Evapo-transpiration Losses Produced by Irrigation in the Snake River Basin, Idaho

Similar documents
HyMet Company. Streamflow and Energy Generation Forecasting Model Columbia River Basin


Modeling of peak inflow dates for a snowmelt dominated basin Evan Heisman. CVEN 6833: Advanced Data Analysis Fall 2012 Prof. Balaji Rajagopalan

Changing Hydrology under a Changing Climate for a Coastal Plain Watershed

PRELIMINARY DRAFT FOR DISCUSSION PURPOSES

Folsom Dam Water Control Manual Update Joint Federal Project, Folsom Dam

APR-SEP. Forecast Are in KAF % Average 10 % 30 Year. Forecast Period

ACCUMULATED PRECIPITATION IN INCHES

Lower Tuolumne River Accretion (La Grange to Modesto) Estimated daily flows ( ) for the Operations Model Don Pedro Project Relicensing

Missouri River Basin Water Management

REDWOOD VALLEY SUBAREA

Albeni Falls Operations Meeting 2015

Integrating Weather Forecasts into Folsom Reservoir Operations

Folsom Dam Water Control Manual Update Joint Federal Project, Folsom Dam

2015 Fall Conditions Report

Chiang Rai Province CC Threat overview AAS1109 Mekong ARCC

The Climate of Oregon Climate Zone 4 Northern Cascades

Climate also has a large influence on how local ecosystems have evolved and how we interact with them.

GAMINGRE 8/1/ of 7

February 27, Jim Ruff, Manager, Mainstem Passage and River Operations. March 2008 Runoff Forecast and Power Supply Status

A Review of the 2007 Water Year in Colorado

Climate Variability. Eric Salathé. Climate Impacts Group & Department of Atmospheric Sciences University of Washington. Thanks to Nathan Mantua

Three main areas of work:

Souris River Basin Spring Runoff Outlook As of March 1, 2019

ACCUMULATED PRECIPITATION IN INCHES

FORECAST-BASED OPERATIONS AT FOLSOM DAM AND LAKE

ACCUMULATED PRECIPITATION IN INCHES

Attachment B to Technical Memorandum No.2. Operations Plan of Ross Valley Detention Basins

ACCUMULATED PRECIPITATION IN INCHES

2003 Water Year Wrap-Up and Look Ahead

A Report on a Statistical Model to Forecast Seasonal Inflows to Cowichan Lake

Impacts of climate change on flooding in the river Meuse

Souris River Basin Spring Runoff Outlook As of March 15, 2018

Colorado s 2003 Moisture Outlook

Climate Change Impact Assessment on Indian Water Resources. Ashvin Gosain, Sandhya Rao, Debajit Basu Ray

NRC Workshop Probabilistic Flood Hazard Assessment (PFHA) Jan 29-31, Mel Schaefer Ph.D. P.E. MGS Engineering Consultants, Inc.

9. PROBABLE MAXIMUM PRECIPITATION AND PROBABLE MAXIMUM FLOOD

3.0 TECHNICAL FEASIBILITY

Disentangling Impacts of Climate & Land Use Changes on the Quantity & Quality of River Flows in Southern Ontario

San Francisco Public Utilities Commission Hydrological Conditions Report For March 2016

Missouri River Basin Water Management Monthly Update

Hydrogeology and Simulated Effects of Future Water Use and Drought in the North Fork Red River Alluvial Aquifer: Progress Report

Water Management for Environmental Restoration Flows In the Big Bend reach, Rio Grande Rio Bravo

ZUMWALT WEATHER AND CLIMATE ANNUAL REPORT ( )

ACCUMULATED PRECIPITATION IN INCHES

Climatography of the United States No

The Climate of Oregon Climate Zone 5 High Plateau

Missouri River Basin Water Management Monthly Update

Drought Characterization. Examination of Extreme Precipitation Events

Missouri River Basin Water Management Monthly Update

2003 Moisture Outlook

Climatography of the United States No

Climatography of the United States No

Assessing bias in satellite rainfall products and their impact in water balance closure at the Zambezi headwaters

