Integrating Social and Biophysical Indicators of Nutrient Reduction Progress in Iowa Watershed Projects
Because there has been limited empirical research on the factors that influence the progress or effectiveness of watershed projects that aim to reduce nutrient loss, this study will address the research question: On a watershed project scale, what are the primary indicators for predicting nutrient reduction effectiveness? These indicators may fall into a variety of operational categories, including availability of funding and financial incentives, participation by diverse stakeholder groups, extent of outreach work among farmers and the wider community, and the extent of conservation practice use.
This study will explore the factors that impact nutrient reduction in watershed project areas at the scale of hydrologic unit code-12 (HUC12) watersheds. The proposed research will accomplish the following outcomes:
- Inventory and compile publicly available data sources measuring watershed project efforts and outcomes;
- Inventory and compile relevant social (e.g. survey research) and biophysical (e.g. land use) data;
- Build datasets for analysis;
- Assess relationships between the identified indicators and modeled nutrient reduction progress on a watershed project scale. In the case that indicators are identified as significant predictors of nutrient reduction progress, indicators will be ranked in order of effect size.
This research will examine watershed projects that were funded through the Iowa Water Quality Initiative, known as the WQI. A subset of key watershed projects will be selected, based on data availability and quality. Additional watershed projects will be incorporated into the research project depending on investigators’ capacity to process the data sources associated with these watershed areas.
A subset of about 20 variables are expected for use in statistical analyses and models, which represent potential indicators of watershed project effectiveness and can be extracted from social and biophysical data sources. Additional data sources may also be identified in the early phases of the project. Modeling will be performed to examine potential contributing effects of financial, social and environmental factors on changes in “watershed performance,” based on nutrient loss reductions. In addition, multi-level modeling or spatial analyses may be performed to assess relationships between nutrient reduction progress of watershed projects and their respective, larger HUC8 watersheds.
Upon finalizing the project, the investigators will share information from this project through development of a technical report, submission to one or more scientific journals, a presentation to a future Watershed Academy, and other outreach and extension-related activities.
The team met three times in-person and plans to meet regularly in 2020 to:
- Develop internal team website in Google Cloud and email list-serve.
- Build out the team to include staff and students. A team member was added through a PSCA (contract) for data ingestion and standardization efforts. An undergraduate student was hired for the fall semester, and a statistics graduate student will begin spring semester.
- Assign priority and secondary status to HUC8 watersheds to prioritize data acquisition, translation, and QC. Begin working on priority watersheds; 8 priority watersheds and 8 secondary.
- Develop project maps for use in presentations, reports, etc.
- Map out social and biophysical data sources in terms of location, type, availability and status. At this point, there are 18 data sources identified, although a few are optional based on their quality and completeness.
- Obtain annual progress reports for priority watersheds from IDALS. Reviewed variability across watersheds in reporting structure, consistency of variables reported. Determined how to structure data into spreadsheets for analyses.
- Interpreted, translated and standardized unstructured data from IDALS reports into structured dataset for two watersheds to-date: Boone and Floyd River. Constructed a data dictionary for this data source to be merged into master data dictionary.
- Extracted BMP data for all watersheds from BMP Mapping Project for conservation practices mapped such as terraces, wetlands, grassed waterways, etc. Constructed a data dictionary for this data source to be merged into master data dictionary.
- Begin developing short, medium and long-term research questions.