Urban water systems β and the economics of building them β are tightly coupled. The modeling has to be too.
Hydrology, hydraulics, green infrastructure, gray infrastructure, storage, pumps, controls, and economics all entangle in urban water design β and the coupling crosses water-stream boundaries. Stormwater, sanitary sewer, drinking-water distribution, rainwater harvesting, and treated reuse share infrastructure, share storage, share pumps, and share the same fundamental hydraulics. The SWMM solver doesn't differentiate between stream types; the architectural reality of urban water is that they are one system. Designing for that reality requires a tool whose cost model is part of the simulation model, not a separate spreadsheet maintained alongside it.
The StormNET cost engine is a peer to the SWMM hydrology/hydraulics/water-quality engine and the 3D CAD digital twin engine, reading the same model inputs. Every CAPEX, OPEX, lifecycle-cost, and unit-cost number derives from the physical model β pipe diameter, trench depth, pond volume, LID layer thickness, pump curve, storage geometry. When the model is georeferenced, the cost engine auto-detects the region from model coordinates and loads labor, material, electricity, and construction-condition assumptions for that location. The same engineering physics adapts to different economies.
The tight coupling is the design problem. The system that handles it is what's needed.
Modern urban water systems are not independent streams of pipes. A bioretention cell's soil depth changes runoff timing, which changes pipe surcharge risk, which changes pond sizing, which changes excavation cost, which changes whether the project is feasible. A rainwater harvesting cistern reduces both drainage demand (a flood hazard) and drinking-water demand (a resource) β the same water, two systems. Stormwater entering combined sewers becomes sanitary load. Treated reuse cascades from higher-quality to lower-quality applications, then recharges groundwater that becomes drinking water again. The hazard-to-resource transformation β treating stormwater as a resource for capture, treatment, and reuse rather than only as a flood hazard to drain β is one expression of how tightly the streams are coupled. Hydrology, hydraulics, green infrastructure, gray infrastructure, distribution, storage, pumps, controls, and economics are not separate design problems. They are one design problem.
Designing for that reality requires a single integrated system. The user draws and places on a data-enabled, georeferenced landscape β polygons for subcatchments, LIDs, and irregular ponds; objects for pipes, channels, sanitary sewers, stormwater drains, water-distribution pipes for harvesting and reuse, manholes, catch basins, and parameterized storage units (surface ponds, underground cube/conduit/vault systems, above-ground storage). From that single act of placement, three peer engines compute in parallel: the SWMM hydrology/hydraulics/water-quality engine, the 3D CAD digital twin engine, and the physics-based cost engine. Multi-mode visualization updates in seconds β 1D time series, 2D map, Sankey water-balance, dynamic profile with synchronized time slider, 3D CAD immersive (pre- and post-simulation), and hierarchical cost view. The full turnaround from a design change to seeing the consequences across all three engines and all visualization modes is typically under a minute. Concept to design to model to economics to visualization, in one cycle. Customizable at every level. Steerable in real time.
Such a system does not exist as established practice today. The components exist β SWMM-based hydraulic modeling, CAD packages, cost estimation modules, GIS overlays, and the underlying data and economic infrastructure built by national agencies and the broader scientific community around the world over decades β often at high quality, each within its own tool. What's not yet established is the architectural integration: one model, one set of inputs, three peer engines computing in parallel, multi-mode visualization in seconds, sub-minute iteration cycle. StormNET is that integrated system.
One set of model inputs
Every design object the user places contributes to a single set of StormNET model inputs β dimensions, materials, slopes, layer thicknesses, control rules, land-use assignments, network connectivity. The SWMM engine, the 3D CAD digital twin engine, and the cost engine all read those same inputs. No separate model representations to maintain; no manual reconciliation between tools.
Georeferenced auto-detection
The cost engine reads the model's spatial coordinates and loads the appropriate regional preset automatically: labor rates, material prices, electricity tariffs, construction-condition factors. Move the project from Detroit to Lagos to Singapore β same engineering physics, different economics. The model knows where it is.
Recompute together, in parallel
Change a pipe diameter, all three engines recompute against the same updated input. Cost is not estimated late; it appears alongside hydraulics and visualization in the same iteration cycle. Hydraulic, visual, and economic consequences surface together.