2011 Flood: Technical Review of Lake Manitoba, Lake St. Martin and Assiniboine River Water Levels Summary

Jackson County 2014 Weather Data

Drought in Southeast Colorado

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Memo. I. Executive Summary. II. ALERT Data Source. III. General System-Wide Reporting Summary. Date: January 26, 2009 To: From: Subject:

Climatography of the United States No

Climatography of the United States No

San Francisco Public Utilities Commission Hydrological Conditions Report For April 2014

Variability of Reference Evapotranspiration Across Nebraska

Hydrologic Conditions in the Delaware River Basin

Climate Change and Water Supply Research. Drought Response Workshop October 8, 2013

Climatography of the United States No

Climatography of the United States No

Promoting Rainwater Harvesting in Caribbean Small Island Developing States Water Availability Mapping for Grenada Preliminary findings

The Colorado Drought : 2003: A Growing Concern. Roger Pielke, Sr. Colorado Climate Center.

Missouri River Basin Climate Outlook 1 May Dr. Dennis Todey State Climatologist South Dakota State Univ.

NATIONAL HYDROPOWER ASSOCIATION MEETING. December 3, 2008 Birmingham Alabama. Roger McNeil Service Hydrologist NWS Birmingham Alabama

Appendix C. AMEC Evaluation of Zuni PPIW. Appendix C. Page C-1 of 34

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

WHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and Rainfall For Selected Arizona Cities

4. THE HBV MODEL APPLICATION TO THE KASARI CATCHMENT

II. HYDROMETEOROLOGY. OBSERVATIONS: Weather Snowpack SWSI Streamflow Flood Events FORECASTS: Runoff Volume Long Range Peaks Daily Streamflows

Climatography of the United States No

Climatography of the United States No

DROUGHT IN MAINLAND PORTUGAL

Climatography of the United States No

Climatography of the United States No

2. PHYSICAL SETTING FINAL GROUNDWATER MANAGEMENT PLAN. 2.1 Topography. 2.2 Climate

Climatography of the United States No

Weather History on the Bishop Paiute Reservation

Jackson County 2013 Weather Data

Technical note on seasonal adjustment for M0

Jackson County 2019 Weather Data 68 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center

Transcription:

Nov 7, 2007 DRAFT Evapo-transpiration Losses Produced by Irrigation in the Snake River Basin, Idaho Wendell Tangborn and Birbal Rana HyMet Inc. Vashon Island, WA Abstract An estimated 8 MAF (million acre-feet) of water is diverted annually from the Snake River in Idaho, a tributary of the Columbia River, for irrigation of about 3.5 million acres of cropland. However, a recently developed water balance model for the Snake basin indicates that approximately 9 MAF (31 inches averaged over the irrigated areas) is lost from the system by evapotranspiration (ET), suggesting that not all diverted water is measured. Reconstructed inflows at Lower Granite Dam on the Snake River do not account for diversions for irrigation or for ET losses, therefore are 20-25 % less than natural flow. The ET model presented in this report simulates daily water losses using observed temperature and precipitation, and is based on the premise that the difference in the timing and quantity of Snake River inflows compared with nearby river basins is caused by large and unusual water losses in the Snake River basin. Addition of simulated ET to the reconstructed inflows produces a more realistic natural flow estimate and significantly improves the accuracy of seasonal streamflow forecasts at Lower Granite Dam. Knowledge of the volume and timing of water that is lost by ET is an important factor in the hydrology of the Snake River basin because: 1. It increases the error of Snake River seasonal runoff forecasts at Lower Granite Dam. 2. It demonstrates the need to improve irrigation distribution efficiency to minimize water losses. 3. It is a essential factor for water conservation and land use planning 4. It provides the missing link for Snake River water balance models A water balance model has been developed to produce daily ET losses for the Snake River basin above Lower Granite Dam for the 1969-2007 period. The model is based on the premise that the difference in runoff patterns (timing and quantity) of reconstructed Snake River inflows, compared with those of the nearby Pend Oreille and Columbia River at Grand Coulee Dam, is caused by an unusual amount of water loss due to agricultural irrigation in the Snake River basin. All three basins have ET losses due to irrigation, however, diversions in the Snake River basin are an order of magnitude or more greater than in the Columbia and Pend Oreille basins. 1

