Accounting for rainfall variability in sediment wash-off modelling using uncertainty propagation

Urban surface sediment is a major source of pollution as it acts as a transport medium for many contaminants. Accurate modelling of sediment wash-off from urban surfaces requires an understanding of the effect of variability in the external drivers such as rainfall on the wash-off process. This study investigates the uncertainty created due to the urban-scale variability of rainfall, in sediment wash-off predictions. Firstly, a rigorous geostatistical method was developed that quantifies uncertainty due to spatial rainfall variability of rainfall at an urban scale. The new method was applied to a unique high-resolution rainfall dataset collected with multiple paired gauges for a study designed to quantify rainfall uncertainty. Secondly, the correlation between calibration parameters and external drivers - rainfall intensity, surface slope and initial load- was established for a widely used exponential wash-off model using data obtained from new detailed laboratory experiments. Based on this, a new wash-off model where the calibration parameters are replaced with functions of these external drivers was derived. Finally, this new wash-off model was used to investigate the propagation of rainfall uncertainty in wash-off predictions. This work produced for the first time quantitative predictions of the variation in wash-off load that can be linked to the rainfall variability observed at an urban scale. The results show that (1) the assumption of constant spatial rainfall variability across rainfall intensity ranges is invalid for small spatial and temporal scales, (2) wash-off load is sensitive to initial loads and using a constant initial load in wash-off modelling is not valid, (3) the level of uncertainty in predicted wash-off load due to rainfall uncertainty depends on the rainfall intensity range and the “first-flush” effect. The maximum uncertainty in the prediction of peak wash-off load due to rainfall uncertainty within an 8-ha catchment was found to be ~15%

A remote sensing based integrated approach to quantify the impact of fluvial and pluvial flooding in an urban catchment

Pluvial (surface water) flooding is often the cause of significant flood damage in urban areas. However, pluvial flooding is often overlooked in catchments which are historically known for fluvial floods. In this study, we present a conceptual remote sensing based integrated approach to enhance current practice in the estimation of flood extent and damage and characterise the spatial distribution of pluvial and fluvial flooding. Cockermouth, a town which is highly prone to flooding, was selected as a study site. The flood event caused by named storm Desmond in 2015 (5-6/12/2015) was selected for this study. A high resolution digital elevation model (DEM) was produced from a composite digital surface model (DSM) and a digital terrain model (DTM) obtained from the Environment Agency. Using this DEM, a 2D flood model was developed in HEC-RAS (v5) 2D for the study site. Simulations were carried out with and without pluvial flooding. Calibrated models were then used to compare the fluvial and combined (pluvial and fluvial) flood damage areas for different land use types. The number of residential properties affected by both fluvial and combined flooding was compared using a combination of modelled results and data collected from Unmanned Aircraft Systems (UAS). As far as the authors are aware, this is the first time that remote sensing data, hydrological modelling and flood damage data at a property level have been combined to differentiate between the extent of flooding and damage caused by fluvial and pluvial flooding in the same event. Results show that the contribution of pluvial flooding should not be ignored, even in a catchment where fluvial flooding is the major cause of the flood damages. Although the additional flood depths caused by the pluvial contribution were lower than the fluvial flood depths, the affected area is still significant. Pluvial flooding increased the overall number of affected properties by 25%. In addition, it increased the flood depths in a number of properties that were identified as being affected by fluvial flooding, in some cases by more than 50%. These findings show the importance of taking pluvial flooding into consideration in flood management practices. Further, most of the data used in this study was obtained via remote sensing methods, including UAS. This demonstrates the merit of developing a remote sensing based framework to enhance current practices in the estimation of both flood extent and damage

Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

There is increasing interest in using Unmanned Aircraft Systems (UAS) in flood risk management activities including in response to flood events. However, there is little evidence that they are used in a structured and strategic manner to best effect. An effective response to flooding is essential if lives are to be saved and suffering alleviated. This study evaluates how UAS can be used in the preparation for and response to flood emergencies and develops guidelines for their deployment before, during and after a flood event. A comprehensive literature review and interviews, with people with practical experience of flood risk management, compared the current organizational and operational structures for flood emergency response in both England and India, and developed a deployment analysis matrix of existing UAS applications. An online survey was carried out in England to assess how the technology could be further developed to meet flood emergency response needs. The deployment analysis matrix has the potential to be translated into an Indian context and other countries. Those organizations responsible for overseeing flood risk management activities including the response to flooding events will have to keep abreast of the rapid technological advances in UAS if they are to be used to best effect

Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modelling

With the extensive use of 2D flood models, the resolution and quality of Digital Elevation Models (DEMs) have come under greater focus especially in urban hydrology. One of the major research areas, in this regard, is the effect of DEM resolution on flood modelling. This study first investigates the root causes of the impact of DEM resolution on urban fluvial flood modelling outputs using DEMs with grid resolutions ranging from 1m to 50m. The study then investigates how DEM resolution affects the definition and characterisation of the river channel and the consequences of this for the modelled results. For this purpose, a separate set of merged DEMs was generated where the river channel as defined by the 1m resolution DEM is merged with coarser resolution DEMs. Data obtained during the flood event caused by Storm Desmond (2015) in Cockermouth (Cumbria, UK) was used for this study. The HEC-RAS 2D model was used for all of the simulations. The benchmark model obtained with the 1m resolution DEM was calibrated using measured water levels at two locations within the rivers. Results show that there is a 30% increase in flood extent from 58.9 ha to 79.0 ha and a 150% increase in mean flood depth from 1.74m to 4.30m when the resolution reduces from a 1m grid to a 50m grid. The main reason for this is the increasing lack of definition of the river channel with an associated reduction in the estimated depth of the river resulting in reduced river channel conveyance. This then leads to an increase in the flood extent and depth especially in the immediate vicinity of the river. This effect is amplified when the DEM grid size is greater than the river width. When the 1m resolution DEM for the river channel is used in conjunction with coarser resolution DEMs for the surrounding areas (merged DEMs), there is a significant improvement in the agreement between the modelled and the reference case (obtained from the benchmark model) flood extents and depths. The use of merged DEMs reduces the error in mean flood depth from 90% to 4% and reduces the overall RMSE in flood depths from 2.6m to 0.9m at 30m resolution. The 30m resolution DEM was tested because this is. The use of merged DEMs, where a higher resolution DEM is used to characterise the river channel in conjunction with a 30m resolution DEM (e.g., NASA Shuttle Radar Topography Mission DEMs) for the wider area could be a cost-effective solution for locations where higher resolution DEMs may not be available

