When rain falls, the water runs off over the surface to the streams as well as infiltrates into the soils and takes parts of fertilizer with it. A similar effect happens during the snowmelt in spring, where the meltwater carries nutrients from the fields. This is an example of diffuse water pollution (DWP). The main pollutants we are looking at are categorized into nitrogen- (N) and phosphorous-based (P) nutrients. Too many nutrients can have negative effects on the overall health of plant and animal life in our lakes and streams.
The main prerequisite for combatting excessive nutrient losses to water bodies is to study how the pollution sources can be traced and reduced. Compared to point pollution, where polluted water is directly pumped into a river, e.g. from wastewater plants or industrial water use, DWP is more difficult to control due to its numerous and dispersed sources, and the difficulties in tracing its pathways. Spatially distributed hydrological models like the Soil and Water Assessment Tool (SWAT) have been successfully used for these analyses.
However, there are challenges: First, the data demand for these models is considerable. Even with the recent advances in standardised data access, i.e. discovery, quality assessment and conversions are major challenges. Up to 50% of research time is still spent on data processing. Secondly, this type of modelling is very computationally expensive. Finally, there is a lack of scientific understanding, if and how these models would change their predictions in relation to different input datasets.
Previous studies have shown that SWAT results are impacted by different choices of input data, but tested typically only one type of input data, i.e. exchanging the soil or land cover dataset for another one, or testing different elevation models. But there are no comprehensive studies on all spatial input data types concertedly and in unison, and the effect at the field, catchment, national or even global level. In particular, the use of high-resolution data has been neglected, mainly because of the unavailability of very high-resolution data and/or the very high computational requirements. That is also the reason why to our knowledge nutrient runoff has not been modelled at a global scale.
If we could automate those analyses, computers would excel at testing various scenarios and analysing and predicting pollution load. At first, we want to enhance and automate data preparation. Subsequently, to improve large scale and high-resolution SWAT modelling we aim to design and test a computation framework that spreads stages of the model computation onto multiple servers, just like nowadays’ cloud-computing.
When the technical basis is ready, we test and estimate the effect of different resolution datasets of climate, topographical, soil and land use inputs on SWAT modelling results of flow and nutrient runoff at the local scale in smaller catchments. Then try to scale up to a global application in order to analyse and predict global nitrogen and phosphorus runoff. This could allow us in the future to more easily create SWAT models at any desired level with reasonable input data and understand its reliability.
We estimated runoff, effects on soil and water quality and applied an extensive parameter sensitivity analysis in Estonian catchments. The results confirm the general patterns, that agriculture is an important contributor in many places. However, predicting future nutrient pollution has limited applicability. Scenario testing prooved very helpful and gave a very detailed insight on sub-catchment level of sources and pathways of pollution.
And finally, although high-resolution spatial maps from satellite, radar, and other sources are available, it does not mean that all data should be used in modelling.
