Development of a window correlation matching method for improved radar rainfall estimation

International audienceThe present study develops a method called window correlation matching method (WCMM) to reduce collocation and timing errors in matching pairs of radar measured reflectivity, Ze, and gauge measured rainfall intensity, R, for improving the accuracy of the estimation of Ze?R relationships. This method was compared with the traditional matching method (TMM), the probability matching method (PMM) and the window probability matching method (WPMM). The calibrated relationship Ze=18.05 R1.45 obtained from 7×7 km of space window and both present and 5 min previous time of radar observation for time window (S77T5) produces the best results for radar rainfall estimates for orographic rain over the Mae Chaem Watershed in the north of Thailand. The comparison shows that the Ze?R relationship obtained from WCMM provide more accuracy in radar rainfall estimates as compared with the other three methods. The Ze?R relationships estimated using TMM and PMM provide large overestimation and underestimation, respectively, of mean areal rainfall whereas WPMM slightly underestimated the mean areal rainfall. Based on the overall results, it can be concluded that WCMM can reduce collocation and timing errors in Ze?R pairs matching and improve the estimation of Ze?R relationships for radar rainfall. WCMM is therefore a promising method for improved radar-measured rainfall, which is an important input for hydrological and environmental modeling and water resources management

An artificial neural network model for rainfall forecasting in Bangkok, Thailand

This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall

Methodological Framework for Analysing Cascading Effects from Flood Events: The Case of Sukhumvit Area, Bangkok, Thailand

This is the final version of the article. Available from MDPI via the DOI in this record.Impacts from floods in urban areas can be diverse and wide ranging. These can include the loss of human life, infrastructure and property damages, as well as other kinds of nuisance and inconvenience to urban life. Hence, the ability to identify and quantify wider ranging effects from floods is of the utmost importance to urban flood managers and infrastructure operators. The present work provides a contribution in this direction and describes a methodological framework for analysing cascading effects from floods that has been applied for the Sukhumvit area in Bangkok (Thailand). It demonstrates that the effects from floods can be much broader in their reach and magnitude than the sole impacts incurred from direct and immediate losses. In Sukhumvit, these include loss of critical services, assets and goods, traffic congestion and delays in transportation, loss of business and income, disturbances and discomfort to the residents, and all these can be traced with the careful analysis of cascading effects. The present work explored the use of different visualization options to present the findings. These include a casual loop diagram, a HAZUR resilience map, a tree diagram and GIS maps.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 603663 for the research project PEARL (Preparing for Extreme and Rare events in coastaL regions). The authors are grateful to Opticits for providing the HAZUR software licence, within the collaboration of the EU H2020 research project RESCCUE (RESilience to cope with Climate Change in Urban arEas—a multisectorial approach focusing on water) Grant Agreement 700174

Developing Intensity-Duration-Frequency (IDF) curves under climate change uncertainty: The case of Bangkok, Thailand

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

Developing Intensity-Duration-Frequency (IDF) curves under climate change uncertainty: The case of Bangkok, Thailand

