[Modeling Tool] [Agriculture] [Human Settlement] [Preservation] [Results] [The Next Step]

Spatial Modeling as an Analysis Tool
A model is an abstract, simplified interpretation of reality.  It strives to imitate and predict how phenomena would occur, or end results would appear, in actual life. Because "reality" is so complex and essentially mysterious it cannot be perfectly imitated.  As in our case, we did not endeavor to include all possible influences that would lead to the answer of a question like "where should human settlement occur on this property".  However given the scope and time constraints of this project we included a selection of data that was available and which we created.  For this selection we combined our firsthand knowledge about the area with the knowledge we had gained from the resources we consulted and our judgment as students of environmental planning. By representing only those factors that are important to our study, a model creates a simplified, manageable view of the real world.

A GIS-Model is a collection of processes performed on spatial data that produces information, usually in the form of a map.  One may use this map for education, decision making, scientific study, and to provide general information. Each process in a model has three components: input data, a function that transforms the input data or derives new information from it, and the output data that the function creates. For this project, input data was digitized from maps and collected in the field.  ArcView's ModelBuilder extension performed the transformation functions, and the output data was created in shapefile format.

Spatial models can do the following:

• Rate geographic areas according to a set of criteria. The type of spatial model we have employed evaluates site suitability to various uses (agriculture, human settlement, and preservation). To choose the most suitable land for these various uses, you must decide how many factors you will consider and which are most important. You might decide that water availability is the most important factor, or proximity to infrastructure, or wildlife habitat considerations.

• Make predictions about what occurs or will occur in geographic areas. To predict which areas are most likely to be suitable for farming, you need to know something about soil depth, organic content of the soil, and availability of water for irrigation.  By overlaying the relevant the data sets in a model, for example, one can generate results in a map representing zones with a range of suitability for agriculture. 

A more complex spatial model may combine quantitative information (for instance, how far away is something or how much does it cost) with qualitative information (for instance, how desirable is something or how important is it).  We established inputs and values for each theme within the model.  See the specific model descriptions below to view the algorithm used for each. The algorithm consists of input themes, percent influence, input labels and scale values. The following definitions refer to all three model algorithms depicted in table format below.

The Agricultural Suitability Model
Soil and water comprise the essential elements for agriculture in this climate, and most of the attributes in the agricultural suitability model refer to these elements.   Minimum soil depth is an obvious necessity for agriculture. However, not just any soil is ideally suited for agriculture.  The percent of organic matter in the soil, the ability of the soil to drain, and the ability of the soil to release nutrients to a growing plant (cation exchange capacity) were also taken into account.  Staying away from areas prone to erosion was also important and was represented in the model.

Next, access to water came into play.  At first we thought the mere proximity to a water source would be considered.  But then we realized that the capacity of the water delivery system needed to be taken into consideration.  Hence a "sub-model" was created which assigned values to both the proximity and the delivery capacity of the water source (depending on elevation) using Boolean logic of if-then scenarios.  For example, if an area (grid cell) of land was at an elevation below a gravity flow earthen acequia it was assigned a higher value than an area of land that had an elevation higher than the same acequia.  Each delivery system was rated accordingly including acequias (both concrete and earthen) center-pivot irrigation systems, wells, and a large water reservoir.

Slopes were included in the model.  Flat slopes are more conducive to agriculture in an area that is periodically affected by flash flooding and erosion, therefore steep slopes were given a low value.  So too were areas that would be difficult to cultivate due to the vegetation already present.  Here we were specifically thinking of several areas of heavy mesquite which is famous for its difficulty of removal.

Finally we considered the areas already developed with buildings as being unsuitable for use as agriculture. Refer to the model-building methodology for a technical description of how this was accomplished using the ArcView ModelBuilder extension and a flowchart depicting the algorithmic process used to convert shapefiles into agricultural suitability results. 

The Human Settlement Suitability Model
In considering what areas would be most suitable for human habitation (dwellings), slopes and aspect (i.e., orientation to the sun) were important considerations.  While exotic forms of architecture can be built on very steep slopes, we valued flatter terrain for its more sustainable potential and to avoid the hazards of erosion that might be caused by developing on the steeper areas.  Soil depth came into consideration here because of the engineering involved in home construction, so deeper soil was given a higher value than shallower soil. Also we wanted to preserve the areas of rocky outcrops, areas with little or no soil, not only for the engineering problems they might pose, but  for their scenic beauty.  Avoiding the southern exposure was considered critical in this arid climate where few tall trees are available for shading from harsh sun.

There was discussion about how to evaluate the floodplain because of the nature and location of the river terraces.  The field research revealed that the terraces had been flooded in recent time as witnessed by extensive debris in these areas.  The 100-year floodplain map, however, showed floodplains extending well beyond the terraces into areas considerably higher in elevation.  We chose not to exclude these areas but assign them a suitability value one-half of that of the area outside the floodplain.  In such situations in the United States, a home owner would be obliged to purchase flood insurance.

Finally somewhat like the agricultural model, proximity and elevation in relation to water infrastructure were evaluated and valued. Refer to the model-building methodology for a technical description of how this was accomplished using the ArcView ModelBuilder extension and a flowchart depicting the algorithmic process used to convert shapefiles into human settlement suitability results. 

The Preservation Suitability Model
Areas were assigned high preservation value based on several factors, including the extent of plant biodiversity.  Higher diversity, we deduced, equaled higher wildlife habitat.  Also since there was not much variability throughout the site, higher plant diversity was valued for its uniqueness alone.  Livestock tanks and the one area of wetlands around the large water reservoir were also valued according to their ability to attract wild life and nurture water loving herbaceous plants.  Arroyos were valued because of their generally larger percentage of vegetative cover and ability to provide shady microclimate.  As mentioned above, rocky outcrops were valued because of their beauty and the views available on top of them.

Finally, there were several cultural features that merited consideration for preservation.  The ruins of an old house located in the southeast quadrant of the ranch property were assigned special value, as was an area with a high concentration of Indian arrowheads and flints. Refer to the model-building methodology for a technical description of how this was accomplished using the ArcView ModelBuilder extension and a flowchart depicting the algorithmic process used to convert shapefiles into preservation suitability results. 

Results
Agricultural Suitability
Natural landscape features used in the model included several soil-related factors, including soil depth, soil cation exchange capacity, organic content, internal soil drainage, slope, aspect (direction of slope), rocky outcrops, and highly erosive zones.  Cultural measures used in modeling suitability for agriculture included the ability to provide pressurized water flow from the existing irrigation system and large water tank.  Numerical values resulting from running the model yielded several categories of suitability, ranging from Restricted Areas to  Lowest to Highest Suitability.   The areas in the vicinity of the existing irrigation systems scored the best in the model.  Water availability was the most determining factor influencing the model results. Soil types were not altogether very variable around the ranch.