Consequences to flood management of using different probability distributions to estimate extreme rainfall

The design of flood defences, such as pumping stations, takes into consideration the predicted return periods of extreme precipitation depths. Most commonly these are estimated by fitting the Generalised Extreme Value (GEV) or the Generalised Pareto (GP) probability distributions to the annual maxima series or to the partial duration series. In this paper, annual maxima series of precipitation depths obtained from daily rainfall data measured at three selected stations in southeast UK are analysed using a range of probability distributions. These analyses demonstrate that GEV or GP distributions do not always provide the best fit to the data, and that extreme rainfall estimates for long return periods (e.g. 1 in 100 years) can differ by more than 40% depending on the distribution model used. Since a large number of properties in the UK and elsewhere currently benefit from flood defences designed using the GEV or GP probability distributions, the results from this study question whether the level of protection they offer are appropriate in locations where data demonstrate clearly that alternative probability distributions may have a better fit to the local rainfall data. This work: (a) raises awareness of the limitations of common practices in extreme rainfall analysis; (b) suggests a simple way forward to incorporate uncertainties that is easily applicable to local rainfall data worldwide; and thus (c) contributes to improve flood risk management. © 2012 Elsevier Ltd

Mapping and valuation of ecosystems and economic activities along the coast of Cameroon: implications of future sea level rise.

The vulnerability of the coastal zone of Cameroon to flooding from sea level rise (SLR) was quantified using Geographic Information System (GIS) flooding analysis. The main economic activities and ecosystems along this area were iden tified using secondary data. Valuations of non- market values of ecosystems were based on the ecosystem service product method. The low-lying coastal areas were found to be physically and socio-economically susceptible to impacts of SLR due to their high ecological and economic value. A digitised land use/land cover (LULC) classification was produced from low resolution topographic maps and Google Earth images of the area. The digital elevation model (DEM) used was acquired by the shuttle radar topography mission. Evaluation of potential land loss due to inundation was based on empirical approaches using minimum and maximum scenarios of 2 and 10 m flooding. These were estimated considering the best available SLR data for the area, mean high water levels and wave heights during storms. The estimated SLR range from 2.3 m to 9.2 m for the low and high scenarios, respectively, by 2050 and from 2.6 m to 9.7 m for the low and high scenarios, respectively, by the year 2100. Results indicate that 112 km 2 (1.2 %) and 1,216 km 2 (12.6 %) of the coastal area will be lost from a 2 m (equivalent to a low scenario by 2050) and 10 m (equivalent to a high scenario by 2100) flooding, respectively. 0.3 % to 6.3 % of ecosystems worth US$ 12.13 billion/yr could be at risk of flooding by the years 2050 and 2100. The areas under a serious threat cont ain mangroves, sea and airport, residential and industrial areas of Douala. Main plantation crops of banana and palms will be slightly affected. The identification of the soci o-economic impacts of projected SLR on vulnerable coastlines and populations is important for timely actions to be taken in mitigating the effects of natural disasters in the coastal zone

Guidance on Setup, Calibration, and Validation of Hydrodynamic, Wave, and Sediment Models for Shelf Seas and Estuaries

© 2017 Jon J. Williams and Luciana S. Esteves. The paper is motivated by a present lack of clear model performance guidelines for shelf sea and estuarine modellers seeking to demonstrate to clients and end users that a model is fit for purpose. It addresses the common problems associated with data availability, errors, and uncertainty and examines the model build process, including calibration and validation. It also looks at common assumptions, data input requirements, and statistical analyses that can be applied to assess the performance of models of estuaries and shelf seas. Specifically, it takes account of inherent modelling uncertainties and defines metrics of performance based on practical experience. It is intended as a reference point both for numerical modellers and for specialists tasked with interpreting the accuracy and validity of results from hydrodynamic, wave, and sediment models

Changes in coastal sediment dynamics due to managed realignment

Consolidation of the ecosystem services approach and concerns about climate change impacts are leading to a paradigm shift in the management of coastal erosion and flooding risk. Working with nature approaches aiming to restore the adaptive capacity of environments to respond to dynamic conditions are now promoted in a growing number of local and national strategies. In England, for example, Shoreline Management Plans foresee 10% of the coastline to be realigned by 2030 and 15% by 2060. Despite over 100 projects implemented in Europe, only few studies discuss the effects of managed realignment on coastal and estuarine sediment dynamics. This paper presents a conceptual model for the longer term evolution within and adjacent to management realignment sites and contrasts with evidence from published field data

X-band radar system to support coastal management decisions

The difficulties and costs associated with the acquisition of hydrodynamic and bathymetry data in the nearshore is widely recognised. While technological advances have enabled in situ measurements at increased precision, the limited spatial and temporal resolution of data continues to hinder evidence-based coastal management. This paper presents selected results from the ‘X-Band radar as a coastal monitoring tool’ (X-Com) project which tests the suitability of a land-based X-Band radar system to provide data required for practical coastal management applications. Results are shown from a radar installation at Thorpeness (Suffolk, eastern England) from August 2015 to October 2016. At this location, coastal erosion threatens clifftop and beach front properties, and the lack of understanding about the local nearshore-shore-cliff interactions has been identified as a key factor limiting the development of a sustainable coastal strategy. A model is used in a preliminary examination of surface current data from the radar. X-Com results indicate that X-Band radar systems have an unrealised potential and could become a cost-effective tool for coastal management applications pending improvements in the automation of data processing, assessment of data accuracy and end user-friendly output formats