Land use change effects on extreme flood in the Kelantan basin using hydrological model

Land use and land cover (LULC) change results in increased of flood frequency and severity. The increase of annual runoff which is caused by urban development, heavy deforestation, or other anthropogenic activities occurs within the catchment areas. Therefore, accurate and continuous LULC change information is vital in quantifying flood hydrograph for any given time. Many studies showed the effect of land use change on flood based on hydrological response (i.e., peak discharge and runoff volume). In this study, a distributed hydrological modeling and GIS approach were applied for the assessment of land use impact in the Kelantan Basin. The assessment focuses on the runoff contributions from different land use classes and the potential impact of land use changes on runoff generation. The results showed that the direct runoff from developmental area, agricultural area, and grassland region is dominant for a flood event compared with runoff from other land-covered areas in the study area. The urban areas or lower planting density areas tend to increase for runoff and for the monsoon season floods, whereas the inter-flow from forested and secondary jungle areas contributes to the normal flow

Estimation of large to extreme floods using a regionalization model

Estimation of large to extreme floods in the range of 100 years return periods to probable maximum floods (PMF) is needed in planning and designing of large water resources management projects. Due to the limited availability of observed flood data, the estimation of large to extreme floods requires significant extrapolation beyond the observed flood and rainfall data. This chapter provides a review of various techniques to estimate large to extreme floods. It also presents a case study in Australia where based on observed flood data, a large to extreme flood regionalization (LEFR) model has been developed which can be applied relatively easily as compared with rainfall runoff modeling. The LEFR model assumes that the maximum observed flood data over a large number of sites in a region can be pooled together by accounting for the at-site variation in the mean and coefficient of variation of the observed annual maximum flood data. The LEFR model has been developed and tested using data from 227 catchments in New South Wales and Victoria States in Australia. The method can easily be adapted to other Australian states and countries

Flood Frequency Analysis Based on Gaussian Copula

Flood duration, volume, and peak flow are important considerations in flood risk analysis and management of hydraulic structures. The conventional flood frequency analysis assumed that the marginal distribution functions of flood parameters follow a certain pattern. However, such assumption is impractical because a flood event is multivariate and the flood parameter distributions can be different. These discrepancies were addressed using bivariate joint distributions and copula function which allow flood parameters having different marginal distributions to be analysed simultaneously. The analysis used hourly stream flow data for 45 years recorded at the Rantau Panjang gauging station on the Johor River in Malaysia. It was found that flood duration and volume are best fitted by the Generalized Extreme Value distribution while peakflow by the Generalized Pareto. Inference Function for Margins (IFM) method was applied to model the joint distributions of correlated flood variables for each pair and the results showed that all the calculated ? values were in acceptable range of Gaussian Copula. By horizontally cutting the joint cumulative distribution function, a set of contour lines were obtained for Gaussian Copula which represented the occurrence probabilities for the joint variables. Also the joint return period for pair of flood variables were calculated