Input to the ET model are: 1. Daily inflow of the basin above Lower Granite (reconstructed by BPA and NWS) 2. Daily precipitation (weighted average of 70 weather stations used for Lower Granite Dam streamflow forecasts) 3. Daily mean temperature (average of three weather stations in the basin) 4. Daily reconstructed inflow of the Pend Oreille basin above Albeni Falls Dam 5. Daily reconstructed inflow of Columbia River above Grand Coulee Dam All input data are daily values for the 1969-2007 period. The Snake River inflow record reconstructed by BPA and NWS takes into account reservoir regulation at dams above Brownlee Reservoir but does not account for diversions from the Snake River or for ET losses produced by irrigation. Therefore, reconstructed inflows are significantly lower than actual natural flow. Linear regression of daily reconstructed inflows of the Snake versus the Pend Oreille inflows for the full period of record (14,235 days) produces an R 2 of 0.76, and regression of Columbia River daily inflows versus the Snake for the same period the R 2 is 0.63. RS (n,i) = a1 (RP(n,i) + b1 And RS (n,i) = a2 (RCn,i) + b2 Where RS(n,i) = Snake River inflow for year n and day i (1969-2007) RP(n,i) = Pend Oreille inflow for year n and day i RC(n,i) = Columbia River inflow for year n and day i a1, b1, a2, b2 = Linear regression coefficients The ET model is designed to calculate daily evapo-transpiration throughout the full period of record so when added to existing inflows significantly increases in the R 2 of these daily regressions. An algorithm that uses daily mean temperatures and precipitation simulates daily ET, which is then added to the reconstructed inflows. Three coefficients are optimized (two for temperature and one for precipitation) to maximize the R 2 of regressing daily inflows of the Snake versus the Pend Oreille and Columbia Rivers. The mean R 2 for the full period regression is used as the objective function to find optimum values for the three ET coefficients. When the maximum R 2 is attained for both the Snake/Pend Oreille and the Snake/Columbia regressions the final coefficients are applied to calculate daily ET losses for the 1969-2007 period. Two main factors determine ET, temperature and the amount of moisture available. An average of three weather station s daily temperature observations (maximum and minimum) in or near the Snake River basin are used to represent basin temperature. A water storage index based on observed daily precipitation (an average of 70 stations) minus simulated ET represents available moisture. Precipitation is cumulated from the previous December 15, thus a ET year begins and ends in mid-winter. Precipitation influences the ET of diverted inflows because the amount of diversion is dependent on 2

precipitation (i.e. more water is diverted during a year of deficient precipitation and vice versa). Daily ET loss in kcfs is determined by: ET = (CF1) (PET) (ST) -CF2 ( dt) Where: PET = Potential evapotranspiration index = 1 e (PET varies from 0 to 1.0) ST = Basin water storage index = Σ (P) ET dt = Mean daily temperature factor = T CF3 T = Average basin temperature CF1, CF2, CF3 = coefficients determined by maximizing R 2 when the following linear regressions are run for each iteration The coefficient CF3 is a threshold temperature equal to 42º F. ET occurs only when the basin mean daily temperature exceeds this threshold. The revised Snake River inflow, after ET has been added, is regressed again with Pend Oreille and Columbia River inflows: RS (n,i) + ET(n,i) = a3 (RP(n,i) + b3 and RS (n,i) + ET(n,i) = a4 (RCn,i) + b4 Where ET(n,i) = Evapotranspiration due to irrigation for year n and day i.. The dependency of the average R 2 (derived from linear regressions of Pend Oreille versus Snake daily inflows and Columbia versus Snake inflows) on coefficient CF1 is shown in Figure 1. Although the Pend Oreille/Snake regression produces higher R 2 values, the Columbia/Snake regressions appear to have greater coefficient sensitivity, likely due to the Pend Oreille having greater irrigation ET losses than the Columbia River above Grand Coulee. 3

