Productivity Corner: Using Bentley WaterGEMS with GIS Data forWater Demand Allocation

9 May, 2006 By: Andres Gutierrez

Automated techniques help you meet current demands and plan for the future.

The consumption of water is the driving force behind the hydraulic dynamics occurring in water distribution systems. When simulating these dynamics in your water distribution model, an accurate representation of system demands is as critical as a precise model of the physical components of the model.

To realize the full potential of the model as a master planning and decision support tool, you must accurately allocate demands while anticipating future demands. You must collect the necessary data and translate it to model loading data regularly to account for changes to the network conditions. Due to the difficulties associated with manually loading the model, you can use automated techniques developed to assist with this task.

Spatial allocation of demands is the most common approach to loading a water distribution model. The spatial analysis capabilities of GIS make these applications a logical tool for the automation of the demand allocation process.

WaterGEMS' LoadBuilder module -- available with stand-alone, MicroStation, AutoCAD, and ArcGIS platforms -- leverages the spatial analysis abilities of your GIS platform to distribute demands according to geocoded meter data, demand density information and coverage polygon intersections. LoadBuilder facilitates the tasks of demand allocation and projection. Every step of the loading process is enhanced from the initial gathering and analysis of data from disparate sources and formats to the employment of various allocation strategies.

The following are descriptions of the types of allocation strategies that you can apply using LoadBuilder.

Allocation uses the spatial analysis capabilities of GIS to assign geocoded (possessing coordinate data based on physical location, such as an x,y coordinate) customer meters to the nearest demand node or pipe. Assigning metered demands to nodes is a point-to-point demand allocation technique, meaning that known point demands (customer meters) are assigned to network demand points (demand nodes).

Assigning metered demands to pipes also is a point-to-point assignment technique, because you still must assign demands to node elements, but an additional step is involved. When using the nearest pipe meter assignment strategy, the demands at a meter are assigned to the nearest pipe. From the pipe, the demand then is distributed to the nodes at the ends of the pipe by using a distribution strategy. Meter assignment is the simplest technique in terms of required data because you don't need to apply service polygons (figure 1).

Figure 1. An example of meter assignment.

Meter assignment can prove less accurate than the more complex allocation strategies because the nearest node is determined by straight-line proximity between the demand node and the consumption meter. Piping routes are not considered, so the nearest demand isn't necessarily the location from which the meter actually receives its flow. In addition, you may not know the actual location of the service meter.

The geographic location of the meter in the GIS is not necessarily the point from which water is taken from the system, but it may be the centroid of the land parcel, the centroid of building footprint or a point along the frontage of the building. Ideally, you should place these meter points at the location of the tap, but you may only know the centroid of the building or land parcel for a customer account.

Billing Meter Aggregation
Billing meter aggregation is the technique of assigning all meters within a service polygon to a specified demand node. Service polygons define the service area for each of the demand nodes (figure 2).

Figure 2. Service polygons define the service area for each of the demand nodes.

Meter aggregation is a polygon-to-point allocation technique because the service areas are contained in a GIS polygon feature class, and the demand nodes are contained in a point feature class. The demands associated with the meters within each of the service area polygons is assigned to the respective demand node points.

Due to the need for service polygons, the initial setup for this approach is more involved than the meter assignment strategy -- the trade-off is greater control over the assignment of meters to demand nodes. Automated construction of the service polygons may not produce the desired results, so you may need to adjust the polygon boundaries manually, especially at the edges of the drawing.

This strategy involves distributing lump-sum area water-use data among a number of service polygons (service areas) and, by extension, their associated demand nodes. The lump-sum area is a polygon for which the total (lump-sum) water use of all of the service areas (and their demand nodes) within it is known (metered) but the distribution of the total water use among the individual nodes is not. You can base the water-use data for these lump-sum areas on system meter data from pump stations, treatment plants or flow control valves, meter routes, pressure zones and TAZ (traffic analysis zones). The lump-sum area for which a flow is known must be a GIS polygon. There is one flow rate per polygon, and you cannot overlap open space between the polygons.

The known flow within the lump-sum area generally is divided among the service polygons within the area using one of two techniques -- equal distribution or proportional distribution:

  • The equal flow distribution option simply divides the known flow evenly between the demand nodes. The lump-sum area in this case is a polygon feature class that represents meter route areas. For each of these meter route polygons, the total flow is known. The total flow is then equally divided among the demand nodes within each of the meter route polygons.
  • The proportional distribution option (by area or by population) divides the lump-sum flow among the service polygons based upon one of two attributes of the service polygons -- the area or the population. The greater the percentage of the lump-sum area or population that a service polygon contains, the greater the percentage of total flow assigned to that service polygon.

Each service polygon has an associated demand node, and the flow that is calculated for each service polygon is assigned to this demand node. For example, if a service polygon is 50% of the lump-sum polygon's area, then 50% of the flow associated with the lump-sum polygon will be assigned to the demand node associated with that service polygon. This strategy requires the definition of lump-sum area or population polygons in the GIS, service polygons in the model and their related demand nodes.

Sometimes you must use the flow distribution technique to assign unaccounted-for water to nodes and when any method that uses customer metering data as opposed to system metering data is implemented.

In figure 3, the total demand in meter route A may be 55 gpm (3.48 L/s) and the demand is 72 gpm (4.55 L/s) in meter route B. Because meter route A has 11 nodes, the demand at each node would be 5 gpm (0.32 L/s) if equal distribution is used, and the demand at each of meter route B's 8 nodes would be 9 gpm (0.57 L/s).

Figure 3. The meter route.

Point Demand Assignment
A point demand assignment technique is used to assign a demand directly to a demand node. This strategy primarily is a manual operation and is used to assign large (generally industrial or commercial) water users to the demand node that serves the consumer in question. This technique is unnecessary if all demands are accounted for using one of the other allocation strategies.

Estimation of Demands Using Land Use and Population Data
Automated techniques also can assist in the estimation of demands using land use and population density data. These techniques are similar to the flow distribution allocation methods, except that the type of base feature class that is used to intersect with the service feature class may contain information other than flow; for example, land use or population.

You can use this type of demand estimation in the projection of future demands; in this case, the demand allocation relies on a polygon feature class that contains data regarding expected future conditions. You can use various data types with this technique, including future land use, projected population or demand density (in polygon form) with the polygons based upon traffic analysis zones, census tracts, planning districts or other classifications. Note that you can use these data sources to assign current demands; the difference between the two being the data that is contained within the source. If the data relates to projected values, you can use it for demand projections.

Many of these data types do not include demand information, so further data conversion is required to translate the information contained in the future condition polygons into projected demand values. This conversion entails translating the data contained within your data source to flow, which you then can apply using LoadBuilder.

After an appropriate conversion method is in place, the service feature class containing the service areas and demand nodes is overlaid with the future condition polygon feature class. You can determine a projected demand for each of the service areas and assign the demand nodes associated with each service polygon. The conversion required will depend on the source data used. You may need to translate the data contained within the source -- such as population, land area, etc. -- to flow, which LoadBuilder can use to assign demands.

Depending on how the feature classes intersect, service areas may contain multiple demand types (land uses) that are added and applied to the demand node for that service polygon.

For more information about Bentley WaterGEMS and the LoadBuilder module, please visit the company's Web site.

About the Author: Andres Gutierrez

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