Understanding urban rainfall-runoff responses using physical and numerical modelling approaches

This thesis provides a novel investigation into rainfall-runoff processes occurring within a unique two-tiered depth-driven overland flow physical modelling environment, as well as within a numerical model context where parameterisation and DEM/building resolution influences have been investigated using an innovative de-coupled methodology. Two approaches to simulating urban rainfall-runoff responses were used. Firstly, a novel, 9 m2 physical modelling environment consisting of a: (i) a low-cost rainfall simulator component able to simulate consistent, uniformly distributed rainfall events of varying duration and intensity, and; (ii) a modular plot surface layer was used. Secondly, a numerical hydroinundation model (FloodMap2D-HydroInundation) was used to simulate a short-duration, high intensity surface water flood event (28th June 2012, Loughborough University campus). The physical model showed sensitivities to a number of meteorological and terrestrial factors. Results demonstrated intuitive model sensitivity to increasing the intensity and duration of rainfall, resulting in higher peak discharges and larger outflow volumes at the model outflow unit, as well as increases in the water depth within the physical model plot surface. Increases in percentage permeability were also shown to alter outflow flood hydrograph shape, volume, magnitude and timing due to storages within the physical model plot. Thus, a reduction in the overall volume of water received at the outflow hydrograph and a decrease in the peak of the flood event was observed with an increase in permeability coverage. Increases in the density of buildings resulted in a more rapid receding limb of the hydrograph and a steeper rising limb, suggesting a more rapid hydrological response. This indicates that buildings can have a channelling influence on surface water flows as well as a blockage effect. The layout and distribution of permeable elements was also shown to affect the rainfall-runoff response recorded at the model outflow, with downstream concentrated permeability resulting in statistically different hydrograph outflow data, but the layout of buildings was not seen to result in significant changes to the outflow flood hydrographs; outflow hydrographs appeared to only be influenced by the actual quantity and density of buildings, rather than their spatial distribution and placement within the catchment. Parameterisation of hydraulic (roughness) and hydrological (drainage rate, infiltration and evapotranspiration) model variables, and the influence of mesh resolution of elevation and building elements on surface water inundation outputs, both at the global and local level, were studied. Further, the viability of crowdsourced approaches to provide external model validation data in conjunction with dGPS water depth data was assessed. Parameterisation demonstrated that drainage rate changes within the expected range of parameter values resulted in considerable losses from the numerical model domain at global and local scales. Further, the model was also shown to be moderately sensitive to hydraulic conductivity and roughness parameterisation at both scales of analysis. Conversely, the parameterisation of evapotranspiration demonstrated that the model was largely insensitive to any changes of evapotranspiration rates at the global and local scales. Detailed analyses at the hotspot level were critical to calibrate and validate the numerical model, as well as allowing small-scale variations to be understood using at-a-point hydrograph assessments. A localised analysis was shown to be especially important to identify the effects of resolution changes in the DEM and buildings which were shown to be spatially dependent on the density, presence, size and geometry of buildings within the study site. The resolution of the topographic elements of a DEM were also shown to be crucial in altering the flood characteristics at the global and localised hotspot levels. A novel de-coupled investigation of the elevation and building components of the DEM in a strategic matrix of scenarios was used to understand the independent influence of building and topographic mesh resolution effects on surface water flood outputs. Notably, the inclusion of buildings on a DEM surface was shown to have a considerable influence on the distribution of flood waters through time (regardless of resolution), with the exclusion of buildings from the DEM grid being shown to produce less accurate results than altering the overall resolution of the horizontal DEM grid cells. This suggests that future surface water flood studies should focus on the inclusion and representation of buildings and structural features present on the DEM surface as these have a crucial role in modifying rainfall-runoff responses. Focus on building representation was shown to be more vital than concentrating on advances in the horizontal resolution of the grid cells which make up a DEM, as a DEM resolution of 2 m was shown to be sufficiently detailed to conduct the urban surface water flood modelling undertaken, supporting previous inundation research

