A Stochastic Nonparametric Technique for Space-time
Disaggregation of Streamflows
Balaji Rajagopalan, Jim Prairie and Upmanu Lall
May 27, 20052005 Joint Assembly
Motivation
• Develop realistic streamflow scenarios at several sites on a network simultaneously
• Difficult to model the network from individual gauges
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Motivation
• Present methods can not capture higher order features
• Present methods can be difficult to implement• Can not easily incorporate climate information• Finding the probability of events• Required for long-term basin-wide planning
– Develop shortage criteria– Meeting standards for salinity
Current Methods
• Parametric– Basic form – Seminal (Valencia and Schaake, 1972)
Variations/Improvements (Mejia and Rousselle; 1976, Lane; 1979; Salas et al.
1980; Stedinger and Vogel, 1984)
• Nonparametric– Kernel-based ( Tarboton et al. 1998)– Nearest-Neighbor based (Kumar et al. 2000)
BεAZX
Drawbacks of Parametric Framework
• Data must be transformed to a normal distribution– During transformation additivity is lost
• There are many parameters to estimate– At least 25 parameters for annual to monthly
disaggregation
• Inability to capture non-Guassian and non- linear features
Proposed Methodology
• Resampling from a conditional PDF
• With the “additivity” constraint• Where Z is the annual flow X are the monthly flows• Or this can be viewed as a spatial problem
– Where Z is the sum of d locations of monthly flows X are the d locations of monthly flow
dXZXf
ZXfZXf
),(
),()(
Joint probability
Marginal probability
Step 1
X = monthly flow matrix.
Z = annual flow vector.
Transform matrix Y = XR
Steps for Temporal Disagg
Step 2
Generate an annual flow z* with an appropriate model
Step 3
Identify k historical years to z*. Pick one of the neighbors with k-nearest neighbor.
Tarbaton el al, 1998
Prairie, 2002
Step 4
Steps for Temporal Disagg
Step 5
Repeat steps 2 through 5 for additional years
Build a vector u* where the first 11 values are first 11 values from Yi and the 12 values is z’, where z’ = z*/√12
Generate disaggregated flows vector x* from
x* = u*RT
2134.1266389556.439
8721.320349112.7
7071068.07071068.0
7071068.07071068.0
6963.1790
7816.453
0435.12066528.584
0874.2326942.221
RXY
R
ZX
Gauge 1 Gauge 2 Gauge 1 +2
Obtain the rotation matrix R via Gram Schmidt orthonormalization
Note the last column of R = 1/√d
RT = R-1
Example.
Generate Zsim let us say 735.6541
Then
1860.5202
6541.735' simz
Next we find the K – nearest neighbors to z’sim
The neighbors are weighted so closest gets higher weight
We pick a neighbor, let us say year 2
Then we build u from y and z’sim
1860.5203896.439',* )1,2( simzyu
52238.678
13172.57
1860.5203896.4397071068.07071068.0
7071068.07071068.0*
sim
T
T
sim
x
uRx
Via back rotation we can solve for the disaggregated components of zsim
Note the disaggregated components add to zsim = 735.6541
The only key parameter is K
which is estimated with a heuristic scheme K=√N
Application
• The Upper Colorado River Basin– 4 key gauges
• Perform 500 simulations each of 90 years length
• Annual Model– a modified K-NN lag-1 model (Prairie, 2002)
Results
• Performance Statistics – Lower order: mean, standard deviation, skew,
autocorrelation (lag-1)– Higher order: probability density function,
drought statistics
• We provide some comparison with a parametric disaggregation model
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Bluff
Lees Ferry
Bluff gauge
June flows
Nonparametric
Parametric
Lees Ferry Gauge
May Flows
Nonparametric
Parametric
Lees Ferry Gauge
Drought Statistics
Annual Model
Modified K-NN lag-1
Annual Model
18 year block bootstrap
Conclusions
• A flexible, simple, framework for space-time disaggregation is presented
• Obviates data transformation• Parsimonious• Ability to capture any arbitrary PDF structure• Preserves all the required statistics and additivity.
• Easily be conditioned on large-scale climate information.
Future Extensions
• Simulate Decision/Policy strategies via passing the simulated flows through Decision Support System
• Incorporate paleo streamflow data to simulate space-time flows back in time
and water resources system scenarios.
• Conditioning on climate
Acknowledgements
BOR Upper Colorado Regional and Boulder Canyon Area (Terry Fulp)
Office for Funding the Study
CADSWES for Logistical Support