Assessing Water Quality Trends and Suspended Sediment Surrogates Above and Below Reservoirs Using High-Frequency Sensors in New Mexico and Southern Colorado | W81EWF 25 SOI 0014

Opportunity Information: Apply for W81EWF 25 SOI 0014

This grant opportunity (Funding Opportunity Number W81EWF 25 SOI 0014) supports a cooperative research and monitoring effort to better understand water quality and sediment behavior in large rivers feeding and exiting several reservoirs managed by SPA in New Mexico and southern Colorado. The work centers on keeping a long-term, real-time network of high-frequency monitoring stations running year-round, then turning those continuous records into usable public datasets and scientific findings that help explain how reservoirs are influenced by upstream conditions, sediment deposition, and major watershed disturbances like droughts, floods, and wildfires.

The core deliverable is operation and maintenance of eleven real-time water-quality monitoring stations located upstream and downstream of key SPA-managed reservoirs. At each station, the project will continuously measure five standard water-quality parameters using multi-parameter sondes (YSI instruments): temperature, specific conductance (conductivity), dissolved oxygen, pH, and turbidity. The vendor is expected to follow established USGS procedures for servicing and quality assurance, with field maintenance occurring roughly every 4 to 6 weeks. The continuous time-series data must be reviewed, corrected as needed, and formally approved using USGS methods, and managed within Aquarius Time-Series software. A notable requirement is that the vendor must purchase and maintain the Aquarius system and provide SPA with remote access, while also keeping site telemetry, data acquisition systems, and photovoltaic power systems (where installed) functioning so data flow remains reliable across all sites.

A major emphasis is open, well-documented data delivery. The opportunity requires that continuous datasets be made publicly available via CUAHSI HydroShare, with robust metadata and documentation that follow FAIR principles (findable, accessible, interoperable, reusable). That means the award is not just about collecting sensor readings, but also about producing clean, traceable records that others can understand and reuse, including clear descriptions of methods, processing steps, and data provenance.

Scientifically, the project aims to use the high-frequency records to assess episodic, seasonal, and year-to-year trends in water quality, and to link those trends to reservoir impacts under changing climatic and hydrologic conditions. The vendor is expected to propose specific research questions that rely on the sonde network data and can be strengthened with other readily available datasets. The intent is to generate findings that matter for both reservoir management and the broader research community, particularly around how altered flow regimes, point and non-point source pollution, and watershed-scale disruptions (especially wildfire) change water quality and sediment delivery.

Sediment monitoring is a central theme, driven by the practical challenge that traditional suspended sediment gaging is expensive and has declined in many places. This project leans on a lower-cost approach: using continuous turbidity and streamflow data as surrogates to compute high-frequency suspended sediment concentrations and loads entering and leaving reservoirs. To calibrate and validate those surrogate relationships, the vendor must also collect discrete suspended sediment samples quarterly and during high-flow events, specifically to capture variability and extremes. Those samples are to be analyzed for total suspended sediment using standard methods and paired with the continuous sensor record to improve sediment load estimates.

The opportunity is also candid about turbidity sensor limitations and why additional analysis is needed. Turbidity sensors can foul or malfunction during sediment-laden events, different sensor models can produce different readings even in the same waterbody due to optical design differences, and monsoon-driven conditions can exceed sensor measurement ranges. Post-fire water can be especially problematic: ash and charcoal can absorb light in ways that cause turbidity readings to drop artificially (even toward zero) despite very high sediment conditions. Because other sonde parameters (like conductivity, dissolved oxygen, and pH) may remain measurable and informative during these events, the project encourages using the full multi-parameter dataset to better interpret sediment behavior when turbidity alone becomes unreliable.

To address these challenges, the project includes a machine learning component. The vendor will apply machine learning methods to high-frequency streamflow, turbidity, and other continuous water-quality variables to identify patterns and develop or test surrogate signals for turbidity and sediment loading across additional reservoirs. This builds on prior work cited in the opportunity where machine learning was used to estimate sediment delivery to Conchas Reservoir and reconstruct turbidity behavior around major wildfire impacts. The expectation is that the vendor will extend that type of analysis across the broader station network to improve understanding of sediment drivers and potentially reduce dependence on turbidity measurements alone when they fail or become biased.

Reporting and publication are required outcomes, not optional extras. The vendor and SPA will co-develop annual reports summarizing conditions above, within, and below each reservoir using both project-collected and other relevant water quality and quantity datasets. In addition, major findings are expected to be written up as technical products suitable for peer-reviewed journal publication, with the vendor responsible for publication fees. In other words, the award supports an operational monitoring network, but it is structured to produce publishable science and practical reservoir-management insights.

The monitoring locations span multiple river-reservoir systems in Colorado and New Mexico, with stations placed above and below certain reservoirs to capture how conditions change across each impoundment. Listed sites include the Arkansas River above and below John Martin Reservoir (CO), the Purgatoire River above and below Trinidad Reservoir (CO), the Canadian River above Conchas Reservoir (NM), the Pecos River above and below Santa Rosa Reservoir (NM), the Rio Chama above and below Abiquiu Reservoir (NM), the Rio Grande above Cochiti Lake (NM), and the Rio Grande downstream of the confluence with the Rio Jemez (NM). This upstream/downstream design is meant to quantify how reservoirs alter water quality and sediment transport, and how much sediment is delivered to reservoirs versus exported downstream.

From an applicant standpoint, the opportunity is geared toward teams with deep, hands-on experience running multi-year, continuously deployed sensor networks in large rivers, ideally in the sediment-rich systems typical of the U.S. Southwest. Preferred qualifications include proven capability integrating discrete sampling with continuous sensor and streamflow records, experience performing rigorous data review and approval workflows in Aquarius (or similar platforms), and a track record of publishing peer-reviewed work using long-term high-frequency water-quality datasets. SPA will provide certain monitoring equipment and materials (referenced as Appendix A), while the vendor must secure whatever additional supplies are needed to deliver the project successfully.

Administratively, this is a discretionary cooperative agreement under CFDA 12.630 (Science and Technology and other Research and Development), issued by the Engineer Research and Development Center. It anticipates a single award with an award ceiling of $150,000, and it is restricted to non-federal partners of the Colorado Plateau Cooperative Ecosystems Studies Unit (CESU). The original closing date is listed as 2025-08-11.

• The Engineer Research and Development Center in the science and technology and other research and development sector is offering a public funding opportunity titled "Assessing Water Quality Trends and Suspended Sediment Surrogates Above and Below Reservoirs Using High-Frequency Sensors in New Mexico and Southern Colorado" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.630.
• This funding opportunity was created on 2025-06-26.
• Applicants must submit their applications by 2025-08-11. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Each selected applicant is eligible to receive up to $150,000.00 in funding.
• The number of recipients for this funding is limited to 1 candidate(s).
• Eligible applicants include: Others.

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