SOI – An index identifying ENSO phase

AO – An index of phase of the Arctic Oscillation

SM – Soil moistureSWE – Snow water equivalent

•Multi-decadal records needed to define variability of soil moisture, snow water, runoff on a seasonal time scale.•Variability of these states and fluxes cannot generally be determined with observations (Roads et al., 2003)

•Benefits of added predictability for a large system are limited•Smaller systems can see greater benefits of improved determination of initial condition and climate state•These benefits can be large amounts, but represent small relative increases over current technology•These results are case-specific and depend on:

•Physical system for management•Operating rules of system•Natural variability (current vs. potential predictability)•Time value of water

ABSTRACTUnderstanding the links between remote conditions, such as tropical sea surface temperatures, and regional climate has the potential to improve streamflow predictions, with associated economic benefits for reservoir operation. Better definition of land surface moisture states (soil moisture and snow water storage) at the beginning of the forecast period provides an additional source of streamflow predictability. We examine the value of long-lead predictive skill added by climate forecast information and land surface moisture states in the Missouri River basin. Forecasted flows were generated that represent predictability achievable through knowledge of climate, snow and soil moisture states at the time of forecast. For the current main stem reservoirs (90 billion m3 storage volume) only a 1.8% improvement in hydropower benefits could be achieved with perfect forecasts for lead times up to one year. This low value of prediction skill is due to the system’s large storage capacity relative to annual inflow. To evaluate the effects of hydrologic predictability on a smaller system, a hypothetical system was specified with a reduced storage volume of 36 billion m3. For this smaller system there was a 7.1% increase in annual hydropower benefits for perfect forecasts, representing $25.7 million. Using realistic streamflow predictability, $6.8 million of the $25.7 million are estimated to be realizable. The climate indices provide the greatest portion of the $6.8 million, and initial soil moisture information provides the largest incremental value above climate knowledge. An analysis of the seasonal variation in the value of runoff predictability provides further insights. In general, the value of predictability is greatest in the spring, when interannual variability is greatest; whereas in winter and spring, the incremental benefits due to soil moisture knowledge (beyond those realizable from knowledge of climate and snow water equivalent state at the time of forecast), are greatest. This illustrates the potential value of soil moisture knowledge in determining spring and summer inflows. The results demonstrate that the use of climate forecast information, along with better definition of the basin (snow and soil) moisture states, can provide modest economic benefits, and that these benefits in general will increase as reservoir storage decreases.

Land Surface Data Used in this Study2

Varying Levels and Sources of Runoff Predictability3

5 Summary

The potential value of hydrologic predictability on Missouri River The potential value of hydrologic predictability on Missouri River main-stem reservoir systemsmain-stem reservoir systems

Edwin P. Maurer1 and Dennis P. Lettenmaier2

1. Department of Atmospheric Sciences, Box 351640, University of Washington, Seattle, WA 981952. Department of Civil Engineering, box 352700, University of Washington, Seattle, WA 98195

GEWEX Americas Prediction Project 2003

PIs Meeting

1) Where is seasonal hydrologic predictability greatest, and through what lead time is it significant?

2) What are the relative contributions of climate conditions, snow and soil moisture content to runoff predictability?

3) What is the value of increased predictive skill to the management of a water resources system?

Science Questions1

dtdWEPQ

To derive W and E, use observations of P (and T), which have better spatial representation to drive a hydrologic model

Illustrate that model reproduces observed QBy water balance, E must be close over long

termUsing a physically-based land surface

representation gives confidence in seasonal variation represented in model

E P

W Q

Resulting Data Set used in this study:Resulting Data Set used in this study:

• 50-year+ simulation using the VIC 50-year+ simulation using the VIC hydrologic modelhydrologic model

• 3-hour time step, aggregated to 3-hour time step, aggregated to monthly and seasonal valuesmonthly and seasonal values

• 1/8 degree (~12 km) resolution1/8 degree (~12 km) resolution• Variables include all water and energy Variables include all water and energy budget componentsbudget components

• Long term spatial data set allows Long term spatial data set allows characterization of variabilitycharacterization of variability

• Described in Maurer et al., 2002Described in Maurer et al., 2002

VIC model used to generate time VIC model used to generate time series of soil moisture, snow, and series of soil moisture, snow, and runoffrunoff

Features:Features:•Developed over 10 years at Princeton Developed over 10 years at Princeton and UWand UW

•Energy and water budget closes at Energy and water budget closes at each time stepeach time step

•Multiple vegetation classes in each cellMultiple vegetation classes in each cell•Sub-grid elevation band definition (for Sub-grid elevation band definition (for snow)snow)

•Subgrid infiltration/runoff variabilitySubgrid infiltration/runoff variability

ForecastSeason

DJF

Initialization Dates for DJF Forecast

Dec 1Dec 1 Mar 1 Jun 1 Sep 1

Lead-0Lead-4 Lead-3 Lead -2 Lead 1

D J F M A M J J A S O N

ClimateClimate

Variables introduced in order of how well indices represent current knowledge of state:

1. SOI/AO2. SWE3. SM

Incremental predictability assessed for each tier

LandLand

r2SOI/AO

r2SWE

Runoff

SOI/AO SWE •At a lead-0 (1.5 month), soil moisture is dominant for predictive At a lead-0 (1.5 month), soil moisture is dominant for predictive capability of runoffcapability of runoff

•At lead times over 1 season, limited potential forecast skill due to land At lead times over 1 season, limited potential forecast skill due to land surface in west and climate signal in eastsurface in west and climate signal in east

