Effect of the Tide On Flood Modeling and Mapping in Kota Tinggi, Johor Malaysia

This study aimed at mapping the Kota Tinggi flood event in 2006/07 that had caused massive damages to properties and the environment. The flood was associated with unusually high intensity and continuous rainfall. Therefore, a reliable technique of floodplain mapping is crucial for the improvement of flood control strategies and for preparing an evacuation plan. The main objective of this study is to compare the effect of tide on flood modeling analysis. The inundated areas were mapped for various annual recurrent intervals using peak flow data from 1965 to 2010. The study used Light Detection and Ranging (LiDAR) data for flood modeling. HEC-HMS, HEC-RAS, and HEC-GeoRAS were used to develop the flood model. The results reaffirm that the GEV model is the best for fitting the annual flood. The HEC-HMS hydrologic model was calibrated and validated using observed hydrographs in Sep 2002 and Jan 2003, respectively. Upon successful calibration and validation, the model was used to simulate flood hydrograph in Jan 2007. The modeling took into account the tidal effect. When the tidal effect was not considered, the simulated flood depth was 43 % lower than the observed flood. However, the inclusion of the tidal effect has reduced the simulation error with an average similarity of 91.4%. The simulation results show that the river flow starts to over bank for ARIs exceeded 25 years.

Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change

An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.

Floodplain mapping and risks assessment of the Orashi River using Remote Sensing and GIS in the Niger Delta Region, Nigeria.

Residents along the shoreline of the Orashi River have yearly been displaced and recorded loss of lives, farmland, and infrastructures. The Government’s approach has been the provision of relief materials to the victims instead of implementing adequate control measures. This research employs Shuttle Radar Topographic Mission and Google Earth imagery in developing a 3D floodplain map in ArcGIS 10.4. The result indicates the drainage system in the study area is observe to be dendritic with catchment of 79 subbasin with 76 pour point indicating the area is floodplain including 3D slope > 8 contain 1.15% and < 8 has 98.85% indicating floodplain area, aspect indicate west-facing slope are dark blue,3D hillshade indicate yellow is very low area and high area is pink and also the buffer analysis result reveals waterbodies reflecting blue with estimated area of 1.88 km2, yellow indicate 0.79 km2 of the shoreline, red indicate 0.81 km2 of the minor floodplain and pink contain 0.82 km2 with length of 32.82km. The result from google earth image in 2007 indicate absent of settlement ,2013 indicate minimal settlement and 2020 indicate major settlement in the study area when correlated with 3D Floodplain mapping before and during the flood in other to analyze and manage flooding for further purpose and majority of the area are under seize with flood like in 2020. Therefore, Remote Sensing and GIS techniques is useful for Floodplain mapping, risk analysis for control measures for better flood management.

Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach

As runoff patterns shift with a changing climate, it is critical to effectively communicate current and future flood risks, yet existing flood hazard maps are insufficient. Modifying, extending, or updating flood inundation extents is difficult, especially over large scales, because traditional floodplain mapping approaches are data and resource intensive. Low-complexity floodplain mapping techniques are promising alternatives, but their simplistic representation of process falls short of capturing inundation patterns in all situations or settings. To address these needs and deficiencies, we formalize and extend the functionality of the Height Above Nearest Drainage (i.e., HAND) floodplain mapping approach into the probHAND model by incorporating an uncertainty analysis. With publicly available datasets, the probHAND model can produce probabilistic floodplain maps for large areas relatively rapidly. We describe the modeling approach and then provide an example application in the Lake Champlain Basin, Vermont, USA. Uncertainties translate to on-the-ground changes to inundated areas, or floodplain widths, in the study area by an average of 40%. We found that the spatial extent of probable inundation captured the distribution of observed and modeled flood extents well, suggesting that low-complexity models may be sufficient for representing inundation extents in support of flood risk and conservation mapping applications, especially when uncertainties in parameter inputs and process simplifications are accounted for. To improve the accuracy of flood hazard datasets, we recommend investing limited resources in accurate topographic datasets and improved flood frequency analyses. Such investments will have the greatest impact on decreasing model output variability, therefore increasing the certainty of flood inundation extents.

Brief communication: Comparing hydrological and hydrogeomorphic paradigms for global flood hazard mapping

Global floodplain mapping has rapidly progressed over the past few years. Different methods have been proposed to identify areas prone to river flooding, resulting in a plethora of available products. Here we assess the potential and limitations of two main paradigms and provide guidance on the use of these global products in assessing flood risk in data-poor regions.