Flood Prediction and Mitigation in Data-Sparse Environments

In the last three decades many sophisticated tools have been developed that can accurately predict the dynamics of flooding. However, due to the paucity of adequate infrastructure, this technological advancement did not benefit ungauged flood-prone regions in the developing countries in a major way. The overall research theme of this dissertation is to explore the improvement in methodology that is essential for utilising recently developed flood prediction and management tools in the developing world, where ideal model inputs and validation datasets do not exist. This research addresses important issues related to undertaking inundation modelling at different scales, particularly in data-sparse environments. The results indicate that in order to predict dynamics of high magnitude stream flow in data-sparse regions, special attention is required on the choice of the model in relation to the available data and hydraulic characteristics of the event. Adaptations are necessary to create inputs for the models that have been primarily designed for areas with better availability of data. Freely available geospatial information of moderate resolution can often meet the minimum data requirements of hydrological and hydrodynamic models if they are supplemented carefully with limited surveyed/measured information. This thesis also explores the issue of flood mitigation through rainfall-runoff modelling. The purpose of this investigation is to assess the impact of land-use changes at the sub-catchment scale on the overall downstream flood risk. A key component of this study is also quantifying predictive uncertainty in hydrodynamic models based on the Generalised Likelihood Uncertainty Estimation (GLUE) framework. Detailed uncertainty assessment of the model outputs indicates that, in spite of using sparse inputs, the model outputs perform at reasonably low levels of uncertainty both spatially and temporally. These findings have the potential to encourage the flood managers and hydrologists in the developing world to use similar data sets for flood management

Flood Damage Analysis: First Floor Elevation Uncertainty Resulting from LiDAR-Derived Digital Surface Models

The use of high resolution ground-based light detection and ranging (LiDAR) datasets provides spatial density and vertical precision for obtaining highly accurate Digital Surface Models (DSMs). As a result, the reliability of flood damage analysis has improved significantly, owing to the increased accuracy of hydrodynamic models. In addition, considerable error reduction has been achieved in the estimation of first floor elevation, which is a critical parameter for determining structural and content damages in buildings. However, as with any discrete measurement technique, LiDAR data contain object space ambiguities, especially in urban areas where the presence of buildings and the floodplain gives rise to a highly complex landscape that is largely corrected by using ancillary information based on the addition of breaklines to a triangulated irregular network (TIN). The present study provides a methodological approach for assessing uncertainty regarding first floor elevation. This is based on: (i) generation an urban TIN from LiDAR data with a density of 0.5 points·m−2, complemented with the river bathymetry obtained from a field survey with a density of 0.3 points·m−2. The TIN was subsequently improved by adding breaklines and was finally transformed to a raster with a spatial resolution of 2 m; (ii) implementation of a two-dimensional (2D) hydrodynamic model based on the 500-year flood return period. The high resolution DSM obtained in the previous step, facilitated addressing the modelling, since it represented suitable urban features influencing hydraulics (e.g., streets and buildings); and (iii) determination of first floor elevation uncertainty within the 500-year flood zone by performing Monte Carlo simulations based on geostatistics and 1997 control elevation points in order to assess error. Deviations in first floor elevation (average: 0.56 m and standard deviation: 0.33 m) show that this parameter has to be neatly characterized in order to obtain reliable assessments of flood damage assessments and implement realistic risk managementThis research as well as the costs for covering the publication in open access were funded by the MARCoNI (CGL2013-42728-R) project. The authors also acknowledge to the National Geographic Institute of Spain for providing LiDAR data and to the Spanish cadastre for supplying the elevation points used to characterize error in first floor elevation.Bodoque, JM.; Guardiola-Albert, C.; Aroca-Jiménez, E.; Eguibar Galán, MÁ.; Martínez-Chenoll, L. (2016). Flood Damage Analysis: First Floor Elevation Uncertainty Resulting from LiDAR-Derived Digital Surface Models. Remote Sensing. 8(7). https://doi.org/10.3390/rs80706048

A rapid flood inundation model for hazard mapping based on least squares support vector machine regression

Versión aceptada de https://doi.org/10.1111/jfr3.12522[Abstract:] Two-dimensional shallow water models are widely used tools for flood inundation mapping. However, even if High Performance Computing techniques have greatly decreased the computational time needed to run a 2D inundation model, this approach remains unsuitable for applications that require results in a very short time or a large number of model runs. In this paper we test a non-parametric regression model based on least squares support vector machines as a computationally efficient surrogate of the 2D shallow water equations for flood inundation mapping. The methodology is initially applied to a synthetic case study consisting of a straight river reach flowing towards the sea. A coastal urban area is then used as a real test case. Discharge in three streams and tide levels are used as predictor variables to estimate the spatial distribution of maximum water depth and velocity in the study area. The suitability of this regression model for the spatial prediction of flood hazard is evaluated. The results show the potential of the proposed regression technique for fast and accurate computation of flood extent and hazard maps.This work was financially supported by the Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad) within the project “CAPRI: Probabilistic flood prediction with high resolution hydrologic models from radar rainfall estimates” (reference CGL2013-46245-R). María Bermúdez gratefully acknowledges financial support from the Spanish Regional Government of Galicia, Postdoctoral Grant Program 2014 (grant reference ED481B 2014/156-0) and 2018 (grant reference ED481B 2018/016).Xunta de Galicia; ED481B 2014/156-0Xunta de Galicia; ED481B 2018/01

