Transcript Prof. Balaji - Lecture-1 - Civil, Environmental and Architectural

Water Resources Systems and Management
CVEN 5393
Lecture 1
Outline
• State of Global Water Resources
– Global watery cycle
– Water availability, demand projections under climate
change
• Water Resources Management Perspective
– Time Scales (hours to decades)
– Weather to decadal climate variability
• Integrated Framework
• Colorado River Water Resources Management - example
– Under climate change
• Optimization
• World Water Resources Development Report 4th edition
(2012). Volume 2, first chapter (co-authored by the
instructors) – links provided on the class page
Regional Renewable Water Supply Estimates
Per Capita Water Usage and Requirement
• Agriculture is the largest
Water user
• With projected population
growth this will increase significantly
– adding to global water stress
Global Water Availability
Population Under Water Shortage
Global Physical and Economic Water Scarcity
Projected Per capita water Availability in 2050
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Water Resources Engineering
21st century
Era of big building is over!
Increasing population
– Rapid urbanization
• Deteriorating rural conditions
• Better opportunities in cities
• 1 billion now living in city slums,
world wide
Municipal services for urban poor are
negligible or non-existent
Limited water availability
Environmental Impacts
Acute water shortages
– Rapid increase in demand with
insufficient capital to develop
– Climate variability
Lack of sanitation
– Rapid increase in generated waste
– Negligible treatment
– Result: disease, environmental
degradation
Vulnerability to natural hazards and
disasters
– Earthquakes, floods, hurricanes,
wildfires, drought, landslides
– Lack of resources to plan for and
mitigate effects
A Water Resources Management Perspective
Inter-decadal
Decision Analysis: Risk + Values
T
• Facility Planning
i
m
– Reservoir, Treatment Plant Size
e
• Policy + Regulatory Framework
H
o
r
i
z
o
n
Climate
– Flood Frequency, Water Rights, 7Q10 flow
• Operational Analysis
– Reservoir Operation, Flood/Drought Preparation
• Emergency Management
– Flood Warning, Drought Response
Data: Historical, Paleo, Scale, Models
Hours
Weather
Climate Variability
• Daily
• Annual
• Diurnal cycle
• Seasonal cycle
• Inter-annual to Interdecadal
• Ocean-atmosphere
coupled modes (ENSO,
NAO, PDO)
• Centennial
• Millenial
• Thermohaline circulation
• Milankovich cycle (earth’s
orbital and precision)
Proposed Integrated Framework
What Drives Year to Year
Variability in regional
Hydrology?
(Floods, Droughts etc.)
Diagnosis
Hydroclimate Predictions –
Scenario Generation
(Nonlinear Time Series Tools,
Watershed Modeling)
Application
Decision Support System
(Evaluate decision
strategies
Under uncertainty)
20
Total Colorado River Use 9-year moving average.
18
NF Lees Ferry 9-year moving average
16
12
10
8
6
4
2
Calnder Year
19
98
20
02
20
06
19
90
19
94
19
86
19
82
19
78
19
70
19
74
19
66
19
62
19
58
19
54
19
46
19
50
19
42
19
38
19
34
19
26
19
30
19
18
19
22
0
19
14
Annual Flow (MAF)
14
Forecast
Colorado River Basin Overview
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1 acre-foot = 325,000 gals, 1 maf = 325 *
1 maf = 1.23 km3 = 1.23*109 m3
109

