Search Results (5)

DEM-based approaches for assessing flood-prone areas have recently gained extensive attention due to their parsimony and cost-effectiveness. This work aims to test the capability of the Geomorphic Flood Index (GFI) to delineate flood-prone areas and the results performances while downscaling the calibration map. The accuracy was tested by examining the sensitivity to the exponent of the power function linking the flow depth in the river network and the upslope contributing area. Two approaches were selected: the first consisted of calibrating the GFI using a flood map generated through a 2D-SWE hydrodynamic model. The second consisted of correlating water depths with their corresponding upslope areas. The geomorphological model is able to effectively delineate flood susceptibility areas which, although on average larger than that obtained using the hydrodynamic model, provide a good starting point for any subsequent in-depth analysis. After calibration, an Objective Function of 0.21 and an Area Under the ROC Curve AUC = 92%, which is among the highest if compared with other cases in the literature, were obtained. Positive feedback was also obtained using a calibration map that covers only a rather limited portion of the basin. However, the small values of the scaling exponent obtained after calibration with the first method indicate substantial independence of the river depths from the upslope contributing areas. This leads to the belief that a simple power function is not particularly suitable for describing the relationships between these two variables. Full article

The objective of this study was to assess the performance of form-based and process-based models, and of local-scale and reach-scale models, used to examine bank retreat and sediment transport in stream restoration. The evaluated models were the Bank Erosion Hazard Index (BEHI), Bank Assessment for Nonpoint Source Consequences of Sediment (BANCS), Bank Stability and Toe Erosion Model (BSTEM), and HEC River Analysis System (HEC-RAS 1D). Model-to-model assessments were conducted to quantify the impact of model selection when predicting applied stress and geomorphic change in a restored stream in North Carolina, USA. Results indicated that the mobility of the bed dictated model selection at the reach-scale. The process-based HEC-RAS 1D was needed to accurately analyze the sand-bed stream, predicting amounts of geomorphic change comparable to measured data and up to three orders of magnitude higher than those from local-scale models. At the local-scale, results indicated that the bank retreat mechanism and flow variability constrained model selection. The form-based BEHI and BANCS did not directly account for geotechnical failure nor capture severe floods, underpredicting amounts of geomorphic change by an order of magnitude when compared to the process-based BSTEM, and failing to characterize erosion potential and applied stresses after short-term morphodynamic adjustments. Full article

Coastal regions are one of the essential spots on the earth as they are hosts to various important ecosystems, natural resources and the increasing population. Based on their proximity to the seas, they are mainly affected by sea-level rise, which is one of the adverse effects of climate change. This has resulted in associated hazards, such as beach erosion, flooding, coastal inundation, habitat destruction, saltwater intrusion into ground water aquifers and ecosystem imbalance. This study quantifies and classifies the vulnerability of the Nigerian coastline to these threats using the analytical hierarchical approach. This involved calculating the coastal vulnerability index (CVI) employing physical and geomorphological variables, and socioeconomic indicators that characterized the coastline vulnerability. The Nigerian coast was divided into seventeen (17) segments based on geomorphic units. The different vulnerability variables were assigned ranks ranging from 1 to 5, with 5 indicating the highest and 1 indicating the lowest vulnerabilities. The geomorphological and physical parameters include coastal slope, bathymetry, geomorphology, wave height, mean tidal range, shoreline change rate and relative sea-level rise, while the socioeconomic parameters include population, cultural heritage, land use/land cover and road network. The calculated CVI values (Saaty method) ranged from 11.25 to 41.66 with a median value of 23.60. Based on Gornitz approach, the calculated measures ranged between 3.51–4.77 and 3.08–5.00 for PVI and SoVI, respectively. However, the aggregated coastal vulnerability index computed using this approach ranged from 3.29 to 4.70. The results obtained from both approaches showed that 59–65% of the entire Nigerian coastline is under moderate to high vulnerability to sea-level rise. Data indicted how the coastal populations are highly vulnerable to both physical–geomorphological and socioeconomic stressors. Coastal vulnerability maps, highlighting the physical–geomorphological and socioeconomic vulnerability status of Nigerian coastline were also generated. The information from this study will assist coastal planners in identifying vulnerable segments in the study area and subsequently aid decisions that would mitigate the predicted impacts in the region. Full article

There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas. Full article

Sediment connectivity is considered a powerful geomorphic indicator for defining the most sensitive areas to geomorphological modifications in a fluvial catchment (hotspots). This encourages the development of methods and models for its assessment, to investigate the interrelation of the various phenomena that occur in a river basin (landslides, floods, etc.). This work explores the potential connection of the processes in flood dynamics, by focusing on induced flood hazard, in order to evaluate the applicability of sediment connectivity to flood monitoring. By applying the recently developed sediment flow connectivity index (SCI) computation method to the Severn River basin, in UK, recurrently affected by floods, we investigate the agreement between the hotspot areas (described by the index) and the areas recurrently flooded (as mapped by aerial photography, satellite imagery and hydrodynamic modelling). Qualitative and quantitative approaches are used for the analysis of past (March 2007 and January 2010) as well as predicted (with return periods of 200 and 500 years) flood events. The results show a good correspondence of areas of high sediment connectivity with flood occurrence. Moreover, the detection performance of the SCI is slightly better than that of a simple flow accumulation map, confirming the importance of the initial mapping of sediment availability and mobility. This experiment extends the direct applicability of the SCI from fluvial analysis to flood monitoring, thus opening interesting future scenarios. Full article