WaterSmartLand
The project aims to develop an analysis, modelling, and machine learning (ML) framework for finding spatially optimal land management scenarios for implementing nature-based solutions (NbS) such as wetlands and riparian buffer strips to reduce agricultural nutrient runoff from catchments at different scales. Moreover, the project will identify the landscape predictor variables at different spatial scales for nutrient concentrations and their cross-scale interactions using ML.
What we do?
Click here for a full view of the imageWe will implement a novel Discrete Global Grid System data cube to manage all environmental data needed for modelling. We will take advantage of the strength and flexibility of existing ML methods to deal with complex ecosystem responses, and to reveal new interactions among water quality predictor variables. ML together with geospatial analysis will help us to develop different spatially explicit NbS allocation scenarios which we will evaluate with process-based hydrological modelling. In addition, we will address the challenges of processing large datasets by using proven parallelisation and distributed computing toolkits.
About the project
WaterSmartLand is 2M € ERC Consolidator Grant project that runs from 2024 to 2029 and is led by Prof. Evelyn Uuemaa, University of Tartu.