Abstract
The magnitude and frequency of hydrological events are expected to increase in coming years due to climate change in megacities of Asia. Intensity–Duration–Frequency (IDF) curves represent essential means to study effects on the performance of drainage systems. Therefore, the need for updating IDF curves comes from the necessity to gain better understanding of climate change effects. The present paper explores an approach based on spatial downscaling-temporal disaggregation method (DDM) to develop future IDFs using stochastic weather generator, Long Ashton Research Station Weather Generator (LARS-WG) and the rainfall disaggregation tool, Hyetos. The work was carried out for the case of Bangkok, Thailand. The application of LARS-WG to project extreme rainfalls showed promising results and nine global climate models (GCMs) were used to estimate changes in IDF characteristics for future time periods of 2011–2030 and 2046–2065 under climate change scenarios. The IDFs derived from this approach were corrected using higher order equation to mitigate biases. IDFs from all GCMs showed increasing intensities in the future for all return periods. The work presented demonstrates the potential of this approach in projecting future climate scenarios for urban catchment where long term hourly rainfall data are not readily available.
Highlights
Almost on daily basis we can observe that the world is being overwhelmed by extreme hydro-meteorological disasters such as hurricanes, widespread flooding and droughts, all of which are associated with devastating losses and suffering (e.g., [1,2,3,4])
The present paper provides contribution in this direction and it presents an approach based on spatial downscaling-temporal disaggregation method (DDM), which appears to give promising results in generating rainfall data for a single site station in an urban catchment
The present paper presents an approach based on spatial downscaling—temporal disaggregation method (DDM)—to develop future IDFs using stochastic weather generator, Long Ashton Research
Summary
Almost on daily basis we can observe that the world is being overwhelmed by extreme hydro-meteorological disasters such as hurricanes (or typhoons), widespread flooding and droughts, all of which are associated with devastating losses and suffering (e.g., [1,2,3,4]). Our search for optimal configurations of water infrastructure systems represents a great challenge for researchers and practitioners (e.g., [5,6,7,8,9,10]). The uncertainty brought by climate change (or climate extremes) make the above challenge even more significant. Addressing climate change effects in the development of disaster management plans has been on the agenda of many governments and institutions. Hydrodynamic flood modelling is an important aspect in the development of flood mitigation and climate adaptation strategies, which requires high resolution time series data of continuous precipitation or design storms. A more effective disaster management planning requires both current and projected climate change scenarios for which a range of optimal measures needs to be
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