WaMDaM source code and instructions to use it and replicate its results
|Resource type:||Composite Resource|
|Storage:||The size of this resource is 2.2 MB|
|Created:||Aug 13, 2018 at 10:28 p.m.|
|Last updated:||Feb 20, 2019 at 2:31 a.m. by Adel Abdallah|
|Citation:||See how to cite this resource|
The Water Management Data Model (WaMDaM) is a database design with companion software that uses contextual metadata and controlled vocabularies to organize water management data from multiple sources and models. The design addressed the problem of using multiple methods to query and analyze water management data to identify input data to develop or extend a water management model. The consistent design allows modelers to query, plot, compare data, and choose input data and serve it to run models.
The instructions here will help you to replicate the process to i) use the WaMDaM Wizard to load 13 different water management datasets into a WaMDaM SQLite database file, ii) execute SQL and Python to query, compare, and plot example data analysis in four use cases to choose it as input to a model in the Bear River Watershed, USA, iii) serve the selected data for a fifth use case into a Water Evaluation and Planning system (WEAP) model in the Bear River Watershed.
Follow this live and public Jupyter Notebook for instructions and to replicate the use cases
Active development of WaMDaM software continues at the WaMDaM project on GitHub at https://github.com/WamdamProject
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation (NSF)||CI-WATER, Cyberinfrastructure to Advance High Performance Water Resource Modeling||1135482|
|National Science Foundation (NSF)||iUTAH-innovative Urban Transitions and Aridregion Hydro-sustainability||1208732|
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