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OMB Individually Reported

Hydrology Copilot

Low riskExact public inventory row

Description

We are building a multi-agent AI copilot that lets users explore and apply the new 1-km, hourly North American Land Data Assimilation System (NLDAS) version 3 dataset through natural-language queries. The copilot—built on NASA Earth Copilot with an agentic Retrieval-Augmented Generation (RAG) stack (Azure AI Search/Foundry + Synapse)—explains variables, retrieves relevant data and workflows, and guides users from question to analysis. It lowers the barrier for scientists, planners, and decision-makers to use NLDAS-3 for drought monitoring, flood assessment, and agricultural risk forecasting.

Detailed example

• Web copilot app (chat + map): variable discovery, data subsetting, previews, and downloads • Auto-generated plots/maps (time series, anomaly maps)

AI / analytics pattern

Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.

Automation level / stage

a) Pre-deployment – The use case is in a development or acquisition status.

Expected benefit

•Time-to-insight : converts plain-language questions into data queries, plots, and subsets in minutes. •Accessibility: non-experts can discover variables/units and relevant documentation without deep tooling knowledge. •Decision support: speeds drought/flood/ag risk assessments by surfacing the right NLDAS-3 variables and spatial/temporal subsets.

Controls / human review

ATO: Not reported; PIA: Not published

Source

OMB 2025 Federal Agency AI Use Case Inventory

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