Water Resources Planning and Management Daene C. McKinney River Basin Modeling.
Water Resources Planning and Management Daene C. McKinney Simulating System Performance.
Transcript of Water Resources Planning and Management Daene C. McKinney Simulating System Performance.
Water Resources Planning and Management
Daene C. McKinney
Simulating System Performance
Reservoir Management
• Important task for water managers around the world.
• Models used to– simulate or optimize
reservoir performance– design reservoirs or
associated facilities (spillways, etc.).
Operating Rules• Allocate releases among purposes, reservoirs, and time
intervals • In operation (as opposed to design), certain system
components are fixed:– Active and dead storage volume– Power plant and stream channel capacities– Reservoir head-capacity functions– Levee heights and flood plain areas– Monthly target outputs for irrigation, energy, water supply, etc
• Others are variable: Allocation of – stored water among reservoirs– stored and released water among purposes– stored and released water among time intervals
Standard Operating Policy
Qt
X2tK
StRt
X1t
Dt
KDQS
KDQSD
DQS
if
KQS
D
QS
R
ttt
tttt
ttt
tt
t
tt
t
Rt
Dt
Dt Dt+K St+ Qt
Release available water &
deficits occur
Release demand spill excess
Sufficient water to meet demands
Reservoir fills and demand met
Release demand &demand met
Demand
• Reservoir operating policy - release as function of storage volume and inflow
Rt = Rt(St,Qt)
Hedging Rule• Reduce releases in times of drought (hedging) to save water
for future releases in case of an extended period of low inflows.
hedging D
K
Done?No
System Simulation
• Create network representation of system• Need inflows for each period for each node• For each period:
Perform mass balance calculations for each node Determine releases from reservoirs Allocate water to users
Start
t = 0St = S0
St+1 = St +Qt -Rt
Stop
Yes
t = t + 1
Read Qt File
ComputeRt, Xit, i=1,…n
DataStorage
Qt
X3tK
St
RX2t
X1t
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0 2 4 6 8 10 12 14 16Release, R
All
ocat
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, X
i x1
x2
x3
Rt
Dt
Dt Dt+K St+ QtK
Release available water
Release demand
Release demand + excess
Rt
Dt
Dt Dt+K St+ QtK
Release available water
Release demand
Release demand + excess
Operating Policy Allocation Policy
Example
• Using unregulated river for irrigation• Proposed Reservoir
• Capacity: K = 40 million m3 (active)• Demand: D = 30 40 45 million m3
• Winter instream flow: 5 mil. m3 min.• 45 year historic flow record available
• Evaluate system performance for a 20 year period
• Simulate• Two seasons/year, winter (1) summer(2)• Continuity constraints• Operating policy
QtX2tK
St RX1t
DEMAND
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Year
De
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(1
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om
m3
)
Flow statistics
R2,t
Dt
Dt Dt+K S2,t+ Q2,tK
Release available water
Release demand
Release demand + excess
Summer Operating Policy
yyyy RQSS ,1,1,1,2
Storage at beginning of summer
KDQS
KDQSD
DQS
if
KQS
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QS
R
yyy
yyyy
yyy
yy
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yy
y
,2,2
,2,2
,2,2
,2,2
,2,2
,2
Performance Evaluation
• How well will the system perform?– Define performance criteria
• Indices related to the ability to meet targets and the seriousness of missing targets
– Simulate the system to evaluate the criteria– Interpret results
• Should design or policies be modified?
Performance Criteria - Reliability• Reliability – Frequency with which demand was satisfied
– Define a deficit as:
• Then reliability is:
• where n is the total number of simulation periods
Performance Criteria - Resilience
• Resilience = probability that once the system is in a period of deficit, the next period is not a deficit.
• How quickly does system recover from failure?
Performance Criteria - Vulnerability
• Vulnerability = average magnitude of deficits
• How bad are the consequences of failure?
Simulate the System
System
Policies
Input Output
x
g(x)
y
h(y)
0
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0 2 4 6 8 10 12 14 16Release, R
All
ocat
ion
, X
i x1
x2
x3
Rt
Dt
Dt Dt+K St+ QtK
Release available water
Release demand
Release demand + excess
Rt
Dt
Dt Dt+K St+ QtK
Release available water
Release demand
Release demand + excess
Reservoir operating policyAllocation policy
Hydrologictime series
Model output
Model
Uncertainty• Deterministic process
– Inputs assumed known. – Ignore variability – Assume inputs are well
represented by average values. – Over estimates benefits and
underestimates losses
• Stochastic process– Explicitly account for variability
and uncertainty– Inputs are stochastic processes – Historic record is one realization
of process.
