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ATTACHMENT(A:(RESUME(AND(STATEMENT(OF(QUALIFICATIONS(BRANDON(CHARLES(
! !
!
STATEMENT OF QUALIFICATIONS OF BRANDON J. CHARLES
Q. Please state your name and business address.
A. My name is Brandon Charles. I am a Senior Associate with MRW & Associates, LLC
(“MRW”). MRW is an energy consulting firm that was founded in 1986. MRW specializes in
power and gas market assessments, regulatory matters, litigation support, expert witness
testimony, contract review, and negotiations. My business address is 1814 Franklin Street, Suite
720, Oakland, California.
Q. Briefly summarize your educational background and professional experience.
A. I have worked at MRW for five years, with a focus on California’s electricity and natural gas
markets, regulations, and policies. During this time, I have worked with energy policy makers,
financial institutions, energy suppliers, and municipal and industrial end-users on a variety of
issues, including electricity ratemaking, departing load exit fees, and wholesale and retail
electricity price forecasting. I have also worked at Bloom Energy, where I analyzed electricity
and natural gas market prices and trends, market strategy and size, and distributed energy project
economics. I hold a bachelor’s degree in economics from Dartmouth College.
Q. Have you ever testified before this Commission?
A. Yes, I have previously testified on behalf of the South San Joaquin Irrigation District and the
California Farm Bureau Federation.
Q. For whom are you submitting this testimony?
A. I am submitting this testimony of behalf of the California Farm Bureau Federation.
!
BRANDON J. CHARLES PROFESSIONAL Senior Associate EXPERIENCE MRW & Associates, LLC (September 2008 – May 2013, March 2016 - present) Analyze electricity and natural gas market data with a focus on markets in the
western U.S. Develop models forecasting retail and wholesale electricity prices. Analyze and develop expert testimony regarding utility ratemaking and policy proposals. Research and interpret policy decisions and proposals affecting the energy market and develop reports on energy policy and market issues.
Senior Market Analyst Bloom Energy (June 2013 – March 2016) Analyzed electricity and natural gas market prices and trends, regulatory policies
impacting distributed generation markets, new market opportunities, stationary fuel cell addressable market size, and the economics of potential product offerings. Developed cash flow models and assumptions for distributed generation project economics for Fortune 500 customers and state policymakers.
Coordinator, Economic and International Policy
Biotechnology Industry Organization (BIO) (September 2006 – August 2008) Analyzed industry trends, legislative and regulatory policy developments, and economic issues in support of industry policy positions and related studies. Legal Assistant White & Case, LLP (July 2005 – June 2006) Managed U.S. litigation and international arbitration cases, including the arbitration of an international power plant development contract.
EDUCATION A.B., Economics, Dartmouth College, 2005
ATTACHMENT(B:(SELECTED(WORKSHEETS(FROM(A.159069001.(PACIFIC(GAS(&(ELECTRIC.(
PREPARED(TESTIMONY,(2016(ENERGY(RESOURCE(RECOVERY(ACCOUNT(AND(
GENERATION(NON9BYPASSABLE(CHARGES(FORECAST(AND(GREENHOUSE(GAS(FORECAST(REVENUE(RETURN(AND(RECONCILIATION.(JUNE(1,(2015.(PG&E(WORKPAPERS(“02.ERRA(20169FORECAST_WP_PGE_20150601_CH02_29
3_PUB(( (
1
2016%ERRA PACIFIC&GAS&AND&ELECTRIC&COMPANYAdjusted%for%EE,%DG,%and%EV.% &&&&&&&&&&&&&&ELECTRIC&DEPARTMENTCommunity%Choice%Aggregation%includes &&&&&&&COMPANY&SALES&AND&LOADSSonoma%Clean%Power%Phase% &(MWH)and%Marin%Clean%Energy.
