Newly published: Statistical Downscaling of Future Precipitation Scenarios

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Newly published: Statistical Downscaling of Future Precipitation Scenarios

Globally, the effects of climate change continue to intensify. To make matters more complex, these effects manifest differently in different localities.

To build a climate-resilient and disaster-prepared world, its nations and cities must first be able to respond to the specific climate-related impacts that they face. Just as the manifestations of climate change are unique in each locality, our responses to them should be as well. This, therefore, underscores the need for historical and future localized climate data that can support area-specific and targeted approaches for adapting and responding to these impacts.

Building resilience and disaster preparedness at the local level requires local participation and appropriate planning. To develop such responses effectively, it is necessary to provide relevant climate data that can mobilize involvement and action from researchers, local government units (LGUs), and other stakeholders in the community. The information can help them better understand the localized impacts of climate change and guide them in developing science-based decisions and initiatives to respond to these impacts. Having said that, enabling access to such data should be the aim of research initiatives, organizations, and governments

Such is the relevance of the study recently published in the Climate, Disaster and Development Journal, entitled “Statistical Downscaling of Future Precipitation Scenarios for Agusan del Norte, Philippines.” The study generated future precipitation scenarios for Agusan del Norte, a province in the Philippines often hit by typhoons due to the fact that it faces the Pacific Ocean.

Using the statistical downscaling technique under the A1B climate scenario, the study was able to generate current and future precipitation scenarios for the province. With a focus on the years 2020, 2050, and 2080, it concluded that there will be a projected increase in precipitation. The generated data can be used by different stakeholders to better understand and study hydrologic models, update vulnerability assessments, and guide policies and programs at the provincial level. Altogether, the possible applications of this data in research, programs, and policy can lead to a more resilient community.

The full article can be downloaded from the Climate, Disaster and Development Journal website.

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