Of bottlenecks and possibilities: Integration between climate data and non-climate data
With Bangladesh being exposed to various climate extremes, this integration can provide critical insights into various aspects of life, including the impact of climate change
In the 21st century, data can be compared with currency in terms of value and usage. It also allows people to make effective decisions without compromising time.
In climate change issues, the usability of climatic data is vital by which research can predict the future impacts of climate change on the planet, and in effect, on people.
Recently, one question came to my mind: "is climate data enough to give us insights into climate change effects on people?" Alternatively, "is climate data sufficient to make the decision for the future?"
I had no clear answer to these questions. I then explored the current practice of different organisations and reviewed scientific papers on the problem of climate data itself.
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I noticed something very interesting: the main problem is the non-integration of climate data with non-climate data – two critical components of information that have far-reaching implications for various aspects of our lives.
Climate data includes information about various meteorological and hydrological conditions, including temperature, rainfall, wind, and atmospheric pressure. Non-climate data, on the other hand, encompasses various socioeconomic, demographic, and land use information.
Integrating these two types of data can provide critical insights into various aspects of life, including the impact of climate change, the economic and social implications, and the potential for adaptation.
Bangladesh is one of the most vulnerable countries to the impact of climate change. The country is exposed to various climate extremes, such as floods, droughts, and cyclones, affecting millions of people's livelihoods.
In order to address these challenges, there is a need to integrate climate data and non-climate data to create a comprehensive understanding of the effects of climate change.
However, integrating these two data types is facing several challenges, hindering the effective management of the impacts of climate change in Bangladesh.
Besides, access to both data is also challenged in Bangladesh, which is no exception in other developed nations due to a lack of accountability and privacy.
There are very few organisations working on climate change data in Bangladesh – some of them very well organise the climate data in detail with the precise prediction of the future impacts of climate change.
But these data are not available for all. The owner makes the data private for-profit purpose, which hinders the many types of research from other fields to access. It is a great challenge for researchers trying to put their efforts into correlating socio-economic variables with climate data.
One example I can share is about a few months ago when one of my professional friends came to Bangladesh to collect data for his PhD research. But he could not get access to climate data as those are not free of cost, which left him with no other choice but to change his field.
Also, he had to manage data purchase fees from his research funds, which was very costly. As a result, he failed to meet his deadline.
I think this needs to be rectified, and many other researchers might not have sufficient funds or financial capacity to purchase these data.
One of the primary challenges of integrating climate and non-climate data is the lack of standardisation in data collection and reporting methods.
Climate data is often collected and maintained by different organisations, such as government agencies, universities, and international organisations, each with its methods and protocols for data collection and dissemination.
Non-climate data, on the other hand, is often collected and maintained by different organisations, each with its own data collection and reporting methods.
This lack of standardisation leads to data quality, format and accuracy inconsistencies, making it difficult to combine and analyse the data effectively.
Another obstacle in integrating climate data and non-climate data is the lack of data management and analysis capacity. In Bangladesh, there is a lack of capacity to integrate climate and non-climate data.
Many organisations and stakeholders in Bangladesh lack the technical skills and expertise to integrate climate and non-climate data effectively, including a lack of knowledge about how to use different data sets and data analysis tools and techniques.
This creates a blockage in the integration of climate data and non-climate data, making it difficult for stakeholders to understand the interlinkages between different sectors and develop effective strategies for addressing the impacts of climate change.
The lack of communication and coordination between stakeholders is another big problem in integrating climate and non-climate data. Several institutions, such as the Bangladesh Meteorological Department, generate climate data, while various other organisations, such as the Bangladesh Bureau of Statistics, collect non-climate data.
The lack of coordination and communication between these institutions limits the integration of the two data types and hinders the development of effective adaptation strategies. The lack of resources and funding for data integration is also a significant obstruction.
Developing and maintaining data systems and integrating data from multiple sources requires significant investments of time, money and technical expertise. Many organisations and stakeholders do not have the resources or the funding to invest in data integration efforts, which limits the scale and impact of these initiatives.
Despite these bottlenecks, there are also many possibilities for integrating climate and non-climate data to support decision-making and to advance our understanding of the impacts of climate change.
One strategy is to create standard data-collecting and reporting methodologies and encourage organisations and stakeholders to use them. This would improve to ensure that the information is collected and reported consistently, making it simpler to aggregate and evaluate data from various sources.
The development of data analysis tools and processes is another critical part of data integration. Climate and non-climate data integration need complex data analysis techniques such as statistical models, machine learning algorithms, and spatial analysis tools.
The development of these tools and approaches may assist in unlocking the data's potential, offering insights into the effects of climate change and the possibility of adaptation and mitigation.
The use of technology, such as geographic information systems (GIS) and remote sensing, is another option for integrating climate and non-climate data.
These technologies may be used to collect and analyse data from various sources, including satellite data, and to generate maps and visualisations that can contribute to determining the effects of climate change and designing successful adaptation strategies.
Furthermore, these technologies may be utilised to gather and evaluate non-climate data, such as socio-economic data, to provide a comprehensive knowledge of Bangladesh's climate change consequences.
One of the most important possibilities is establishing a centralised database for climate and non-climate data. This database would allow for simple access to the data while also ensuring its accuracy and reliability.
This database would also serve as a platform for collaboration and communication among many stakeholders, facilitating the integration of the two data types.
Another approach is to increase the resources and funding for data integration efforts to support the development and implementation of data systems and data integration initiatives.
It would help to ensure that data integration efforts are well-resourced, and that data is widely available and accessible to stakeholders and decision-makers.
Developing partnerships and collaborations between different stakeholders is also possible for integrating climate data and non-climate data in Bangladesh.
Partnerships between government agencies, non-government organisations, and academic institutions can be formed to enhance the integration of the two data types, which would provide an opportunity for cross-disciplinary collaboration and would lead to the development of innovative solutions for climate change adaptation.
Finally, combining climate and non-climate data can provide useful insights into the implications of climate change and support decision-making.
However, significant bottlenecks prevent this information from being used effectively in real-world applications, such as a lack of standardisation in data collection and reporting methods, a lack of interoperability between data systems, and a lack of resources and funding for data integration.
Despite these hurdles, there are several options for combining these two types of data, including the establishment of standard data collecting and reporting techniques, the application of data interoperability standards, and the growth of resources and financing for data integration activities.
M Manjurul Islam, currently working as Domain Monitoring, Evaluation, Accountability and Learning (MEAL) Lead at HELVETAS Swiss Cooperation.
Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.
