La dieta mediterránea y su evolución en el tiempo

Not all water systems are universally metered as is customary in North America. For example, in the United Kingdom, only roughly 10 percent of the domestic customers are metered.

Instead of metering individual customers, distribu-tion systems in the UK are divided into smaller zones, called District Metered Areas (DMAs), which are isolated by valving and are fed through a smaller number of inlet and outlet meters (WRc, 1985). The number of properties in a DMA is known fairly precisely, and usually varies from 500 to 5,000 properties but can go as high as 10,000.

The flows are recorded using data-logging tech-nology or telemetered to a central location.

Per capita consumption at the unmetered resi-dences is estimated to be on the order of 150 liters per capita per day, although there is consid-erable variation (Ofwat, 1998). Some of the variation is attributed to different socioeconomic classes as accounted for by ACORN (A Classifi-cation of Residential Neighborhoods), which classifies properties in England and Wales into categories such as “modern family housing with higher income” to “poorest council estates.”

Demand patterns in the UK are similar to most other developed nations, and the patterns can be established by DMA or groups of DMAs. Data log-ging is used to determine individual demand pat-terns only for the largest users.

Because most residences are not metered, unac-counted-for water in the UK is large, but most of this water is delivered to legitimate users and can be estimated fairly reasonably. The amount of actual leakage depends on pressure, burst fre-quency, leakage control policy, and age of pipes.

Despite the differences in metering practices between the UK and North America, loading of the model still involves many of the same steps, and the system metering data collected in the UK can make calibration easier than in locations with-out pervasive distribution metering.

Two basic approaches exist for filling in the data gaps between water production and computed customer usage: top-down and bottom-up. Both of these methods are based on general mass-balance concepts and are shown schematically in Figure 4.2.

Figure 4.2 Approaches to model loading

Nodes Nodes Nodes Nodes Nodes

Large Users Nodes / Service Area Production

Meter Routes

Billing Records Unaccounted-For

Nodes Start: Production Data

Goal: Nodal Demands

Start: Meter Records

Top-down demand determination involves starting from the water sources (at the

“top”) and working down to the nodal demands. With knowledge about the produc-tion of water and any large individual water customers, the remainder of the demand is disaggregated among the rest of the customers. Bottom-up demand determination is exactly the opposite, starting with individual customer billing records and summing their influences using meter routes as an intermediate level of aggregation to deter-mine the nodal demands.

Most methods for loading models are some variation or combination of the top-down and bottom-up approaches, and tend to be system-specific depending on the availabil-ity of data, the resources for data-entry, and the need for accuracy in demands. For some systems, the decision to use top-down or bottom-up methods can be made on a zone-by-zone basis.

Cesario (1995) uses the terms estimated consumption method and actual consumption method to describe these two approaches. However, both methods involve a certain level of estimation. An intermediate level of detail can be achieved by applying the top-down approach with usage data on a meter-route-by-meter-route basis (AWWA, 1989).

Most design decisions, especially for smaller pipes, are controlled by fire flows, so modest errors in loading have little impact on pipe sizing. The case in which loading becomes critical is in the tracking of water quality constituents through a system, because fire flows are not typically considered in such cases.

Section 4.1 Baseline Demands 139

„ Example — Top-Down Demand Determination. Consider a system that serves a community of 1,000 people and a single factory, which is metered. Over the course of a year, the total production of potable water is 30,000,000 gallons (114,000 m³). The factory meter registered a usage of 10,000,000 gallons (38,000 m³). Determining the average per capita residential usage in this case is straightforward:

Total volume of residential usage = (Total usage) – (Non-residential usage)

= 30,000,000 gallons – 10,000,000 gallons

= 20,000,000 gallons

Residential volume usage per capita = (Total volume of residential usage) / (no. of residents)

= 20,000,000 gallons / 1,000 capita

= 20,000 gallons/capita

Residential usage rate per capita (given that prior volume calculations were for a period of one year)

= (Residential volume usage per capita) / time

= (20,000 gallons/capita/year) u(1 year / 365 days)

= 55 gallons/capita/day = 210 liters/capita/day

Models usually require demands in gallons per minute or liters per second, which gives 0.038 gpm/capita = 0.0024 l/s/capita

Next, the approximate number of people (or houses) per node (e.g., 25 houses with 2.5 residents per house = 62.5 residents/node) is determined to give average nodal demand of

2.37 gpm/node =0.15 l/s/node

These average residential nodal demands can be adjusted for different parts of town based on popula-tion density, amount of lawn irrigapopula-tion, and other factors.

„ Example — Bottom-Up Demand Determination. Each customer account is assigned an x-y coordinate in a GIS. Then, each account can be assigned to a node in the model based on poly-gons around each node in the GIS. (If a GIS is not available, customer accounts can be directly assigned to a node in the customer service information system used for billing purposes.) Then, each account in the customer information database records can be assigned to a model node. By querying the customer information database, the average demand at each node for any billing period can be determined.

The billing data must now be corrected for unaccounted-for water. Consider a user who decides to allocate unaccounted-for water uniformly to each node. The daily production is 82,000 gpd, and metered sales are 65,000 gpd. For each node, the demand must be corrected for unaccounted-for water. One approach is to assign unaccounted-for water in proportion to the demand at a node using:

Corrected demand = (Node consumption) u [(Production) / (Metered Sales)]

For a node with a consumption of 4.2 gpm, the corrected demand is:

Corrected demand = (4.2 gpm) u (82,000/65,000) = 5.3 gpm = 0.33 l/s

As can be seen in the preceding examples, bottom-up demand allocation requires a great deal of initial effort to set up links between accounts and nodes, but after this work is done, the loads can be recalculated easily. Of course, the corrections due to unaccounted-for water and the fact that instantaneous demands are most likely not equal to average demands suggest that both approaches are subject to error.

„ Example — Demand Allocation. In a detailed demand allocation, a key step is determin-ing the customers assigned to each node. Figure 4.3 demonstrates the allocation of customer demands to modeled junction nodes. The dashed lines represent the boundaries between junction associations.

For example, the junction labeled J-1 should have demands that represent nine homes and two com-mercial establishments. Likewise, J-4 represents the school, six homes, and one comcom-mercial building.

Figure 4.3 Allocating demands to network junctions

Commercial Establishments Homes

School

J-1 J-2

J-3 J-5 J-4

Node Service Area Boundary (typ.)

Following demand allocation, the modeler must ensure that demands have been assigned to junction nodes in such a way that (1) the sums of the nodal demands system-wide and in each pressure zone are in agreement with production records, and (2) the spatial allocation of demands closely approxi-mates actual demands.

When working with high-quality GIS data, the modeler can much more precisely assign demands to nodes. Nodal demands can be loaded using several GIS-related methodologies, ranging from a simple inverse-pipe-diameter allocation model to a comprehensive polygon overlay. The inverse-pipe-diameter approach assumes that demands are associated with small-diameter pipes, whereas large-diameter pipes are mainly used for transmission and thus should have less “weight” associated with them. More detailed methods make use of extensive statistical data analysis and GIS processing by combining layers of data that account for variables such as population changes over time, land use, seasonal changes, planning, and future development rates. Davis and Brawn (2000) describe an approach they employed to allocate demands using a GIS.