Overview of the Envision Model
This project utilizes a model called Envision, a powerful platform that allows for the analysis of feedbacks across both natural and human-influenced systems. Two specific advantages to this model are the ability to represent differences across the study area and that it allows for the comparison of alternate scenarios to support planning and/or management decisions. The model was completed in three major steps:
At the core of this application, the model represents the dynamics of water resources and land resources in the Big Wood Basin. Following is a conceptual description of the model. For additional information, refer to the
Overview of the Water Resources Model
Evapotranspiration: The movement of water from the land surface to the air through evaporation plus transpiration from plants
Catchment: borders of drainage areas; any water that falls within a catchment or drainage area will all drain to the same place
Crop Curve: a coefficient used to estimate the amount of evapotranspiration from a specific crop type
Hydric Soils: soil that is permanently or seasonally saturated by water
To represent the water resource dynamics, the model requires an input of precipitation which is then simulated as snow or rain based on the air temperature. Snow melt and rain either infiltrate into soil water/shallow groundwater or evapotranspire, which removes water from the model. The amount of evapotranspiration (ET) is dependent on both the land cover as well as the climate. For example, evapotranspiration rates are higher in hot/dry weather than cool/wet weather. Water in the soil water or shallow groundwater can run off into a stream or reservoir. Much of the streamflow is withdrawn or diverted and used for purposes such as irrigation; the amount of irrigation water depends on the crop type and the efficiency of the delivery system. Some streamflow is diverted and transferred to areas outside the study area. Lastly, some streamflow continues to flow through the entire Big Wood River basin and eventually flows into the Malad River which flows into the Snake River, outside the boundary of the model.
Input data required for the water resources model include:
- Daily climate data
- >Spatial representation of:
- Stream & reservoir network
- Catchment boundaries
- Land cover type
- Surface water rights for irrigation
- Magic Reservoir operating constraints
- Crop curves
- Irrigation system conveyance and field application efficiencies
- Out-of-basin water transfers
- Historic reservoir, streamflow, & Snow Water Equivalent (SWE) data
Overview of the Land Resources Model
To simulate the land resources of the study area, we divided the basin into thousands of small polygons. For each polygon, we knew the land use or land cover in 2010 based on satellite data. The model then simulates the transition of land from one use or cover to another. For example, the amount of developed land may increase over time due to population growth. The pattern of this growth varies across the scenarios we explored, which are discussed later. The amount and type of agricultural land also changes depending on the scenario.
Input data required for the land resources model include:
- Spatial representation of
- 2010 land use/land cover
- 2010 population
- Zoning restrictions
- Hydric soils
- Prime farmland
- Surface water rights for irrigation
- Anticipated population growth rate
Calibration to Historical Data
Once the major pieces of the model were developed, we used what modelers refer to as a ‘calibration procedure’ to ensure the water resources model accurately represented historical observations - our best measure of the model’s performance - in the study area. This involved defining the values for 14 ‘parameters’, or settings, that dictate exactly how the model works. For example, one parameter defines the temperature above which precipitation is rain and below which precipitation is snow. Other parameters quantify how quickly snow melts or how quickly water travels from the soil water into the stream network.
To identify these parameter values, we ran the model 1,000 times using randomly generated parameter values. Each of these runs simulated the same five-year historic period. At the end of each run, we calculated how well the model simulated the observed conditions. From those 1,000 runs, we selected the 10 parameter sets that best represented historic observations and then repeated the process over a different five-year historic period with only those parameter sets. Finally, we selected the single parameter set that best represented the entire ten-year historic period. The graphs below show the modeled data and the historic observed data over the ten-year historic period. The observed data are from U.S. Geological Survey stream gaging locations within the watershed. See
Study Area for locations of the gages. Each graph lists a value for the
Nash-Sutcliffe efficiency; this is a metric used to quantify how well the model simulates historic data. A Nash-Sutcliffe value of 1 would indicate a perfect fit.
|Modeled and observed historic streamflows at USGS Gage 13139510, Big Wood River at Hailey. Nash-Sutcliffe efficiency value over 10 years: 0.79|
|Modeled and observed historic streamflows at USGS Gage 13140800, Big Wood River at Stanton Crossing. Nash-Sutcliffe efficiency value over 8 years: 0.80 (Note: no gage data available prior to October 1997)|
|Modeled and observed historic streamflows at USGS Gage 13141500, Camas Creek Near Blaine. Nash-Sutcliffe efficiency value over 10 years: 0.74|
|Modeled and observed historic storage volumes in Magic Reservoir, USGS Gage 13142000. Model efficiency was not calculated for this location. Note that the model did not fully represent the historic dynamics of reservoir volume. However, the model was able to accurately predict whether or not the reservoir filled (191,500 ac-ft) each year during the historic period.|
Exploration of Alternative Future Scenarios
The previous graphs show good correlation between the modeled, or simulated, data to the historic observations in the study area. This indicates that the model represents the basin dynamics reasonably well and thus can be used to explore how the water resources might look if the climate or management practices were different. For the third phase of model development, we explored how some of these different scenarios might affect water resources over the time period
The goal of an alternative futures analysis is to explore a wide range of possibilities that could occur in the future under different drivers of change in order to help inform planning and decision making. The primary drivers of change that we considered were changes in
future climate and human activity.
Future Climate: Through the first two stages of model development, we simulated the basin’s response to historic climate conditions. In this stage, we explored how things might look in the future under three different climate scenarios: Low Change from historic; Warmer/Wetter than historic; and Hotter/Drier than historic.
These climate inputs came from downscaled global climate models and were selected to represent a range of future possible conditions in the Big Wood Basin. Additional information is provided under
Climate Model Selection.
Human Activity: Human activity strongly influences the way land and water resources are used and managed. We explored future changes in human activity through three mechanisms:
- Economic base of the region. We considered two ends of an economic spectrum. On one end, the major economic base of the region would be agriculture, and at the other end would be tourism.
- Population growth rate. The model simulates an annual growth rate of 2.8%.
- Land/Water Management. We considered a range of management regimes, from little management of land and water resources to a regime where land and water are highly managed.
The various types of economic base and management regime were combined into four management scenarios, which are described in further detail on the next page.
With the input of the alternative scenarios into the model framework, we are able to explore a variety of model outputs that can provide information on how the Big Wood Basin might change under these conditions.