SOI

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The potential value of hydrologic predictability
on Missouri River main-stem reservoir
systems Edwin P. Maurer1 and Dennis P.
Lettenmaier2 1. Department of Atmospheric
Sciences, Box 351640, University of Washington,
Seattle, WA 98195 2. Department of Civil
Engineering, box 352700, University of
Washington, Seattle, WA 98195
GEWEX Americas Prediction Project 2003 PIs Meeting
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ABSTRACT Understanding 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
systems 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.
Varying Levels and Sources of Runoff
Predictability
Season of runoff being predicted
Runoff predictability due to climate
Runoff predictability due to snow
Runoff predictability due to soil moisture
Selection of Indices Characterizing Sources of
Predictability
SOI An index identifying ENSO phase AO An
index of phase of the Arctic Oscillation SM
Soil moisture SWE Snow water equivalent
Increasing Lead Time
Climate
Land

• Variables introduced in order of how well indices
  represent current knowledge of state
• SOI/AO
• SWE
• SM
• Incremental predictability assessed for each tier

Varying Lead Times between Initial Conditions
(IC) and Forecast Runoff Only Use Indices in
Persistence Mode
Multiple linear regression between selected
predictors (SOI/AO/SM/SWE) and runoff at
different lead times

• At a lead-0 (1.5 month), soil moisture is
  dominant for predictive capability of runoff
• At lead times over 1 season, limited potential
  forecast skill due to land surface in west and
  climate signal in east
• Important runoff forecast skill at long lead
  times is limited, and due to modest predictive
  skill in areas with high runoff

Shaded areas are locally significant at 95
confidence Color indicates r2 of regression at
each grid cell X indicates no basin-wide field
significance at 95 confidence level
r2 of regression is indicator of predictability
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Science Questions
Water Management Implications of Runoff
Predictability in the Missouri River basin

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?

Seasonal Distribution of Predictability Benefits
with Reduced-Volume System
Development of Predicted Inflow Sequences for
each Reservoir
Synthetic forecast inflows derived for each
reservoir by adding noise to served inflows
Predictability level set for chosen predictors in
designated season zero predictability in all
other seasons.
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
Average Annual Hydropower Benefits
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Land Surface Data Used in this Study
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

• 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)

Inflow to 3 Upstream Reservoirs

• 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 Q
• By water balance, E must be close over long term
• Using a physically-based land surface
  representation gives confidence in seasonal
  variation represented in model

• 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.

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

• 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

Benefits with zero predictability 530
million/year Benefits with perfect forecast
540 million/year 1.8 gain This is within
trajectory of past studies Benefits with MOSIM
without any forecast component 510 million

• VIC model used to generate time series of soil
  moisture, snow, and runoff
• Features
• Developed over 10 years at Princeton and UW
• Energy and water budget closes at each time step
• Multiple vegetation classes in each cell
• Sub-grid elevation band definition (for snow)
• Subgrid infiltration/runoff variability

• 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

• This shows
• Change to benefits due to modification of system
  operation to incorporate forecast information
  exceeds benefits added by predictability
• System capacity is large (multi-year storage), so
  seasonal predictability effect is small

Results with Reduced Volume System
Scenario/Forecast Knowledge Annual Hydropower Benefits, million
Zero Predictability 359.8
Climate State 363.2
Climate Snow 364.5
Climate Snow Soil Moisture 366.6
Perfect Predictability 385.5

• Resulting Data Set used in this study
• 50-year simulation using the VIC hydrologic
  model
• 3-hour time step, aggregated to monthly and
  seasonal values
• 1/8 degree (12 km) resolution
• Variables include all water and energy budget
  components
• Long term spatial data set allows
  characterization of variability
• Described in Maurer et al., 2002

Domain coincides with LDAS-NA
Validation of MOSIM Storage and Energy Simulations
To investigate the potential effect of
predictability on a smaller system in this
geographical setting, a reduced-volume scenario
was developed

• 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).