Development of a Binational Geospatial Decision Support System to Protect Water Quality in the Lower Rio Grande: An Innovative Use of Open Source Geographic Information System Software
Keywords
Geospatial Decision Support System, Water Quality Modeling, Open Source Software, Lower Rio Grande/Río Bravo, Surface Water Quality Modeling, Nonpoint Source Pollutant Loading, Wastewater Infrastructure, Python-based Application, QGIS, SQLite
Start Date
26-6-2018 3:40 PM
End Date
26-6-2018 5:00 PM
Abstract
Development of a Binational Geospatial Decision Support System to Protect Water Quality in the Lower Rio Grande: An Innovative Use of Open Source Geographic Information System Software
Alexander Sun1 and Roger M. Miranda2
The Lower Rio Grande/Río Bravo downstream of Falcon Reservoir suffers from number of persistent water quality problems, including high levels of fecal bacteria and increasing salinity levels. Realizing the need to address water quality concerns in the river in an integrated fashion, the governments of the US and Mexico created a binational forum for cooperation and information exchange under the auspices of the International Boundary and Water Commission. The effort, known as the Lower Rio Grande/Río Bravo Water Quality Initiative (LRGWQI), is designed to establish binational mechanisms to protect water quality in the river. Among the technical goals of the LRGWQI is the modeling of pollutant loadings and instream water quality in the river, including simulation of scenarios associated with changing population, infrastructure and management practices. To facilitate binational decision making, UT’s Bureau of Economic Geology, in collaboration with the UT’s LBJ School of Public Affairs, developed a geospatial decision support system in Python. The GIS functionalities are enabled using the Python-based Application Programming Interface of QGIS, an open source GIS application. SQLite, a serverless, self-contained database system is used for managing user-specified and system configuration data. We present the principal capabilities of the LRGWQI DSS, discuss some of the challenges we encountered using the QGIS software and present some of the modifications and additions we are currently incorporating into the system.
1Bureau of Economic Geology, The University of Texas at Austin
2Lyndon B. Johnson School of Public Affairs, The university of Texas at Austin
Development of a Binational Geospatial Decision Support System to Protect Water Quality in the Lower Rio Grande: An Innovative Use of Open Source Geographic Information System Software
Development of a Binational Geospatial Decision Support System to Protect Water Quality in the Lower Rio Grande: An Innovative Use of Open Source Geographic Information System Software
Alexander Sun1 and Roger M. Miranda2
The Lower Rio Grande/Río Bravo downstream of Falcon Reservoir suffers from number of persistent water quality problems, including high levels of fecal bacteria and increasing salinity levels. Realizing the need to address water quality concerns in the river in an integrated fashion, the governments of the US and Mexico created a binational forum for cooperation and information exchange under the auspices of the International Boundary and Water Commission. The effort, known as the Lower Rio Grande/Río Bravo Water Quality Initiative (LRGWQI), is designed to establish binational mechanisms to protect water quality in the river. Among the technical goals of the LRGWQI is the modeling of pollutant loadings and instream water quality in the river, including simulation of scenarios associated with changing population, infrastructure and management practices. To facilitate binational decision making, UT’s Bureau of Economic Geology, in collaboration with the UT’s LBJ School of Public Affairs, developed a geospatial decision support system in Python. The GIS functionalities are enabled using the Python-based Application Programming Interface of QGIS, an open source GIS application. SQLite, a serverless, self-contained database system is used for managing user-specified and system configuration data. We present the principal capabilities of the LRGWQI DSS, discuss some of the challenges we encountered using the QGIS software and present some of the modifications and additions we are currently incorporating into the system.
1Bureau of Economic Geology, The University of Texas at Austin
2Lyndon B. Johnson School of Public Affairs, The university of Texas at Austin
Stream and Session
C14: Towards Interdisciplinary and Transdisciplinary Collaboration in Environmental Modelling: Innovative Practices to Address Wicked Problems