FIGURE 1 R-SQUARED VERSUS COEFFICIENT CF1 0.85 SNAKE VS COLUMBIA SNAKE VS PEND OREILLE 0.8 R-SQUARED 0.75 0.7 0.65 0.6 0.0000 0.0041 0.0083 0.0125 COEFFICIENT CF1 Figure 1. The R 2 for regressions of daily inflows of the Snake River versus the Pend Oreille and the Snake versus the Columbia River for the 1969-2007 period, as a function of coefficient CF1. When CF1 = 0.0, ET = 0.0 and the reconstructed inflow is unadjusted. The maximum R 2 for both regressions occurs at CF1 = 0.0150. The relationship between the average R 2 determined from linear regressions of Snake/Pend Oreille daily inflows and Columbia/Snake inflows is shown in Figure 2. The optimum value of CF1 to produce the maximum R2 is 0.0155. Coefficients CF2 and CF3 were found by the same procedure. 4

FIGURE 2 0.8 MEAN R-SQUARED VERSUS COEFFICIENT CF1and CF2 CF1 = 0.0155 AT MAXIMUM R2 CF2 = 0.0523 AT MAXIMUM 0.78 MEAN R-SQUARED 0.76 0.74 0.72 0.7 0.68 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 COEFFICIENT CF Figure 2. Mean R 2 ( average of linear regressions of Pend Oreille versus Snake and Columbia versus Snake) as a function of coefficients CF1 and CF2. 5

FIGURE 3 0.8 0.78 MEAN R-SQUARED VERSUS ANNUAL ET FOR COEFFICIENTS CF1 AND CF2 ET = 9.6 MAF AT MAX R2 ET = 9.6 MAF AT MAX MEAN R-SQUARED 0.76 0.74 0.72 0.7 0.68 0 1 2 3 4 5 6 7 8 9 10 11 12 MEAN ANNUAL ET LOSS (MAF) Figure 3. The relationship between the mean R 2 and annual ET water loss for coefficients CF1 and CF2. When ET equals zero, the R 2 is equal to that produced by a linear regression of unadjusted (before ET is added) Snake River inflows versus Pend Oreille and Columbia River inflows. ET at the maximum R 2 is 9.6 MAF, compared with 9.0 MAF that results when both coefficients are determined simultaneously. 6

FIGURE 4 MEAN DAILY DISCHARGE IN KCFS 180 160 140 120 100 80 60 40 SNAKE RIVER AT LOWER GRANITE DAM BPA RECONSTRUCTED INFLOW PLUS SIMULATED ET INFLOW + ET BPA INFLOW ET 20 0 1-Oct 1-Nov 1-Dec 1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep Figure 4. Hydrographs of the mean daily inflows at Lower Granite Dam reconstructed by BPA and NWS (pink line), and the resulting inflows after the simulated ET has been added (black line) to the reconstructed inflows. The mean daily ET (green line) is in kcfs for comparison with inflows. Also shown is the mean daily ET, converted to kcfs for comparison with the inflows. The daily water loss due to irrigation ET, averaged for the 1969-2007 period, for 2001 - a low runoff year, and for 2007, an above normal year, is shown in Figure 2. 7

