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.

The Phetchaburi River in Phetchaburi province, Thailand, has a watershed with many different water resource projects. The surrounding farms rely on the Phetchaburi River for irrigation water and the drinking water companies rely on it as a source of water. However, the Phetchaburi basin has problems with yearly floods, salt intrusion and pollution. Water monitoring stations in the region are scarce. A new telemetering system has been put in place, but due to the cost of these stations they are few in number. This project presents a showcase for a cheap and robust water monitoring system in terms of both quantity through water level data and quality through various water quality parameters using apps on an android or iPhone device to gather and analyse the data. The app from Mobile Water Management (MWM) is used to measure the water level through reading a photo of a staff gauge. The Akvo app uses various methods like electronic devices and reading strips via a photo to measure several water quality parameters. It was proven that construction of the staff gauges needed for the MWM app is cheap and does not require highly skilled workers. The resulting data is reliable, if the app is handled by someone trained in handling the app, and/or the data that is created is checked by a trained person. The fact that the pictures taken by the app are uploaded to the database makes for easy verification of the data. This makes verification of telemetric data possible, which as it turns out is not always reliable when compared to the MWM data. The Akvo app has a similar advantage in the sense that verification of the data at a later moment is not only possible, but also easy. This eliminates several human errors in the data collection process and effectively increases the data quality. Right now, several RID officers are needed to collect this data. Using the Akvo app, the required manpower can be lowered. Data analysis shows that the Phetchaburi River has significant levels fecal contamination (E. coli) and issues with low oxygen concentrations at certain moments. For this reason, it is not recommended to use as recreational, fishing or irrigation water. The boundary between salt and freshwater is constantly changing depending on weather conditions and can cause serious problems for local farmers. When constructing the staff gauge there are multiple possible human errors that need to be avoided in order for the MWM app to work correctly. This mainly has to do with the placement of the staff gauge sticker, keeping it straight and unobstructed and also directed towards the user. It turned out that several of the Akvo strips are not working correctly. Other than that, taking data from many parameters can also be time consuming. We recommend that the RID looks into this method of data collection further, both as a cheap and easy way to expand their water monitoring network, and in the case of the MWM app to verify the effectiveness of the telemetering systems.