Recorded2014 2015 2016
PG&E*SALES*AND*LOADS:
RESIDENTIAL:
**INDIVIDUAL 29,071,674 28,908,719 28,528,119
**MASTER*METER 751,268 751,629 741,734
****TOTAL 29,822,942 29,660,348 29,269,853
****Percent*Change G0.5% G1.3%
LIGHT*AND*POWER:
**SMALL 8,536,502 8,447,545 8,360,282
**MEDIUM 24,075,635 23,697,765 23,452,966
****TOTAL 32,612,137 32,145,311 31,813,248
****Percent*Change G1.4% G1.0%
LARGE*LIGHT*AND*POWER: 15,608,469 15,684,316 15,674,654
****Percent*Change 0.5% G0.1%
PUBLIC*AUTHORITY: 19,998 20,000 20,000
AGRICULTURE 7,576,024 6,750,475 6,991,044
STREET*LIGHTING 397,973 399,662 390,821
RAILWAY 359,978 360,000 360,000
RESALE: 0 0 0
INTERDEPARTMENTAL 305,941 300,000 300,000
TOTAL*PG&E*SALES 86,703,461 85,320,112 84,819,619
G1.6% G0.6%
NONGUTILITY*PROCUREMENT
**DIRECT*ACCESS 9,628,014 9,646,887 9,646,887
**COMMUNITY*CHOICE*AGGREGATION 1,838,222 3,547,960 3,943,156
**RAILWAY*(BART) 359,978 360,000 360,000
****TOTAL 11,826,213 13,554,847 13,950,043
1
Electric(Model(Variables
Sales'(PG&E'billing'system)
AG_SALES'8'Sales,'Agriculture
COM_SALES'8'Sales,'Commercial
IND_SALES'8'Sales,'Large'Light'and'Power
RES_SALES'8'Sales,'Individual'Meter
Billings'(PG&E'billing'system)
AG_ACCTS'8'Billings,'Agriculture
COM_ACCTS'8'Billings,'Commerical
IND_ACCTS'8'Billings,'Large'Light'and'Power
RES_ACCTS'8'Billings,'Residential
Exogenous
AFFORD'8'Housing'affordability'metric,'Moody's'Analytics
CDD_PGE'8'Cooling'Degree'Days,'75'Base'(PG&E'Meteorology)
Forecast'from'2013'NCAR'(Nat'l'Climate'and'Atmospheric'Research)'project
COM_RATE'8'Average'Price,'Commercial'Class'8'Inflated'by'AET'in'2015'and'CPI+1%'2016.
CPI_PGE'8'Consumer'Price'Index,'PG&E'Service'Area'(Moody's'Analytics)
EMP_TOT_PGE'8'Total'Employment,'PG&E'Service'Area'(Moody's'Analytics)
EFIN_PGE'8'Financial'Employment,'PG&E'Service'Area'(Moody's'Analytics)
EMP_INFO'8'Information'Employment,'PG&E'Service'Area'(Moody's'Analytics)
EMP_TOT_SVC'8'Service'sector'employment,'PG&E'Service'Area'(Moody's'Analytics)
GDP_MANUF'8'Gross'Metro'Product,'Manufacturing,'PG&E'Service'Area,'$2005'Constant''(Moody's'Analytics)
HDD_PGE'8'Heating'Degree'Days,'60'Base'(PG&E'Meteorology)
Forecast'from'2013'NCAR'(Nat'l'Climate'and'Atmospheric'Research)'project
HH_PGE'8'Households,'PG&E'Service'Area'(Moody's'Analytics)
Jan,'Feb,'Mar,'Apr,'May,'Jun,'Jul,'Aug,'Sep,'Oct,'Nov,'Dec'8'Monthly'Dummy'variables
"Jun'8'10"'and'others'like'it'8'dummy'variables'accounting'for'outlier'data'points.
