Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a dual polarimetric X-Band radar instrument at the CAESAR site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this case study, and (4) scaling factors allow the adequate rainfall resolution to be selected to obtain a given level of accuracy in the calculation of hydrological response.

@en

Cities are vulnerable to local floods due to heavy rainfall. Urban flooding causes damage to buildings and contents, and also disturbs daily city activities as it entails drainage, transportation, and electricity interruptions. Urban flooding is expected to increase as climate change drives heavier rainfall events. Population and assets densification, as well as infrastructure aging, increasingly hamper cities from tackling pluvial flooding. Climate adaptation measures can help cities to face the challenge of heavier weather and urban flooding. Examples of those measures are: smart drainage maintenance and emergency responses, urban climate-proofing and retrofitting, and provision of real-time flooding information to citizens and government officials, among others. To plan and perform such measures it is required to know, and even predict before a heavy storm is onset, where, when, and why urban flooding occurs. This knowledge is not always available though. Required knowledge to design and implement adaptation measures against urban flooding is insufficient in cases such as Amsterdam and Rotterdam. In these cities, urban drainage models are limited to certain districts or uncalibrated; they cannot validly predict where or when the drainage system will surcharge or flood, and thus, they cannot be used for flood damage modeling. Moreover, urban flooding may not only depend on hydraulic parameters of underground drainage systems; other physical and socioeconomic characteristics of the urban fabric may also influence the flooding likelihood at a particular urban location. Urban flooding can be better understood by using non-hydraulic and unconventional sources of information. Available public data, curated by statistics, cadastral, or municipal call-center services, can provide insights about urban flooding damage. Using mainstream technology, such as web, traffic, and smart-phone cameras, can also afford for valuable data about urban flooding impacts, which contributes to the development of climate adaptation measures in lowland cities. This dissertation aimed to determine the potential of such alternative data sources in better explaining urban flooding incidents. Employed methods combined techniques from geographic information systems, graph theory, community ecology, and computer vision. The exploration done in this research follows three main steps: testing previously proposed models, exploring currently available data sources, and evaluating the usefulness of attainable and affordable technology to gather key, nonexistent data about the timing, location, and extent of urban flooding incidents. @en

Cities are increasingly prone to urban flooding due to heavier rainfall, denser populations, augmenting imperviousness, and infrastructure aging. Urban pluvial flooding causes damage to buildings and contents, and disturbs stormwater drainage, transportation, and electricity provision. Designing and implementing efficient adaptation measures requires proper understanding of the urban response to heavy rainfall. However, implemented stormwater drainage models lack flood impact data for calibration, which results in poor flood predictions. Moreover, such models only consider rainfall and hydraulic parameters, neglecting the role of other natural, built, and social conditions in flooding mechanisms. This paper explores the potential of open spatial datasets to explain the occurrence of citizen-reported flood incidents during a heavy rain event. After a dimensionality reduction, imperviousness and proximity to watershed outflow point were found to significantly explain up to half of the flooding incidents variability, proving the usefulness of the proposed approach for urban flood modelling and management.@en

Urban hydrological applications require high resolution precipitation and catchment information in order to well represent the spatial variability, fast runoff processes and short response times of urban catchments (Berne et al., 2004). Although fast progress has been made over the last few decades in high resolution measurement of rainfall at urban scales, including increasing use of weather radars, recent studies suggest that the resolution of the currently available rainfall estimates (typically 1 × 1 km2 in space and 5 min in time) may still be too coarse to meet the stringent requirements of urban hydrology (Gires et al., 2012). What is more, current evidence is still insufficient to provide a concrete answer regarding the added value of higher resolution rainfall estimates and actual rainfall input resolution requirements for urban hydrological applications. With the aim of providing further evidence in this regard, a collaborative study was conducted which investigated the impact of rainfall input resolutions on the outputs of the operational urban drainage models of four urban catchments in the UK and Belgium (Figure 1).@en

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density. @en