Smart Sewer Weekly
Esri UC 2026 Special Edition | July 21, 2026
From GIS records to an operational sewer-network intelligence system
The 2026 Esri User Conference ran July 13-17; recordings and supporting materials are now being released. The strongest sewer-related theme was not a single new CCTV tool, but an architecture connecting Utility Network, automated data quality, real-time sensors, AI and digital twins.
1. Strengthening utility management through Utility Network integration
Source: Esri User Conference technical and utility sessions
Published: June 12, 2026; sessions delivered July 15–16
Explore the Utility Network conference guide
The conference covered Utility Network implementation, migration, advanced network management and integration with other utility systems.
Why it matters: AI, hydraulic modeling and PACP automation become unreliable when pipe connectivity, flow direction, facility IDs and inspection-to-asset relationships are inconsistent.
How to apply it:
Establish a persistent GlobalID for every pipe and manhole.
Model upstream/downstream connectivity and gravity-flow direction.
Relate CCTV inspections, PACP observations, work orders and sensor records to the same asset.
Validate terminal manholes and inspection direction automatically.
Pilot Utility Network migration in one sewershed before attempting a systemwide conversion.
2. Real-Time GIS and the transition to ArcGIS Velocity
Source: Esri ArcGIS Blog and UC technical sessions
Published: June 15, 2026
Review the real-time GIS technical program
ArcGIS Velocity is now positioned for both ArcGIS Online and Enterprise environments. The conference emphasized ingesting IoT data, detecting incidents, enriching feeds with GIS attributes, identifying anomalies and migrating from GeoEvent Server.
Why it matters: A digital twin needs an updating representation of actual operating conditions. Static GIS and CCTV condition data alone produce an asset model not an operational twin.
How to apply it:
Stream manhole level, flow, rainfall and pump-station data into GIS.
Spatially join each reading to its manhole, pipe and sewershed.
Detect rate-of-rise, surcharge and dry-weather anomalies.
Trigger alerts only after persistence and data-quality rules are satisfied.
Retain historical sensor data for I&I, blockage and capacity analysis.
Evaluate whether self-hosted Velocity better suits utility cybersecurity requirements.
3. ArcGIS Data Reviewer and AI-assisted data quality
Source: Esri ArcGIS Blog and UC demonstration sessions
Published: June 16, 2026
Read the Data Reviewer conference briefing
Esri previewed expanded automated quality control and emerging AI-assisted data-quality workflows, including utility-network applications.
Why it matters: This may have more immediate value for PACP programs than generative AI. Many inspection problems originate in incorrect AssetIDs, missing attributes, invalid geometry, reversed direction or unmatched manholes.
How to apply it:
Create automated rules that flag:
CCTV records with no matching GIS pipe.
Upstream and downstream manholes inconsistent with GIS connectivity.
Inspection length materially different from mapped pipe length.
Missing PACP-required fields or operator information.
Invalid diameter, material or installation-year values.
Duplicate inspections and conflicting condition scores.
Critical defects without coordinates, footage or review status.
Keep PACP coding validation in the inspection platform, but use GIS rules to verify the asset and spatial context.
4. Building a utility ready for digital twins
Source: East Valley Water District and Esri technical presentation
Published: June 10, 2026
Watch “Building a Utility Ready for Digital Twins”
East Valley Water District describes modernizing its GIS and transitioning toward ArcGIS Utility Network as the foundation for future digital-twin workflows.
Why it matters: The practical lesson is that a utility should become “twin-ready” before purchasing a large digital-twin platform. Asset identity, connectivity, governance and system integration come first.
How to apply it:
Assess GIS completeness, connectivity and positional accuracy.
Define authoritative systems for assets, work, CCTV and sensor data.
Build APIs or repeatable integrations instead of manual exports.
Select a limited operational problem such as surcharge or blockage risk.
Document update frequency, responsible staff and required decisions.
Measure operational outcomes not the visual quality of the 3D model.
5. FlowsDT: A geospatial digital twin for urban flood dynamics
Source: Mandal et al., research preprint
Published: July 8, 2025
Read the technical paper
FlowsDT combines GIS, LiDAR, land cover, drainage infrastructure and a coupled 1D-2D PCSWMM model to simulate flood depth, extent, duration and velocity in four dimensions.
Why it matters: Although focused on urban flooding, its architecture is directly relevant to sewer surcharge, basement-backup and overflow analysis. It demonstrates how GIS can connect network models with surface consequences.
How to apply it:
Combine sanitary-sewer hydraulics with terrain and building elevations.
Map locations where surcharge could reach streets or basements.
Compare sensor observations with simulated hydraulic grade lines.
Prioritize monitoring in high-consequence areas.
Test rainfall scenarios and proposed capacity improvements.
Use modeled consequences as part of your LoF/CoF risk framework.
Conference gap: CCTV and PACP
The available Esri UC material does not show a mature, native ArcGIS system that automatically performs PACP-compliant CCTV coding. The more credible architecture remains:
ArcGIS should serve as the spatial and network backbone. Specialized inspection software should manage video, AI detections and PACP observations, with certified personnel responsible for final acceptance.
Recommended 60-day pilot
Select one sewershed containing 10-20 monitored manholes and approximately 25-50 recent CCTV inspections.
Connect:
Pipes and manholes in GIS.
PACP observations and video links.
Work-order and service-request history.
Level, flow and rainfall measurements.
Hydraulic-model results.
Consequence and criticality attributes.
Measure inspection-to-GIS match rate, data-quality failures, review time, critical-defect response time, anomaly detection and the number of maintenance decisions supported. This would create a realistic foundation for your sewer digital-twin program without overbuilding the first phase.