EVA and scenario definition
Exploratory semi-structured interviews were conducted with a set of 11 stakeholders to identify priorities, interests and to help base the definition of scenarios on local knowledge. Stakeholders were chosen from professionals with extended knowledge of the Broads, and active residents with a long-lasting interest in the area’s overall management. Specific experience in flood management varied greatly as participants covered a wide range of sectors such as farming, angling, environmental protection, engineering and coastal management. The interviews confirmed the importance of tidal and coastal sources of flooding in the Broads and highlighted vulnerable locations such as—but not limited to—Great Yarmouth or several protected areas. One of the main recurring statements emphasised in the interviews was a concern for the risk of combined events. More specifically, the occurrence of a storm surge during high river discharge was identified as a worry for different stakeholders. Although the small sample of participants does not allow for statistically significant conclusions, this information was used to guide modelling choices and define future scenarios.
A comparison of the available data on past peak sea levels, non-tidal residuals and discharge shows that these events do not tend to occur simultaneously (Fig. 7). However, Fig. 7 also shows that it is physically possible for the peak of the storm surge to occur during a high discharge event and therefore near peak flow.
The EVA served to find return levels of both extreme sea level and extreme discharge to define representative downstream and upstream boundary conditions, respectively. The purpose of the EVA was not to provide a robust probabilistic assessment of flooding risk from different or combining sources. Without an analysis of the probability of joint occurrence of high tide and extreme storm surge, it was not possible to assign return levels to entire extreme sea level events. The EVA performed on total sea levels however did provide return levels for the peak of recreated extreme events.
The GP distribution performed relatively well to describe both extreme sea level (Fig. 8a) and extreme discharge (Fig. 8b). It should be noted that the most extreme values were found above the fitted distribution curves. These events corresponded to the December 2013 storm surge and a peak river flow in April 1981. Both occurrences were verified using data from other nearby gauges, and it was therefore decided not to discard them as recording errors. These points were by far the most extreme observations and did not provide strong evidence against the choice of the GP distribution function compared to other tested distribution functions. The lack of data is a common issue in EVA. More investigation using other sources of data (such as news reports if they exist) that extend past the recorded data period would allow for more confidence in this estimation.
Evidence suggests that changes in MSL are the primary factor leading to an increase in extremes sea levels (Menéndez and Woodworth 2010). Relative MSL (RMSL) is not only rising, but has also been found to accelerate at various rates around the world, with a trend of 4.4 ± 1.1 mm a−1 estimated at Lowestoft from 1993 to 2011 by Wahl et al. (2013). It indeed remains highly uncertain how climate change will impact local storm surge patterns. A linear increase in RMSL was assumed to determine future conditions and return levels up to the year 2100. Uncertainty moreover resides in current projections of the rate of SLR in the twenty-first century. Pfeffer et al. (2008) found that accelerated sea-level rise between 0.8 m and 2 m up to 2100 was physically plausible depending on glaciological conditions. To account for such possibilities, extreme scenarios of 1 m and 2 m MSL rise by 2100 were also considered.
While seasonal precipitation changes are expected in the UK, notably with an increased proportion of heavy precipitation events occurring during winter months, current projections do not show significant changes in annual precipitation in East Anglia (Jenkins 2009). Moreover, little is known on the intensity of extreme precipitation events in coming decades and therefore which trajectory river discharge will also follow. Patterns of extreme river discharge were therefore assumed to the same up to 2100 as in 2015 in the presented scenarios. This assumption is moreover warranted by the much greater influence of tidal processes in the Broads.
The chosen scenarios are presented in Table 2. They included three scenarios of 100-year return peak sea levels under different MSL rise pathways. As explained in Sect. 3.2.2, only the peak sea level is assigned a 100-year return period as opposed to the entire event. Each storm surge event was then also combined with a simultaneous 100-year return river discharge to test the sensitivity of the study area to coinciding extreme events. The timing of events can have significant impacts on flooding occurrence and extent. It is therefore important to note that previous studies have found it most likely for these types of events to not coincide with up to several days separating the different extremes (Klerk et al. 2015). With these caveats taken into account, the proposed scenarios provide a basis to assess the sensitivity of the Broads to compound flooding.
