Gauge Networks
Collect valuable data, but don't provide a clear operating picture on their own.
Prepared for U.S. Geological Survey
AquaMesh helps the U.S. Geological Survey (USGS) move from fragmented gauge data and manual review to one operating layer for streamflow monitoring, water level trends, risk scoring, forecasting, and AI-generated operational summaries.
Watershed teams often manage large monitoring networks with limited staff. Stream gauges, rainfall stations, reservoir readings, field reports, and historical datasets may all hold useful information, but that data is spread across different systems — making it harder to answer urgent operational questions.
Collect valuable data, but don't provide a clear operating picture on their own.
Built for infrastructure monitoring, not watershed-wide risk interpretation.
Can explain conditions, but take time and may be outdated by the time they're shared.
Useful, but rarely tailored to internal thresholds or agency-specific response needs.
AquaMesh sits above these systems as an intelligence layer for watershed operations — centralizing readings, scoring risk, forecasting change, and explaining it in plain language.
A cloud-based hydrological monitoring platform that combines real-time sensor data, predictive analytics, and AI-assisted decision support across rivers, streams, and reservoirs.
Hydrological measurements are continuously processed through the AquaMesh platform, where forecasting models evaluate changing conditions and generate operational risk indicators — so operators see current conditions alongside projected trends.
Operators monitor streamflow, river levels, rainfall indicators, and watershed conditions through a centralized operational dashboard, with measured data and short-term forecasts shown side by side.
Monitoring stations are displayed geographically, letting teams quickly identify affected regions and understand watershed-wide conditions at a glance.
Predictive models estimate future streamflow trends, helping operators prepare for changing environmental conditions before they occur, with forecast max, range, and threshold context built in.
The platform automatically evaluates incoming data to classify operational risk levels across conductance, dissolved oxygen, gage height, streamflow, turbidity, and temperature — so teams can prioritize locations requiring attention.
AI-generated summaries provide concise explanations of current watershed conditions, highlight emerging risks such as sensor connectivity loss or stale data, and surface the priority findings operators need to act on.
The goal is to prove that AquaMesh can improve watershed visibility, reduce manual analysis, and help teams identify developing flood or low flow risk earlier.
Faster identification of developing flood or low-flow conditions
Centralized visibility across distributed monitoring stations
Reduced manual review time across historical datasets
Clearer communication of watershed status to stakeholders
Better prioritization of field review based on calculated risk
Earlier operational planning using forecasted trends
These are target pilot outcomes, not verified production claims.
Centralize live monitoring data, forecast emerging risk, prioritize locations, and communicate operational status clearly.