Abstract. Flood hazard is increasing in frequency and magnitude in major South East Asian metropolitan areas due to fast urban development and changes in climate, threatening people's property and life. Typically, flood management actions are mostly focused on large-scale defences, such as river embankments or discharge channels or tunnels. However, these are difficult to implement in town centres without affecting the value of their heritage districts and might not provide sufficient mitigation. Therefore, urban heritage buildings may become vulnerable to flood events, even when they were originally designed and built with intrinsic resilient measures, based on the local knowledge of the natural environment and its threats at the time. Their aesthetic and cultural and economic values mean that they can represent a proportionally high contribution to losses in any event. Hence it is worth investigating more localized, tailored mitigation measures. Vulnerability assessment studies are essential to inform the feasibility and development of such strategies. In this study we propose a multilevel methodology to assess the flood vulnerability and risk of residential buildings in an area of Kuala Lumpur, Malaysia, characterized by traditional timber housing. The multiscale flood vulnerability model is based on a wide range of parameters, covering building-specific parameters, neighbourhood conditions and catchment area conditions. The obtained vulnerability index shows the ability to reflect different exposure by different building types and their relative locations. The vulnerability model is combined with high-resolution fluvial and pluvial flood maps providing scenario events with 0.1 % annual exceedance probability (AEP). A damage function of generic applicability is developed to compute the economic losses at individual building and sample levels. The study provides evidence that results obtained for a small district can be scaled up to the city level, to inform both generic and specific protection strategies.
Abstract. The estimation of extreme floods is associated with high uncertainty, in part due to the limited length of streamflow records. Traditionally, statistical flood frequency analysis and an event-based model (PQRUT) using a single design storm have been applied in Norway. We here propose a stochastic PQRUT model, as an extension of the standard application of the event-based PQRUT model, by considering different combinations of initial conditions, rainfall and snowmelt, from which a distribution of flood peaks can be constructed. The stochastic PQRUT was applied for 20 small- and medium-sized catchments in Norway and the results give good fits to observed peak-over-threshold (POT) series. A sensitivity analysis of the method indicates (a) that the soil saturation level is less important than the rainfall input and the parameters of the PQRUT model for flood peaks with return periods higher than 100 years and (b) that excluding the snow routine can change the seasonality of the flood peaks. Estimates for the 100- and 1000-year return level based on the stochastic PQRUT model are compared with results for (a) statistical frequency analysis and (b) a standard implementation of the event-based PQRUT method. The differences in flood estimates between the stochastic PQRUT and the statistical flood frequency analysis are within 50 % in most catchments. However, the differences between the stochastic PQRUT and the standard implementation of the PQRUT model are much higher, especially in catchments with a snowmelt flood regime.
This paper presents the regionalisation of the three parameter event-based PQRUT model, which is used for design flood analyses. The PQRUT model is used for the analysis of peak flows for which a sub-daily temporal resolution is required. The availability of high-resolution discharge data and disaggregated precipitation data have made it possible to re-evaluate the regional regression equations currently in use. We also assess whether the model parameters show spatial dependency. Event-based calibration was performed for the 45 highest flood events for each of 55 selected catchments across Norway, representing peak flows generated predominantly by rainfall. Due to the geographical heterogeneity of most areas in Norway, a statistically significant homogeneous region was only identified for catchments in southeastern Norway. Multiple linear regression and weighted regression were, therefore, used to develop a single set of equations, applicable to the entire country. The results for the weighted regression indicate a decrease in the median Kling–Gupta efficiency from 0.64 to 0.51 for calibration and regionalisation, respectively. The results also suggest that regression methods may perform better than methods based on spatial proximity in regions with varying topography when a parsimonious model is used.
A novel approach to consider local‐scale defence infrastructure in an urban environment, coupled with a broadscale hydraulic model framework, is applied to the capital city of Kuala Lumpur, Malaysia. Broadscale hydraulic modelling frameworks are often able to employ more complex models, but are typically limited to homogenous decision‐making to ensure standardised outputs across large regions. Conversely, small‐scale hydraulic modelling frameworks tend to better integrate local‐scale features but can be computationally expensive to scale up beyond a regional view. Improvements to the broadscale hydraulic model framework through the incorporation of defence systems yield a more accurate representation of fluvial flood risk. This study incorporates defences in Kuala Lumpur, yielding a reduction in our estimates of fluvial flood extent by around 40%. The results of this study are validated against a set of high‐quality observations, demonstrating the capability of the model framework in capturing flood risk in more than 95% of known flood risk zones in the city. Incorporating defence infrastructure using data‐driven decision making and existing functionality in the hydraulic model could be automated in future model builds. This new approach bridges the gap between local‐scale model frameworks and the broadscale, homogenous 2D hydraulic modelling studies.
In this study, we utilise Artificial Neural Network (ANN) models to estimate the 100- and 1500-year return levels for around 900,000 ungauged catchments in the contiguous USA. The models were trained and validated using 4,079 gauges and several selected catchment descriptors out of a total of 25 available. The study area was split into 15 regions, which represent major watersheds. ANN models were developed for each region and evaluated by calculating several performance metrics such as root-mean-squared error (RMSE), coefficient of determination (R2) and absolute percent error. The availability of a large dataset of gauges made it possible to test different model architectures and assess the regional performance of the models. The results indicate that ANN models with only one hidden layer are sufficient to describe the relationship between flood quantiles and catchment descriptors. The regional performance depends on climate type as models perform worse in arid and humid continental climates. Overall, the study suggests that ANN models are particularly applicable for predicting ungauged flood quantiles across a large geographic area. The paper presents recommendations about future application of ANN in regional flood frequency analysis.
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