Background
Ensemble Streamflow Forecasting becomes a well-established technique in operational (flood) forecasting centers to assess forecast uncertainty. Currently, these forecasts are communicated to decision makers; however, taking decisions is still up to the subjective experience of the specific stakeholder. Due to the large amount of information in ensemble forecasts, this task is a major challenge in particular when time is limited during ongoing flood events.
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RTC-Tools 2.02.0 beta1 released on Friday October 7, 2016May 28, 2018
- License condition: GNU Lesser General Public License V3 (Lesser GPL)
- Website: https://www.deltares.nl/en/software/rtc-tools/Download
- RTC-Tools 2.2 python pip installation package: https://downloadpypi.deltares.nl/en/downloadpython.org/pypi/rtc-tools
- ChannelFlow Modelica library pip installation package: https://pypi.python.org/pypi/rtc-tools/-channel-flow
- Python code repository: https://gitlab.com/deltares/rtc-tools
- ChannelFlow Modelica library repository: https://gitlab.com/deltares/rtc-tools-channel-flow
- Documentation: http://rtc-tools.readthedocs.io/
- Optimization API: http://rtc-tools.readthedocs.io/en/latest/optimization.html
- Simulation API (BMI): http://rtc-tools.readthedocs.io/en/latest/simulation.html
- Tutorial: http://rtc-tools.readthedocs.io/en/latest/examples.html
RTC-Tools 2.0 uses JModelica
Dependencies are all managed within the installation package. RTC-Tools 2.2 uses pymola.hsa a strong co-development relation with pymoca.
A beta version RTC-Tools 2.2 (named rtc-tools 0.0.1.dev1) is available as python pip installation package on pypi.org: https://pypi.python.org/pypi/rtc-tools
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