Spanning across a wide range of climatic regimes, Texas relies on water extracted from rivers and aquifers within its own borders. Climate change and population growth are adding unprecedented stress on Texas waters and other types of natural resources. Quantifying the nexus between natural resources, energy generation, and food production is critically important for sustainable regional planning. This need also echoes one of the primary pillars of PT2050—Understanding the spatiotemporal dynamics of water availability and other natural resources, in relation to energy production and urban demands.
Data science for geoscience is still at its nascence. Currently, natural resources-related datasets for Texas are scattered across disparate data sources and in heterogeneous spatial and temporal resolutions and formats. Frictions related to the extraction, processing, and interpretation of data hinder timely decision-making activities using this valuable natural resource information. This is especially true for many domain users who may not have the necessary training in using contemporary data tools. The main objectives of this Data Imputation, Scaling, and Homogenization (DISH) project were thus two folds. First, we aimed to develop and expand toolsets (on top of the effort that already started under the PT2050 Water Averaging project) for automating data processing pipelines. Second, we aimed to demonstrate the developed toolsets through a number of meaningful use cases related to both natural resources and socioeconomics by using an integrated Web platform.
Specifically, this project consisted of the following major tasks:
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Identify and collect natural resources data for Texas for use in specific use cases
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Develop and test a suite of algorithms/scripts for performing DISH
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Demonstrate the utility of the developed DISH framework on several use cases at both the local and regional scales