The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

Abstract

Abstract. The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data are required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam ( ∼ 575 km 2 ) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental setup. The sensor performance in the experimental setup and the density of the PWS network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low-intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.

Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

“…With recent advances in technology, there emerges a new crowdsourcing approach to incorporate measurements made by common citizens as a source of observational rainfall data in the form of, for example, precipitation-type (rain, fog, and hail) reports (Binau, 2012), smartphone or surveillance camera image-derived estimates of rainfall intensity (Allamano et al, 2015;Yang & Ng, 2017), car sensor signals (Haberlandt & Sester, 2010;Rabiei et al, 2013), and amateur rain gauge readings (de Vos et al, 2017). Following Yang and Ng (2017), in this present study, we consider two forms of crowdsourced data:…”

“…(Though such microwave data cannot rightly be said to be from the crowd, they have been identified as broader examples of crowdsourced data in review papers by Buytaert et al (2014) and Zheng et al (2018)). de Vos et al (2017) obtained crowdsourced rainfall measurements from amateur automatic weather stations around Amsterdam and showed the crowdsourced data to outperform unadjusted radar data in representing rainfall and to perform comparably well compared to gauge-adjusted radar data. This is despite the high individual observation errors of the crowdsourced measurements.…”

Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model

“…On the other hand, remote sensing and, more recently, citizen science initiatives provide useful data for hydrological purposes (Buytaert et al, 2014). The use of crowdsourced observations for hydrological modeling applications has rapidly increased in the last years (Assumpção et al, 2018;de Vos et al, 2017;Walker et al, 2016;Yu et al, 2016;Yang & Ng, 2017). In particular, different studies have been carried out for integrating quantitative expert-based and citizens' observations within hydrological and hydraulic models to improve model calibration and validation in ungauged basins (Seibert & McDonnell, 2002;van Meerveld et al, 2017), to better characterize the spatial and temporal dimensions of a catchment hydrological processes (Starkey et al, 2017;Weeser et al, 2018), and to improve flood predictions (Etter et al, 2018;Mazzoleni et al, 2015, Mazzoleni, Verlaan, et al, 2017Mazzoleni, Cortes Arevalo, et al, 2018).…”

The Future of Earth Observation in Hydrology

“…Since these early satellite missions there have been considerable and dramatic advances in remote observation platforms and the types of measurements available from them. Evolving from early panchromatic and red-green-blue (RGB) or R-G-near-infrared (NIR) imagery (De Wulf et al, 1990), sensor technology has expanded to include multi-and hyperspectral visible to near-infrared bands (VNIR) (Houborg et al, 2015), multi-band thermal (Roberts et al, 2012), multi-channel microwave emissions (Njoku and Li, 1999), as well as radar and lidar techniques (Mace et al, 2009), all of which have advanced and redefined our knowledge and understanding of the Earth system. From a hydrological sciences perspective, remote sensing has driven process insights and provided new and independent datasets that span the range of water cycle components.…”