1. Predictability of Seasonal Runoff in the Mississippi River Basin
Mississippi River Climate and Hydrology Conference
May 14, 2002
E.P. Maurer and
D.P. Lettenmaier
Univ. of Washington, Seattle, WA USA
We are all familiar with the hydrologic cycle, showing the movement of water through the environment
Improved understanding variability, with added predictive skill, can aid in
better use of water resources
prevention of adverse impacts
adaptation
This motivates my interest on the prediction of runoff, on large scales and at long lead times (months to a year)
To study predictability requires an understanding of the variability runoff and the sources of that variability.
If we are fortunate to find the sources of variability, they may exhibit enough persistence, or have a predictable evolution, to add predictive skill at very long lead times.
Source: NASA

2. Motivation
Better observation and prediction of climate signals and their teleconnections to land areas
Improved understanding of continental-scale hydrologic variability through data collection and modeling
Potential benefits of improved long-lead prediction
Better understanding of climate signals and their teleconnections (to precipitation, snow, streamflow,…) and new remotely sensed data sources
Where are these sources important – climate relative to land surface, i.e., where would additional land surface observations be most valiable?
This understanding is leading to study of improvements in forecasting skill, though water management has largely not responded to it yet.

3. Outline of Study Describe Variability of Land Surface Water Budget
Observations
Modeling
Determine Relative Long-Lead Hydrologic Predictability attributable to:
Initial State of Land Surface
Remote Climate Forcing
Identify where and when the greatest improvement in seasonal forecasts might be expected with improved observations.
Need to understand the hydrologic cycle and its variability.
Need to understand presence of and sources of predictability.
Where can one expect the greatest impact of predictability? This can ultimately help guide research and development efforts and future studies.
I will fully resolve all of these issues in the next 25 minutes.

4. The Land-Surface Water Budget
Examine variability in water budget components E P
(Near Surface) Water Balance Equation W Q
For the first question, the challenge is to fully describe the components of the water budget, their maginitude and variation.
W is termed the “surface water” and includes both snow and soil moisture.
Variability and prediction of Q requires an understanding of the variability of the terms on the RHS.
We can consider three time scales of variability:
Chaotic weather – essentially unpredictable
Slowly varying signals -- states (SM and SWE) and low frequency variations in climate (P,T).
Long term Trends (decades to centuries)
4) Since seasonal time scale fluctuations are the focus here, we need decades of records to establish variability
5) So, how well can we describe the components of the equation?
Need long records of observations to define variability and predictability

5. Precipitation and Evaporation Observations
Ameriflux (flux towers) provides measurements of E, since mid 1990’s
Precipitation appears well defined, generally since 1948
1) US has much better coverage than many other parts of globe.
2) Great deficiencies remain, especially in areas of complex topography and finer temporal resolution
3) Spatial resolution OK, but much is left unknown a the scale of smaller storms.
4) Many stations reporting since 1948, and have long records – important in determining variability
5) Radar helps with spatial distribution and evolution of storms, but estimates of precipitation volume is problematic. Begun in 1988, but wasn’t filled out until 1996 or so, and data wasn’t archived.
U.S. Station density: 1 per 700 km2
U.S. Station Density: 1 per 130,000 km2

6. Snow Water and Soil Moisture Observations
About 600 SNOTEL sites in western US
Snow water content measured since 1977
NCDC Cooperative observer stations report snow depth at many locations, but the water content is not recorded.
Spatial coverage poor at continental scale
Source: A. Robock, Rutgers U.

7. Runoff (Streamflow) Observations
Streamflow in the U.S. measured at roughly 7,000 active gauging stations.
Stations can represent regulated flow conditions
Streamflow is a spatially integrated quantity
Hydro Climatic Data Center looked for:
Continuity (average 45 year record length)
Limited effect of upstream water management
Measured streamflow does not indicate where in the basin the runoff was generated, so spatial variability is unknown
Spatial runoff only measured in its integrated state as streamflow wihtout knowledge of where it came from. For studying the spatial variation of runoff and its predictability, we need to be able to characterize the variability of the components affecting it.
HCDN selected 1,700 most representative of natural conditions, shown here.
Source: U.S.G.S.

8. Hydrologic Model
Drive a Hydrologic Model with well-known P, T, reproduce Q, derive snow, soil moisture, ET
VIC Model Features:
Developed over 10 years
Energy and water budget closure at each time step
Multiple vegetation classes in each cell
Sub-grid elevation band definition (for snow)
Subgrid infiltration/runoff variability
VIC has been developed over the past 10 years at Princeton and UW.
The VIC Model has been regionally (river basins like the Delaware and Potomac to larger ones like the Missouri and Columbia) and globally.
It is designed to represent the land surface at scales from 1/8 degree (10-12 km) to 2 degrees (~200km)
Performs full energy and water budget solution at time steps from hourly to daily.
Distinguishing features are statistical sub-grid variability of vegetation, infiltration capacity, and elevation/temperature/snowfall.

