Abstract
Investments in watershed services programs hold the promise to protect and restore ecosystems and water resources. The design and implementation of such programs is often accompanied by hydrologic modeling and monitoring, although the role of hydrologic information in meeting the needs of program managers remains unclear. In the Camboriú watershed, Brazil, we explored the value of hydrologic modeling and monitoring with respect to two dimensions: scientific credibility and use of generated knowledge in the design, implementation, and evaluation of the watershed management program. We used a combination of semi-structured interviews, focus groups, and hydrologic modeling under various levels of data availability to examine when improved models and data availability might build credibility and provide more useful information for decision makers. We found that hydrologic information was not actually used for the detailed design, but rather contributed to broad-scale support of the program by increasing scientific credibility. Model sophistication and data availability improved the credibility of hydrologic information but did not affect actual decisions related to program design. Hydrologic monitoring data were critical for model calibration, and high-resolution land use and land cover data, obtained via remote sensing, affected some model outputs which were not used to design the program. Our study suggests that identifying how hydrologic data will inform decision making should guide the level of effort used in hydrologic modeling and monitoring.
| Original language | English (US) |
|---|---|
| Article number | 135871 |
| Journal | Science of the Total Environment |
| Volume | 705 |
| DOIs | |
| State | Published - Feb 25 2020 |
Bibliographical note
Funding Information:The study benefited from data generated under grants to Camboriú WPP program partners from the Inter-American Development Bank and the U.S. Agency for International Development . Hamel, Bremer, Ponette-González, Steele, and Brauman were supported by the Belmont Forum collaborative research (NSF # 1624329 to Brauman, and NSF # 1624407 to Ponette-González). This is contributed paper WRRC-CP-2020-07 of the Water Resources Research Center, University of Hawaii at Manoa, Honolulu, Hawaii. We are grateful to Paulo Petry of The Nature Conservancy for supporting this research with his time and expertise. Patricia Pompeu and Susan Seehusen assisted with stakeholder interviews and Eric Wilburn carried out landholder focus groups. We thank our stakeholder interviewees and supporters: Kelly Dacol and Rafaela Comparim Santos from EMASA, Pedro Francez (City Council of Camboriú); Carla Rosana Krug and Mauro Eichler (FUCAM- Muncipality of Camboriú); and Patricia Zimmerman (Municipality of Balneario-Camboriú). We dedicate this work to Christof Schneider, our colleague and friend who will be greatly missed. Appendix A Table A1 Summary of InVEST and SWAT input data. Table A1 Input Sources Climate inputs SWAT : Precipitation data from five weather stations ( Fisher et al., 2018 ) including Louro station InVEST : Erosivity calculated from two sources: i) national average over the 1980–2013 period ( Xavier et al., 2016 ), converted to erosivity with a relationship by Oliveira et al. (2012) ; ii) local hourly precipitation in 2016 (from Louro weather station), converted to erosivity following the methods described in Section 3.4 of the main text Soil erodibility raster Both models : Data provided by Santa Catarina state used in the SWAT model ( Fisher et al., 2018 ) Digital elevation model raster Both models : 1 m data from Secretariado Desenvolvimento Econômico Sustentável ( Fisher et al., 2018 ) InVEST : 1 m data resampled to 10 m and pit-filled in Qgis ( Wang and Liu, 2006 ); 30 m data from SRTM and pit-filled. LULC raster Both models : 30 m data from Landsat8 SWAT : 1 m data obtained from Worldview 2 imagery ( Fisher et al., 2018 ) InVEST : 10 m data resampled from the 1 m Worldview 2 data used in the SWAT model C and P factors Both models: literature values used in the SWAT model (Fisher et al.,2018) Appendix B
Funding Information:
The study benefited from data generated under grants to Cambori? WPP program partners from the Inter-American Development Bank and the U.S. Agency for International Development. Hamel, Bremer, Ponette-Gonz?lez, Steele, and Brauman were supported by the Belmont Forum collaborative research (NSF #1624329 to Brauman, and NSF #1624407 to Ponette-Gonz?lez). This is contributed paper WRRC-CP-2020-07 of the Water Resources Research Center, University of Hawaii at Manoa, Honolulu, Hawaii. We are grateful to Paulo Petry of The Nature Conservancy for supporting this research with his time and expertise. Patricia Pompeu and Susan Seehusen assisted with stakeholder interviews and Eric Wilburn carried out landholder focus groups. We thank our stakeholder interviewees and supporters: Kelly Dacol and Rafaela Comparim Santos from EMASA, Pedro Francez (City Council of Cambori?); Carla Rosana Krug and Mauro Eichler (FUCAM- Muncipality of Cambori?); and Patricia Zimmerman (Municipality of Balneario-Cambori?). We dedicate this work to Christof Schneider, our colleague and friend who will be greatly missed.
Publisher Copyright:
© 2019 The Authors
Keywords
- Decision support
- InVEST
- SWAT
- Uncertainty
- Watershed management
PubMed: MeSH publication types
- Journal Article
