A global model for mean annual river discharge

15-12-2016 | Publicatie

This paper describes a global-scale regression model that quantifies mean annual river discharge based on a set of easily retrievable catchment characteristics (catchment area, precipitation, temperature, elevation, and slope).

Quantifying mean annual river flow

Quantifying the mean annual flow (MAF) of rivers is essential for various applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF is typically quantified with spatially explicit process-based models. However, these might be overly time-consuming and data-intensive for this purpose.

A quick alternative

To provide a quick alternative, we developed a global-scale regression model for MAF. Our model quantifies MAF based on easily retrievable catchment characteristics (catchment area and catchment-averaged mean annual precipitation and air temperature, slope and elevation). The model is calibrated based on observations from 1885 catchments worldwide and explains nearly 90% of the variance in MAF. Our study also shows that the regression model performs slightly better than the spatially explicit process-based hydrological model PCR-GLOBWB. 


Our regression model can be applied globally to estimate MAF at any point of the river network, based on only a few characteristics of the upstream catchment.

This article is available on the publisher’s website via restricted access.