Browsing by Author "Ramachandran, Rakhee"

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    Accuracy assessment of surveying strategies for the characterization of microtopographic features that influence surface water flooding
    (MDPI, 2023-04-02) Ramachandran, Rakhee; Bajón Fernández, Yadira; Truckell, Ian; Constantino, Carlos; Casselden, Richard; Leinster, Paul; Rivas Casado, Monica
    With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies.
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    Data relating to "High resolution (cm scale) elevation data of Cockermouth Town, UK"
    (Cranfield University, 2024-08-07) Mukherjee, Kriti; Rivas Casado, Monica; Ramachandran, Rakhee; Leinster, Paul
    The project is focused on 'Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding'. The data provides the microtopography information of Cockermouth Town in England and the property resilience and resistance information. Three data sets are provided; 1.Elevation model at 25 cm resolution generated from lidar point clouds captured from aircraft; 2. Elevation model at 10 cm resolution generated from stereo photos captured by photographic cameras mounted on UAV; 3. shapefile having attributes related to flood resilience and resistance information for the residential buildings of Cockermouth Town.
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    ItemOpen Access
    Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding
    (Public Library of Science (PLoS), 2024-09-24) Mukherjee, Kriti; Rivas Casado, Mónica; Ramachandran, Rakhee; Leinster, Paul; Schumann Guy J-P
    Climate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures. To date, most research on SWF has focused on modelling and prediction, but these models have been limited to 2 m resolution for England to avoid excessive computational burdens. The lead time for predicting convective rainfall responsible for SWF can be as little as 30 minutes for a 1 km x 1 km part of the storm. Therefore, it is useful to identify the locations most vulnerable to SWF based on past rainfall data and microtopography to provide better risk management measures for properties. In this study, we present a framework that uses long-term gridded rainfall data to quantify SWF hazard at the 1 km x 1 km pixel level, thereby identifying localized areas vulnerable to SWF. We also use high-resolution photographic (10 cm) and LiDAR (25 cm) DEMs, as well as a property flood resistance and resilience (PFR) database, to quantify SWF exposure at property level. By adopting this methodology, locations and properties vulnerable to SWF can be identified, and appropriate SWF management strategies can be developed, such as installing PFR features for the properties at highest risk from SWF.