Identify Data Gaps and Collect Additional Data

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Watershed Management Process
Build Partnerships
Characterize the Watershed
Set Goals and Identify Solutions
Design an Implementation Program
Implement the Watershed Plan
Measure Progress and Make Adjustments

One of the most difficult challenges in watershed management is understanding when you have enough data to identify relationships between impairments and their sources and causes. There will always be more data to collect, but you need to keep the process moving forward and determine whether you can reasonably characterize watershed conditions with the data you have. Once you have gathered all the necessary data related to the watershed goals identified by your stakeholders, you must examine the data to determine whether you can link the impairments seen in the watershed to the causes and sources of pollutants.

Ask the following questions

  • Do I have the right types of data to identify causes and sources?
  • What is the quality of the data?

The answers to these questions will tell you whether you need to collect additional data before proceeding with data analysis. Several different types of data gaps might require you to collect additional information. Further information on data gaps (informational, temporal, and spatial) is available in [ Chapter 6

Data Quality and Measurement Quality

<p>Data are often available in various types from different sources and collected for a wide range of purposes. The acceptability of this data should be examined before used in your analyses. Data acceptability is determined by comparing the type and quality of data with the minimum criteria necessary to address the monitoring questions of interest. For each data source, focus on two areas: data quality and measurement quality. Data quality pertains to the purpose of the monitoring activity, the types of data collected, and the methods and conditions under which the data were collected. These characteristics determine the applicability of the data to your watershed management effort and the decisions that can be made on the basis of the data. Measurement quality describes data characteristics like accuracy, precision, sensitivity, and detection limit. These are critical issues for any monitoring activity, and you should consider them in detail when you design your own data collection program.

Reevaluate your data

At this point, you have collected existing data for your watershed, assessed its quality and relevance, and identified gaps. Compare your available resources against your tasks:

  • Can we identify and quantify the water quality problems in the watershed?
  • Can we quantify pollutant loads?
  • Can we link the water quality impairments to specific sources and source areas in the watershed?
  • Do we know enough to select and target management measures to reduce pollutant loads and address water quality impairments?

If you were able to answer yes to each of these questions, congratulations! You are ready to move on to the next phase and begin data analysis. If you answered no to any of them, the next step is to come up with a plan to fill the gaps. If you have determined that additional data must be collected to complete your watershed characterization, you should develop a sampling plan. Sampling plans often include the collection different types of data, so the data collection techniques should be carefully selected to ensure that new data is useful to meet project goals.

For more information on designing a sampling plan, see EPA's Guidance on Choosing a Sampling Design for Environmental Data Collection (PDF, 1.02 Mb, 178 pp.) and EPA's Quality Management Tools Web site at

Useful Tools

Also, using a mathematical model for your watershed is a very helpful way to identify gaps in the data. Models will have specific data needs that can be used as a starting point for identifying data needed to meet goals.