9. 6 Sample Hydrographs Good agreement of Seasonal cycle Low Flows
Peak Flows
Model
Obs.
This shows a blow-up of 6 of the hydrographs.
Resulting monthly hydrographs, aggregated from daily flows, show good agreement of seasonal cycle, peak flows, and baseflow dominated periods
Many additional incremental calibration points exist within each sub-basin.

10. Case Study - Estimation of Runoff Predictability
Mississippi River Basin
Strong gradient in precipitation and runoff
Winter runoff concentrated in SE
High snowmelt runoff in summer
In continuing this examination of persistence, I focus on the Mississippi River basin, which coincides with GCIP region, a project established in 1992 to demonstrate skill in predicting changes in water resources on time scales up to seasonal, annual, and interannual.
By narrowing the region, I also reduce the computational demands. I addition aggregate to ½-degree (about 50km), so there are 1500 grid cells in the basin.
This area has been heavily studies, so there are many resources to draw from, and interest in the results obtained.
World Climate Research Programme, Global Energy and Water Cycle Experiment Continental-scale International Project GCIP

11. Methods for Determining Runoff Predictability
Indices Characterizing Sources of Predictability:
SOI – An index identifying ENSO phase
AO – An index of phase of the Arctic Oscillation
SM – Soil moisture level (normalized)
SWE – Snow water equivalent (normalized)
Varying Lead Times between IC and Forecast Runoff
Only Use Indices in Persistence Mode
Climate
Land
Forecast
Season
DJF
Initialization Dates for DJF Forecast
Dec 1
Mar 1
Jun 1
Sep 1
Lead-0
Lead-4
Lead-3
Lead -2
Lead 1 D J F M A S O N
1) Predictability in the context of this study is that due to linear relationships of runoff with the state of the climate and land surface at some earlier date.
2) Variables are introduced in a manner similar to step-wise regression, except instead of the most significant variables first, the best known are introduced first.
3) SM and SWE represent perfect knowledge of these states, and therefore a maximum obtainable knowledge of initial state.

12. Methods 2 Multiple linear regression used between IC and runoff
Variance explained (r2) indicates level of predictability
Variables introduced in order
of how well indices represent
current knowledge of state:
SOI/AO
SWE SM
Incremental predictability
r2SOI/AO
r2SWE
Runoff
SOI/AO
SWE

13. Methods 3 Test for Significant Predictability (r2) in 2 steps
Local Significance:
Tested at each grid cell
Accounts for temporal autocorrelation
95% confidence level estimated
Field Significance (Livezey and Chen, 1983):
Tests area showing local significance over entire basin
Accounts for limited sample size, spatial correlation in both predictors and predictand
95% confidence for field significance

14. Total Runoff Predictability
Lead,
months
1.5
4.5
7.5
10.5
13.5
Uses all 4 indices to predict runoff
“X”  no field significance
Predictability deteriorates with time
Significance – first local significance is tested, accounting for autocorrelation in both series. Second, field significance is tested to account for spatial correlation in predictor and predictand fields. All at 95%.
Winter – highest predictability in mountains, but very low runoff – combined with some overestimation by using VIC model. Mild, significant predictability in SE, where most runoff is occurring, with some predictability at 3 season lead.
Summer – significant predictability of high mountain runoff at 4 season lead

15. Predictability due to Climate Signals
Predictors currently available
Moderate levels of r2
Greater influence in winter, in area and lead time
Difficulty in long-lead persistence prediction with climate signals
Generally moderate levels of predictability – this is not a physical forcing of the runoff by the index, but is remotely teleconnected, so weaker predictability is understandable.
This is a level of predictability that is currently achievable.
Even with small areas of moderate predictability in summer at leads of 4 seasons, a lot of runoff is at stake
In a moment of weakness I accept a posteriori statistics, and changed one X to light green, since it is within 1% of significant, and would probably pass a 90% test.
Long-lead predictability of winter runoff in southeast is due to climate influences.

16. Predictability Due to Snow
r2 represents incremental increase
Focus at 1 or more season lead is in Rocky mountains
At level of Mississippi basin, predictability limited to 1-2 seasons
Analysis by sub-area could reveal greater predictability
Persistence from snow only possible through snow melt – limited scale
This corresponds to current use of snow forecasts beginning Jan1, as little information is available before this date.
Predictability represents incremental predictability above that from climate signals

17. Predictability due to Soil Moisture
Widespread predictability at 0 lead (1½ month)
Winter Runoff: little predictability where runoff is high
Summer Runoff: limited predictability to 3 seasons
Predictability at greater than 1 season only in west.
Some prediction of JJA runoff the previous Sept 1 in the west!
What is evident from this is that the coincidence of runoff magnitude and predictability may be a better gauge of the importance of these predictabilities.

18. Scaled Runoff Predictability
3 season lead: climate signal provides predictability of winter runoff in SE
1 season lead: mild climate influence in SE
snow influence on summer runoff in W
2 season lead: Important predictability limited
Snowmelt signal in W mountains
0 season lead: soil moisture signal dominant
snow signal dominant in W in summer
climate signal strong in SE in winter

19. Conclusions
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 snow 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