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  5. ›Hindcasting/Forecasting Dixon Glacier Runoff Trends

Awarded contract·Alaska Energy Authority·Alaska

Hindcasting/Forecasting Dixon Glacier Runoff Trends

Won by Alaska Pacific University$250kSingle Award
Contract source

Awarded vendors

  • Alaska Pacific University

Description

This contract tasks a vendor with hydrologic and climate analysis of Dixon Glacier to support potential hydropower development. The work includes installing a meteorological station, collecting and processing glacier runoff data, calibrating a Distributed Enhanced Temperature Index Model, hindcasting historic runoff (1993‑present), and forecasting future runoff (to 2100) using downscaled climate scenarios. All raw data and a comprehensive final report are to be delivered by spring 2024. The solicitation is a cost‑reimbursement professional‑services RFQ (23101) issued by the Alaska Energy Authority, with a single award to Alaska Pacific University. • Field installation of a meteorological station on Dixon Glacier (spring 2023) • Data analysis, model calibration (DETIM), hindcasting (1993‑present) and forecasting (to 2100) using SNOTEL and SNAP climate data • Deliverables: draft and final technical reports plus complete raw datasets, all due by spring 2024.

Attached files

Key dates

Bids closed
3/16/2023
Duration
12 months

Last updated

Details

Award type
Single Award
Procurement
Full and Open Competition
Industry
Engineering Services
NAICS
541330
Solicitation №
RFQ 23101

Contract value

$250,000

The solicitation does not disclose a definitive ceiling (“$XXXXXX” placeholder). Based on comparable Alaskan state research contracts for glacier monitoring and modeling—typically ranging from $150,000 to $350,000 for a one‑year scope that includes field installation, data collection, modeling, and a final report—a reasonable midpoint estimate would be roughly $250,000. This accounts for equipment, travel to a remote site, personnel (scientists, technicians), permitting costs, and overhead associated with a cost‑reimbursement contract.

AI estimate from the award documents

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