FY(y)
Simulate the System
Policies
Simulate each Input sequence
X
FX(x)
x
g(x)
y
h(y)
y
h(y)
Computestatistics of
outputs
System
Generate multiple input sequences
x
g(x)Get multiple
output sequences
0
2
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0 2 4 6 8 10 12 14 16Release, R
All
ocat
ion
, X
i x1
x2
x3
Rt
Dt
Dt Dt+K St+ QtK
Release available water
Release demand
Release demand + excess
Rt
Dt
Dt Dt+K St+ QtK
Release available water
Release demand
Release demand + excess
Reservoir operating policyAllocation policy
Model
Distribution of inputs
The Simulation• Simulate reservoir operation
– Perform 23 equally likely simulations– Each simulation is 20 years long– Each simulation uses a different sequence of inflows
(realization)
Example – Realization 1Rmin 0.5 K 4
Realization 1
Winter SummerYear S1y Q1y S+Q R1y S2y Q2y S+Q D2y R2y Deficit1 0.000 4.740 4.740 0.740 4.000 1.805 5.805 3.000 3.000 0.0002 2.805 2.918 5.723 1.723 4.000 1.499 5.499 3.200 3.200 0.0003 2.299 2.747 5.045 1.045 4.000 1.548 5.548 3.400 3.400 0.0004 2.148 2.819 4.966 0.966 4.000 1.753 5.753 3.600 3.600 0.0005 2.153 3.871 6.023 2.023 4.000 2.229 6.229 3.800 3.800 0.0006 2.429 3.585 6.015 2.015 4.000 2.235 6.235 4.000 4.000 0.0007 2.235 4.736 6.971 2.971 4.000 2.984 6.984 4.100 4.100 0.0008 2.884 3.275 6.159 2.159 4.000 2.212 6.212 4.200 4.200 0.0009 2.012 3.188 5.200 1.200 4.000 2.666 6.666 4.300 4.300 0.00010 2.366 3.401 5.767 1.767 4.000 1.240 5.240 4.300 4.300 0.00011 0.940 3.811 4.750 0.750 4.000 2.371 6.371 4.400 4.400 0.00012 1.971 3.435 5.407 1.407 4.000 2.421 6.421 4.400 4.400 0.00013 2.021 2.460 4.481 0.500 3.981 1.317 5.298 4.400 4.400 0.00014 0.898 2.377 3.275 0.500 2.775 1.896 4.671 4.400 4.400 0.00015 0.271 3.692 3.963 0.500 3.463 1.831 5.293 4.500 4.500 0.00016 0.793 3.302 4.095 0.500 3.595 1.300 4.895 4.500 4.500 0.00017 0.395 2.548 2.944 0.500 2.444 2.047 4.491 4.500 4.491 -0.00918 0.000 2.454 2.454 0.500 1.954 1.658 3.612 4.500 3.612 -0.88819 0.000 3.139 3.139 0.500 2.639 2.768 5.407 4.500 4.500 0.00020 0.907 2.910 3.816 0.500 3.316 1.445 4.762 4.500 4.500 0.000
Deficit -0.897Number 2.000% 0.100
ResultsTotal # of Frequency
SimulationShortage Failures of Failure1 -1.031 2 0.1002 -10.050 8 0.4003 -0.516 1 0.0504 -0.184 1 0.0505 -1.159 2 0.1006 -10.747 8 0.4007 -4.627 6 0.3008 -1.134 4 0.2009 -1.446 4 0.20010 0.000 0 0.00011 -1.735 4 0.20012 -3.384 5 0.25013 -3.639 3 0.15014 0.000 0 0.00015 -0.067 1 0.05016 -1.561 3 0.15017 -3.586 6 0.30018 -0.223 1 0.05019 -1.347 1 0.05020 -0.977 2 0.10021 -4.758 0 0.25022 -4.966 5 0.25023 -3.641 4 0.200
Average -2.643 0.165Std. Dev. 2.93704 0.118163
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Simulation
# o
f F
ailu
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Average failure frequency = 0.165Average reliability = 1- 0.165 = 0.835 = 83.5%Actual failure frequency [0, 0.40]Actual Reliability [100%, 60%]
Physical Environment Feedback Sub Model to FAV
Salinity
Temp
Surface, Subsurface
Light
FAVEstablishment
and Growth
FAVPatch
Local Physical Environment (tides, freshwater flow)
NutrientsHeavymetals
Riparian Vegetation
DO
Wind, Flow Velocity
Dispersal
SubstrateOrg Matter
Subsurface Light
-
Small SubstrateGrain Size
Understanding:
High – green arrow
Med – blue arrow
Low - red arrow
Importance:
High – thick line
Med – medium line
Low – thin line
Predictability:
High – solid line
Med – dashed line
Low – dotted line
Lars Anderson, UC DavisStuart Siegel, WWRMark Stacey, UCB
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