The Power of Environmental Observatories for Advancing Multidisciplinary Research, Outreach, and Decision Support: The Case of the Minnesota River Basin

Karen B Gran, C. Dolph, A. Baker, M. Bevis, S. J. Cho, J. A. Czuba, Brent J Dalzell, M. Danesh-Yazdi, A. T. Hansen, S. Kelly, Z. Lang, J. Schwenk, P. Belmont, Jacques C Finlay, P. Kumar, S. Rabotyagov, Gillian H Roehrig, P. Wilcock, E. Foufoula-Georgiou

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Observatory-scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation-funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes-Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south-central Minnesota, USA, a 43,400-km2 tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change, allowing for transdisciplinary research into the complex cascade of environmental changes that occur due to hydrology and land use alterations from intensive agricultural management and climate change. Data sets collected include water chemistry and biogeochemical data, geochemical fingerprinting of major sediment sources, high-resolution monitoring of river bluff erosion, and repeat channel cross-sectional and bathymetry data following major floods. The data collection efforts led to development of a series of integrative reduced complexity models that provide deeper insight into how water, sediment, and nutrients route and transform through a large channel network and respond to change. These models represent the culmination of efforts to integrate interdisciplinary data sets and science to gain new insights into watershed-scale processes in order to advance management and decision making. The purpose of this paper is to present a synthesis of the data sets and models, disseminate them to the community for further research, and identify mechanisms used to expand the temporal and spatial extent of short-term observatory-scale data collection efforts.

Original languageEnglish (US)
Pages (from-to)3576-3592
Number of pages17
JournalWater Resources Research
Volume55
Issue number4
DOIs
StatePublished - Apr 2019

Bibliographical note

Funding Information:
This research was primarily funded by NSF grant EAR‐1209402 under the Water Sustainability and Climate Program (WSC): REACH (REsiliance under Accelerated CHange) as well as NSF Grant EAR‐1331906 under the Critical Zone Observatory (CZO) program. Portions of this research were also funded from the Minnesota Pollution Control Agency, the Minnesota Agricultural Water Resource Center, the Minnesota Department of Natural Resources, and the Minnesota Department of Agriculture through Minnesota's Clean Water Land and Legacy Amendment. Data supporting this paper can be obtained through references cited throughout the paper, which include links to relevant open source data repositories. Public data utilized in the project can be found with links in the supporting information (Table S1).

Funding Information:
data collection efforts are now providing opportunities for integrated research in diverse landscapes throughout the United States. Through the National Science Foundation (NSF)‐funded REACH (REsilience under Accelerated CHange) project as part of the Water Sustainability and Climate initiative, and in collaboration with the NSF‐funded Intensively Managed Landscapes‐Critical Zone Observatory (IML‐CZO), we have undertaken a strategic campaign to collect a series of detailed, multiprocess, multidisciplinary data sets throughout the Minnesota River Basin (MRB) in south‐central Minnesota, USA. Within the MRB, an intensive focus was placed on the Le Sueur River Basin, a key subwatershed that contributes a disproportionately large amount of sediment and nutrients to the Minnesota River (Figure 1; Belmont et al., 2011; Gran et al., 2009; Musser et al., 2009). These targeted data sets were integrated into stream gage and precipitation monitoring networks run by state and federal agencies so that the longer‐term hydrologic data could provide a richer context for the shorter‐term data collection efforts described here. Collectively, these data sets allow for the exploration of interactions and feedbacks between hydrologic and land use change and the geomorphic, geochemical, and biophysical responses in a large intensively managed agricultural basin. Much of this work was done in collaboration with local stakeholders including local, state, and federal resource managers, agricultural interest groups, land owners, scientists, and nonprofits and reflects an intentional, iterative, and participatory process in data collection and model building (Cho et al., 2019; Cho, 2017). In addition, our data collection and modeling efforts were explicitly integrated with the development of educational materials for teachers and high school students in the region, to broaden student participation in scientific inquiry motivated by challenges facing the natural world in their home landscapes.

Funding Information:
This research was primarily funded by NSF grant EAR-1209402 under the Water Sustainability and Climate Program (WSC): REACH (REsiliance under Accelerated CHange) as well as NSF Grant EAR-1331906 under the Critical Zone Observatory (CZO) program. Portions of this research were also funded from the Minnesota Pollution Control Agency, the Minnesota Agricultural Water Resource Center, the Minnesota Department of Natural Resources, and the Minnesota Department of Agriculture through Minnesota's Clean Water Land and Legacy Amendment. Data supporting this paper can be obtained through references cited throughout the paper, which include links to relevant open source data repositories. Public data utilized in the project can be found with links in the supporting information (Table?S1).

Publisher Copyright:
©2019. American Geophysical Union. All Rights Reserved.

Keywords

  • Minnesota River Basin
  • agricultural watersheds
  • intensively managed landscapes
  • observatory data collection
  • reduced complexity modeling

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