Big Data and Sediment Delivery Ratio: Evaluating its Application in Modelling with Google Earth Engine and InVEST

Abstract

Ecosystem services modelling is emerging as a key tool for land management. However, the challenge of input data remains significant. This study evaluates the applicability of Big Data as a fast and reliable source of information for ecosystem services modelling, focusing on sediment delivery ratio in the province of Bizkaia using InVEST software. Key variables (land use, topography, rainfall erosivity, soil erodibility, vegetation cover, and conservation practices) were collected for the years 1990 and 2018 using platforms such as Google Earth Engine and European environmental data repositories. The results show a general improvement in sediment retention capacity, attributed to increased forest cover, although presenting significant spatial variability. Some microbasins showed increased sediment export despite vegetation gains, due to factors such as topography or land cover changes from low to high erosion potential. The study demonstrates that the use of Big Data enables more agile and detailed modelling, although it is limited by data resolution and the need for field validation. It concludes that, while Big Data is a promising tool for environmental planning, its integration must be complemented with local information and increased field monitoring efforts aimed at validating and reducing the uncertainty of the modeling results.