gals
7 States, 2 Nations
 Upper Basin: CO, UT, WY, NM
 Lower Basin: AZ, CA, NV
Fastest Growing Part of the U.S.
Over 1,450 miles in length
Basin makes up about 8% of total
U.S. lands
Highly variable Natural Flow
which averages 15 MAF
60 MAF of total storage
 4x Annual Flow
 50 MAF in Powell + Mead
Irrigates 3.5 million acres
Serves 30 million people
Very Complicated Legal
Environment ‘Law of the River’
Denver, Albuquerque, Phoenix,
Tucson, Las Vegas, Los Angeles,
San Diego all use CRB water
DOI Reclamation Operates
Mead/Powell
Scale Matters
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Runoff Efficiency (How much Precip actually runs off) Varies Greatly from
~5% (Dirty Devil) to > 40% (Upper Mainstem)
You can’t model the basin at large scales and expect accurate results
 GCMs (e.g. Milly, Seager) and H&E 2006 may get the right answer, but
miss important topographical effects
% of Total
Runoff
14.4%
16.1%
9.9%
2.4%
24.9%
6.3% 14.1%
11.8%
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Most runoff comes from small part of the basin > 9000 feet
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Very Little of the Runoff Comes from Below 9000’ (16% Runoff, 87% of Area)
84% of Total Runoff Comes from 13% of the Basin Area – all above 9000’
Basin Area and Runoff By Elevation
20%
Elevation
% Total Runoff
9000-10,000
25%
10,000-11,000
27%
11,000-12000
22% %
12,000-13,000
11%
Sums 9-13
84%
Below 9000
16%
18%
16%
14%
12%
% Total Area "Productivity"
6.3%
3.9
4.3%
6.2
10.4
Total2.1%Runoff
0.5%
20.4
13.2%
87%
0.2
Runoff
10%
8%
Basin Area
6%
4%
2%
0%
0
2000
4000
6000
Runoff as % of Total
8000
10000
Area as % of Upper Basin Total
12000
14000
Current State
20
18
Annual Flow (MaF)
16
14
12
10
8
6
4
Total Colorado River use 9-year moving average
2
NF Lees Ferry 9-year moving average
0
1914
1924
1934
1944
1954
1964
1974
1984
1994
Year
30
25
20
15
10
5
0
Lake Mead Volume in Millions of
Acrefeet 1935-2008
2004
Recent conditions in the
Paleo Context
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Below normal flows into
Lake Powell 2000-2004
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62%, 59%, 25%, 51%, 51%,
respectively
 2002 at 25% lowest
inflow recorded since
completion of Glen
Canyon Dam
Woodhouse et al., WRR, 2007
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Some relief in 2005
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105% of normal inflows
Not in 2006 !
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73% of normal inflows
2007 at 68% of Normal
inflows
2008 at 111% of Normal
inflows
2009 at 88% and 2010 at
72.5%
5 year running average
Decadal Variability!
Paleo Perspective
Reconstruction of Colorado River at Lees Ferry streamflow, 7622005, with 10-year running mean
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Six 10-year periods before 1900 with reconstructed mean flow lower than
12 MaF (lowest: 1146-1155)
1905-1930 one of the three wettest ~25-year periods in 1200 years
Mid-1100s: 57-year period with mean flow of ~13 MaF
Century-scale non-stationarity: 100-year mean varies from 13.9 to 15.4 MaF
* Slide courtesy of Jeff Lukas, NOAA/WWA
Wavelet Power Spectrum of Lees Ferry
Flow
Features of interest 1) decadal (active past 30
years) 2) Low frequency (more persistent)
Wavelet Spectra of Temp and Precip data
• Are there features in the T and P data that may be linked to the
2 scales of interest found in the Lees Ferry spectrum?
Temperature
Precipitation
Low frequency feature similar
to that of the flow data
Decadal scale feature similar
to that of the flow data
Winter and Summer Precipitation
Changes at 2100 – High Emissions
Hatching Indicates
Areas of Strong
Model Agreement
Summer
Green = 2010-2039
Blue = 2040-2069
Red = 2070-2099
120
110
100
90
80
-40% to
+30%
Runoff
changes in
2070-2099
~80%
70
Up
= Increase
Down = Decrease
2C to 6 C
60
Triangle size
proportional to
runoff changes:
~115%
Precip Change in %
CRB
Runoff
From
C&L
Precipitation, Temperatures and Runoff in 2070-2099
0
1
2
3
Temp Increase in C
4
5
6
Future Flow Summary
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Future projections of Climate/Hydrology in the basin
based on current knowledge suggest
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Increase in temperature with less uncertainty
Decrease in streamflow with large uncertainty
Uncertain about the summer rainfall (which forms a reasonable
amount of flow)
Unreliable on the sequence of wet/dry (which is key for system
risk/reliability)
The best information that can be used is the
projected mean flow
Clearly, need to combine paleo + observed + projection
to generate plausible flow scenarios
System Risk
•Streamflow Simulation
•Prairie et al. (2008) WRR
• System Water Balance
Model
•Management Alternatives
(Reservoir Operation +
Demand Growth)
Rajagopalan et al. (2009),
WRR
Proposed Framework for flow generation
Prairie et al. (2008, WRR)
Nonhomogeneous Markov Chain
Model on the observed & Paleo
data
Natural
Climate
Variability
Generate system state
( St )
Generate flow conditionally
(K-NN resampling of historical flow)
f ( xt St , St 1 , xt 1 )
10000 Simulations
Each 50-year long
2008-2057
Superimpose Climate Change
trend (10% and 20%)
Climate
Change
Water Balance Model: Our version
Climate Change
-20% LF flows over
50 years
Lees Ferry Natural Flow (15.0)
+
Intervening flows (0.8)
Upper Basin Consumptive Use (4.5+)
Evaporation (varies
with stage; 1.4 avg
declining to 1.1)
LB Consumptive Use
+ MX Delivery + losses (9.6)
“Bank Storage is near
long-term equilibrium’
Initial Net Inflow = +0.4
Combined Area-volume Relationship
ET Calculation
ET (MaF)
2
1.5
1
0.5
0
0
10
20
30
40
Storage (MaF)
50
ET coefficients/month
(Max and Min)
0.5 and 0.16 at Powell
