3 Scientific Tools to Help You Better Understand Your Water Quality

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Courtesy of Adedge Water Technologies, LLC

There are many ways of testing and understanding your water quality with online scientific tools and models. One such resource is the National Water-Quality Assessment (NAWQA) project run by the U.S. Geological Survey (USGS). The project, which was founded in 1991 to develop reliable information about water sources to support decisions on water quality management and policy at every governmental level, uses several different types of modeling and analysis tools to estimate nutrient fluxes, pesticide concentrations and even understand sources of contaminants.

The more you know about what these models measure and how they are used, hopefully the better you can understand your own water quality.

SPARROW Modeling
SPAtially-Referenced Regression On Watershed attributes, or SPARROW, is a model developed by USGS scientists to predict long-term average values of water characteristics. The models estimate the amount of a contaminate from inland watersheds to larger bodies of water by linking monitoring data with information about the watershed characteristics and the sources of the contaminant.

SPARROW models predict load (mass per time) for all stream reaches in a modeling reaches, and can also be modified to predict constituent yields, concentrations and source contributions to stream loads.

MODFLOW
MODLOW is considered an international standard for simulating and predicting groundwater conditions and interactions between groundwater and surface water. Developed in 1984 as a groundwater-flow simulation code, it now is a whole family of programs that can simulate coupled groundwater/surface water systems, solute transport, variable-density flow, aquifer system compaction and land subsidence, parameter estimation and groundwater management.

WARP Models
Watershed Regression for Pesticides (WARP) models are statistical/geographic information system (GIS) hybrids used to estimate pesticide concentrations for unmonitored streams throughout the United States. The models are developed using linear regression methods to establish quantitative linkages between the concentrations of pesticides measured at sites and the human and natural factors that affect pesticides in streams. These factors include pesticide use, soil characteristics, hydrology and climate.

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