Question 7 of 7

Problem 7 (20 marks)

Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.

(i) Why a dry pond meets the 24-hour detention BMP principle (7 marks)

1. Sediment and pollutant settling. Holding the runoff volume for roughly a day gives suspended solids — and the pollutants (nutrients, metals, hydrocarbons) that adsorb to them — time to settle out under quiescent conditions before the water is released, so a 24-hour dry pond functions as a passive sedimentation basin and measurably improves discharge water quality compared with immediate conveyance to the receiving water.
2. Peak-flow attenuation. By temporarily storing the storm volume and releasing it slowly through a small controlled outlet (an orifice or weir sized for the drawdown target), the pond spreads the outflow hydrograph over many hours instead of passing the sharp inflow peak straight through, reducing downstream peak discharge and the associated flooding and channel-erosion risk.
3. Time for losses that mimic pre-development hydrology. The extended residence time allows some infiltration through the pond floor (where soils permit) and evapotranspiration/biological uptake within the vegetated pond, reducing the net volume ultimately discharged and moving the site's hydrologic response closer to what it was before development, which is the underlying intent of stormwater BMPs.

(ii) On-site and off-site runoff control — operational strategies for a 20-year design life (8 marks)

An on-site control system is provided at the individual development parcel — for example a rooftop or parking-lot detention facility, or a bioretention cell in a commercial lot — sized to manage that parcel's own runoff before it leaves the site. An off-site control system serves multiple upstream developments collectively, typically a regional stormwater management pond or wetland located downstream in the drainage network, sized to the combined contributing area rather than any single parcel.

Two key operational strategies that keep either system performing over a 20-year design life:

1. A scheduled inspection and maintenance program. Inlets, forebays, outlet control structures (orifices, weirs, trash racks) and vegetated surfaces must be inspected on a regular cycle and cleared of accumulated sediment and debris, since even modest sedimentation progressively reduces available storage volume and can eventually block the outlet, defeating the facility's design function well before the end of its intended life.
2. Periodic performance verification against the as-built design. Re-surveying pond bathymetry (to confirm design storage volume has not been lost to sedimentation), checking outlet structure geometry against its design rating curve, and confirming maintenance access and easements remain available all guard against slow, unnoticed degradation of hydraulic performance over two decades of operation — a facility that passed its commissioning test can still under-perform years later if it is not periodically re-checked.

(iii) Determining event magnitude from frequency and probability analysis (5 marks)

The magnitude of an extreme event (flood or drought) associated with a chosen return period $T$ is estimated from the frequency-factor equation: $$X_T = \bar{X} + K_T\,s$$ where $\bar{X}$ is the mean of the annual extreme-event series (e.g. annual maximum flood peaks, or annual minimum flows for a drought analysis), $s$ is the standard deviation of that same series, and $K_T$ is a frequency factor that depends on both the chosen return period $T$ and the probability distribution assumed to fit the data (e.g. Log-Pearson Type III, Gumbel Extreme Value I, Normal). $K_T$ is obtained from tables (or the inverse CDF of the fitted distribution) at the exceedance probability $1/T$ corresponding to the desired return period, and increases with $T$ (a rarer, larger-magnitude event corresponds to a larger $K_T$). This lets an engineer estimate the magnitude of an event at any desired return period directly from the sample statistics of the observed record, without needing to construct and read a full plotted frequency curve for every case — for example, a 100-year flood is obtained by looking up $K_{100}$ for the chosen distribution and applying the same equation to the gauge record's $\bar{X}$ and $s$.