Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Ensuring Water in a Changing World
The Abdus Salam ICTP Summer School on: Climate Impact Modeling for Developing Countries: Water, Agriculture & Health Trieste, Italy: Sept. 5th – 16th 2011
Lecture I Hydrological modeling requirements for Water
Resources Applications - Model Issues Soroosh Sorooshian
Center for Hydrometeorology and Remote Sensing University of California Irvine
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
University of California Irvine (UCI) and Arizona (UA)
and many more …
CHRS & Affiliates: A truly International Team
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Two Primary Water Resources/Hydrology Challenges:
• Hydrologic Hazards ( Floods and Droughts) • Water Supply Requirements ( Quantity and Quality)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Hydrologic Forecasting Needs: Flash Floods
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Droughts: The Other hydrologic Extreme
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Two Primary Water Resources/Hydrology Challenges:
• Hydrologic Hazards ( Floods and Droughts) • Water Supply Requirements ( Quantity and Quality)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Projected Regions of Water Stress
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Increasing Population: Number of Mega Cities
Global Urban population 1970: ~37% 2010: ~53%
Projected Global Population: 8.3 Billion by 2025
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Distribution of Fresh Water Use
90.8 33.4%
17.1% 49.5%
460
7.0% 6.0%
87.0%
36.47 18.6%
22.0% 59.4%
117
60.0% 17.0%
23.0%
467.34
45.2%
13.1%
41.7%
380
4.0% 3.0%
93.0% Agriculture
Industry
Domestic
Fresh Water Use (109 Cubic Meters)
Water Source
Water Use
USA China India
Russia Japan Brazil
92% 6%
2%
70.3 Iran
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Primary Solution To Meet Hydrologic Extremes and Water Resources Needs
Engineering Approach: Control, Store, Pump and Transfer
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Hoover Dam
A Century of Water Resources Development: Engineering success
Central Arizona Project Aqueduct
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
1800 1900
1950
Impact on Design and Operation of Global Infrastructure
2000
More than 70,000 Dams in the U.S.
Provided by: C. J. Vörösmarty
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Short Range Long Range
hours days weeks months seasons years decades
Required Hydrometeorologic Predictions
Forecast Requirements
Short-range Mid-range Long-range
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Short Range Long Range
hours days weeks months seasons years decades
Required Hydrometeorologic Predictions
Forecast Requirements
Mid-range Long-range
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
A Key Consideration:
The Link Between Climate and Hydrology
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Global Warming And Hydrologic Cycle Connection
Heating
Temperature Evaporation
Water Holding Capacity
Atmospheric Moisture
Temperature oF Satu
rate
d Va
por P
ress
ure
t t+20
Green House Effect
Rain Intensity
Drought Flood
Flood Drought
Created by: Gi-Hyeon Park
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
P
E
Qs
∆ Ss
∆ Sg Qg
Ig
Coupled Ocean-Atmosphere
Models
Mesoscale Models
SVATs
Hydrologic/Routing Models
Water Resources Applications
GEWEX
CLIVAR
Hydrologic Services
Water resources management agencies
From the Global- to Watershed-Scale
CLiC
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Hydroclimate Science and Hydrologic/Water Resources Engineering
Hydrologic/Hydraulic Routing, Water Resources Models
SCIENCE ENGINEERING
Hydrologic/Hydraulic and Water Resources Engineering
Hydroclimate Science
P
E
Qs
∆ Ss
∆ Sg Qg
Ig
Mesoscale Models
SVATs
GCMs
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
River Basins and Watersheds
Continental Scale:
Watershed Scale: Where hydrology happens Where stakeholders exist
Different Scales Different Issues
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Short Range Long Range
hours days weeks months seasons years decades
Climate-Scale approaches to addressing hydrologic extremes
Forecast Requirements
Long-range
•Use of climate models: down-scaling and ensemble schemes •Traditional statistical hydrology methods:
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Prec
ipita
tion
p
redi
ctio
n
Time (years) Present Future 20 40 60 80
Climate Model Downscaling to Regional/Watershed Scales
Generation of Future Precipitation Scenarios
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Ensemble Approach Generation of Future Precipitation Scenarios
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Present Future 20 40 60 80 Time (years)
Prec
ipita
tion
p
redi
ctio
n
Present Future Time (years)
Flo
w
Generation of Future Runoff Scenarios
Downscaled Precipitation to Runoff Generation
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
1 0.5 0.2 0.1 0.05 0.02 0.01 0.005 0.002 0.001 Probability of Exceedance
Return Period (Years) 1 2 5 10 20 50 100 200 500 1000
600
500
400
300
200
100
0
Dis
char
ge
(100
0 cf
s)
Alternative Approach to Climate Model Downscaling
traditional statistical hydrology methods
