A data warehouse to explore multidimensional simulated data from a spatially distributed agro-hydrological model to improve catchment nitrogen management. (November 2017)

• Record Type: Journal Article
• Authors: Bouadi, Tassadit; Cordier, Marie-Odile; Moreau, Pierre; Quiniou, René; Salmon-Monviola, Jordy; Gascuel-Odoux, Chantal
• Is Part Of: Environmental modelling & software. Volume 97(2017)
• Page Start: 229
• Page End: 242
• Publication Date: 2017-11
• Subjects: Multidimensional modeling -- Simulation data -- Data warehouse -- OLAP -- Water quality -- Nitrogen -- Catchment -- Distributed agro-hydrological model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.07.019
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Spatially distributed agro-hydrological models allow researchers and stakeholders to represent, understand and formulate hypotheses about the functioning of agro-environmental systems and to predict their evolution. These models have guided agricultural management by simulating effects of landscape structure, farming system changes and their spatial arrangement on stream water quality. Such models generate many intermediate results that should be managed, analyzed and transformed into usable information. We describe a data warehouse (N-Catch) built to store and analyze simulation data from the spatially distributed agro-hydrological model TNT2. We present scientific challenges to and tools for building data warehouses and describe the three dimensions of N-Catch: space, time and an original hierarchical description of cropping systems. We show how to use OLAP to explore and extract all kinds of useful high-level information by aggregating the data along these three dimensions and how to facilitate exploration of the spatial dimension by coupling N-Catch with GIS. Such tool constitutes an efficient interface between science and society, simulation remaining a research activity, exploration of the results becoming an easy task accessible for a large audience. Highlights: A data warehouse (DW) as a tool to explore simulated agro-environmental data. N-Catch as an example of a DW for analyzing N emissions across a catchment. DWs for catchment N management.