Title: An R Decision Support Framework for the Identification of BMP in Catchments
Publisher: useR!2017
Publication Year: 2017
JRC N°: JRC106946
URI: https://user2017.brussels/
Type: Articles in periodicals and books
Abstract: The work presents and illustrates the application an R framework designed for Decision Making (DM), related to the identification of Best Management Practices (BMPs), for restoring and protecting the good ecological status of freshwater bodies without reducing the farmer’s income. R-SWAT-DM combines the use of the SWAT watershed model, the spatial representation of BMPs and an economic component. The SWAT model served as the nonpoint source pollution estimator for current conditions (base line) as well as for scenarios with modified agricultural practices (fertilization and irrigation) and PS nutrient concentrations, after considering waste water treatment upgrading. R-SWAT-DM easily communicates with the SWAT and economic model through simple ASCII files and/or wrapper functions for exchanging information. It includes tools, to launch individual or iterative BMPs simulations or search for optimal strategies. The current version integrates the state of the art in mono and multi-objective optimization libraries that were already implemented in R. It also includes advanced plotting, mapping and statistical analysis functionalities to facilitate the interpretation and assessment of the results. We illustrate the application of R-SWAT-DM in two real-world case study: the Upper Danube Basin and the Crete Island. In both cases the framework is able to identify management solutions that provides important nutrient reduction (Upper Danube), important water savings (Crete Island) and increase global income (both cases). R has proven to be a great platform for this development, since taking advantage of routines and libraries available in CRAN development times have been very short, in addition to allowing to try different alternatives, greatly facilitate the communication with the data or allow multiple possibilities to visualize and analyse the results. We are currently working on the preparation of the R package from the framework described in this paper
JRC Directorate:Sustainable Resources

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