The Potential of Citizen Observatories for Improving Spatial Measurements of Rainfall in Cities

Abstract

Urban pluvial flooding occurs when the run-off converted rainfall exceeds the capacity of the sewer or stormwater system (Houston et al., 2011). This can result in damage to ecology, infrastructure, disruption to human activities, injuries and in the worst-case scenario, loss of life (Biniyam et al., 2017). With the expected increase in the world’s population living in urban areas to 68% by 2050 (UN, 2018) and a more frequent occurrence of extreme weather events with a longer duration and higher intensity in the future (IPCC, 2012), there is a need to improve early warning system and disaster management (Restrepo-Estrada et al., 2017). Lopez et al. (2005) mentions that one of the key factors in hydrological models to determine accurate flood estimates is to have accurate rainfall input. However, many urban areas might lack this information because sensors are not available, or the number of sensors is too few to cover the entire region with an acceptable resolution (Restrepo-Estrada et al., 2017). Kidd et al. (2017) mentions that the density of rain gauges varies per region, with Europe and Eastern-Asia (including Japan) having a decent coverage by rain gauges, while the rest of the world has a sparse coverage of rain gauges. This study assesses the potential of citizen observatories for improving the spatial measurements of rainfall in comparison to a single professional station. Two types of citizen observatories are used, semi-professional stations (TU Delft) and citizen weather stations (Netatmo), that are located near the city of Rotterdam. In total there are 9 TU Delft stations and 73 Netatmo stations in a region of 256.7 km2. Based on a spatial variance analysis between these two types of citizen observatories and a professional station (KNMI) in the region, weighing factors are determined in order to merge and interpolate the data from the citizen observatories into a single rainfall map. This interpolated map is then compared to the radar rainfall maps, which are bias-corrected based on a network of professional ground stations (Overeem et al., 2016), and provide rainfall data with a 1x1 km spatial resolution. The baseline for improvement comes from the assumption that the rainfall measured by the KNMI station is uniform over the entire study area. By comparing the differences between KNMI and radar and citizen observatories and radar, it can be assessed whether the citizen observatories are an improvement. The results show that the interpolated rainfall maps are better at capturing the spatial structure of the rainfall event, both on the small-scale (10-minute timestep) and event-scale (total accumulation). When it comes to the actual rainfall values, it became clear that rainfall intensities within a 4 km radius from the KNMI station were better represented by the KNMI station while after a distance of 8 km, the citizen stations clearly helped give a better representation.