DISCHARGE (KCFS) FIGURE 5 45 40 35 30 25 20 15 10 5 2001 2007 1969-2007 AVE SNAKE RIVER AT LOWER GRANITE DAM EVAPOTRANSPIRATION 0.30 0.25 0.20 0.15 0.10 0.05 INCHES PER DAY AVERAGED OVER IRRIGATED AREA 0 0.00 1-Oct 1-Nov 1-Dec 1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep Figure 5. Mean daily ET simulated by the model in kcfs (left side) and in inches per day averaged over 3.5 million acres of irrigated area (right side): 1969-2007 average (dark blue line), 2001 (pink), 2007 (green). The high rates of ET in the spring of 2007 reduced inflows and affected seasonal forecasts at Lower Granite Dam. Average annual ET is approximately 31 inches averaged over the 3.5 million acres of irrigated area. The average annual evapotranspiration (ET) loss for the Snake River above Lower Granite Dam derived from this model is 9 MAF, therefore average annual ET per unit area of irrigated cropland is approximately 2.6 feet (31 inches). Total annual inflow based on the BPA/NWS reconstruction is 25 MAF, thus total natural runoff including ET is approximately 34 MAF, and ET losses due to irrigation is 26% of total runoff. However total ET losses would include natural evapotranspiration and would be greater than 9 MAF. Figure 6 shows the annual irrigation ET that is lost from the Snake basin above Lower Granite Dam for the period 1969 2007. 8

FIGURE 6 18 16 9.0 MAF MEAN SNAKE RIVER ABOVE LOWER GRANITE DAM ID ANNUAL ET LOSS DUE TO IRRIGATION (MAF) ANNUAL ET LOSS (MAF) 14 12 10 8 6 4 2 0 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Figure 6. Annual water loss due to ET of water distributed for irrigation. The 1969-2007 average is 9 MAF, equal to 31 inches averaged over the irrigated area. A maximum of 16.4 MAF (56 inches) occurred in 1978 and a minimum of 8.0 MAF (27 inches) in 1977. Verification of the revised Snake inflows is by independently comparing Snake River mean annual inflow (both with and without ET) with observed annual precipitation Figures 7 and 8 demonstrate the increase in R 2 resulting from the addition of simulated ET to daily reconstructed inflows. The R 2 of regressing annual reconstructed inflow versus annual precipitation is 0.76 without ET, and increases to 0.83 after ET is added to the initial inflows. The mean annual simulated ET of 31 inches is in good agreement with evapotranspiration measurements made with a satellite-based energy balance model, which produced annual ET losses of 30 and 32 inches in 2000 and 2002, respectively (Richard G. Allen, personal communication, 2007). 9

FIGURE 7 100 SNAKE RIVER AT LOWER GRANITE DAM RECONSTRUCTED INFLOW VERSUS PRECIPITATION BPA RECONSTRUCTED ANNUAL INFLOW (KCFS) 90 y = 2.3289x - 26.806 R 2 80 = 0.7574 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 PRECIPITATION (70-STATION AVERAGE) IN INCHES Figure 7. Annual reconstructed inflow (unadjusted for ET) of the Snake R at Lower Granite Dam versus weighted average precipitation observed at 70 stations 10

FIGURE 8 BPA INFLOW PLUS ET (KCFS) 120 100 80 60 40 SNAKE RIVER AT LOWER GRANITE DAM INFLOW PLUS ET VERSUS PRECIPITATION y = 2.8031x - 29.684 R 2 = 0.8273 20 0 0 10 20 30 40 50 60 PRECIPITATION IN INCHES (70-STATION AVERAGE) Figure 8. Annual reconstructed inflow plus simulated ET of the Snake River at Lower Granite versus weighted average precipitation at 70 stations. The addition of simulated ET increased the R 2 from 0.76 (Figure 4) to 0.83, verifying the ET simulations. Improved accuracy in Lower Granite seasonal forecasts results when the simulated irrigation ET is added to reconstructed inflows. The procedure is to first calibrate the forecasting model using the reconstructed inflows that includes ET (natural inflow, or as close to natural flow as can be attained). A real-time forecast is then run and the mean ET during the forecast season is calculated and subtracted from the forecast. For example, the Lower Granite forecast to September 30 on March 1, 2007 was 22 MAF and average ET from March 1 September 30 is 4.0 MAF. Therefore, the actual forecast is revised from 22 to 18 MAF, compared with observed inflow of 18.5 MAF. The reduction in forecast error for hindcast forecasts made from January 1 to July 1 for a season ending September 30 is approximately 12%. However, the real test of incorporating simulated ET into the seasonal forecasting model will be real-time forecasts made in the future. 11