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Probability density function
Time (years) Present
Future
10 20 30 40 50 60 70 80 90 100 -100 -75 -50 -25 0
Flo
w (
m3 /s
ec)
Past
0255075
100125150175200225250
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000Flo
w (m
3 /sec
)
Time (years)
Statistical Hydrology: “synthetic” stream flow Generation
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Brief Review of Rainfall
Runoff modeling:
Progress in Hydrologic Modeling
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Fundamental Law
Input Output
Change In Storage
I O
∆S
I – O =∆S
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Nature’s Way Terrain
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
The Watershed
Area km2 12.78Perimeter km 19.344Min Elevation m 478.00Max Elevation m 1756.00Mean Elevation 930.34Max Flow Length 8.878
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Eva
pora
tion
(Oce
an)
Eva
potra
nspi
ratio
n
Pre
cipi
tatio
n
Pre
cipi
tatio
n
Pre
cipi
tatio
n
Sub
limat
ion
Eva
pora
tion
(Lak
es &
Res
ervo
irs)
Vap
or D
iffus
ion
Lake
Infil
tratio
n
Dee
p P
erco
latio
n
River
Aquifer
Eva
pora
tion
(Lan
d S
urfa
ce)
Vegetation
Interflow
Ponce, 1989
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Trace The Water Drop
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Distributed
Physically-based
API Model A
C
D
B
Lumped Conceptual
Distributed (Mike SHE)
VIC Model
Evolution of Hydrologic R-R Models
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
MODEL
PARAMETER ESTIMATION
DATA
If the “World” of Watershed Hydrology Was Perfect!
Hydrologic Modeling: 3 Elements!
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Flow in Channels: How far can we go simplifying?
V = n-1 R2/3 S1/2
n – Manning Coefficient R – Hydraulic Radius S – Energy Slope
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Hydrologic Modeling
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Observed Lumped Model
Animation Assisted by: Q. Xia
Hydrologic Modeling: “Lumped”
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
UCt
UM
LM
Streamflow
Rainfall
percolation
LCt
UK
LK
PD = f(Z, X)
A look into the “heart” of R-R Models
Percolation Process is the Core element in Partitioning the rain between the various stores
PD = f(Z, X)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Free water
Tension water
Tension water
Percolation RATE=PBASE(1+ZPERC*DEFR REXP)
NWS Soil Moisture Accounting Model: SMA-NWSRFS
Nonlinear structure with large number of parameters
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
1 2
3 4
6
1 2
A
B
4 3
5
Combination
Rou
ting
Runoff
“Semi-distributed” Hydrologic Models
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Observed Lumped Model Semi-Distributed Model
Animation Assisted by: Q. Xia
“Semi-distributed” Hydrologic Models
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Large basin
Sub-basin 4
Sub-basin 2
Sub-basin 1
Sub-basin 3
Suz
Srz qv Su
z Srz
qv Suz
Srz qv
Suz
Srz qv
Suz
Srz qv Su
z Srz
qv q
v qv
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Srz qv
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Srz qv
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Srz qv
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qv Suz
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Srz qv
qv
Suz
Srz qv Su
z Srz
qv Suz
Srz qv
Sub-basin k Rek
qv
ai grid-cell i
qv
Suz
Srz qv Su
z Srz
qv
grid-cell i
qv
qv qv
Suz
Srz qv
Suz
Srz qv Su
z Srz
qv
Suz
Srz qv
Suz
Srz qv
SDi
Funada, 2004
Example of Distributed Model Appl. in large Basins
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Observed Lumped Model Semi-Distributed Model Distributed Model
Animation Assisted by: Q. Xia
Example of Distributed Model
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Excess
Loss
Partition precipitation Loss + Runoff Transform to outlet of
Sub-watershed Rout through channel
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Cross-Validation
Continued need for Some Form of Calibration
Model Complexity Matches
System Complexity
Underfitting Overfitting
Source: Gershenfeld, 1999
Calibration
Model Complexity
Erro
r
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Reviewing some recent model evaluation studies
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
DMIP-1 Findings: In a Nutshell
No Major Difference between the performance
of Lumped and distributed models
Sacramento Model
DMIP 1 Results (From Reed et al., 2004)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Recent Assessment of Seasonal Climate Forecasts
Livezey &Timofeyeva - BAMS, June 2008.
• “About the only time forecasts had any success predicting precipitation was for winters with an El Nino or a La Nina”
Quoting from Science, Vol. 321, 15th August 2008
Drought Predictability Sp
atia
l Sca
les
Time Scales Minute Day Week Season 1 yr 10 yrs 100 yrs
1 km
10 km
100 km
1000 km
10000 km
User Needs Current Skill
Provided by Siegfried Schubert 2011
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Recent Assessment of Climate Models
Regional trends in extreme events are not always captured by current models
It is difficult to assess the significance of these discrepancies and to distinguish between model deficiencies and natural variability