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CRDSS_Task1_14-22_IrrigationCULosses_GunnisonBasin
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CRDSS_Task1_14-22_IrrigationCULosses_GunnisonBasin
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9/25/2011 10:18:50 AM
Creation date
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Decision Support Systems
Title
CRDSS Task 1.14-22 - Consumptive Use Model - Irrigation Consumptive Uses and Losses in the Gunnison River Basin
Description
This task memorandum formalizes the verification/implementation of the CRDSS consumptive use (CU) model for the Gunnison River basin as a prototype.
Decision Support - Doc Type
Task Memorandum
Date
1/9/1995
DSS Category
Consumptive Use
DSS
Colorado River
Basin
Gunnison
Contract/PO #
C153658, C153727, C153752
Grant Type
Non-Reimbursable
Bill Number
SB92-87, HB93-1273, SB94-029, HB95-1155, SB96-153, HB97-008
Prepared By
Riverside Technology inc.
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average annual use). Figure 2 shows the 6-year average annual IWR by subarea. It should be noted that <br />the highest volumetric water use by subarea, by county, and by HU is in Motrose6 (99,850 and 109,240 <br />acre-ft), Delta (146,400 and 160,590 acre-ft), and HU14020006 (117,550 and 131,040 acre-ft) for the <br />SCS Blaney-Criddle with and without enhancements, respectively. <br />Figure 3 shows the 6-year average annual IWR in terms of unit depth for each subarea. The average <br />value for the whole Gunnison River basin is 16.7 and 18.8 inches using the SCS Blaney-Criddle with and <br />without enhancements, respectively. The highest per unit depth water use by subarea, by county, and by <br />HU is in Mesa5 (27.5 and 28.9 inches), Mesa (27.5 and 28.9 inches), and HUC14020005 (22.6 and 24.5 <br />inches) for the SCS Blaney-Criddle with and without enhancements, respectively. The least per unit <br />depth water use is estimated in Gunnison County (9.0 and 11.2 inches for the SCS Blaney-Criddle with <br />and without enhancements, respectively). <br />The IWR estimate by the two methods differs by approximately 45,000 acre-ft, with the enhanced <br />version of the SCS Blaney-Criddle method giving the lower estimate. The bulk of this difference can be <br />attributed to an assumption that in the first year (1985), the soil moisture capacity is full. In the <br />enhanced version where soil moisture budget is performed, the crop is assumed to be able to deplete this <br />soil moisture storage. It should be noted that in 1985, the difference in IWR is approximately 43,000 <br />acre-ft. Other factors that contribute to the difference in the estimates include 1) replenishment of the <br />soil moisture storage (from winter precipitation) prior to the growing season, and 2) revised values for <br />effective rainfall that are applicable for the CRDSS enhanced CSC Blaney-Criddle, which includes a soil <br />moisture budget. The latter could be a positive or a negative change depending on whether the excess <br />rainfall is stored in the soil root zone for later use, or lost through deep percolation. <br />A more detailed tabular result is included in Section 4.0. <br />Data Set 2 - Each Crop Associated with an Aggregate Soil Type <br />All the soil types associated with a certain crop type in a subarea have been combined and represented by <br />an aggregate soil type. The soil type with the largest area in the combination is chosen to be the <br />aggregate soil type. The AWC for the aggregate soil type is calculated by areal average of the AWCs of <br />each soil type. This reduces the number of crop-soil combinations to 61. The advantage of this <br />reduction becomes obvious when the other characteristics, such as planting/harvesting dates, are <br />permutated for a given crop and soil combination, i.e consideration of alfalfa-short and pasture-short <br />areas. <br />Figure 4 shows the total annual IWR for the Gunnison River basin computed using the two forms of the <br />SCS Blaney-Criddle ET estimation method (with and without enhancements). The IWR values are not <br />significantly different from the results of the first data set. For instance, the 6-year average annual IWR <br />is 355,640 and 400,730 acre-ft for the SCS Blaney-Criddle with and without enhancements, respectively. <br />This means that identifying the same crop separately by the type of soil (as in the first data set) seems to <br />have little effect on the results. By assigning each crop just one type of soil that has the aggregate <br />property of all the types of soil associated with the crop, the size of the input data can be dramatically <br />reduced. <br />A more detailed tabular result is included in Section 4.0 <br />3 <br />A275 01.0995 1.14-22 Manguerra <br />
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