OCCI_DUMMY'8'Dummy'variable'denoting'the'presence'of'Occidenal'Petroleum
@SEAS(2),'@SEAS(3)'8'Quarterly'Seasonal'Dummy'Variables'
RAIN1'8'Cumulative'rainfall,'4th'Q'(previous)'through'1st'Q'(Weather.com'website)
RAIN2'8'Cumulative'rainfall,'4th'Q'(previous)'through'2nd'Q'(Weather.com'website)
RAIN3'8'Cumulative'rainfall,'4th'Q'(previous)'through'3rd'Q'(Weather.com'website)
RECESSION'8'Manufactured'variable'characterizing'the'recessionary'period
RES_ACCTS_FORE'8'Residential'Accounts'Forecast
Res_Rate'8'Average'price,'Residential'Class'8'Inflated'by'AET'in'2015'and'CPI+1%'2016.
Real_Pers_Inc_PGE'8'Real'income,'C09$000,'PG&E'Service'Area'(Moody's'Analytics)
1
Dependent(Variable:(AG_SALESMethod:(Least(SquaresDate:(03/08/15(((Time:(18:52Sample:(2001Q1(2014Q4Included(observations:(56Convergence(achieved(after(22(iterationsMA(Backcast:(1999Q3(2000Q4
Variable Coefficient Std.(Error tQStatistic Prob.((
C 353,000,000 32,678,599(((((((((( 10.8118 0RAIN1 Q8,730,308 1,427,586(((((((((((( Q6.1154 0RAIN2 Q23,958,042 2,332,654(((((((((((( Q10.2707 0RAIN3 Q16,461,316 2.39E+06 Q6.8834 0@SEAS(2) 1,340,000,000 8.39E+07 16.0016 0@SEAS(3) 1470000000 78253163 18.7297 0
@TREND 16226733 477675 33.9703 0
MA(6) Q0.928423 0.021049 Q44.1068 0
RQsquared 0.9836 ((((Mean(dependent(var 1.27E+09Adjusted(RQsquared 1(((((((((((((((((((((((((((( ((((S.D.(dependent(var 584000000S.E.(of(regression 8.02E+07 ((((Akaike(info(criterion 39.3689Sum(squared(resid 3.09E+17 ((((Schwarz(criterion 39.6583Log(likelihood Q1094.33 ((((HannanQQuinn(criter. 39.4811FQstatistic 410.0974 ((((DurbinQWatson(stat 1.605781Prob(FQstatistic) 0
Inverted(MA(Roots 0.99 (((((.49Q.86i (((.49+.86i Q.49Q.86iQ.49+.86i Q0.99
Agricultural*Usage
ATTACHMENT(C:(A.159069001.(PACIFIC(GAS(&(ELECTRIC.(PREPARED(TESTIMONY,(2016(ENERGY(RESOURCE(RECOVERY(ACCOUNT(AND(GENERATION(NON9BYPASSABLE(
CHARGES(FORECAST(AND(GREENHOUSE(GAS(FORECAST(REVENUE(RETURN(AND(RECONCILIATION.(JUNE(1,(2015.(P.(295(
( (
Application: 15-06- (U 39 E) Exhibit No.: Date: June 1, 2015 Witness(es): Various
PACIFIC GAS AND ELECTRIC COMPANY
PREPARED TESTIMONY
2016 ENERGY RESOURCE RECOVERY ACCOUNT AND GENERATION NON-BYPASSABLE CHARGES FORECAST AND GREENHOUSE GAS
FORECAST REVENUE RETURN AND RECONCILIATION
PUBLIC VERSION
-i-
PACIFIC GAS AND ELECTRIC COMPANY 2016 ENERGY RESOURCE RECOVERY ACCOUNT AND GENERATION
NON-BYPASSABLE CHARGES FORECAST AND GREENHOUSE GAS FORECAST REVENUE RETURN AND RECONCILIATION
TABLE OF CONTENTS
Chapter Title Witness
1 INTRODUCTION AND POLICY Alexandra R. MacKie
2 SALES AND PEAK DEMAND FORECAST David Millar Mark D. Randazzo
3 GENERATION RESOURCE AND COST
SUMMARY George P. Clavier Andrew S. Klingler Steven Ng
4 FUEL COSTS FOR UTILITY-OWNED
GENERATION ASSETS Alexander Allan Mark Mayer John Schuknecht
5 QUALIFYING FACILITY AND PURCHASED
POWER COSTS Ruth E. Oscar
6 PROCUREMENT COSTS: POST-2002
CONTRACTED RESOURCES, RESIDUAL MARKET TRANSACTIONS AND OTHER COSTS
Alexander Allan
7 GREENHOUSE GAS COSTS Alexander Allan
8 HEDGING AND COLLATERAL COSTS Michael Kowalewski Patricia Martin
9 GENERATION NON-BYPASSABLE CHARGES Donna L. Barry Attachment A: Resolution E-4475, Exhibit A:
Proposed Formula to Calculate the Market Price Benchmark Per Decision 11-12-018
Attachment B: Excerpt From Decision 07-09-044,
Appendix A, Section IX: Implementation of Joint Parties’ Proposal
10 REVENUE REQUIREMENTS Angelia Lim
PACIFIC GAS AND ELECTRIC COMPANY 2016 ENERGY RESOURCE RECOVERY ACCOUNT AND GENERATION
NON-BYPASSABLE CHARGES FORECAST AND GREENHOUSE GAS FORECAST REVENUE RETURN AND RECONCILIATION
TABLE OF CONTENTS
(CONTINUED)
-ii-
Chapter Title Witness
11 GREENHOUSE GAS FORECAST REVENUE AND RECONCILIATION – COST CALCULATIONS
Alexander Allan Richard Lee William Reinwald
