Spatial Rainfall Variability in Urban Environments—High-Density Precipitation Measurements on a City-Scale

Abstract

Rainfall runoff models are frequently used for design processes for urban infrastructure. The most sensitive input for these models is precipitation data. Therefore, it is crucial to account for temporal and spatial variability of rainfall events as accurately as possible to avoid misleading simulation results. This paper aims to show the significant errors that can occur by using rainfall measurement resolutions in urban environments that are too coarse. We analyzed the spatial variability of rainfall events from two years with the validated data of 22 rain gauges spread out over an urban catchment of 125 km2. By looking at the interstation correlation of the rain gauges for different classes of rainfall intensities, we found that rainfall events with low and intermediate intensities show a good interstation correlation. However, the correlation drops significantly for heavy rainfall events suggesting higher spatial variability for more intense rainstorms. Further, we analyzed the possible deviation from the spatial rainfall interpolation that uses all available rain gauges when reducing the number of rain gauges to interpolate the spatial rainfall for 24 chosen events. With these analyses we found that reducing the available information by half results in deviations of up to 25% for events with return periods shorter than one year and 45% for events with longer return periods. Assuming uniformly distributed rainfall over the entire catchment resulted in deviations of up to 75% and 125%, respectively. These findings are supported by the work of past research projects and underline the necessity of a high spatial measurement density in order to account for spatial variability of intense rainstorms.

Hydro-Meteorological Trends in an Austrian Low-Mountain Catchment

“…To exclude as much doubtful data as possible from the subsequent analysis the 21 available measurements were first validated on a daily basis according to the following inter-stational variability [33,34]. The validation steps (i)-(vi) were applied for the rainfall and discharge observations.…”

“…An appropriate validation strategy depends on several factors, such as the spatial distribution of stations, the recording and analysis frequency or the type of measurement device. While there is no standardized procedure that is generally applicable, validation strategies commonly comprise the following steps: (i) identification of documented defects, (ii) device specific boundaries, (iii) climatological boundaries, (iv) temporal variability, (v) intra-stational validation, and (vi) inter-stational variability [33,34].…”

Hydro-Meteorological Trends in an Austrian Low-Mountain Catchment

“…An appropriate validation strategy depends on several factors, such as the spatial distribution of stations, the recording and analysis frequency or the type of measurement device. While there is no standardized procedure that is generally applicable, validation strategies commonly comprise the following steps: (i) identification of documented defects, (ii) device-specific boundaries, (iii) climatological boundaries, (iv) temporal variability, (v) intra-stational validation, and (vi) inter-stational variability [36,37].…”

Small Catchment Runoff Sensitivity to Station Density and Spatial Interpolation: Hydrological Modeling of Heavy Rainfall Using a Dense Rain Gauge Network

“…Since precipitation is the most important input in hydrological models [5][6][7], it is crucial to understand its uncertainty and how this uncertainty affects the simulated runoff. Assessing the spatial and temporal heterogeneity of precipitation is becoming increasingly important, especially with respect to heavy precipitation events [8,9]. Convective storm cells with large volumes of precipitation can easily trigger hazards, but the limited spatial and temporal extent of these cells is associated with huge levels of measurement uncertainty [10].…”