HS1.2.7 | PICO

Hydrology relies strongly on heterogeneous data sets and a multitude of computational models. However, several challenges remain in order to obtain all information from the data and model results and, at the same time, carry out scientific work that is reproducible and repeatable.

Data collection is generally the first step in the scientific process, but collecting spatially and temporally dense data sets can be challenging, especially in extreme environments, such as dry, humid or cold areas. Therefore, environmental data sets are often sparse and do not allow us to fully understand the hydrological and associated environmental processes dominant in these areas. Therefore, innovative ideas are needed to build methods able to extract information from the available data and make use of the many signatures in the observations that are still to be explored.

On the other hand, an increasing amount of heterogenous data becomes available from diverse sources such as remote sensing, social media or citizen science. Platforms and tools are needed to interpret such data, identify and understand patterns, trends, and uncertainty and to draw conclusions and implications from data-driven research. New methods for data visualization can be a pivotal for our ability to make new sense of heterogeneous data and to communicate complex datasets and findings in an appropriate way to other researchers and the public.

Eventually, the full scientific process should be open, reproducible and repeatable. Many data sets contain a wide range of derived variables that cannot be easily re-computed from the raw data, either because the raw data is not available or because the computational steps are not adequately described. As a result, very few published results in hydrology are reproducible for the general reader. However, more and more software tools and platforms are becoming available to support open science, partly as a result of collaborations between software experts and hydrologists.

This session invites contributions on topics ranging from data collection and visualization to sharing model code and reproducible workflows, e.g.:

- Platforms and tools for improved data visualization, open science, scientific collaboration and/or communication with a larger audience
- Use of innovative data and data collection techniques, with a focus on data sparse environments
- Case studies illustrating challenges and solutions related to open science
- Innovative types of data and their visualizations

This session is organized in cooperation with the Young Hydrologic Society (youngHS.com).

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Co-organized as EOS8.1/GM2.16
Convener: Remko C. Nijzink  | Co-conveners: Jonathan Dick , Sebastian Gnann , Stan Schymanski , Lina Stein , Fi-John Chang 
PICOs
| Fri, 12 Apr, 08:30–12:30
 
PICO spot 5b
Hydrology relies strongly on heterogeneous data sets and a multitude of computational models. However, several challenges remain in order to obtain all information from the data and model results and, at the same time, carry out scientific work that is reproducible and repeatable.

Data collection is generally the first step in the scientific process, but collecting spatially and temporally dense data sets can be challenging, especially in extreme environments, such as dry, humid or cold areas. Therefore, environmental data sets are often sparse and do not allow us to fully understand the hydrological and associated environmental processes dominant in these areas. Therefore, innovative ideas are needed to build methods able to extract information from the available data and make use of the many signatures in the observations that are still to be explored.

On the other hand, an increasing amount of heterogenous data becomes available from diverse sources such as remote sensing, social media or citizen science. Platforms and tools are needed to interpret such data, identify and understand patterns, trends, and uncertainty and to draw conclusions and implications from data-driven research. New methods for data visualization can be a pivotal for our ability to make new sense of heterogeneous data and to communicate complex datasets and findings in an appropriate way to other researchers and the public.

Eventually, the full scientific process should be open, reproducible and repeatable. Many data sets contain a wide range of derived variables that cannot be easily re-computed from the raw data, either because the raw data is not available or because the computational steps are not adequately described. As a result, very few published results in hydrology are reproducible for the general reader. However, more and more software tools and platforms are becoming available to support open science, partly as a result of collaborations between software experts and hydrologists.

This session invites contributions on topics ranging from data collection and visualization to sharing model code and reproducible workflows, e.g.:

- Platforms and tools for improved data visualization, open science, scientific collaboration and/or communication with a larger audience
- Use of innovative data and data collection techniques, with a focus on data sparse environments
- Case studies illustrating challenges and solutions related to open science
- Innovative types of data and their visualizations

This session is organized in cooperation with the Young Hydrologic Society (youngHS.com).