12 GREENHOUSE GAS FORECAST REVENUE AND RECONCILIATION – ADMINISTRATIVE AND OUTREACH EXPENSES
Lisa R. Long Jessica Tsang
13 GREENHOUSE GAS FORECAST REVENUE AND
RECONCILIATION – REVENUE CALCULATION Angelia Lim Tysen Streib
14 RATE PROPOSAL Tysen Streib
Attachment A: Illustrative Total Rates by Class
Appendix A TABLE OF GREENHOUSE GAS HISTORY OF REVENUE, COSTS AND EMISSIONS INTENSITY (TEMPLATE D-5)
Alexander Allan
Appendix B STATEMENTS OF QUALIFICATIONS Alexander Allan
Donna L. Barry George P. Clavier Andrew S. Klingler Michael Kowalewski Richard Lee Angelia Lim Lisa R. Long Alexandra R. MacKie Patricia Martin Mark Mayer David Millar Steven Ng Ruth E. Oscar Mark D. Randazzo William Reinwald John Schuknecht Tysen Streib Jessica Tsang
PACIFIC GAS AND ELECTRIC COMPANY
CHAPTER 2
SALES AND PEAK DEMAND FORECAST
2-i
PACIFIC GAS AND ELECTRIC COMPANY CHAPTER 2
SALES AND PEAK DEMAND FORECAST
TABLE OF CONTENTS
A. Introduction ....................................................................................................... 2-1
B. PG&E’s Load Requirements ............................................................................. 2-1
1. Sales and Peak Demand Forecast ............................................................ 2-2
a. Economic Backdrop ............................................................................. 2-2
b. Sales Forecast ..................................................................................... 2-3
c. Peak Demand Forecast ....................................................................... 2-5
2. Post-Regression Forecast Adjustments ..................................................... 2-5
a. Distributed Generation Adjustments .................................................... 2-5
b. Conservation and Energy Efficiency Adjustments ............................... 2-6
c. Electric Vehicles .................................................................................. 2-6
d. Transmission and Distribution Losses and Unaccounted for Energy ................................................................................................. 2-6
C. Departing Load ................................................................................................. 2-7
1. Departing Load Sales Methodology ........................................................... 2-7
1. Departing Load Settlements ..................................................................... 2-10
2. Departing Load Due to Direct Access ...................................................... 2-10
3. Departing Load Due to Community Choice Aggregation.......................... 2-11
D. Conclusion ...................................................................................................... 2-12
2-4
economic drivers from the Moody’s Analytics December 2014 update of 1
PG&E service territory economic forecast outlook. 2
PG&E expects total retail sales to decline 2.3 percent from the 2015 3
ERRA Forecast Application forecasted sales for 2015 to the 2016 ERRA 4
Forecast Application forecasted sales for 2016. 5
On a recorded basis, residential sector sales were down 1.1 percent 6
in 2013 from 2012 and down an additional 3.1 percent in 2014. After 7
controlling for the effect of weather, residential sales declined 8
2.5 percent between 2012 and 2013 and another 0.5 percent in 2014. 9
PG&E primarily attributes the drop in residential sales to increases in EE 10
savings and record installations of DG, primarily via customer-owned 11
photovoltaic (PV) systems. The effect of EE and DG are discussed in 12
Section 2 below. Given these trends, PG&E’s 2016 residential sales 13
forecast is projected to be 4.4 percent lower than PG&E’s forecasted 14
residential sales filed in the 2015 ERRA Forecast Application for 2015. 15
Between 2013 and 2014, commercial sales declined by 0.3 percent 16
on a recorded basis and 0.7 percent on a weather-adjusted basis. 17
Similar to the residential sector, the commercial sector has also become 18
more efficient and has adopted increasing amounts of DG, including 19
rooftop solar, fuel cells, Combined Heat and Power (CHP), and 20
micro-wind. Given these trends, PG&E’s 2016 commercial sales 21
forecast is projected to be 3.6 percent lower than PG&E’s forecasted 22
commercial sales filed in the 2015 ERRA Forecast Application for 2015. 23
Unlike residential and commercial, recorded industrial class sales 24
increased in 2014 by 2.6 percent, driven primarily by strong economic 25
growth. PG&E’s 2016 industrial sales forecast is projected to be 26
2.6 percent higher than PG&E’s forecasted industrial sales filed in the 27
2015 ERRA Forecast Application for 2015. 28
Agriculture continues to post record sales, which since 2012 have 29
grown about 23 percent. This surge in demand is driven by the extreme 30
drought, since farmers pump groundwater for irrigation needs during 31
periods when there are low or zero allocations of surface water from the 32
State Water Project. PG&E has observed growing numbers of 33
agricultural accounts as well as larger sales per account due to new 34
2-5
groundwater well installations coupled with large electric engines. Even 1
under PG&E’s forecast methodology, which assumes a return to normal 2
hydrological conditions, agricultural sales are forecast to be 2 percent 3
higher than PG&E’s forecasted agricultural sales filed in the 2015 ERRA 4
Forecast Application for 2015. 5
c. Peak Demand Forecast 6
PG&E also develops a retail peak load forecast for the total system, 7
using a regression methodology where the main model drivers are 8
heating and cooling degree days, household growth, and gross 9
metropolitan product as forecast by Moody’s Analytics. For 2016, PG&E 10
projects a retail peak demand shown in Table 2-3, line 19. Determining 11
PG&E’s bundled procurement need requires that the demand 12
associated with DA and CCA customers, along with their associated 13
losses, be removed from the system retail peak demand. Line 34 of 14
Table 2-3 shows PG&E’s procurement need during the annual peak. 15
2. Post-Regression Forecast Adjustments 16
Although the model-based forecast described above remains at the core 17
of PG&E’s sales and peak demand forecast process, the regression forecast 18
alone cannot incorporate all of the impacts of new technologies and policy 19
impacts. For this reason, PG&E takes an additional step to adjust its 20
regression forecast before arriving at final sales and peak demand 21
forecasts. In general, the adjustments described in this section are 22
designed to incorporate the latest EE, DG, Electric Vehicle (EV), DA and 23
CCA expectations and account for transmission and distribution (T&D) 24
losses and Unaccounted for Energy (UFE). 25
a. Distributed Generation Adjustments 26
The PG&E service territory is a national leader in rooftop solar 27
installations. Approximately 20 percent of the nation’s distributed solar 28
capacity is interconnected in PG&E’s territory.6 PG&E expects 29
significant growth of rooftop solar as the economics continue to become 30
more favorable. Table 2-3, lines 5 and 22, show reduced consumption 31
6 Solar Energy Power Association 2014 Utility Solar Rankings Database.
ATTACHMENT(D:(CALIFORNIA(DEPARTMENT(OF(WATER(RESOURCES.(STATE(WATER(PROJECT(ALLOCATION(INCREASED,(STORMS(IMPROVE(CONDITIONS,(BUT(DROUGHT(REMAINS.(APRIL(
21,(2016(
( (
!
!
April 21, 2016 Contact: Ted!Thomas,!DWR!Information!Officer!–!(916)!653>[email protected]!Doug!Carlson,!DWR!Information!Officer!–!(916)!653>[email protected]!
State Water Project Allocation Increased Storms Improve Conditions, but Drought Remains
SACRAMENTO – With runoff from spring storms boosting reservoir levels, the Department of Water Resources (DWR) today increased its water delivery estimate for most recipients to 60 percent of requests for the calendar year. DWR’s initial State Water Project (SWP) allocation, announced in December, was 10 percent of requests. As storms developed, the allocation was increased to 15 percent on January 26, then to 30 percent on February 24 and 45 percent on March 17. Today’s boost to a 60 percent allocation is mostly due to March storms that soaked Northern California after a mostly dry February. Still, the state’s historic drought is far from over. The storms that have nearly filled key northern reservoirs, including Shasta, Oroville and Folsom, largely skipped the San Joaquin Valley and Southern California where reservoir storage remains low and some communities have seen their wells go dry. It normally takes more than one wet year to erase the impacts of multi-year droughts, and decades to replenish groundwater levels. Accurately predicting whether water year 2017 will be wet, dry, or average is beyond the skill of climate forecasters, so California must be prepared for the possibility that next year will be dry. “Conservation is the surest and easiest way to stretch supplies,” said DWR Director Mark Cowin. “We all need to make the sparing, wise use of water a daily habit.” The 29 public agencies that receive SWP water (State Water Project Contractors) requested 4,172,786 acre-feet of water for 2016. With today’s allocation increase, they will receive 2,527,629 acre-feet.
Collectively, the SWP Contractors serve approximately 25 million Californians and just under a million acres of irrigated farmland. The SWP provides the same allocation percentages to cities and farms Through the drought that began in 2012, agriculture and many communities have felt the pain of low allocations from the SWP and federal Central Valley Project (CVP), with vast tracts of farmland fallowed and drinking water systems failing in some communities. It is important to note that nearly all areas served by the SWP also have other sources of water, among them streams, groundwater and local reservoirs. Key reservoirs are rising from winter storms, but some remain below average for the date. Lake Oroville in Butte County, the State Water Project’s principal reservoir, early this morning was holding 3,314,718 acre-feet, 94 percent of its 3.5 million acre-foot capacity and 118 percent of its historical average for the date. Shasta Lake north of Redding, California’s and the CVP’s largest reservoir, was holding 4,184,298 acre-feet, 92 percent of its 4.5 million acre-foot capacity and 109 percent of its historical average. But San Luis Reservoir, a critical south-of-Delta pool for both the SWP and CVP, was holding 1,013,003 acre-feet, only 50 percent of its 2 million acre-foot capacity and 55 percent of its historical average for the date. Low San Luis Reservoir levels reflect restrictions on SWP and CVP pumping plants in the Delta in order to protect threatened and endangered fish. Folsom Lake, a CVP reservoir near Sacramento, has risen to 82 percent of its 977,000 acre-foot capacity, 115 percent of historical average for the date. Under federal rules, Shasta, Oroville and Folsom have had to make flood control releases to retain storage space for heavy inflow. Groundwater aquifers recharge much more slowly than surface reservoirs, and many in the Central Valley have fallen to record levels in this drought. Last year’s (2015) 20 percent SWP allocation was the second lowest since 1991, when agricultural customers of the SWP got a zero allocation and municipal customers received 30 percent of requests. In 2014, SWP deliveries were five percent of requested amounts for all customers. The last 100 percent allocation was in 2006. SWP allocations in recent years: 2015 – 20 percent
2014 – 5 percent
2013 – 35 percent
2012 – 65 percent
2011 – 80 percent
2010 – 50 percent
2009 – 40 percent
2008 – 35 percent
2007 – 60 percent
2006 – 100 percent
DWR’s California Data Exchange Center (CDEC) Web sites show current water conditions at the state’s reservoirs and weather stations.
Reservoirs: http://cdec.water.ca.gov/cdecapp/resapp/getResGraphsMain.action
Precipitation: http://cdec.water.ca.gov/snow_rain.html
Snow: http://cdec.water.ca.gov/cdecapp/snowapp/sweq.action
Governor Edmund G. Brown Jr. declared a drought state of emergency in January 2014 and directed state agencies to take all necessary actions to respond to drought conditions. In April 2015, Governor Brown announced the first-ever 25 percent statewide mandatory water reductions and a series of actions to help save water, increase enforcement to prevent wasteful water use, streamline the state's drought response and invest in new technologies that will make California more drought resilient. Californians have responded with unprecedented conservation efforts.
To date, guided by the California Water Action Plan, the state has committed hundreds of millions of dollars - including Water Bond funds - to emergency drought relief, disaster assistance, water conservation and infrastructure projects across the state. To learn about all the actions the state has taken to manage our water system and cope with the impacts of the drought, visit Drought.CA.gov.
-30-
!!Every Californian should take steps to conserve water; find out how at!
SaveOurWater.com.
!!
!
To unsubscribe from the DWR_NEWSRELEASES list, click the following link: https://LISTSERV.STATE.CA.GOV/wa.exe?SUBED1=DWR_NEWSRELEASES&A=1
!
!
!
!
vi"
ATTACHMENT"E:"CALIFORNIA"DEPARTMENT"OF"1"
WATER"RESOURCES."CALIFORNIA"DATA"2"
EXCHANGE"CENTER"–"RESERVOIRS."3"
CONDITIONS"FOR"MAJOR"RESERVOIRS"ON"4"
SELECTED"DATE5"
8/22/16, 4:57 PMMajor Reservoir Current Conditions Graphs
Page 1 of 1http://cdec.water.ca.gov/cdecapp/resapp/getResGraphsMain.action
California Data Exchange Center - ReservoirsCalifornia Data Exchange Center - Reservoirs
Pine Flat23% | 54%
(Total Cap.) (Hist. Avg.)
Exchequer46% | 79%
(Total Cap.) (Hist. Avg.)
CONDITIONS FOR MAJOR RESERVOIRS: 19-AUG-2016
Castaic Lake76% | 93%
(Total Cap.) (Hist. Avg.)
Don Pedro69% | 94%
(Total Cap.) (Hist. Avg.)
Folsom Lake38% | 57%
(Total Cap.) (Hist. Avg.)
Lake Perris37% | 47%
(Total Cap.) (Hist. Avg.)
Millerton Lake54% | 104%
(Total Cap.) (Hist. Avg.)
San Luis12% | 26%
(Total Cap.) (Hist. Avg.)
New Melones23% | 39%
(Total Cap.) (Hist. Avg.)
Trinity Lake45% | 59%
(Total Cap.) (Hist. Avg.)
Lake Shasta72% | 109%
(Total Cap.) (Hist. Avg.)
Lake Oroville56% | 83%
(Total Cap.) (Hist. Avg.)
Change Date: 19-Aug-2016
Refresh Data
LEGENDBlue Bar: Storage level for dateGold Bar: Total reservoir capacity.Red Line: Historic level for date.
(Click reservoir name for details)
Capacity(TAF)
% of Capacity | % Historical Avg
HistoricalAvg Mark
Data as of Midnight: 19-Aug-2016
Click for printable version of current data. Report Generated: 22-Aug-2016 3:54 PM
8/23/16, 10:17 AMMajor Reservoir Current Conditions Graphs
Page 1 of 1http://cdec.water.ca.gov/cdecapp/resapp/getResGraphsMain.action
California Data Exchange Center - ReservoirsCalifornia Data Exchange Center - Reservoirs
Pine Flat23% | 54%
(Total Cap.) (Hist. Avg.)
Exchequer52% | 90%
(Total Cap.) (Hist. Avg.)
CONDITIONS FOR MAJOR RESERVOIRS: 20-AUG-2012
Castaic Lake84% | 102%
(Total Cap.) (Hist. Avg.)
Don Pedro66% | 91%
(Total Cap.) (Hist. Avg.)
Folsom Lake54% | 82%
(Total Cap.) (Hist. Avg.)
Lake Perris55% | 72%
(Total Cap.) (Hist. Avg.)
Millerton Lake54% | 104%
(Total Cap.) (Hist. Avg.)
San Luis31% | 70%
(Total Cap.) (Hist. Avg.)
New Melones66% | 112%
(Total Cap.) (Hist. Avg.)
Trinity Lake81% | 107%
(Total Cap.) (Hist. Avg.)
Lake Shasta64% | 97%
(Total Cap.) (Hist. Avg.)
Lake Oroville67% | 98%
(Total Cap.) (Hist. Avg.)
Change Date: 20-Aug-2012
Refresh Data
LEGENDBlue Bar: Storage level for dateGold Bar: Total reservoir capacity.Red Line: Historic level for date.
(Click reservoir name for details)
Capacity(TAF)
% of Capacity | % Historical Avg
HistoricalAvg Mark
Data as of Midnight: 20-Aug-2012
Click for printable version of current data. Report Generated: 23-Aug-2016 10:16 AM
!
7"
"ATTACHMENT"F:"SELECTED"PAGES"FROM"
FUCHS,"BRIAN."“PALMER"DROUGHT"SEVERITY"INDEX"(PSDI"AND"SCPDSI).”"NATIONAL"
DROUGHT"MITIGATION"CENTER,"UNIVERSITY"OF"NEBRASKA:LINCOLN"PRESENTATION,"
CARRIBEAN"DROUGHT"WORKSHOP."MAY"22:24,"2012"
Palmer Drought Severity Index (PSDI and scPDSI)
Brian Fuchs
National Drought Mitigation Center University of Nebraska-Lincoln
Caribbean Drought Workshop May 22-24, 2012
How the PDSI works Responds to both wet and dry conditions.
The PDSI is calculated based on precipitation and temperature data, as well as the local Available Water Content (AWC) of the soil. From the inputs, all the basic terms of the water balance equation can be determined, including evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer.
Running the PDSI requires temperature, precipitation, and soils information
Drawbacks of the Palmer Drought Severity Index
The values quantifying the intensity of drought and signaling the beginning and end of a drought or wet spell were arbitrarily selected based on Palmer’s study of central Iowa and western Kansas and have little scientific meaning.
The Palmer Index is sensitive to the available water content (AWC) of a soil type. Thus, applying the index for a climate division may be too general.
The two soil layers within the water balance computations are simplified and may not be accurately representative of a location.
Snowfall, snow cover, and frozen ground are not included in the index. All precipitation is treated as rain, so that the timing of PDSI or PHDI values may be inaccurate in the winter and spring months in regions where snow occurs.
The natural lag between when precipitation falls and the resulting runoff is not considered. In addition, no runoff is allowed to take place in the model until the water capacity of the surface and subsurface soil layers is full, leading to an underestimation of runoff.
Potential evapotranspiration is estimated using the Thornthwaite method. This technique has wide acceptance, but it is still only an approximation.
Other limitations described over time by additional research
PDSI is designed for agriculture but does not accurately represent the hydrological impacts resulting from longer droughts.
The PDSI does not do well in regions where there are extremes in the variability of rainfall or runoff.
The “extreme” and “severe” classifications of drought occur with a greater frequency in some parts of the country than in others (U.S.)
“Extreme” droughts in the Great Plains occur with a frequency greater than 10%. This limits the accuracy of comparing the intensity of droughts between two regions and makes planning response actions based on a certain intensity more difficult.
Brian Fuchs
[email protected] 402-472-6775
National Drought Mitigation Center School of Natural Resources
University of Nebraska-Lincoln
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