Calibration and validation
The HEC-RAS model was calibrated and validated with storm surge events from October 2014 and December 2013, respectively. The calibration parameter used was the Manning’s n roughness coefficient. Data on past flooding inundation extent in the Broads are lacking in both availability and accuracy. While there have not been major flooding events since 1953, localised defence failures have been observed during extreme storm surge events. Spencer et al. (2015) provided an account of the impact of the December 2013 storm surge along the Norfolk coast. Tidal flooding was however also observed further inland due to overtopping and reported in parts of the Broads (Broads Authority, 2014). As there is no record of the spatial footprint of this inundation, the validation process was carried out using river levels at different stations on the Bure and the Yare (Fig. 6), as well as reports from the Broads Authority, news articles, dated photos, and local accounts of flooding.
Descriptions of the local environments and recommended ranges obtained from Chow (1959) served to make initial benchmarks for Manning’s n values. The model’s calibration was performed on the Manning’s n within river channels to reach final values as shown in Table 3. A roughness coefficient was also applied to land classes out of the river banks in the 2D modelling domain. These values were not used during the model’s calibration as flood extent data were not available (Table 4). In tidally influenced rivers, the inertial terms in the momentum equation are important and rivers levels are not highly sensitive to adjustments in the roughness coefficient (USACE 2016). Theta is a weighting factor that ranges between 0.6 (more accurate) and 1.0 (more computationally stable) applied to the finite difference approximations when solving the unsteady flow equations. A Theta value of 0.6 was used to improve the accuracy in the representation of the propagating tidal wave, which did not decrease the model’s stability.
As expected, the model performed well at recreating river levels near the model’s downstream boundary condition in Great Yarmouth at Haven Bridge (Fig. 9a) with an NSE of 0.92. The model also performed well upstream on both the River Bure and the River Yare, at the Three Mile House (Fig. 9b) and Burgh Castle (Fig. 9c) gauges, respectively. It should be noted that the instrument at Three Mile House was unable to measure the river level during the peak of the tide on 06/12/2013. The NSE remained relatively high at 0.84. The gauge at Burgh Castle is a flood warning monitoring station only and due to the position of its pressure sensor instrument, it therefore does not measure any levels below 0 maOD. Still, the model produced a good fit to both the level of the peaks and their timing at Burgh Castle. The model’s performance decreased upstream of the River Bure. At Acle, once the tidal wave had propagated, the NSE dropped to 0.67 and there was a slight shift in the timing of the tide (Fig. 9d). The modelled peak river level remained within 0.03 m of the observed value. Nearly 40 km from the sea, the error increased further upstream towards Hoveton Broad, where the model overestimated the river level by a maximum of 0.1 m. While river levels were high during this event, the defences were largely successful in holding back the water from the floodplains. This was also the case in the model’s recreation of the event, where only localised flooding was visible at moorings located near Berney Arms, which allowed water to flow into Halvergate Marshes.
Model results derived from simulations in HEC-RAS were exported to ArcGIS and R for analysis. The maximum flooding depth from each simulation run can be found in Fig. 10. The inundation extent shown in these profiles represents an aggregation of the overall runs rather than a specific simulation time. The profiles should therefore be differentiated with the extents occurring during maximum sea level, since flooding is dynamic and its timing varies across various locations. Extreme sea levels cause flooding both downstream and upstream in the Broads when assuming a linear mean SLR up to 2100 (Fig. 10a). The largest affected area is Halvergate Marshes, where water is able to flow throughout the large floodplain located north of Breydon Water. Elevated roads and railway tracks are well captured by the model’s 2D mesh and slow the propagation of the flood wave. Flooding is minimal in the more densely populated Great Yarmouth as there is almost no overtopping of high defences. With the exception of Halvergate Marshes, flood walls and levees are successful in preventing extensive flooding. Upstream of Ranworth Broads, the floodplains are unprotected and consist mostly of marshes that are well connected to the river. While buildings near the riverbanks in the towns of Horning and Hoveton are affected, the flood depth remains relatively low. As Fig. 10b shows, combining this event with a 1:100 return river discharge has significant consequences on flooding on the upstream boundary of the tidal Bure. Impacts downstream remain limited. As SLR has been observed to accelerate in the last decades, a linear increase in RMSL over the next century is a conservative assumption. Scenarios representing an accelerated rise leading up to 1 m and 2 m increase in MSL are shown in Fig. 10c–f.
