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Enhancing data fusion, parallelisation for hydrological modelling and estimating sensitivity to spatialparameterization of SWAT to model nitrogen and phosphorus runoff at local and global scale

Total Cost €


EC-Contrib. €






 GLOMODAT project word cloud

Explore the words cloud of the GLOMODAT project. It provides you a very rough idea of what is the project "GLOMODAT" about.

parallelization    causing    tested    successfully    partitioning    water    computing    pollution    programmed    mapreduce    transport    interface    computations    world    soil    distributed    hpc    load    data    limited    effort    global    paradigm    becomes    message    clusters    ways    computation    flow    datasets    mpi    dem    published    specialised    simulate    model    catchments    diffuse    compromised    quality    passing    balancing    transparently    scales    either    framework    limitations    swat    tool    power    performance    estimate    limitation    partition    computational    pressure    resolution    accessible    sensitivity    entails    automate    spreading    coupled    nodes    population    countries    understand    computers    landscape    core    runoff    spatial    precipitation    size    impacts    input    local    types    underlying    loosely    land    automated    tightly    nutrient    multiple    surpass    demands    economy    calibration    put    too    variety   

Project "GLOMODAT" data sheet

The following table provides information about the project.


Organization address
address: ULIKOOLI 18
city: TARTU
postcode: 51005

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Estonia [EE]
 Total cost 148˙582 €
 EC max contribution 148˙582 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-RI
 Starting year 2019
 Duration (year-month-day) from 2019-09-01   to  2021-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TARTU ULIKOOL EE (TARTU) coordinator 148˙582.00


 Project objective

A growing economy and population in the world is causing landscape changes and an increasing pressure is put on water resources. Diffuse water pollution is considered to be one of the major problems for water quality in many countries. Modelling has been successfully used to simulate water quality in catchments to better understand the underlying landscape processes. 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.

In current published studies typically only one or two parameters of precipitation, DEM, land use or soil properties are used in. 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. The limitation to one or two parameters in current published studies is due to the computational demands. Due to the way the SWAT model is programmed using a tightly coupled Message Passing Interface (MPI) approaches the available computing power needs to accessible within specialised High Performance Computing (HPC) clusters of limited size. Thus, either scale or resolution is typically compromised.

As for higher resolution or global scale data the computational effort becomes too large for automated calibration, we aim to develop a novel method to automate data processing and balancing computational load transparently between many computers.

In order to surpass these limitations we test the MapReduce framework as a novel method for parallelization. This entails new ways of data management, model data partitioning and spreading the model partition computations transparently over multiple computing nodes fostering a loosely coupled distributed computation paradigm.


year authors and title journal last update
List of publications.
2019 Kmoch, Alexander
EstSoil-EH v1.0: An eco-hydrological modelling parameters dataset derived from the Soil Map of Estonia - Poster
published pages: , ISSN: , DOI: 10.5281/zenodo.3613441
X Mullapäev (World Soil Day) 2019 2020-03-05

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