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

A framework for assessing benefits of implemented nature-based solutions

Nature-based solutions (NBS) are solutions that can protect, sustainably manage, and restore natural or modified ecosystems in urban and rural areas, while providing many benefits and co-benefits including stormwater mitigation, biodiversity enhancement, and human well-being. As such, NBS have the potential to alleviate many of the environmental, social, and economic issues that we face today. Grey infrastructure, such as lined trenches and catch basins, pipes, and concrete dikes are frequently used for stormwater management and flood protection, but they do not provide many of the co-benefits that are common with NBS. Grey infrastructure is designed to quickly collect and remove rainwater, whereas NBS keep rainwater where it falls, and where it can be used by the environment. Many stakeholders lack knowledge of the capabilities and benefits of NBS, and as a result, they continue to rely on grey infrastructure in their projects. When information is made available on the benefits and how they can be quantitatively measured, it is hoped that NBS will be promoted to a mainstream infrastructure choice. A valuable way to quantify and highlight the benefits of NBS is by using an evaluation framework. There are several evaluation frameworks that qualitatively assess the potential benefits of possible NBS, however there is a need for quantitative frameworks that can assess the actual benefits (or performance) of implemented (or existing) NBS. This article presents an evaluation framework that aims to quantify the benefits and co-benefits of implemented NBS. The framework involves five main steps: (1) selection of NBS benefit categories, (2) selection of NBS indicators, (3) calculation of indicator values, (4) calculation of NBS grade, and (5) recommendations. The outcome of the framework is a single numerical grade that reflects the benefit functioning for an NBS site and values for each performance indicator. This information may be used by decision makers to determine their budget allocations to expand or construct a new NBS site, to update maintenance plans that will improve the benefits of that site, to set up programs to monitor the NBS benefits and co-benefits over time, and to schedule labour and resources for other NBS projects. The framework was tested and validated on a case study of NBS in Thailand. Through conversations with stakeholders and knowledge of the case study area, relevant categories and indicators were chosen. Using data and information obtained through various means, values for each indicator and the overall NBS grade were calculated. The values revealed which benefits were pronounced, those that were weak, and where improvements were required

Variations in the wave climate and sediment transport due to climate change along the coast of Vietnam

This study quantifies the climate change (CC)-driven variations in wave characteristics and the resulting variations in potential longshore sediment transport rate along the ~2000 km mainland coast of Vietnam. Wind fields derived from global circulation models (GCM) for current and future (2041–2060 and 2081–2100) climate conditions are used to force a numerical wave model (MIKE21 SW) to derive the deep water wave climate. The offshore wave climate is translated to nearshore wave conditions using another numerical model (Simulating WAves Nearshore—SWAN) and finally, a sediment transport model (GENEralized model for Simulating Shoreline Change—GENESIS) is used to estimate potential sediment transport for current and future climate conditions. Results indicate that CC effects are substantially different in the northern, central and southern parts of the coast of Vietnam. The 2081–2100 mean significant wave height along the northern coast is estimated to be up to 8 cm lower (relative to 1981–2000), while projections for central and southern coasts of Vietnam indicate slightly higher (increases of up to 5 cm and 7 cm respectively). Wave direction along the northern coast of Vietnam is projected to shift by up to 4° towards the south (clockwise) by 2081–2100 (relative to 1981–2000), up to 6° clockwise along the central coast and by up to 8° anti-clockwise (to the north) along the southern coast. The projected potential longshore sediment transport rates show very substantial and spatially variable future changes in net transport rates along the coast of Vietnam, with increases of up to 0.5 million m3/year at some locations (by 2081–2100 relative to 1981–2000), implying major changes in future coastline position and/or orientation. The vicinity of the highly developed city of Da Nang is likely to be particularly subject to coastline changes, with potentially an additional 875,000 m3 of sand being transported away from the area per year by the turn of the 21st century

Classifying Headland-Bay Beaches and Dynamic Coastal Stabilization

In this paper, a framework is developed for classifying bay types using stability and a sediment supply source. The framework is used to classify a total of 212 headland-bay beaches in Southeast Asia. The results show that static bays, bays with no sediment supply, and dynamic bays (with a sediment supply), account for 36% and 64%, respectively, while stable bays, bays that can maintain their long-term shoreline stability, and unstable bays (changing their shape over time), account for 69% and 31%, respectively. The results reveal the importance of dynamic bays. The dynamic parabolic bay shape and bay characteristic equations have been verified to bridge the knowledge gap of coastal stabilization and management in dynamic bays. The verification of bay characteristic equations shows an efficiency index of more than 78%. The bay characteristic equation shows that dynamic bays are highly sensitive to low sediment supply and become less sensitive when the sediment supply increases. Knowledge of the coastal stabilization concept successfully implemented for static unstable bays has been extended in this study and applied to stabilize dynamic unstable bays using the verified equations. Sediment control and the combined method are developed in this study, and a case study is presented on the stabilization of a dynamic unstable bay