The costing chain β and why it stays traceable.
Model input β derived physical quantity β regional unit price β financial assumption β CAPEX, OPEX, lifecycle, and unit-cost output.
Three categories of input are architecturally separated and individually refinable: physical quantities (extracted from SWMM β geometry, materials, layer depths; come directly from the model), regional economics (location-dependent unit prices; auto-loaded from georeferenced coordinates, fully overridable with local bid data), and project financial assumptions (contingency, design life, discount rate, maintenance β user-controlled, depends on the planning stage). The user can identify which category of assumption is uncertain and refine accordingly.
Four cost layers are computed together.
StormNET should not be read as a static estimating table. It is a design-linked economic framework for the full urban hydrologic system: rainfall, runoff, grading, conveyance, storage, green infrastructure, controls, treatment, pumping, and long-term economics.
CAPEX
- Grading, excavation, trenching, bedding, backfill
- Pipes, culverts, manholes, inlets, outfalls
- Ponds, vaults, wetlands, channels, LIDs
- Engineering, administration, and contingency
OPEX
- Pump electricity where pumps are present
- LID maintenance and rehabilitation
- Sediment removal, vegetation, pond upkeep
- Inspection and recurring operating assumptions
Lifecycle cost
- Present value of future operating cost
- Design life and discount-rate assumptions
- Replacement and rehabilitation cycles
- Capital-intensive versus maintenance-intensive alternatives
Unit cost
- Cost per storage volume
- Cost per drainage area treated
- Cost per flow capacity
- Cost per runoff or pollutant reduction where performance metrics are available
The cost model is part of the design loop.
A larger LID soil layer can improve hydrologic performance but increases excavation and media cost. A smaller pipe may reduce capital cost but increase surcharge or pumping. An underground vault may preserve land but sharply increase structural cost. StormNET keeps these tradeoffs visible.
Hydrology, hydraulics, infrastructure, ecology, and cost are tightly coupled.
Urban water systems are continuous. Rainfall becomes runoff, runoff moves across graded land, flow enters inlets and pipes, storage routes peaks, LIDs infiltrate and treat water, pumps consume energy, and every decision has a construction and lifecycle consequence.
Hydrologic propagation
Changing land cover, imperviousness, subcatchment routing, or LID placement changes runoff generation and timing.
Hydraulic propagation
Runoff changes inlet demand, conduit flow, surcharge risk, storage volume, outfall behavior, and pumping requirements.
Economic propagation
Pipe size, trenching, pond volume, LID layer thickness, channel lining, pump power, and local prices propagate into CAPEX, OPEX, lifecycle cost, and unit cost.
StormNET costs the entire urban water system β not isolated components.
The model computes physical quantities first and then prices those quantities with regional economic parameters. This preserves engineering transparency and supports comparison across cities, countries, and design alternatives.
Site drainage and grading
Subcatchment area and imperviousness drive grading, paving, curb and gutter, inlets, laterals, landscaping, and erosion-control quantities.
Conveyance and nodes
Conduits, culverts, manholes, outfalls, dividers, channels, and inlet structures are costed from geometry, material, length, depth, and connected hydraulic context.
Storage and ponds
Storage volume is computed from SWMM depth-area curves or predefined tank geometry. Cost reflects excavation, embankment, forebay, lining, fencing, outlet works, structural vault quantities, andβwhere elevated storage is usedβheight-dependent support, foundation, and hydraulic-lift effects.
Pumps and controls
Pump equipment and energy are linked to flow, head, efficiency, and operating assumptions. Weirs and orifices are costed from control geometry.
Green infrastructure
Bio-retention, rain gardens, permeable pavement, green roofs, infiltration trenches, swales, rain barrels, and rooftop disconnection are computed from SWMM LID controls and usage areas.
Treatment and landscape water assets
Constructed wetlands, infiltration basins, eco-creeks, open channels, and treatment-oriented storage are treated as part of water infrastructure, not decorative site work.
LID design directly couples performance and cost.
This is one of StormNETβs strongest differentiators. LIDs (Low Impact Development practices β also called SuDS in the UK and EU, WSUD in Australia, and sponge-city infrastructure in China) are not assigned a lump-sum placeholder. The model reads SWMM LID layers and placement areas, converts them into construction quantities, and prices each layer.
