ABSTRACT:

This resource contains the code to generate the EU-MOHP v013.1.1 dataset as static code repository. For updates of the code and further information, please visit the corresponding Github repository: https://github.com/MxNl/macro_mohp_feature. The EU-MOHP v013.1.1 dataset can also be found on Hydroshare.

ABSTRACT:

The presented dataset EU-MOHP v013.1.1 provides cross-scale information on the hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures “lateral position” (LP) as a relative measure of the position between the stream and the catchment divide, “divide to stream distance” (DSD) as sum of the distances to the nearest stream and divide and “stream distance” (SD) as an absolute measure of the distance to the nearest stream. These three measures are calculated for several hydrologic orders to reflect different spatial scales. Its spatial extent covers major parts of the European Economic Area (EEA39) which also largely coincides with physiographical Europe. Although there are multiple potential use cases, this dataset serves predominantly as valuable static environmental feature or predictor variable for hydrogeological and hydrological modelling such as mapping or forecasting tasks using machine learning. The generation of this dataset uses free open source software only and therefore can be transferred to other regions or input datasets.

ABSTRACT:

This resource contains the code to generate the EU-MOHP v013.1.0 dataset as static code repository. For updates of the code and further information, please visit the corresponding Github repository: https://github.com/MxNl/macro_mohp_feature. The EU-MOHP v013.1.0 dataset can be downloaded here: https://doi.org/10.4211/hs.0f02af18e5344ae7a65dfa7fe1444f34

ABSTRACT:

The presented dataset EU-MOHP v013.1.0 provides cross-scale information on the multiorder hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures “lateral position” (LP) as a relative measure of the position between the stream and the catchment divide, “divide stream distance” (DSD) as sum of the distances to the nearest stream and divide and “stream distance” (SD) as an absolute measure of the distance to the nearest stream. These three measures are calculated for several hydrologic orders to reflect different spatial scales. Its spatial extent covers major parts of the European Economic Area (EEA39) which also largely coincides with physiographical Europe. Although there might be many potential use cases, this dataset serves predominantly as valuable static environmental predictor variable for hydrogeological and hydrological modelling such as mapping or regionalization tasks using machine learning.