Research Topic

Advancement in Big Data Science in Hydroclimatology Research

About this Research Topic

The hydroclimatology science community has been utilizing large datasets and quantitative methods for many decades, such as data that represents land-atmosphere parameters at multiple spatial and temporal resolutions and scales. These have been used to reveal complex processes and interactions between these parameters and have been used in a variety of applications such as near real-time flood monitoring. Two common approaches for investigating land-atmosphere interactions and hydroclimatological processes are physical models and data-driven models. Due to the fact that hydroclimatological processes are extremely complex, more recently data-driven models have attracted much research attention and are considered complementary or as a potential alternative to physically-based models.

This attention has been driven by: the availability of large-volumes of data from satellites, climate models and various non-traditional sources such as social media; the development and use of advanced data science algorithms; and high-performance computing facilities. Together these are helping to improve hydroclimatological understanding and modeling, and development of applications. There are already review articles, research papers, and projects focused on aspects of big data science, machine learning (including deep learning) and artificial intelligence in flood modeling, precipitation-runoff modeling, urban runoff and flooding, precipitation forecasting, model validation and comparison, etc. There is also large interest in the commercial sector, such as Google's recent release of its deep-learning informed regional flood prediction model. Therefore, the time is right to create a Research Topic on big data science in hydroclimatology research and applications. This topic emphasizes the use of big data methodologies in hydroclimatology research, including:

1) High performance and cloud computing platforms
2) Data mining
3) Machine learning (particularly deep learning)
4) Model parameterization and optimization.

Submission to this Research Topic may include literature reviews, original research papers, and applied research case studies. This topic represents an emerging research direction with diverse opportunities for understanding and approaches. Therefore, our intention is to create a forum for new research and users to share views and results, to generate consensus of related issues, and help to promote a research agenda within the next 5 years.


Keywords: Deep learning, hyroclimatology, big data, model, remote sensing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The hydroclimatology science community has been utilizing large datasets and quantitative methods for many decades, such as data that represents land-atmosphere parameters at multiple spatial and temporal resolutions and scales. These have been used to reveal complex processes and interactions between these parameters and have been used in a variety of applications such as near real-time flood monitoring. Two common approaches for investigating land-atmosphere interactions and hydroclimatological processes are physical models and data-driven models. Due to the fact that hydroclimatological processes are extremely complex, more recently data-driven models have attracted much research attention and are considered complementary or as a potential alternative to physically-based models.

This attention has been driven by: the availability of large-volumes of data from satellites, climate models and various non-traditional sources such as social media; the development and use of advanced data science algorithms; and high-performance computing facilities. Together these are helping to improve hydroclimatological understanding and modeling, and development of applications. There are already review articles, research papers, and projects focused on aspects of big data science, machine learning (including deep learning) and artificial intelligence in flood modeling, precipitation-runoff modeling, urban runoff and flooding, precipitation forecasting, model validation and comparison, etc. There is also large interest in the commercial sector, such as Google's recent release of its deep-learning informed regional flood prediction model. Therefore, the time is right to create a Research Topic on big data science in hydroclimatology research and applications. This topic emphasizes the use of big data methodologies in hydroclimatology research, including:

1) High performance and cloud computing platforms
2) Data mining
3) Machine learning (particularly deep learning)
4) Model parameterization and optimization.

Submission to this Research Topic may include literature reviews, original research papers, and applied research case studies. This topic represents an emerging research direction with diverse opportunities for understanding and approaches. Therefore, our intention is to create a forum for new research and users to share views and results, to generate consensus of related issues, and help to promote a research agenda within the next 5 years.


Keywords: Deep learning, hyroclimatology, big data, model, remote sensing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

11 January 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

11 January 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..