General LID cost logic
CLID = Ξ£(layer depth Γ area Γ unit cost) + surface materials + underdrain/check-dam features
Layer depths come from SWMM LID controls. Placement area and number come from SWMM LID usage. Regional unit costs supply soil media, gravel, geotextile, plants, membrane, pavers, underdrain, and labor rates.
Design-cost tradeoff
- Increase soil depth β more storage and treatment, higher media and excavation cost
- Add underdrain β better drainage control, added pipe and installation cost
- Increase LID area β more runoff control, larger material quantities
- Change from rain garden to permeable pavement β different surface, subbase, and maintenance profile
| LID type | Key cost quantities | Decision implication |
|---|---|---|
| Bio-retention / rain garden | Excavation depth, soil media, gravel storage, mulch, plants, geotextile, underdrain | Direct tradeoff between storage/treatment performance and layer cost |
| Permeable pavement | Paver or porous surface, gravel subbase, geotextile, underdrain | Links pavement design, runoff reduction, and structural/site cost |
| Green roof | Membrane, drainage mat, lightweight media, sedum, structural upgrade | Compares roof runoff control with high unit cost and structural constraints |
| Infiltration trench / swale | Excavation, stone fill, geotextile, underdrain, observation well, check dams | Shows how distributed practices scale by area, depth, and length |
Cost is computed from the SWMM / StormNET model, not re-entered manually.
The simulation model becomes the cost-model input. Users should understand which values come from the model, which values come from the regional preset, and which values are user assumptions.
Extracted from SWMM
- Subcatchment area, slope, imperviousness
- Conduit diameter, length, shape, tag, culvert code
- Junction depth and outfall type
- Storage depth-area curves and pond dimensions
- LID controls, layer depths, LID usage
- Pump curves: flow and head
Regional economic preset
- Labor rate and construction productivity
- Concrete, steel, HDPE, PVC, gravel, soil media
- Excavation and reinstatement rates
- Electricity tariff
- Solar and climate-related parameters where relevant
- Seismic or construction-condition factors
User-controlled assumptions
- Pipe materials by tag category
- Channel lining and storage type
- Soil class, reinstatement class, land class
- Contingency and engineering/administration
- Design life, discount/interest rate
- Pump efficiency and energy cost override
| SWMM element | Fields read | Cost use |
|---|---|---|
| Subcatchments | Area, imperviousness, slope | Grading, paving, inlets, laterals, runoff context |
| Conduits | Length, diameter/maxDepth, shape, tag, culvert code | Pipe material mass, trench volume, bedding, culverts, channels |
| Junctions / outfalls | Depth, invert, outfall type, connected conduit | Manholes, outfall headwalls, wingwalls, riprap, flap gates |
| Storage units / curves | Depth-area curve, max depth, drawn dimensions | Excavation volume, pond surface, fencing, forebay, vault or tank cost |
| LID controls / usage | Type, layer depths, area, number | Layer-by-layer LID quantities and construction cost |
| Pumps / curves | Flow and head curve | Hydraulic power, equipment cost, energy OPEX |
Physical quantity first. Local price second. Financial interpretation third.
Read the model
StormNET reads the SWMM JSON: subcatchments, pipes, junctions, outfalls, storage, curves, LID controls, LID usage, tags, pump curves, and georeferenced model location.
Compute engineering quantities
Geometry becomes quantities: cubic meters of excavation, kilograms of pipe material, square meters of liner, meters of underdrain, pond volume from depth-area integration, hydraulic power from flow and head.
Apply regional economics
Quantities are multiplied by local labor, material, electricity, excavation, reinstatement, and construction-condition rates from the selected regional preset or user overrides.
Add project financial assumptions
Contingency, engineering and administration, design life, interest/discount rate, maintenance, replacement, and energy assumptions convert construction quantities into CAPEX, OPEX, lifecycle cost, and annualized cost.
Report decision metrics
The system reports component totals, water-infrastructure totals, associated site costs, annual operating cost, present-value lifecycle cost, annualized cost, and unit costs for cross-project comparison.
StormNET converts model inputs into quantities, then converts quantities into cost.
