Overview:
Scenarios for 2050 using UPlan Model
Background of UPlan
UPLAN is a GIS-based expert decision rule urban growth model that was conceived by Robert Johnston at UC Davis and built by David Shabazian. The model runs on a PC in ArcView, a common desktop GIS application that is used by most planning and natural resource agencies. The data required to run the model are generally available from regional or state resource and planning agencies. Read A Detailed Description of the Uplan Model for more information about how the model was created and how it works.
Scenarios
Base Case Scenario
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This is a view of the Sacramento watershed area in the year 2050, showing where development will occur if no changes in planning or development are made. It is used to compare with the other scenarios, which take into account possible effects from natural disasters or limited natural resources.
Detail
Limiting Factors for Disaster Risk: Flood and Fire
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This scenario outlines the possible outcome if insurance rates were to increase drastically or be altogether denied for homes in certain areas.
Detail
Limiting Factors for Resources: Groundwater
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This scenario takes into account the availability of groundwater in determining where new development occurs.
Detail
Population Explosion
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This scenario considers the possibility that the Department of Finance projection figures may be a considerable underestimate of what the population will actually be in the year 2050.
Detail
Maximum Pressure
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This scenario combines the maximum population pressures of the Population Explosion Scenario with the limiting factors of both the disaster risk scenario and the limited resources scenario. It essentially forces the most amount of development in the smallest area of all the scenarios.
Detail
Methodology
The Sacramento Watershed was divided into eight groups, based on an attempt to group counties with similar demographic inputs such as residential percentages and projected population growth and similar local and county goals for new development. Three counties did not fit well into a group and were run as single counties.
| Group |
Counties |
| 1 |
Siskiyou, Modoc, Shasta, Lassen, Tehama, Plumas |
| 2 |
Butte, Sutter, Yuba |
| 3 |
Colusa, Glenn, Lake |
| 4 |
Yolo, Napa, Solano |
| 5 |
Sacramento, Placer, El Dorado, Amador |
| 6 |
Nevada |
| 7 |
Sierra |
| 8 |
Alpine |
For a 2050 model run, the projected population is assigned space in five residential and three business land use categories. Each residential land use type receives a percentage of the total new households and similarly each employment type receives a percentage of the new employees. For example, in Group 5 the highest density for residential use will receive 9.6% of the new households. For each residential density type there is an average lot size, which the model multiplies by the number of new households in that land use to determine the needed space. A similar process is used to calculate the needed space for each employment type.
| Land Use Type |
Average Lot Size |
| Residential High |
0.05 acre |
| Residential Medium |
0.2 acres |
| Residential Low |
2 acres |
| Residential Very Low Estates |
20 acres |
| Residential Very Low |
20 acres |
The percentages of each land use type vary by group. They are calculated using a weighted average of each county's percentage within the group, with population as the weighting factor.
| Group |
Residential High |
Residential Medium |
Residential Low |
Residential Very Low Estates |
Residential Very Low |
| 1 |
10.1% |
42.2% |
35.9% |
5.9% |
5.9% |
| 2 |
13.4% |
63.5% |
17.6% |
2.8% |
2.8% |
| 3 |
15.1% |
37.6% |
36.2% |
5.6% |
5.6% |
| 4 |
10.2% |
80.9% |
7.0% |
1.0% |
1.0% |
| 5 |
9.6% |
79.2% |
9.3% |
1.0% |
1.0% |
| 6 |
6.1% |
45.1% |
40.5% |
4.1% |
4.1% |
| 7 |
3.2% |
9.0% |
65.0% |
11.4% |
11.4% |
| 8 |
12.8% |
30.6% |
41.8% |
7.4% |
7.4% |
The percentages for each employment type by group were calculated in the same way as the residential percentages.
| Group |
Industrial |
High Density Commercial |
Low Density Commercial |
| 1 |
12.1% |
22.7% |
65.2% |
| 2 |
13.0% |
23.4% |
63.7% |
| 3 |
11.7% |
23.3% |
65.0% |
| 4 |
15.0% |
26.6% |
58.4% |
| 5 |
14.0% |
31.7% |
54.3% |
| 6 |
14.3% |
28.6% |
57.0% |
| 7 |
7.8% |
30.7% |
61.5% |
| 8 |
7.3% |
2.2% |
68.9% |
The amount of space allotted for each employment type was in the same manner for all groups and depends on three figures: the number of new employees, an average number of square feet per employee and the Floor Area Ratio (FAR), or the total building square footage (building area) divided by the site size square footage (site area).
| Employment Type |
Square Feet/Employee |
Floor Area Ratio |
| Industrial |
500 |
.23 |
| High Density Commercial |
200 |
.35 |
| Low Density Commercial |
300 |
.15 |
These figures remained constant for all of the scenarios, even if population projections numbers were changed.
The model works by assigning geographic layers values, based on likelihood of attracting growth. For example, highways can be given a high value because they tend to attract growth while floodplains can be given a negative value because they tend to discourage growth. These Attractors and Discouragers are summed by the model and given an overall net value. This, in addition to the available general plan and the demographic values like current population, future population, numbers of households, etc., gives a basic picture of where growth is likely to occur.
The Attractors for the Base Case Scenario remain unchanged in all the subsequent scenarios. They include: highways, interstate ramps, major roads, minor roads, census blocks with growth (1990-2000), and existing spheres of influence. The Discouragers changed depending on the circumstances we wished to show for each scenario, and are explained for each scenario.
