A remote sensing cloud-assisted pipeline to estimate river stream power at catchment scale

Descriptive historical cartography of river morpho-dynamics is crucial for understanding the impacts of climate change, the evolution of river catchment land use and land cover and the degree of human influence on river systems.  
Beyond studying specific river sections, analysing fluvial dynamics at large spatio-temporal scales, such as national or continental water basins (i.e., Atlantic, Mediterranean) over a 20 year-period, presents numerous limitations and challenges. These include issues in hydrological and geomorphological calculation procedures, as well as data availability. 
A key metric for understanding river dynamics and their temporal evolution is the stream power, which is essentially the energy exerted by water flow on different parts of the riverbed (i.e., banks and bottom). The calculation inputs, along with water density and recorded discharge, include the slope and wetted channel width. These latest two inputs are essential due to the challenges in accurately extracting riverbed elevation data and measuring wetted channel width along the entire river length. These challenges arise from factors such as vegetation coverage masking the river and the limitations in the spatial resolution of worldwide satellite-borne remote sensing products used for historical studies (e.g., Landsat products). To address these problems, we developed an automated and cloud-based methodology, that follows the next steps:

i)    To implement a cloud-computed procedure using Google Earth Engine to process historical data in large areas, such as trends in vegetation indices and frequency of floods;

ii)    To treat wetted channel width as a variable subject to random errors and outliers, but model it as a function of more reliably measurable variables (e.g., surrounding vegetation, flood frequency, slope, transverse channelling and discharge return period) to estimate it in non-measurable river sections;

iii)    To perform a series of filtering operations on the input data, such as the RANSAC algorithm, to minimize outliers and eliminate topological inconsistencies in height and derived slope of the riverbed; 

iv)    To compute error propagation in the calculation of stream power, considering the significant sources of error in the river longitudinal slope and wetted channel width variables, as well as the longitudinal variability of stream power, both as indicators of the calculation reliability.

In the final phase, we examine the correlation and potential causality between the stream flow results and socio-environmental aspects of the study areas (e.g., number of cities, population, land use) to broadly understand the patterns that may influence its spatiotemporal variability.
The proposed cloud-assisted pipeline enables the analysis of large-scale river systems, accounting for their temporal evolution and providing an initial estimate of stream power with an associated confidence index. This method advances previous global studies. It automatically generates essential data for river basin management, assesses the level of human impact on river systems, and facilitates comparisons across different hydrographic regions.

 

ACKNOWLEDGMENTS: This work is funded by the European Research Council (ERC) through the Horizon Europe 2021 Starting Grant program under REA grant agreement number 101039181 - SEDAHEAD.