Users should be able to trace every major cost back to something in the StormNET / SWMM model or to an explicit economic assumption. The model provides intelligent defaults so an estimate can be generated immediately, but the assumptions remain visible and customizable.
The costing chain
Model input β derived physical quantity β regional unit price β financial assumption β CAPEX, OPEX, lifecycle, and unit-cost output.
| StormNET / SWMM input | Derived quantity | Cost impact | User can refine |
|---|---|---|---|
| Subcatchment area, imperviousness, slope | Bulk grading area, runoff generation, inlet demand | Site grading, drainage inlets, paving assumptions, erosion control | Average cut/fill depth, road fraction, inlet capacity, site class |
| Pipe diameter, length, shape, tag, invert / cover assumptions | Pipe material mass, trench volume, bedding volume, backfill volume | Pipe CAPEX, excavation, bedding, reinstatement, installation labor | Pipe material, cover depth, soil class, reinstatement class, trench assumptions |
| Junction / manhole depth | Manhole structural height and excavation depth | Precast rings, base slab, excavation, frame/cover, access cost | Minimum depth, manhole type, local unit prices, coatings or special structures |
| Outfall type and connected conduit diameter | Headwall size, wingwall size, riprap apron, flap gate need | Outfall structure CAPEX and erosion protection | Outfall structure type, riprap thickness, tidal gate assumptions |
| Storage depth-area curve and drawn pond dimensions | Detention / retention excavation volume, embankment volume, surface area, perimeter | Pond excavation, liner, forebay, outlet structure, fencing, seeding | Pond type, liner, side slope, forebay fraction, fence/landscape assumptions |
| Underground storage geometry or predefined tank type | Excavation depth, tank volume, slab/wall/roof quantities, structural demand | Underground detention CAPEX, waterproofing, backfill, surface restoration | Tank type, burial depth, soil class, groundwater/dewatering, structural assumptions |
| Elevated storage geometry and height | Support height, structural loading, hydraulic grade, pumping lift | Foundation/support CAPEX, wind/seismic sensitivity, energy OPEX, lifecycle cost | Tank elevation, seismic/wind assumptions, foundation type, pump efficiency |
| LID controls: soil, storage, pavement, drain, berm, media depths | Layer-by-layer material volumes and areas | Excavation, engineered soil, gravel, geotextile, underdrain, plants, surface materials | Layer depths, underdrain, media cost, planting density, local LID material prices |
| Channel cross-section, lining, length | Excavation volume and wetted lining area | Earthwork, lining, stabilization, riprap, concrete or vegetated channel cost | Channel lining, side slope, roughness/lining choice, excavation rate |
| Pump curve: flow and head | Hydraulic power, annual energy, equipment scale | Pump CAPEX, electricity OPEX, lifecycle energy cost | Pump efficiency, operating hours, electricity tariff, control strategy |
Intelligent defaults
When the user does not yet know every local price or design parameter, StormNET starts with region-specific defaults for labor, material, electricity, excavation, reinstatement, contingency, engineering/administration, and design-life assumptions.
Customizable assumptions
As the project matures, users can replace defaults with local bid tabs, supplier quotes, geotechnical data, utility tariffs, selected materials, construction method, and agency-specific contingency or lifecycle assumptions.
For elevated tanks, height is a cost driver β not just a display attribute.
When StormNET routes a storage unit to predefined tank or water-distribution storage logic, elevation and tank height should be treated as physical design inputs. Height affects hydraulic grade, structural demand, foundation design, wind and seismic sensitivity, access requirements, andβin pumped systemsβthe energy required to lift water.
Structural cost
Greater tower or tank height increases support structure, bracing, foundation, access, safety, and constructability requirements. Elevated tanks therefore cannot be priced only by storage volume.
Hydraulic and energy cost
Where pumps fill elevated storage, added elevation increases lift. Higher lift increases pump head, power demand, annual energy cost, and lifecycle energy cost.
Lifecycle and risk
Height can increase inspection, coating, maintenance, wind/seismic checks, and replacement assumptions. These effects should be visible when users compare elevated, ground-level, and underground storage alternatives.
