Creating water-smart landscapes 

Funded by Estonian Research Council Mobilitas+, PI Evelyn Uuemaa, 1.01.2021−30.06.2022 mobilitas plus

The world’s population is increasing, which means that food production must increase significantly. Therefore, it will be necessary to sustainably intensify agriculture while simultaneously decreasing environmental impacts. Nature-based solutions (NBS) such as wetlands and riparian buffer strips can efficiently reduce the nutrient runoff from agricultural catchments. Identification of priority areas will be important for ensuring cost-effective interventions to reduce the impact of intensive agriculture. The aim of the proposed project is to develop an analysis, modelling, and machine learning (ML) framework. With this novel framework, we will find spatially optimal land management scenarios for implementing NBS to reduce agricultural nutrient runoff from catchments from global to local scale. We will take advantage of the strength and flexibility of existing ML methods to deal with complex ecosystem responses, and interactions among water quality predictor variables. The project is a preparation project for ERC proposal.


Creating spatial layers of pollution sensitivity for water protection zones and determining the need for establishing riparian buffer zones
Funded by Environmental Investment Centre (KIK), PI: Evelyn Uuemaa, 1.09.2019-15.02.2021

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The aim of the project is 1) to develop a dataset of pollution sensitivity of water protection zones based on digital soil map, digital terrain model and canopy height model; 2) based on pollution sensitivity estimation to create a dataset with estimation of the need for riparian buffer zones; 3) to add numerical parameters needed for hydrological modelling to Estonian digital soil map.
The result of the project enables to more efficiently plan creating riparian buffer zones and to reduce the agricultural non-point source pollution. The resulting digital datasets enable to identify quickly the most problematic areas where riparian buffer zones are the most needed.

Enhancing data fusion, parallelisation for hydrological modelling and estimating sensitivity to spatial parameterization of SWAT to model nitrogen and phosphorus runoff at local and global scale (GLOMODAT)
H2020 Marie Sklodowska Curie Individual Fellowship, PI Alexander Kmoch 1.09.2019−31.08.2021

The widely used Soil and Water Assessment Tool (SWAT) is a spatially distributed model that can be used to estimate flow and nutrient transport at a variety of scales. The proposed project aims to investigate how spatial resolution of core input datasets of all types (precipitation, DEM, land use and soil) impacts SWAT modelling results and estimate the nutrient runoff on a local and global scale. Sensitivity analysis to all of precipitation, DEM, land use and soil will therefore be tested. However, due to higher resolution or global scale data the computational effort becomes too large for automated calibration. In order to surpass these limitations, we test novel ways of data management and model data partitioning and apply the MapReduce framework as a method for parallelization.

Finding optimal size and location for wetland restoration sites for best nutrient removal performance using spatial analysis and modelling (OPTWET)
2020 Marie Sklodowska Curie Global Fellowship, PI Evelyn Uuemaa, 01.05.2015−30.04.2018).

Wetlands play a vital role in removing excess nutrients from agricultural catchments, which can have a negative impact on water quality. An EU-funded project identified the optimal size for wetlands and mapped their location within agricultural areas to reduce nutrient levels. This EU-funded Horizon 2020 project developed a fast method of identifying suitable locations for wetland construction. The suitability of a site for restoring or creating a wetland depends on many factors, like the underlying geology, soils, topography, hydrology, drainage, and land ownership. This technique also determined the effectiveness of wetlands in removing nutrients and the best use of land in agricultural catchments for reducing nitrogen and phosphorus levels.