The topology of the rivers and floodplains in the Broads causes flooding to occur rapidly and spread significantly when a defence is overtopped. Figure 10 shows that certain areas are susceptible to lower thresholds of embankment failure, thereby flooding first and highlighting potential vulnerabilities. A notable observation from the scenarios with a 1 m and 2 m RMSL rise is the increased impact on Great Yarmouth. Not only are more tidal defences overtopped, but coastal waters are also able to flow into the town directly from the sea and cause more flooding at some simulation time steps. These interacting sources of flooding lead to an important increase in impacted buildings (Table 5). While a 2 m increase in MSL by 2100 is still considered unlikely and would require a drastic acceleration of SLR, this scenario is useful to highlight the area’s sensitivity. For example, the model showed flooding outside of some of the left banks of the Bure only during scenarios 2mQ0 and 2mQ100. The main urban zone in the study area is Great Yarmouth, located near the coast. Sea level is therefore the main driver for the number of flooded buildings. Other towns located farther upstream in the Broads are also affected. Centres of activity for tourism and sailing in Horning and Hoveton lie in close proximity to the River Bure, and several buildings in both towns are susceptible to flooding in all scenarios.
While flooding occurs in all the presented scenarios, both extent and depth vary greatly between the different simulations. Depth is important to consider for risk management as it is used in determining flood damage. Figure 11 shows the density of flooded 2-m cells by depth in all six scenarios. Although the flooding extent was already high in scenario 2100Q0, most of the flooding occurred at low depths between 0 m and 0.5 m, meaning actual damages would be limited or easier to cope with (Fig. 11a). The maximum density shifts towards 0.5 m and 1 m for scenario 1mQ0 (Fig. 11b) and increases considerably to over 2 m for scenario 2mQ0 (Fig. 11c).
Both Table 5 and Fig. 11 emphasise that increasing RMSL has a significant impact on inundation extent and depth in the Broads. While sea level is indeed the main driver for flooding in the study area, the results also show that coinciding high river flows can exacerbate these impacts. The average depth of cells below 5 m in depth increased from 0.82 m to 1.08 m (Fig. 11a), from 0.92 m to 1.16 m (Fig. 11b) and from 1.9 m to 2.09 m (Fig. 11c) for the three scenario pairs, respectively. A similar pattern can be observed for the total area of the flooding in each scenario. For both average depth and inundation area however, the influence of high discharge decreases as the maximum sea level increases. Average flood depth increases by 40% from scenarios 2100Q0 to 2100Q100, while it increases by 5% from scenarios 2mQ0 to 2mQ100. Similarly total inundated area increases by 32% from scenarios 2100Q0 to 2100Q100 compared to a 10% rise from scenarios 2mQ0 to 2mQ100.
The simulated compound events did not have significant added consequences in Great Yarmouth on either flooding extent or depth, compared to unique events of extreme sea level. The longitudinal profile of the modelled rivers indeed shows that the influence of the combined extreme discharge decreases going downstream (Fig. 12). Near the mouth of the River Yare, the extreme discharge has almost no impact on the water level in all three envisaged cases. Figure 12 also shows that the difference in water level between Q0 and Q100 events is greater for a lower MSL. Upstream areas are much more affected. The flooded area of broadleaf woodland, which occurs mostly upstream of Ranworth Broads along the River Bure, is highly influenced by the occurrence of a combined event (Fig. 12, Table 6). The Bure Broads and Marshes are well connected to the river, and the encroachment of water is therefore not a direct concern or a rare occurrence.
