Newton Fund (Invitation Only) Disaster Resilient Cities: Forecasting local climate extremes and physical hazards for Kuala Lumpur

Recent natural disasters in Malaysia, such as the wide-spread floods in 2014/15 and the flash flooding of Kuala Lumpur in 2007, have revealed that improvements are required in the prediction of damaging natural hazards and in the capacity to manage the associated risks and consequences.

Appropriate to the theme of 'future cities', the focus of this project is the prediction and management of physical risks relevant to Kuala Lumpur, which is the Malaysian capital and the most populated city in Malaysia with around 8 million inhabitants. The particular hazards to be targetted in this project, that are common in Kuala Lumpur, are floods, landslides, sink holes, strong winds, urban heat and air pollution.

A consortium of 16 research and business partners from the UK and Malaysia has been assembled for this project. The basic strategy is to adapt and combine existing technologies to enhance hazard forecasting ability, and the main objective is to develop a prototype multi-hazard information platform suitable for communicating risks. The primary beneficiaries will be risk managers and decision-makers in Malaysian local government and the insurance sector.

The project objectives relate to the Malaysian Science to Action initiative, which has an aim of mobilising science for societal well-being.

The University of Cambridge (UoC) is the lead UK partner in the project, and its main role is in the meteorological forecasting package. As the UoC Principal Invesigator has particular expertise in urban hazard modelling and has had a leading role in assembling the consortium, UoC will also have a prominent role in the overall management of the whole project.

Within the meteorological workplans, the main task of UoC will be to assess long-range forecast capability for Malaysia and develop data-processing models with specific objectives for hazard risk outlooks. This is timely because the global long-range forecast systems developed by leading weather and climate centres now have demonstrable predictive skill on seasonal timescales, and have spatial resolution sufficient to represent important tropical meteorological processes relevant to the Malaysian sector. UoC will also contribute to the mesoscale weather modelling workpackage led by the Malaysian Meteorological Department.

Benefits of the project will include:

Improved information regarding the risks of occurrence of natural hazards will enable Malaysian local authorities to make better contingency plans to mitigate the effects of such hazards. This will in turn provide economic benefits and improve the quality of life for Malaysian citizens. Improved information about geophysical hazards will also aid the development of insurance services.

The hazard information system developed for Kuala Lumpur will have relevance to cities elsewhere in Malaysia and in the ASEAN region. There is scope for broadening commercial development of such systems, with benefit both to the commercial sector and to the future urban management of cities.

With regard to the UoC long-range meteorology component, the results of the skill investigation will be relevant to the wider seasonal prediction research community, both through the methodology used and as an example of the potential for beneficial application in the Asian region.

The project will have a wide range of economic, societal and academic impacts.

The main outcome will be a prototype for an operational source of information about multiple physical hazards that may threaten Kuala Lumpur (flash-flood, flood, landslide, sinkholes, heat, air pollution). The immediate beneficiaries will be the contingency planners and decision makers for that major city who will be able to manage better the risks and impacts associated with those hazards. To ensure that such benefits can be realised, these stake-holders will be engaged with the project, through representation on the steering committee and through regular dialogues with the project participants. Through improved planning the economic and societal damage of these physical hazards will be reduced. The methodologies developed in the project (both scientific and in construction of a knowledge dissemination network) will be applicable to other cities in Malaysia and beyond, so these benefits will broaden geographically beyond the lifetime of the project. Expansion of the applications will stimulate commercial exploitation to the benefit of the companies that provide the underlying models, which will in turn underpin investment in further model development to improve accuracy and efficiency.

Improved detailed information about the nature and distribution of the risks associated with the physical hazards will impact the insurance industry by aiding national insurance programmes that are expected to develop in Malaysia over the next several years.

Improved planning will bring social benefits, through improvements in the response of emergency services and through actions to mitigate the severity of damaging physical events.

The deliverables from the project will benefit the research community in several sectors. Results from the multi-scale meteorological modelling component will provide examples and case studies relevant to atmospheric and climate science. Methodologies employed in and data from the mapping and assessment of geophysical hazards will be useful for research in the earth sciences and environmental sciences. Information will be disseminated through publications and through presentations at national and international workshops and conferences.

The main effort by the University of Cambridge in this project regards the evaluation of long-range meteorological predictive skill. The resulting information about performance for the Malaysian sector for a variety of parameters will benefit the continuing development of the comprehensive dynamical models that underly long-range forecasting systems. This will be effected through interaction with the Met Office monthly-to-decadal variability and prediction group.