NASA · NASA-897 · record R0495

Hydrology Copilot

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: Software development assistance, Search and knowledge retrieval, Forecasting and prediction Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

•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.

System outputs

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

Technology

AI classification: Agentic AISource: Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported