Decision Tree Analysis Of Processes Generating Water-Related Building Damage: A Case Study In Rotterdam, The Netherlands

The objective of this study was to identify the main failure mechanisms behind water-related building damage and to investigate to what extent these processes are related to characteristics of buildings and rainfall events. Results are based on the mining of property level insurance damage data, for a case study in Rotterdam, the Netherlands. This study has found that most frequent causes of water-related damage relate to roof leakages (28%), bursts of household water supply pipes (19%) and blocked household wastewater systems (18%). Cases of sewer flooding or depression filling were less present (2.4% and 0.6%), but showed stronger correlations with heavy rainfall events than any other failure mechanism. Classification tree analysis revealed that water discharges from neighbours is the main damage cause for high-rise buildings on days with no or minor rainfall (\u3c 7.5 mm/h). Moreover, damage due to blocked household wastewater systems is associated with low-rise buildings younger than 50 years

Predicting streamflow with LSTM networks using global datasets

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing

High resolution radar rainfall for urban pluvial flood management: Lessons learnt from 10 pilots in North-West Europe within the RainGain project

ABSTRACT Precipitation and catchment information needs to be available at high resolution to reliably predict hydrological response and potential flooding in urban catchments. While recent advances have been made in weather radar technology and availability of DTM for urban flood modelling, the question is whether these are sufficient to provide reliable predictions for urban pluvial flood control. The RainGain project (EU-Interreg IVB NWE) brings together radar technologists and hydrologists to explore a variety of rainfall sensors, rainfall data processing techniques and hydrodynamic models for the purpose of fine-scale representation of urban hydrodynamic response. High resolution rainfall and hydrodynamic modelling techniques are implemented at 10 different pilot locations under real-life conditions. In this paper, the pilot locations, configurations of rainfall sensors (including X-Band and C-Band radars, rain gauges and disdrometers) and modelling approaches adopted within the RainGain project are introduced. Initial results are presented of hydrodynamic modelling using high resolution precipitation inputs from dual-polarisation X-band radar, followed by a discussion of differences in hydrodynamic response behaviour between the pilots

Statistical analysis of hydrological response in urbanising catchments based on adaptive sampling using inter-Amount times

Urban catchments are typically characterised by a more flashy nature of the hydrological response compared to natural catchments. Predicting flow changes associated with urbanisation is not straightforward, as they are influenced by interactions between impervious cover, basin size, drainage connectivity and stormwater management infrastructure. In this study, we present an alternative approach to statistical analysis of hydrological response variability and basin flashiness, based on the distribution of inter-Amount times. We analyse inter-Amount time distributions of high-resolution streamflow time series for 17 (semi-)urbanised basins in North Carolina, USA, ranging from 13 to 238 km2 in size. We show that in the inter-Amount-Time framework, sampling frequency is tuned to the local variability of the flow pattern, resulting in a different representation and weighting of high and low flow periods in the statistical distribution. This leads to important differences in the way the distribution quantiles, mean, coefficient of variation and skewness vary across scales and results in lower mean intermittency and improved scaling. Moreover, we show that inter-Amount-Time distributions can be used to detect regulation effects on flow patterns, identify critical sampling scales and characterise flashiness of hydrological response. The possibility to use both the classical approach and the inter-Amount-Time framework to identify minimum observable scales and analyse flow data opens up interesting areas for future research.Water ResourcesAtmospheric Remote Sensin

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

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.Water ResourcesSanitary Engineerin

Decision Tree Analysis Of Processes Generating Water-Related Building Damage: A Case Study In Rotterdam, The Netherlands

The objective of this study was to identify the main failure mechanisms behind water-related building damage and to investigate to what extent these processes are related to characteristics of buildings and rainfall events. Results are based on the mining of property level insurance damage data, for a case study in Rotterdam, the Netherlands. This study has found that most frequent causes of water-related damage relate to roof leakages (28%), bursts of household water supply pipes (19%) and blocked household wastewater systems (18%). Cases of sewer flooding or depression filling were less present (2.4% and 0.6%), but showed stronger correlations with heavy rainfall events than any other failure mechanism. Classification tree analysis revealed that water discharges from neighbours is the main damage cause for high-rise buildings on days with no or minor rainfall (< 7.5 mm/h). Moreover, damage due to blocked household wastewater systems is associated with low-rise buildings younger than 50 years