be studied and determine the boundaries of each corresponding site. 2.Determine the type of cover found on each site as Building, Paved Plumbed, Paved Not-plumbed, Tree- covered, or Fine Vegetation (grasses, shrubs). 3.Determine the area of each of the five different types of cover using grids and scales.

4. For each cover identify the percentage of water that will Evaporate/Transpire, Run-off or Infiltrate by Ground Work Investigation. 5. Calculate the total volume of water for the entire site. 6. Calculate the total volume of water for each cover type. 7. Calculate the amount of water distributed to each output for each cover type. 8. Estimate water quality for each cover type.

This graph represents the total rainfall in gallons and the amount calculated that was lost to each output.

The urban site had the highest run off and lowest infiltration causing the site to lose the highest percentage of water as run off.

Hereford Owings Mills Digital Harbor The site with highest amount of impervious surfaces had the highest percentage of run off as expected. However the Owings Mills site had a higher percentage of impervious surfaces than Hereford, but a lower percentage of run off

Hereford Owings Mills Digital Harbor The Owings Mills site was calculated to have the highest quality of water, but did not have the lowest percentage of impervious surfaces. Hereford had the lowest percentage of impervious surfaces, but had the lowest water quality.

In our hypothesis we predicted that higher percentages of impervious surfaces would result in a higher percentage of run off and lower quality of water. Therefore we were unable to determine a correlation between percentage of impervious surfaces and percentage of run off. While our site with the highest percentage of impervious surfaces did have the highest run off, our site with the lowest percentage of impervious surfaces did not have the lowest percent of run off.

According to our data, we did see a correlation between pervious surfaces and infiltration and transpiration. Digital Harbor had the highest amount of impervious surfaces but did not have the lowest quality of water. Our site with the lowest amount of impervious surfaces had the lowest quality of water. When looking at water quality we did not see a correlation between impervious surfaces and water quality.

We sited several sources of error. Many of the percentages for outputs were estimated using individual assignment of value. Using multiple groups allowed for a greater deviation in values and possibly a higher percentage of human error. We could also use previous study models online to get more accurate estimations for output and use updated arial maps. We would also need to find a more quantitative approach to determine water quality. If we were to duplicate this water budget study, we would include more sites, allowing us to calculate an average and provide more accurate and reliable data.

Additional Inputs not measured are city water usage: Digital Harbor= 2.2 million gallons Owings Mills= 2.65 million gallons Hereford High= data unaccounted for We calculated the roof of the building as losing 2 million gallons to runoff, if this volume was captured it would sustain the building. Additional Outputs not measured are city sewer: The amount of water lost to city sewage would be approximately the same as the water coming in from city plumbing since little is retained.

For the evaporation part, the study group intended to determine the differences in rates of evaporation in different locations and conditions in the study area. Unfortunately, the storm left only 2 evaporation pans out of 5 evaporation pans that were placed in the different locations within the study area. The group did not find meaningful data because of the increase in the amount of water in the two evaporation pans that were found. Initial volume of water placed in the pans was 600 ml each. One pan has 640 ml (an increase of 40 ml) and the other pan has 940 ml (an increase of 340 ml).

To measure precipitation we use rain gauge. Two rain gauges were placed in Owings Mills High school. One was placed on the shaded area and the other one was placed in an open field. After seven days, we get the amount of precipitation.

AreaRain Gauge (unit) Volume of Water (ml) Unshaded Shaded Difference The data above shows the amount of precipitation during the time of study. The volume of water was measured using a graduated cylinder.

Transpiration- To measure transpiration we enclosed several leaves on a tree branch into a plastic bag. As transpiration occurs the water exiting the stomas will be collected in the plastic bag. The bags were sealed with tape, labeled and location noted on satellite map. Once the bags were collected water was removed and the volume was measured. Then we used grids to determine the surface area. We were then able to calculate a rate of transpiration.

Interception- The difference in amount of rainfall that makes it to the ground through the canopy and that that falls in an open area. We placed 5 solo cups buried half way in the ground in various canopy coverage areas, 10%, 35%, 65%, 75%, and 90%. We were going to measure the amount of water in each cup and compare how the different amount of cover allows different amounts of rain reach the ground. Do to the storm all data was lost!

Bdya bdya bdya, that’s all folks!! Thanks to Trevor the grad student for constructing our graphs!