What users should verify
For elevated storage, verify tank volume, base elevation, water level, tower/standpipe height, pump curve, operating pattern, seismic/wind assumptions, and selected regional cost preset before interpreting CAPEX, OPEX, lifecycle cost, or unit storage cost.
Operating cost computed from hydraulic physics. Across whatever streams include pumps.
Pumps, controls, and pressure links appear across the full urban water system β stormwater drainage, sanitary lift stations, drinking-water distribution, rainwater-harvesting and reuse loops. Wherever they appear, operating cost is governed by the same physics: hydraulic work over time. StormNET reads the pump curve, the operating pattern, and the regional electricity tariff directly from the model β and computes annual energy, lifecycle energy, and CAPEX-OPEX tradeoffs as part of the same iteration cycle as construction cost.
Hydraulic power
P = ΟgQH / Ξ·
Flow and head from pump curves determine pump power. Efficiency assumptions convert hydraulic power into electrical demand.
Annual energy cost
Annual energy cost = kWh Γ $/kWh
Operating duration, pump utilization, electricity tariff, and pump efficiency determine energy OPEX.
Lifecycle energy
Energy cost can be discounted over the design life and compared against construction alternatives that reduce pumping, headloss, or operating hours.
Design changes can shift both CAPEX and OPEX.
Deeper storage may require more lift. Longer force mains may increase headloss. Larger pipes may increase capital cost but reduce pumping energy. This is where StormNET connects hydraulic behavior directly to economic consequence.
From system design to decision-ready economics.
StormNET should help users answer not only βwhat does it cost?β but βis this cost reasonable for what the system delivers?β
Capital outputs
- Total CAPEX
- Water infrastructure versus associated site costs
- Cost by section: grading, conveyance, nodes, storage, pumps, LID, treatment
- Line-item quantities for review
Operating outputs
- Annual pump energy where applicable
- Maintenance assumptions for LIDs, ponds, vegetation, sediment
- Rehabilitation or replacement assumptions
- Operating sensitivity to efficiency and tariff
Lifecycle outputs
- Present-value lifecycle cost
- Annualized cost
- Design-life and discount-rate sensitivity
- Lifecycle comparison of grey, green, distributed, and centralized alternatives
Unit cost enables comparison across different kinds of stormwater projects.
Stormwater projects are not all comparable by total cost. A detention basin, permeable pavement retrofit, trunk storm drain, pump station, and green roof deliver different benefits. StormNET should present multiple unit-cost views so users can judge cost relative to delivered function.
| Metric | Best used for | Interpretation |
|---|---|---|
| $/mΒ³ or $/acre-ft of storage | Ponds, vaults, tanks, detention/retention alternatives | How expensive the storage capacity is |
| $/mΒ³ runoff managed | LID, detention, watershed-scale comparisons | How much it costs to manage runoff volume |
| $/cfs or $/(mΒ³/s) | Pipes, channels, culverts, pumps, outlets | How expensive the conveyance or pumping capacity is |
| $/acre or $/hectare treated | Site retrofit, LID, watershed BMP programs | How expensive coverage is relative to drainage area |
| $/m or $/km | Pipe, canal, channel, culvert corridors | Useful for alignment and corridor alternatives |
| Annualized $/year | Long-term planning and finance | Combines capital and recurring cost into one comparable annual number |
Same physics. Different economics.
StormNET uses a location-aware cost methodology built for project locations anywhere in the world. The model knows where it is on Earth β and the cost engine adapts accordingly. Regional preset defaults provide immediate estimates; local overrides move the estimate toward project specificity.
Auto-detected region
When the model is georeferenced β which is the default for any project drawn on the data-enabled landscape β the cost engine reads the model's coordinates and loads the appropriate regional preset automatically. Labor rates, material prices, electricity tariffs, and construction-condition assumptions come from the location, without the user picking from a dropdown. Move the project to a different city: the physics stays the same; the economics adapts.
258 presets
Regional presets cover countries worldwide, US states/DC, and selected sub-national zones β so the same model can be compared from a project in Detroit to one in Lagos or Singapore. The presets are the starting point β fully overridable when better local data are available.
User overrides
Users can replace default unit rates with utility data, contractor quotes, local bid tabs, supplier pricing, or agency cost guidance β from whatever sources are authoritative in their region. The overrides apply within the same architectural chain: model input β physical quantity β your unit price β financial assumption β cost output.