Flood risk assessment of the Crocodile River, Mpumalanga

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science at the University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. Johannesburg, 2016.The Crocodile River East in Mpumalanga Province, South Africa, has seen three major floods in a twenty-four-month period, specifically January 2011, January 2012 and January 2013. The damage included the loss of life, damage and/or loss of public or private properties, agricultural land loss, and damage to biodiversity and river geomorphology. The purpose of this study was to understand the consequences and risks to livelihoods and river basin systems due to flooding of the river. The study focused on a segment of the Crocodile River East, between Riverside and Tekwane. The study used historic hydro-climatic data for the Crocodile River to determine the critical threshold for past flood events and to predict the extent of future flood events. Hydrological modelling coupled with the HEC-RAS hydraulic model enabled the simulation of these future flood events. The use of orthophotos and digital elevation models (DEMs) allowed for a spatial representation of the areas affected during the flood events. Flood hazard maps and flood risk maps were then developed for the identified flood events within a Geographical Information System (GIS). The maps enabled the identification of high risk and flood prone areas along this segment of the Crocodile River Basin. The results showed that when discharge reaches 241.75 m3/s, both locations (Riverside and Tekwane) are at risk to flooding. This is therefore the threshold for which the two locations are likely to be flooded. This study provides a methodology to determine the spatial extent of past and modelled future river flood events. As such, outcomes of this study may aid in the understanding of flood hazard extent and flood prone areas, and may thus help catchment management authorities and institutions in flood reconstruction and flood risk management. The employed methodology can aid effective spatial planning, and can also be extended at the basin scale through integration with the existent flood warning system to gain an estimate of flood extent and flood risk.TG201

Coupled modeling of storm surge and coastal inundation: a case study in New York City during Hurricane Sandy

In this paper we describe a new method of modeling coastal inundation arising from storm surge by coupling a widely used storm surge model (ADCIRC) and an urban flood inundation model (FloodMap). This is the first time the coupling of such models is implemented and tested using real events. The method offers a flexible and efficient procedure for applying detailed ADCIRC storm surge modeling results along the coastal boundary (with typical resolution of ∼100 m) to FloodMap for fine resolution inundation modeling ( 70 m). In further testing, we explored the effects of mesh resolution and roughness specification. Results agree with previous studies that fine resolution is essential for capturing intricate flow paths and connectivity in urban topography. While the specification of roughness is more challenging for urban environments, it may be empirically optimized. The successful coupling of ADCIRC and FloodMap models for fine-resolution coastal inundation modeling unlocks the potential for undertaking large numbers of probabilistically-based synthetic surge events for street-level risk analysis

Assessing the contribution of precipitation to urban flood inundation using a hydraulic modelling approach

Improving the capacity to model urban flood inundation was identified by Wheater (2002) as a key priority within contemporary flood risk science. Although an increasing emphasis has been placed upon urban environments within flood modelling studies, current approaches remain somewhat rooted within the context of rural areas. This has begun to be addressed through the development of model codes specifically designed for application to urban flooding problems (Yu and Lane, 2006a, Bates et al., 2010). However studies of urban flooding have thus far failed to address the potential importance of rainfall, which is hypothesised to attain a greater significance within urban environments due to the pre eminence of impermeable land cover (Hall, 1984). This is particularly relevant in the light of recent increases in pluvial flooding (Pitt, 2007). Accordingly, this study provides the first attempt to include rainfall within a hydraulic flood inundation model. An improvised representation of rainfall has subsequently been developed using a negative manipulation of the infiltration and evaporation terms within a simple storage cell model, LISFLOOD-FP. This has facilitated testing of the potential significance of rainfall to flooding within urban areas, with specific reference to the flood event which occurred on 25th-26th June 2007 in Sheffield. The proliferation of uncertainty from various sources has necessitated analysis with respect to bulk contribution of precipitation here. Addition of rainfall to the parameterisation of the model has lead to an increase in model performance from F=0.56 to F=0.60, suggesting that precipitation provided a modest but significant contribution to the aforementioned flood event. The findings of this modelling study are in agreement with several independent assessments of the June 2007 flooding within Sheffield (Dickson and Berry, 2008, Environment Agency, 2007). Moreover, this study illustrates that the utilisation of new, more efficient modelling tools (Bates et al., 2010), may facilitate further comprehensive assessment of the potential contribution of rainfall to urban flood inundation