USGS ScienceSearch

USGS · 70210903

Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey

Abstract

The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation’s greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km 2 . Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as “Integrated Water Science Basins”; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 25.08° to 49.38905° latitude; -124.68721° to -66.96466° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Peter C. Van Metre, Sharon L. Qi, Jeffrey R. Deacon, Cheryl A. Dieter, Jessica M. Driscoll, Michael N. Fienen, Terry A. Kenney, Patrick M. Lambert, David P. Lesmes, Christopher Allen Mason, Anke Mueller-Solger, MaryLynn Musgrove, Jaime A. Painter, Donald O. Rosenberry, Lori A. Sprague, Anthony J. Tesoriero, Lisamarie Windham-Myers, David M. Wolock. 2020-06-27. Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey. https://doi.org/10.1007/s10661-020-08403-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

An alternative process model of preferential contaminant travel times in the unsaturated zone: Application to Rainier Mesa and Shoshone Mountain, Nevada

Simulating contaminant transport in unsaturated zones with sparse hydraulic property information is a difficult, yet common, problem. When contaminant transport may occur via preferential flow, simple modeling approaches can provide predictions of interest, such as the first arrival of contaminant, with minimal site characterization. The conceptual model for unsaturated zone flow at Rainier Mesa and Shoshone Mountain, Nevada National Security Site, establishes the possibility of preferential flow through lithologies between potential radionuclide sources and the saturated zone. After identifying preferential flow as a possible contaminant transport process, we apply a simple model to estimate first arrival times for conservatively transported radionuclides to reach the saturated zone. Simulated preferential flow travel times at Rainier Mesa are tens to hundreds of years for non-ponded water sources and 1 to 2 months for continuously ponded water sources; first arrival times are approximately twice as long at Shoshone Mountain. These first arrival time results should then be viewed as a worst-case scenario but not necessarily as a timescale for a groundwater-contamination hazard, because concentrations may be very low. The alternative approach demonstrated here for estimating travel times can be useful in situations where predictions are needed by managers for the fastest arrival of contaminants, yet budgetary or time constraints preclude more rigorous analysis, and when additional model estimates are needed for comparison (i.e., model abstraction).

Nevada

Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m 2 cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r 2 . For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r 2 values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.

Wyoming