Improving understanding of the underlying physical process of sediment wash-off from urban road surfaces

Among the urban aquatic pollutants, the most common is sediment which also acts as a transport medium for many contaminants. Hence there is an increasing interest in being able to better predict the sediment wash–off from urban surfaces. The exponential wash-off model is the most widely used method to predict the sediment wash-off. Although a number of studies proposed various modifications to the original exponential wash-off equation, these studies mostly looked into one parameter in isolation thereby ignoring the interactions between the parameters corresponding to rainfall, catchment and sediment characteristics. Hence in this study we aim (a) to investigate the effect of rainfall intensity, surface slope and initial load on wash-off load in an integrated and systematic way and (b) to subsequently improve the exponential wash-off equation focusing on the effect of the aforementioned three parameters. A series of laboratory experiments were carried out in a full-scale setup, comprising of a rainfall simulator, a 1 m 2 bituminous road surface, and a continuous wash-off measuring system. Five rainfall intensities ranging from 33 to 155 mm/h, four slopes ranging from 2 to 16% and three initial loads ranging from 50 to 200 g/m 2 were selected based on values obtained from the literature. Fine sediment with a size range of 300–600 µm was used for all of the tests. Each test was carried out for one hour with at least 9 wash-off samples per test collected. Mass balance checks were carried out for all the tests as a quality control measure to make sure that there is no significant loss of sand during the tests. Results show that the washed off sediment load at any given time is proportional to initial load for a given combination of rainfall intensity and surface slope. This indicates the importance of dedicated modelling of build-up so as to subsequently predict wash-off load. It was also observed that the maximum fraction that is washed off from the surface increases with both rainfall intensity and the surface slope. This observation leads to the second part of the study where the existing wash-off model is modified by introducing a capacity factor which defines this maximum fraction. This capacity factor is derived as a function of wash-off coefficient, making use of the correlation between the maximum fraction and the wash-off rate. Values of the modified wash-off coefficient are presented for all combinations of rainfall intensities and surface slopes, which can be transferred to other urban catchments with similar conditions

HECRAS 2D model files "A Remote Sensing Based Integrated Approach to Quantify the Impact of Fluvial and Pluvial Flooding in an Urban Catchment"

This HECRAS 2D model setup files and results were produced to compare fluvial and pluvial flood properties at Cockermouth during storm Desmend (2015). For more details please refer the following publication Muthusamy, Manoranjan, Monica Rivas Casado, Gloria Salmoral, Tracy Irvine, and Paul Leinster. 2019. €œA Remote Sensing Based Integrated Approach to Quantify the Impact of Fluvial and Pluvial Flooding in an Urban Catchment.€ Remote Sensing . doi:10.3390/rs11050577. Note: This folder contains DEM data downloaded from Environment Agency, UK. This metadata record is for Approval for Access product AfA458. Attribution statement: (c) Environment Agency copyright and/or database right 2019. All rights reserved.This research was funded by the Natural Environment Research Council, grant numbers NE/N020316/1 and NE/P018890/1 and the Engineering and Physical Sciences Research Council, grant Impact Acceleration Award and grant number EP/P02839X/1 (Emergency flood planning and management using unmanned aircraft systems

Bradford University Rainfall Data (2012-2013)_ Intensity

<p>This data set consists of rainfall data collected from a network of eight paired rain gauges located in Bradford university for 10 months during 2012-2013. There are 5 files where intensities are calculated for time scales of 1 min, 2 min, 5 min, 15 min, 30 min.</p> <p> </p

Physics-based simulations of multiple natural hazards for risk-sensitive planning and decision making in expanding urban regions

Rapid urban expansion in many parts of the world is leading to increased exposure to natural hazards, exacerbated by climate change. The use of physics-based models of natural hazards in risk-informed planning and decision-making frameworks may provide an improved understanding of site-specific hazard scenarios, allowing various decision makers to more accurately consider the consequences of their decisions on risk in future development. We present results of physics-based simulations of flood, earthquake, and debris flow scenarios in a virtual urban testbed. The effect of climate change, in terms of increasing rainfall intensity, is also incorporated into some of the considered hazard scenarios. We use our results to highlight the importance of using physics-based models applied to high-resolution urban plans to provide dynamic hazard information at the building level for different development options. Furthermore, our results demonstrate that including building elevations into digital elevation models is crucial for predicting the routing of hazardous flows through future urban landscapes. We show that simulations of multiple, independent hazards can assist with the identification of developing urban regions that are vulnerable to potential multi-hazard events. This information can direct further modelling to provide decision-makers with insights into potential multi-hazard events. Finally, we reflect on how information derived from physics-based hazard models can be effectively used in risk-sensitive planning and decision-making