• Important runoff forecast skill at long lead times is limited, and due to Important runoff forecast skill at long lead times is limited, and due to modest predictive skill in areas with high runoffmodest predictive skill in areas with high runoff

Missouri Main Stem hydropower:•Constitutes the largest current system benefit•Provides a metric for benefits of runoff predictability

MOSIM system simulation model developed to simulate system operation and hydropower generation at monthly time step

•Simulates operation of upstream 3 reservoirs – downstream are run-of-river•March 1 reservoir evacuation target for each dam: drain during fall and winter to base of Multiple Use zone•Uses model constraints from Corps of Engineers

•Physical limits of dams, penstocks•Release constraints:

•Navigation•Endangered species•Spawning, water supply, irrigation•Minimum hydropower generation•Maximum release for flooding•Maximum winter release for ice

Missouri River Basin

Ft. Peck Garrison Oahe Big Bend Ft. Randall Gavins Pt.

31% 47% 12% 6% 4%

90% of inflow

90% of inflow and storage capacity at upstream 3 reservoirs

Inflows dominated by spring and early summer snowmelt

Variability greatest in spring and early summer

Predictability of spring and summer flows should provide greatest benefits

Validation of MOSIM Storage and Energy Simulations

Multiple Use Zone

CarryoverStorage

DeadPool

Flood Control

For each season For each season and lead time:and lead time:

•Establish average Establish average predictability for predictability for each contributing each contributing areaarea

•Weight each grid Weight each grid cell by runoffcell by runoff

Predictability

Contributing Areas

4 x 5 grid of 4 x 5 grid of average average predictabilities for predictabilities for each areaeach area

Benefits with zero predictability: $530 Benefits with zero predictability: $530 million/yearmillion/yearBenefits with perfect forecast: $540 Benefits with perfect forecast: $540 million/year – 1.8% gainmillion/year – 1.8% gainThis is within trajectory of past studiesThis is within trajectory of past studiesBenefits with MOSIM without any forecast Benefits with MOSIM without any forecast component: $510 millioncomponent: $510 million

Multiple Use Zone

CarryoverStorage

DeadPool

Flood Control

Reductions to Reductions to Carryover Storage Carryover Storage

and Dead Pool and Dead Pool ZonesZones

Scenario/Forecast Scenario/Forecast KnowledgeKnowledge

Annual Hydropower Annual Hydropower Benefits, $ millionBenefits, $ million

Zero Predictability 359.8

Climate State 363.2

Climate + Snow 364.5

Climate + Snow + Soil Moisture 366.6

Perfect Predictability 385.5

References:•Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen, 2002, A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States, J. Climate 15(22), 3237-3251.•Roads, J., E. Bainto, M. Kanamitsu, T. Reichler, R. Lawford, D. Lettenmaier, E. Maurer, D. Miller, K. Gallo, A. Robock, G. Srinivasan, K. Vinnikov, D. Robinson, V. Lakshmi, H. Berbery, R. Pinker, Q. Li, J. Smith, T. von der Haar, W. Higgins, E. Yarosh, J. Janowiak, K. Mitchell, B. Fekete, C. Vorosmarty, T. Meyers, D. Salstein S. Williams, 2003, GCIP Water and Energy Budget Synthesis, J. Geophys. Res. (in review).

Domain coincides with LDAS-NA

Selection of Indices Characterizing Sources of Predictability

Multiple linear regression between selected predictors (SOI/AO/SM/SWE) and runoff at different lead times

r2 of regression is indicator of predictability

Varying Lead Times between Initial Conditions (IC) and Forecast Runoff

Only Use Indices in Persistence Mode

Runoff predictability due to climate Runoff predictability due to snow Runoff predictability due to soil moisture

Shaded areas are locally significant at 95% confidenceColor indicates r2 of regression at each grid cellX indicates no basin-wide field significance at 95% confidence level

Increasing Lead Time

Season of runoff being

predicted

Predictability level set for chosen predictors in designated season; zero predictability in all other seasons.

Ben

efits

abo

ve z

ero

pred

icta

bilit

y, $

mill

ion

• Greatest value of predictability in DJF and MAM – affecting large future inflows.

• Knowledge of soil moisture in winter and spring provides the greatest incremental increase in benefits above that already attainable with climate signals.

• Increased predictability in JJA with soil moisture lowers annual value, due to variable monthly value of hydropower.

Seasonal Distribution of Predictability Benefits with Reduced-Volume System

Inflow to 3 Upstream Reservoirs

Synthetic forecast inflows derived for each reservoir by adding noise to served inflows: tXX tt

~

Development of Predicted Inflow Sequences for each Reservoir

Step month by month, for 99 years (1898-1996) making new forecasts for 12 months ahead

The 90th percentile flows (upper decile) are the assumed level of risk (for flooding) used for this study

4 Water Management Implications of Runoff Predictability in the Missouri River basin

Effect of differing levels of predictability on Missouri River mainstem hydropower generation

This shows:This shows:1.1. Change to benefits due to modification of system Change to benefits due to modification of system

operation to incorporate forecast information operation to incorporate forecast information exceeds benefits added by predictabilityexceeds benefits added by predictability

2.2. System capacity is large (multi-year storage), so System capacity is large (multi-year storage), so seasonal predictability effect is smallseasonal predictability effect is small

Reduced-volume Missouri River system applies proportional reductions to 3 upstream dams

Existing System

Hypothetical Reduced system

To investigate the potential effect of predictability on To investigate the potential effect of predictability on a smaller system in this geographical setting, a a smaller system in this geographical setting, a reduced-volume scenario was developed:reduced-volume scenario was developed:

Results with Reduced Volume System

Average Annual Hydropower Benefits