2 Dimensional Hydrodynamic Flood Routing Analysis on Flood Forecasting Modelling for Kelantan River Basin

Abstract

Abstract. Flood disaster occurs quite frequently in Malaysia and has been categorized as the most threatening natural disaster compared to landslides, hurricanes, tsunami, haze and others. A study by Department of Irrigation and Drainage (DID) show that 9% of land areas in Malaysia are prone to flood which may affect approximately 4.9 million of the population. 2 Dimensional floods routing modelling demonstrate is turning out to be broadly utilized for flood plain display and is an extremely viable device for evaluating flood. Flood propagations can be better understood by simulating the flow and water level by using hydrodynamic modelling. The hydrodynamic flood routing can be recognized by the spatial complexity of the schematization such as 1D model and 2D model. It was found that most of available hydrological models for flood forecasting are more focus on short duration as compared to long duration hydrological model using the Probabilistic Distribution Moisture Model (PDM). The aim of this paper is to discuss preliminary findings on development of flood forecasting model using Probabilistic Distribution Moisture Model (PDM) for Kelantan river basin. Among the findings discuss in this paper includes preliminary calibrated PDM model, which performed reasonably for the Dec 2014, but underestimated the peak flows. Apart from that, this paper also discusses findings on Soil Moisture Deficit (SMD) and flood plain analysis. Flood forecasting is the complex process that begins with an understanding of the geographical makeup of the catchment and knowledge of the preferential regions of heavy rainfall and flood behaviour for the area of responsibility. Therefore, to decreases the uncertainty in the model output, so it is important to increase the complexity of the model.

High-Resolution Hydrological-Hydraulic Modeling of Urban Floods Using InfoWorks ICM

“…This well-established PDM model has been shown to work with high flow and rainfall records for continuous rainfall−runoff simulations [38][39][40][41]. The concept of PDM is based on three main conceptual storage components, namely: probability distributed soil moisture storage, surface storage, and groundwater storage.…”

“…Here, 64 sub-catchments were created, where all significant tributaries to the mainstream were represented as one or more sub-catchments. The catchment characteristics such as the slope, land use, and soil type were extracted from a GIS database and were This well-established PDM model has been shown to work with high flow and rainfall records for continuous rainfall−runoff simulations [38][39][40][41]. The concept of PDM is based on three main conceptual storage components, namely: probability distributed soil moisture storage, surface storage, and groundwater storage.…”

The validation of hydrodynamic modelling for 2014 flood in Kuala Krai, Kelantan

“…Kuala Krai area formed as a hilly land area. The catchment area is 2,329 km 2 where it comprises about 104,356 of population recorded in 2010 with the density of 45 km 2 /people. Figure 1 shows the area of study.…”

“…One has to live with the flood disaster by reducing the impact since avoiding it is impossible [1]. There are 714 floods events have been recorded in 5 years between 2011 and 2015 which comprised of 556 events of flash flood, 147 events of monsoon flood, 4 events of mud flood, 7 flood events due to dam release and over than 200 flash floods have been occurred in 2015 on that occasion [2]. However, the extreme flood disaster occurred in 2014 particularly in the east coast part of Peninsular Malaysia has caused great loss of assets and lives.…”

One Dimensional Shallow Water Equation Streamflow Modeling using WASH123D Model

“…There are many available hydrological models and can be categorized into three main types which are, the transfer function (empirical black box), lumped conceptual and physically based models [7]. Characterization of rainfall-runoff models is made by a different level of complexity and data requirement [8]. The model selection criteria can be made based on the type of application, the catchment characteristics and the data availability, different spatial and temporal scales, and different model conceptualizations and parameterizations [9].…”