The deeper upstream flooding observed in Fig. 10b, c and d remains significant as it can lead to longer residence times of saline waters. Large areas of improved grassland, notably used for grazing, are predisposed to flooding under each scenario, with arable and horticulture land classes also highly impacted (Table 6). There are moreover several protected areas, such as sites of specific interests (SSSI), located in the Broads. A topic for future research would be the impact of extreme events on salinity in the Broads. Salinity can cause damage to agricultural land and therefore lead to significant economic losses as well as representing a threat to sensitive species. Studying the impact of combined events may lead to counter-intuitive results as several processes affect salinity. Indeed, high river flows add freshwater to the system, while surges push saline water upstream into the Broads. River salinity and conductivity can be simulated in HEC-RAS’s water quality module.
A significant benefit of the described 1D–2D approach in portraying overtopping is the use of specific lateral structures for flood defences to guarantee that maximum crest heights were accounted for, regardless of the chosen mesh resolution. It is a fundamental requirement for 2D cells in HEC-RAS to be set up such that cell edges (or “faces”) align with high ground or structures impeding the movement of water. This task can be difficult for narrow flood defences, even with a relatively fine resolution of 2 m. Cells that are too large or that are not adequately oriented can cause issues with the model’s calculations, leading water to incorrectly “leak” through natural or man-made barriers. The results in such cases are fragmented and therefore produce unrealistic outputs of flooding extents. The Broads is a highly engineered area with many embankments protecting large expanses of land from rivers. It was therefore essential to use lateral structures between 1D and 2D domains that capture the height of defences for their entire lengths. Until computational capabilities increase to allow for extremely fine mesh resolutions, this study finds that a 1D–2D method remains the most feasible approach for the geographical location in question.
The HEC-RAS 1D–2D model was able to highlight vulnerabilities and weak points within the study area as well as account for complex interactions between different sources of flooding. The model structure could still be improved by including building footprints in the 2D mesh to better represent the flow of water in urban areas. Such levels of accuracy were however not necessary to assess the overall sensitivity of the case study area and the fitness for use of the HEC-RAS model version 5.0. Further developments for the model could moreover be to include other parts of the Broads that currently lie outside the modelling domain. Areas in the River Yare, Waveney, Thurne and Ant basins, as well as in Lowestoft have experienced flooding in the past.
Several important considerations should be made when interpreting the results derived from the presented hydraulic model. The first is that while flood defence infrastructure can fail in a number of ways, only the overtopping of defences was considered here. The erosion and breaching of dunes, embankments and walls are a common concern in coastal regions (Hall et al. 2015). Although these processes can be simulated in HEC-RAS and can be useful to represent catastrophic or “what if” scenarios, their impacts fell outside of the scope of this study.
A more comprehensive study of flooding risk would moreover need to incorporate processes of wind and waves, which were omitted in this simplified hydraulic modelling framework. Wind is a key parameter that plays a role in the dynamics of both waves and surges and can therefore have important consequences on coastal flooding. With the necessary data, the EVA and the scenarios used for simulations could therefore be refined by setting up local wave and storm surge models (e.g. Villatoro et al. 2014). Similarly, the lack of available discharge data was also a limitation for this work. A hydrological model could be used in future research to determine more accurate upstream boundaries for the HEC-RAS hydraulic model. A hydrological model would moreover make it possible to account for projected changes in temperature and precipitation in the Broadland catchment to better understand the impact of these climatic changes on flooding hazard.
This study highlighted the potential for multiple extreme events occurring simultaneously to exacerbate flooding risk in the Broads. Validating the proposed modelling framework to assess the sensitivity of the Broads, the aim of this research was however not to understand the probabilities of co-occurrence of these events. The assumption was made that peak river discharge and peak sea level occurred simultaneously in scenarios where both events occurred. While it helped in interpreting the created scenarios, this assumption may not be representative of likely events in the Broads. Past studies in other regions, such as the Netherlands, have, for example, shown a dependency between discharge peaks and water levels, but with a lag time of several days (Klerk et al. 2015). More analysis should be performed to determine the dependency between discharge peaks and sea levels in the East coast of England. Moreover, understanding the types of weather patterns associated with different events could provide some useful insights into flooding hazard in the region. As the timing of events can have significant consequences not only of flooding extent but also on the usefulness of flood mitigation strategies, joint probabilities should be carefully considered to make robust planning recommendations on flood risk management.