Proposed Modification to Method for Determining Reasonable In-Season Demand for the Surface Water Coalition:

July 1 Prediction of Reasonable In-Season Demand

Presented to the SWC Methodology Technical Working Group by Matt Anders

February 19, 2015Settlement Document Subject to I.R.E. 408

Methodology Update

• Wildman Decision 2014 (pg 40)

• Revise the April forecast in mid-July

http://idwr.idaho.gov/files/legal/CV-2010-382/CV-2010-382_20140926_Judgment.pdf

• Proposed change

• July 1

• Forecast supply

• Calculate remaining Reasonable In-Season Demand (RISD)

• July 15

• Issue updated order with forecast supply and expected shortfall

Current Method Results

• Change in demand shortfall calculation from July 1 to November 1

• Shortfall = Water Supply - Demand

A&B AFRD2 BID Milner Minidoka NSCC TFCC SWSI2010 -13,861 -88,126 613 4,710 -8,048 -138,738 -17,873 0.52011 -19,246 -147,852 -33,391 -9,876 -39,110 -19,625 -26,700 3.72012 11,697 7,495 -7,079 12,169 -32 151,580 -34,979 0.72013 2,982 -2,063 11,047 4,741 43,243 977 -130,287 -2.22014 11,392 -57,500 -25,356 4,963 -25,460 -173,600 -265,167 1.3

• Units = AF• “-” indicates demand shortfall decreased

Current Method

• Predict remaining RISD using the average of the monthly baseline demand for the 2006/2008 baseline year.

• Can we use data from the current irrigation year to predict the remaining RISD?

Month

06/08 Monthly Baseline Demand

(ac-ft)Actual Monthly Demand (ac-ft)

Cumulative Actual Demand (ac-ft)

Actual Monthly CWN (ac-ft) PE

Monthly RISD (ac-ft)

Cummulative RISD (ac-ft)

Apr 774 1,765 1,765 1,316 1.02 1,294 1,294

May 6,755 10,546 12,311 4,695 0.53 8,901 10,195

Jun 9,768 12,367 24,678 7,665 0.59 12,954 23,150

Jul 12,709 0.66 12,709 35,858

Aug 9,464 0.64 9,464 45,323

Sep 5,242 0.58 5,242 50,565

Oct 1,619 0.37 1,619 52,185

• Table from “RISD & DS Calculator” tab in the “DS RISD Calculator_2014.xlsx” Excel spreadsheet.

RISD Plots

4 5 6 7 8 9 10 11 120

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

TFCC

200020012002200320042005200620072008200920102011201220132014

Month

Cum

ulat

ive

RISD

(AF)

• Plots for all SWC Members located in “RISD Summary” tab of “DS RISD Calculator_2014.xlsx”.

Predict RISD Using Regression

• Used RStudio to test various regression combinations:

• Diversions• Evapotranspiration (ETr)• Growing Degree Days (GGD)• Precipitation• First day of storage use by TFCC• Crop Water Need (CWN)• Surface Water Supply Index (SWSI)• Palmer Drought Severity Index (PDSI)

• Regression did not produce robust prediction of RISD.

• Data and R scripts used are located in the “Regression” folder.

RISD Plots

4 5 6 7 8 9 10 11 120

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

TFCC

200020012002200320042005200620072008200920102011201220132014

Month

Cum

ulat

ive

RISD

(AF)

• Plots for all SWC Members located in “RISD Summary” tab of “DS RISD Calculator_2014.xlsx”.

Proposed Method: Predict RISD Using Analog Year

• Tested in RStudio to determine if cluster analysis would help identify analog year(s).

• Different methods produced similar results

• Hierarchical• Agnes• Ward

• Non-Hierarchical• PAM• K-Means

• R script of testing (cluster_testing.R ) is located in the “Cluster” folder

Proposed Method: Predict RISD Using Analog Year

• Clustering in RStudio: Sum of Squared Error (SSE) to get an estimate of the natural clusters for use in K-Means.

• R script (cluster.R ) and process description (R_cluster_notes.docx) are located in the “Cluster” folder

Proposed Method: Predict RISD Using Analog Year

• Clustering in RStudio: K-Means.

Proposed Method: Predict RISD Using Analog Year

• Clustering in RStudio: Cluster Results

• The first cluster solution that puts all the data points into a cluster generally produces the best results for predicting RISD

• This can cause adjacent data points to be grouped with further away data points.

Proposed Method: Predict RISD Using Analog Year

• Predict RISD in Excel: Exclude abnormal years where after July 1 (unless the abnormal is the only analog):

• The cumulative RISD deviates from the general trend of the data and crosses the lines for multiple other years.

• There was an abnormal climate event that affected RISD.