The georeferenced chain β what makes this architecturally distinctive.
Model coordinates β regional preset β local labor, materials, electricity, construction conditions β cost adapts. No manual region picker, no spreadsheet of regional adjustments to maintain, no separate cost-by-region lookup. The architectural commitment is that the model knows where it is, and the cost engine reads that location as just another model input β same as it reads pipe diameter or pond depth.
Start with intelligent defaults. Refine toward project-specific estimates.
The refinement workflow explains how users can start quickly, understand the assumptions, and improve the estimate as better project-specific information becomes available.
Early planning
Use auto-detected regional defaults, default materials, typical site conditions, standard LID assumptions, default contingency, and standard design life. Best for rapid screening and education.
Preliminary design
Update pipe material, storage type, channel lining, LID depths, soil class, reinstatement class, energy tariff, pump efficiency, contingency, E&A, and design life.
Project-specific estimate
Replace defaults with survey data, geotechnical conditions, local bid tabs, supplier quotes, agency unit prices, maintenance plans, utility tariffs, permitting assumptions, and contractor feedback.
How users improve the estimate
- Verify that the SWMM model geometry is correct: areas, pipe lengths, diameters, depths, storage curves, LID placements, and pump curves.
- Confirm that the region and cost preset match the project location.
- Override default unit prices with known local data.
- Adjust design life, discount rate, contingency, E&A, and O&M assumptions to match the decision stage.
- Run alternatives and compare lifecycle/unit cost, not only initial capital cost.
Rigorous enough for planning. Transparent enough to refine.
The model is intended for planning-level and feasibility-level decisions, alternative comparison, grant and capital planning, education, and preliminary budgeting. It is not a substitute for final bid documents, detailed design, geotechnical investigation, permitting review, or contractor pricing.
Core assumptions
- Open-cut trenching unless otherwise represented
- Standard cover, bedding, backfill, and reinstatement assumptions
- Average cut/fill depth for conceptual grading
- Layer depths in SWMM LID controls are taken at face value
- Storage depth-area curves represent actual pond/vault geometry
- Regional costs are averages, not site-specific bids
- Contingency and E&A are applied consistently across cost sections
Common exclusions or refinements
- Traffic control, utility conflicts, special crossings, dewatering
- Detailed phasing, escalation, financing, and cash-flow schedule
- Permitting, environmental mitigation, cultural resources
- Detailed SCADA, monitoring, water-quality instrumentation
- Major treatment plants or non-stormwater facilities unless separately modeled
- Final contractor means and methods
Professional judgment remains essential.
StormNET provides a transparent, physics-based planning estimate. Users should validate quantities, assumptions, local unit prices, and design standards before using results for procurement, final design, or regulatory submittals.
An integrated cost-and-design system for tightly coupled urban water infrastructure.
Urban water is one tightly coupled system across multiple streams β stormwater, sanitary, drinking water distribution, rainwater harvesting, and treated reuse β sharing infrastructure, storage, pumps, and the same fundamental hydraulics. Designing for that reality requires a tool where hydrology, hydraulics, green and grey infrastructure, storage, treatment, pumps, controls, and economics all compute together, against one set of model inputs, in one iteration cycle. StormNET is that integrated system, with cost as a peer engine β not a separate workflow.
Multi-stream by architecture
The SWMM solver doesn't differentiate between stream types; StormNET inherits that property. Stormwater drainage, sanitary conveyance, drinking-water distribution, rainwater-harvesting-and-reuse loops β the same physics-aware logic prices pipes, pumps, storage, energy, lifecycle cost, and unit-cost comparison across all of them.
Integrated design loop
Draw and place on the data-enabled landscape β three peer engines (SWMM hydrology/hydraulics/water-quality, 3D CAD digital twin, physics-based cost) compute in parallel β multi-mode visualization updates in seconds β typical turnaround under a minute. Concept to design to model to economics to visualization, in one cycle, steerable in real time.
MAGNET4WATER ecosystem
One modeling ecosystem where physical simulation, infrastructure design, economics, reporting, and decision support are connected across water domains β the same architectural principle (physical models generate the quantities that drive cost) applied consistently across the platform family.