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National Water Watch Charter
Rivers, lakes and streams are considered sentinels of environmental change. Deforestation, urbanization, and nutrient runoff are increasingly recognized as drivers of change for freshwaters, yet most research analyzing the impact of these forcings occurs at the watershed scale. While smaller scale studies provide valuable insight into physical processes, few studies describe the vulnerability of inland water quality (WQ) to climate change and anthropogenic activities at larger scales. This type of synthesis knowledge is crucial for informing policy-making, water resources management and conservation, yet is lacking at a national scale. However, advances in cloud-based data analytics has created a new research landscape making possible the rapid analysis of public datasets to monitor changes in surface waters at large spatial and temporal scales.
This project seeks to create a national tool for relating changes in water quality signals to land use and precipitation change, representing a significant step forward for understanding the impact of human activities and climate change on US surface waters. In our data synthesis and visualization system, large datasets will be queried to create simple geovisualizations of historic WQ changes and to establish foundations for distilling broad national patterns related to satellite remote sensing. We hypothesize regions with rapid land use change will also experience shifts in WQ signals.
(1) Build an open-source tool for querying time series data for parameters of interest (pH, carbon, water temperature) held within the National Water Quality Portal (NWQP). Approach: Use a database solution for query implementation by running PostgreSQL and the PostGIS spatial extension built on an Amazon Relational Database Service (RDS) Instance. Outcome: Github-hosted code base for programmatically extracting parameters from the NWQP.
(2) Create a geovisualization website for monitoring changes to WQ at any given US location. Approach: Associate time series data with a mapped coordinates and visualize time series plots on an interactive national map of the United States. Create simple rasters of percent change for each parameter that can be toggled on and off to show change direction for each selected parameter. Outcome: Dynamic map empowering users to better monitor and manage freshwater resources.
(3) Relate time series water quality data to spatial variables using the National Hydrography Dataset. Approach: Use GDAL to call the National Hydrography Dataset (NHD) into our RDS instance and spatially join mapped station points from the NWQP to the NHD delineated streams. Test associations between WQ time series data and changes in vegetation, air temperature, precipitation and percent imperviousness (proxy for urbanization) from the NHD dataset using machine learning algorithms. Outcome: Publication-ready figures depicting changes in selected WQ parameters over time for each major US region and relating those changes to land use and climate parameters from the NHD dataset.
Our project seeks to mine the National Water Quality Dataset for water quality and chemistry relevant for potential remote sensing retrieval. The success of our project will be demonstrated by:
Publication of open-source tool for querying the National Water Quality Database where users can specify a location, time range and parameter types and visualize time series outputs.
A heatmap showing spatial coverage of water quality data for all US major rivers and can identify the density and type of available parameters, thus identifying potential areas for future remote sensing analysis.
Time series of key chemical (pH, temperature, chlorophyll-a, organic carbon) parameters for all major US rivers.
Raster maps identifying areas of rapid changes in water quality.
Weeks 1-2: Identify variables and regions of interest, build REST Query, create S3 bucket to store data
Weeks 3-4: Mine NWQP data out of to reshape time series out of variables of interest.
Week 5-6: Design an EC2 instance to pull in NHD and NWQP data and do a spatial join to map time series on top of points.
Week 7-8: Test time series for trends
Week 9-10: Visualize changes for each region for each parameter through time.