2013 & 2014

• AFRD2• Milner• NSCC• TFCC

2014

• A&B• BID• Minidoka

Proposed Method: Predict RISD Using Analog Year

4 5 6 7 8 9 10 11 120

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

TFCC

200020012002200320042005200620072008200920102011201220132014

Month

Cum

ulat

ive

RISD

(AF)

Proposed Method: Predict RISD Using Analog Year

• Predict RISD in Excel: Calculate predicted remaining RISD

• Use clusters select analog year(s)

• Result = average RISD used in analog year(s)

Enter Analog Proj-Aug 1 Proj-Sept 1 Proj-Oct 1 Proj-Nov 1

1-Jul Years 8 9 10 11

2011 343,892 2000 Avg. Analog 580551 774823 909123 987838

2010

 

  Actual 570828 773195 942080 1020795

%Error 2% 0% -3% -3%

Analog-Actual 9724 1628 -32957 -32957

Comparison of Method Results

Error (AF) in July 1 RSID - Cluster Method

A&B AFRD2 BID Milner Minidoka NSCC TFCC2010 983 5,736 -4,207 2,166 -4,632 -14,429 -21,3832011 -153 -20,240 -5,563 -2,166 -8,835 -100,812 -32,9572012 292 -22,083 2,934 166 6,530 -45,488 -35,0252013 -3,102 33,780 -20,028 2,034 -19,933 54,277 85,3852014 1,355 88,451 -15,837 5,217 -11,265 121,595 221,373

% Error - RSID from July 1 to November 1 - Cluster Method

A&B AFRD2 BID Milner Minidoka NSCC TFCC2010 2% 2% -2% 5% -2% -1% -2%2011 0% -5% -2% -4% -3% -10% -3%2012 0% -5% 1% 0% 2% -4% -3%2013 -5% 8% -7% 4% -5% 5% 8%2014 2% 24% -5% 12% -3% 14% 23%

Comparison of Method Results

Error (AF) in July 1 RSID - Cluster Method

A&B AFRD2 BID Milner Minidoka NSCC TFCC2010 983 5,736 -4,207 2,166 -4,632 -14,429 -21,3832011 -153 -20,240 -5,563 -2,166 -8,835 -100,812 -32,9572012 292 -22,083 2,934 166 6,530 -45,488 -35,0252013 -3,102 33,780 -20,028 2,034 -19,933 54,277 85,3852014 1,355 88,451 -15,837 5,217 -11,265 121,595 221,373

Sqrt of Residual Sum

of Squares (RSS)

2010-2012

1037 30500 7566 3067 11923 111537 52632

Error (AF) in July 1 RSID - 06/08 BLY

A&B AFRD2 BID Milner Minidoka NSCC TFCC2010 -2,958 -15,090 -1,120 1,833 -4,818 38,652 14,3922011 -4,534 -3,583 -9,636 2,978 -14,702 74,757 40,9752012 -3,343 8,971 -4,985 3,110 -9,663 72,788 64,9072013 -1,562 -31,499 3,671 550 965 -30,255 -33,4732014 -6,482 -78,723 -10,319 -6,210 -24,563 -170,709 -156,200

Sqrt of Residual Sum

of Squares (RSS)

2010-2012

6362 17917 10907 4679 18241 111269 78096

Information on Website

http://idwr.idaho.gov/News/WaterCalls/Surface%20Coalition%20Call/

• Analog Folder

• Remaining RISD Calculations: Predict_RISD.xlsx

• Cluster Folder

• R script for cluster method: cluster.R• R script for cluster method testing: cluster_testing.R• Process notes for cluster method: R_cluster_notes.docx• RISD Data for clustering in R: RISD_July_1.csv

• Loose Files

• This PowerPoint: CDL_MidYear_RISD.pptx• RISD Calculator for 2014: DS RISD Calculator_2014.xlsx

Information on Website

http://idwr.idaho.gov/News/WaterCalls/Surface%20Coalition%20Call/

• Regression Folder - All

• R script for regression testing: RISD2.R• Data for regression in R: v1_2000_2013.csv & v1_wet.csv

• Regression Folder - TFCC

• R script for regression testing: Test1.R• Data for regression in R:

• TFCC_for_R_v1.csv• TFCC_for_R_v2.csv• TFCC_for_R_v2_aug1_colinearity.xlsx• TFCC_for_R_v2_july1_colinearity.xlsx• TFCC_for_R_v2_sept1_colinearity.xlsx• TFCC_for_R_v2b.csv• TFCC_for_R_v2c.csv

• Regression Folder - CWN

• R script for regression testing: cwn_Test2.R• Data for regression in R: TFCC_for_R_v4.csv

Questions?

(208) 287-4932matthew.anders@idwr.idaho.gov