Probabilistic hydrologic simulation of urbanized catchments with sparse data

Deterministic modeling of urban stormwater systems requires detailed data describing the sewer network and areas contributing to each sewer. Records describing utility positions, when available, are often incomplete and inaccurate with errors as high as 30%. Similarly, subcatchment data are often unavailable, particularly at the scale of distributed models. Hence, urban hydrologic modeling often requires either costly sewer mapping and field measurements, gross simplification, or both. This paper uses the probabilistic Illinois Urban Hydrologic Model (IUHM), which predicts the hydrologic response of urbanized catchments using the sewer layout and statistical properties of sewers and subcatchments of each order. This allows random sampling, stratified by pipe order, to produce the necessary model inputs, at a fraction of the effort and cost required to obtain data for deterministic modeling. Example applications to catchments in the Chicago area are compared to monitored flows for different levels of sampling.