Difference between revisions of "Tracing how Landsat data helps foster fire-adapted communities in Colorado through data-driven, science-informed planning"
| Line 1: | Line 1: | ||
='''Mitigation planning to enhance resilience of communities living near Wildland-Urban Interfaces (WUIs)'''= | ='''Mitigation planning to enhance resilience of communities living near Wildland-Urban Interfaces (WUIs)'''= | ||
| − | Shifting patterns of ambient temperature and precipitation introduce a new level of uncertainty into risk management practices for communities in the proximity of Wildland-Urban Interfaces (WUIs). Adaptive management of these risks has to be the new norm for such communities at risk. This reinforces the value of risk management that are, to an extent, repeatable year after year, informed by the best available science and data. Capturing the provenance of science-informed, data-driven risk management facilitates the "swapping out" of outdated | + | Shifting patterns of ambient temperature and precipitation introduce a new level of uncertainty into risk management practices for communities in the proximity of Wildland-Urban Interfaces (WUIs). Adaptive management of these risks has to be the new norm for such communities at risk. This reinforces the value of risk management practices that are, to an extent, repeatable year after year, informed by the best available science and data. Capturing the provenance of such science-informed, data-driven risk management practices facilitates the "swapping out" of outdated components of the process (e.g. data, policies, protocols) and replacing them with updated ones. |
<BR> | <BR> | ||
<BR> | <BR> | ||
| − | The narrative below, assembled from draft concept maps developed for the specific purpose of demonstrating | + | The narrative below, assembled from draft concept maps developed for the specific purpose of demonstrating the potential for capturing transdisciplinary processes as concept maps, is an informal way to tell the story of how Landsat data can be used to inform sections of a Community Wildfire Protection Plan that describes wildfire hazards threatening a community at risk. A more formal representation of this provenance trace is beyond the scope of this experiment. |
<BR> | <BR> | ||
<BR> | <BR> | ||
| − | However, we hope that this narrative gives you an idea of how you can | + | However, we hope that this example narrative gives you an idea of how you can assemble concepts in the c-soup repository to tell a story about how data could be used to inform a hazard mitigation plan. In this example, the mitigation plan - a Community Wildfire Protection Plan - has a role to play in a law passed by an act of the US Congress (Healthy Forests Restoration Act of 2003, Public Law 108-148) which prioritizes funding for hazard mitigation projects. |
| + | <BR> | ||
| + | <BR> | ||
| + | Nodes below that are clickable retrieve the underlying concept maps that provide more details relevant to the narrative. These underlying concept maps will not tell the whole story: references in each concept map point to documents and websites for future information. | ||
<BR> | <BR> | ||
<BR> | <BR> | ||
Revision as of 19:14, April 27, 2020
Mitigation planning to enhance resilience of communities living near Wildland-Urban Interfaces (WUIs)
Shifting patterns of ambient temperature and precipitation introduce a new level of uncertainty into risk management practices for communities in the proximity of Wildland-Urban Interfaces (WUIs). Adaptive management of these risks has to be the new norm for such communities at risk. This reinforces the value of risk management practices that are, to an extent, repeatable year after year, informed by the best available science and data. Capturing the provenance of such science-informed, data-driven risk management practices facilitates the "swapping out" of outdated components of the process (e.g. data, policies, protocols) and replacing them with updated ones.
The narrative below, assembled from draft concept maps developed for the specific purpose of demonstrating the potential for capturing transdisciplinary processes as concept maps, is an informal way to tell the story of how Landsat data can be used to inform sections of a Community Wildfire Protection Plan that describes wildfire hazards threatening a community at risk. A more formal representation of this provenance trace is beyond the scope of this experiment.
However, we hope that this example narrative gives you an idea of how you can assemble concepts in the c-soup repository to tell a story about how data could be used to inform a hazard mitigation plan. In this example, the mitigation plan - a Community Wildfire Protection Plan - has a role to play in a law passed by an act of the US Congress (Healthy Forests Restoration Act of 2003, Public Law 108-148) which prioritizes funding for hazard mitigation projects.
Nodes below that are clickable retrieve the underlying concept maps that provide more details relevant to the narrative. These underlying concept maps will not tell the whole story: references in each concept map point to documents and websites for future information.
The nodes below are clickable, but you would be left scrolling around madly within the restricted size window. Click here instead to render the full-scale concept map below in your browser window.
