ABSTRACT:

The Measurement Infrastructure Gap Analysis in Utah’s Great Salt Lake Basin was a comprehensive inventory and analysis of existing diversion and stream measurement infrastructure along 19 primary river systems, as well as a preliminary investigation of measurement infrastructure gaps around Great Salt Lake proper. The purpose of this “Gap Analysis” was to develop methods to identify and prioritize areas throughout the Great Salt Lake basin where new or updated measurement infrastructure is needed to serve diverse objectives. The following gaps were identified: (1) existing measurement infrastructure quality and completeness gaps, (2) stakeholder identified gaps, and (3) potential spatial gaps in hydrologic understanding. By adapting the prioritization schema originally presented in the Colorado River Metering and Gaging and Gap Analysis to equally weight these three gap types at the HUC12 scale, a flexible framework for prioritizing new or updated measurement infrastructure in areas with large cumulative measurement gaps was developed, and high, medium, and low priority HUC12s were identified.

Results showed that 250 diversion and 28 stream measurement devices along primary systems had at least one quality and/or completeness gap. The most common quality and completeness gaps were insufficient device types, lack of telemetry, and data record interval. Stakeholders suggested 50 instances of new or updated diversion measurement infrastructure, 95 instances of new or updated stream measurement infrastructure, and 39 recommendations for continued funding of existing measurement infrastructure—totaling 185 stakeholder-identified gaps. To provide a spatially consistent approach to identifying potential gaps in hydrologic understanding, geospatial datasets describing features or attributes that can impact local hydrology were used to identify measurement gaps at the HUC12 scale. Among HUC12s that overlapped with the river systems included in this analysis, HUC12s with the greatest number of potential spatial gaps were in the Bear River sub-basin and near reservoirs in the Weber River sub-basin.

Based on the prioritization schema applied to synthesize these three gap types, there were 52 HUC12s along primary systems classified as high priority for measurement improvement. Of the 250 existing diversion and 28 stream measurement devices with at least one quality and/or completeness gap, 217 and 10 devices, respectively, were located within high priority HUC12s. Most stakeholder-identified gaps identified in the Weber and Jordan River sub-basins also fell within high-priority HUCs. Eighteen unique agencies suggested new or updated measurement infrastructure or continued funding of existing measurement infrastructure in high-priority HUC12s, demonstrating some consensus regarding measurement gaps in critical areas. There were 6 high priority HUC12s with no existing measurement infrastructure quality and completeness gaps, and 11 high priority HUC12s with no stakeholder-identified gaps. High priority HUC12s highlighted only due to potential spatial gaps may warrant additional investigation to further understand potential measurement gaps in these HUC12s.

Because the prioritization schema applied equally weighted all three gap types, it likely does not fully represent the diverse missions and priorities of different stakeholder groups. To facilitate an adaptable approach to prioritize measurement gaps within the Great Salt Lake basin, raw data for each of the three gap types are provided to allow varied prioritization schemes to be developed by weighting gap types differently or considering subsets of data. These data provide the basis for stakeholders within the Great Salt Lake basin to collectively prioritize future investments in gaging infrastructure and better manage water throughout the Great Salt Lake basin.

ABSTRACT:

Data and code referenced by Turney et al. (2024). Article abstract:

Aquatic habitat suitability models are increasingly coupled with water management models to estimate environmental effects of water management. Many types of habitat models exist, but there are no standard methods to compare predictive performance of habitat model types for use with water management models. In this study, we compared three common aquatic habitat model types: a hydraulic-habitat model, a habitat threshold model, and a geospatial model. Each of the models predicted native Bonneville Cutthroat Trout distribution in the Bear River Watershed (Utah, Idaho, and Wyoming, USA) at a monthly timestep. We compared the differences in predictive performance among models by validating 1) environmental predictors of the models with field observations from summer 2022, using the coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE) index, and percent bias (PBIAS) and 2) habitat suitability estimates generated by each model with fish presence data and three accuracy metrics developed for this study. Validation of environmental predictors revealed observed conditions were not well represented by any of the three models—a function of either outdated, incorrect, or over-generalized input data. Validation of habitat suitability predictions using Bonneville Cutthroat Trout presence data showed the habitat threshold model most accurately classified fish presence observations in suitable habitat, but suitable habitat was likely overpredicted. While more precise habitat modeling methods may be useful to support generalized habitat estimates for native fish, overall, simple models, like the habitat threshold model, are promising for incorporating ecological objectives into water management models.