A high-resolution streamflow and hydrological metrics dataset for ecological modeling using a regression model.

Area of Science:

• Ecology
• Hydrology
• Environmental Science

Background:

• Hydrological variables significantly influence stream ecosystems.
• Limited spatiotemporal resolution of available hydrological data hinders ecological applications like species distribution modeling.

Purpose of the Study:

• To generate high-resolution, long-term daily stream flow data for Germany.
• To create a comprehensive dataset of hydrological indices for ecological research.
• To provide a transferable methodology for other geographical regions.

Main Methods:

• A regression model was applied to a 1 km gridded stream network in Germany.
• Estimated daily stream flow data (1950-2013) were calculated.
• Hydrological indices characterizing stream flow regimes were computed.
• Temporal and spatial validations were conducted.
• Generalized Linear Models (GLMs) compared predicted and observed hydrological indices.

Main Results:

• Estimated daily stream flow data were generated for Germany's stream network at a 1 km grid.
• A set of 53 hydrological metrics characterizing stream flow regimes was calculated.
• Validation confirmed the adequacy of predicted flow data for ecological models.
• An R script for the methodology was provided, enabling application to other regions.

Conclusions:

• The generated hydrological data and metrics are suitable for predictive ecological modeling.
• The developed methodology offers a solution for data-scarce regions in hydrological and ecological studies.
• The provided R script facilitates the application of this approach globally.