Fig 1.
Calculation algorithm of the model used for analyzing the influences of the local catchment and rainfall characteristics on flooding in a small urban catchment.
Fig 2.
Location of the 32 rain gauges employed in the present study, corresponding to Polish landforms (1 –Baltic coastal lowlands, 2 –lakelands, 3 –central lowlands, 4 –uplands, 5 –sub-mountain basins, 6 –mountains).
Fig 3.
Mean high of convective rainfall depths as a function of their duration at selected meteorological stations (names in Fig 1) located in different regions of Poland.
Fig 4.
Location of partial catchments A, B, C, and D in the Si9 urban catchment.
Table 1.
Physical and geographic characteristics of the A, B, C, and D catchments.
Table 2.
Number of flooding events of logistic regression, SWMM and measurements in catchments A, B, C, and D.
Fig 5.
Influence of the convective rainfall event duration on the probability of stormwater flooding.
(a) Influence of the convective rainfall event duration on the probability of stormwater flooding in small urban catchments (Imp = Impb = 0.55) in selected Polish cities. (b) Influence of the Imp = Impb values for tr = 10 min on the probability of stormwater flooding in the selected Polish cities. (c) Influence of the rainfall duration and spatial distribution of the impervious area on the probability of stormwater flooding in the selected Polish cities. (d) Influence of the spatial distribution of the impervious area in a small catchment on the probability of stormwater flooding on the example of Kielce.
Fig 6.
Influence of catchment imperviousness on the rainfall duration (tcr) and critical rainfall intensity (icr) in selected Polish cities.
Fig 7.
Variability in the stormwater flooding sensitivity index (ps) in Poland for selected cities and regions (region names are listed in Fig 1).
Fig 8.
Maximum catchment imperviousness (Impgr) for the analyzed rainfall stations, the exceedance of which results in stormwater flooding in the small urban catchments (region names are listed in Fig 1).