0.85 and 0.33 at Mead
Average ET coefficient : 0.436
ET = Area * Average coefficient * 12
60
70
Flow and Demand Trends
applied to the simulations
Red – demand trend
13.5MAF – 14.1MAF
by 2030
Blue – mean flow trend
15MAF – 12MAF
By 2057
-0.06MAF/year
Under 20% - reduction
Management and Demand Growth Combinations
Alternative
Demand
Shortage Policy
Initial
Storage
A
7.5 MaF to LB, 1.5 MaF to MX
and UB deliveries per EIS
depletion schedule
333 KaF DS when S < 36%, 417
KaF DS when S < 30% and 500
KaF DS when S <23%
30 MAF
B
7.5 MaF to LB, 1.5 MaF to MX
and UB deliveries per EIS
depletion schedule
5% DS when S < 36%, 6% DS
when S < 30% and 7% DS when
S < 23%
30 MAF
C
7.5 MaF to LB, 1.5 MaF to MX
and UB deliveries at a 50% rate of
increase as compared to the EIS
depletion schedule
5% DS when S < 36%, 6% DS
when S < 30% and 7% DS when
S < 23%
D
7.5 MaF to LB, 1.5 MaF to MX
and UB deliveries at a 50% rate of
increase as compared to the EIS
depletion schedule
5% DS when S < 36%, 6% DS
when S < 30% and 7% d DS
when S < 23%
E
7.5 MaF to LB, 1.5 MaF to MX
and UB deliveries at a 50% rate of
increase as compared to the EIS
depletion schedule
5% DS when S < 50%, 6% DS
when S < 40%, 7% d DS when S <
30% and 8 % DS when S < 20%
30 MAF
60
MAF*
30 MAF
Table 1 Descriptions of alternatives considered in this study.
(LB = Lower Basin, MX = Mexico, UB = Upper Basin, DS = Delivery Shortage and S = Storage).
Per EIS depletion schedule the total deliveries are projected to be 13.9 MaF by 2026 and 14.4 MaF by 2057.
* One alternative with full initial storage (E) illustrates the effects of a full system.
Natural Climate Variability
Rajagopalan et al. 2009, WRR
Climate Change – 10% reduction
Climate Change – 20% reduction
Shortage Volume Under Climate Change
10% Reduction
20% Reduction
Summary
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Water supply risk (i.e., risk of drying) is small (< 5%) in the near term ~2026, for
any climate variability (good news)
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Risk increases dramatically by about 7 times in the three decades thereafter
(bad news)
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Risk increase is highly nonlinear
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There is flexibility in the system that can be exploited to mitigate risk.
 Considered alternatives provide ideas
Smart operating policies and demand growth strategies need to be instilled
 Demand profiles are not rigid
Delayed action can be too little too late
Water supply risk occurs well before any ‘abrupt’ climate change – even
under modest changes
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Nonlinear response
Complimentary Question
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In Rajagopalan et al. (2009)
 what is the risk for water supply under climate change?
 can management mitigate?
What is the probability distribution of 'optimal yield' from
the flow scenarios (climate change) given the system
capacity and constraints ?
 ensemble of streamflow sequences
 PDF of yield, storage mean & std dev
 provide policy makers with estimates of risk/reliability of
various growth targets
 12.7, 13.5, 15.0 MaF
Systems Approach - Optimization
Optimal Yield
Y = Yield (MaF)
Spillt= Overflow (MaF)
Qt = Paleo-reconstructed inflow
(MaF/yr)
K = Reservoir capacity (MaF)
St-1 = Previous year storage (MaF)
St = Current storage (MaF)
Minimum Storage is specified
System storage = 60MaF
• Average storage is computed for
the optimal yield Yopt, as the
average of:
Why not build more storage?
Plot of Optimal yield
vs. storage potential
 larger tub does
NOT lead to greater
yield
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
optimal yield
plateaus at ~15MaF
at around 70 MaF
Temporal
Variability! The
cause
Outline of Study
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More realistic approach
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Two ‘tubs’ representing
Lakes Mead and Powell
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Implementing operational
rules and evaporation...
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Generate PDFs of optimal
yields from given storage
capacity in the upper and
lower basins and ensembles
of streamflow sequences
Methodology – Constrained Linear
Optimization (Two-Basin Model)
• Evaporation is included in linear model as
coefficient weighted on current year’s
storage. To solve for evaporation, rearrange
the formula on the right.
• Evaporation coefficient is greater for Lower
Basin than Upper Basin
Climate Change Scenarios –
0 MaF Minimum Storage

Natural Flows
10% Reduction
20% Reduction
Two basins are let
run dry

optimal median
yield at least
15MAF (projected
demand)
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No Equalization...
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
natural flows
optimal median
yield decreases by
~1MAF in each
climate change
scenario
optimal yield
decreases as
streamflow
decreases
standard
deviation increases

less yield and
more variable
Climate Change Scenarios –
24 MaF Minimum Storage

Natural Flows
10% Reduction
20% Reduction
Two basins with
combined 24 MaF
Minimum
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No Equalization...
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
optimal median
yield never greater
than15MAF
(projected
demand)
optimal yield
decreases to scary
lows as streamflow
decreases
standard
deviation low

minimum storage
prevents variable
release
Reliability
Systems Approach
• enables a broader perspective
of the problem
• provide optimal solutions of
decision variables
• risk/reliability information
• enable robust decision making
NEW MODEL
What do we do?