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Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Role of Observations in Model testing, Parameterization & Modification: Case
Studies from Hydroclimatology Soroosh Sorooshian
Center for Hydrometeorology and Remote Sensing University of California Irvine
1st Workshop on: Understanding Climate Change from Data August 15-16 2011, Univ. of Minnesota
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
PERSIANN System “Estimation” Global IR
MW-RR (TRMM, NOAA, DMSP Satellites)
Merged Products - Hourly rainfall - 6 hourly rainfall - Daily rainfall - Monthly rainfall
ANN
Error Detection
Quality Control
Merging
Sate
llite
Dat
a G
roun
d O
bser
vatio
ns
Products
High Temporal-Spatial Res. Cloud Infrared Images
Feed
back
Hourly Rain Estimate Sampling
MW-PR Hourly Rain Rates (GSFC, NASA; NESDIS, NOAA)
Hourly Global Precipitation Estimates
Gauges Coverage
GPCC & CPC Gauge Analysis
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
(CPC, NOAA)
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
http://chrs.web.uci.edu/
Real Time Global Data: Cooperation With UNESCO
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
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
How Accurate Are Global Climate Models?
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Recent Assessment of Seasonal Climate Forecasts •“ of the dozens of forecast techniques proffered by government, academic, private-sector climatologists, all but two are virtually useless, according to a new study” 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
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Status of Forecast Skill in Hydrologic 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
Hydrologic Modeling
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
The Concept of Model Calibration Measured Outputs
Yt
t
Real World
Measured Inputs
MODEL (θ) Computed Outputs
Prior Info θ
Computed Outputs
+ -
Optimization Procedure θ
“Calibration: constraining the model to be consistent with observations”
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Calibration components
Objective Function
Search Algorithm
Sensitivity Analysis
Problems with identifiability
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Data information content
“Bucket Model” Simple two parameter Model
Cm
ax
K S
P
Q
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Data information content C
max
K S
P
Q
Multiple spills
Cmax
K
No spills
Cmax
K
Cm
ax
K S
P
Q
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Parameter X
Obj
ectiv
e Fu
nctio
n
True Parameter Set
The Ideal case: Convex Optimization
Created By G-H Park
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Obj
ectiv
e Fu
nctio
n
Parameter X
True Parameter Set
Difficulties in Global Optimization
Created By G-H Park
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Parameter X
Obj
ectiv
e Fu
nctio
n
Global Optimum
Created By G-H Park
Parameter Estimation (non-convex, multi-optima)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
SAC-SMA model 13 parameters to be estimated
The U.S. National Weather Service Flood Forecast Model ( SAC-SMA)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Optimization Strategy – Local Direct Search
Calibration of the Sacramento Model Downhill Simplex Method, Nelder & Mead, 1965
Duan, Gupta, and Sorooshian, 1992, WRR
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
More than one main convergence region
1.- Regions of Attraction
2.- Local Optima
Many small "pits" in each region
Difficulties in Optimization
Duan, Gupta, and Sorooshian, 1992, WRR
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
2.- Local Optima
Many small "pits" in each region
More than one main convergence region
1.- Regions of Attraction
3.- Roughness Rough surface with discontinuous derivatives
Difficulties in Optimization
Duan, Gupta, and Sorooshian, 1992, WRR
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
4.- Flatness Flat near optimum with significantly different parameter sensitivities
3.- Roughness Rough surface with discontinuous derivatives
2.- Local Optima
Many small "pits" in each region
More than one main convergence region
1.- Regions of Attraction
Difficulties in Optimization
5.- Shape Long and curved ridges
Duan, Gupta, and Sorooshian, 1992, WRR
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
The SCE-UA Algorithm …
Duan, Sorooshian, and Gupta 1992, WRR
The Shuffled Complex Evolution Algorithm
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Global Optimization – The SCE-UA Algorithm
Simplex Method
Shuffled Complex Evolution (SCE-UA)
Duan, Gupta & Sorooshian, 1992, WRR
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Evolving Directions
Advances in Parameter Estimation
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Land-Surface Model
RHODE ISLANDCONNECTICUT
FLORIDA
MISSISSIPPI
WESTVIRGINIA
WASHINGTON
OREGONIDAHO
MONTANA
WYOMING
NORTHDAKOTA
SOUTHDAKOTA
NEBRASKAIOWA
MINNESOTA
WISCONSIN
ILLINOIS
INDIANA OHIO
MISSOURIKANSASCOLORADO
UTAHNEVADA
CALIFORNIA
ARIZONA NEW MEXICO
OKLAHOMAARKANSAS
KENTUCKY
VIRGINIA
TEXAS
GEORGIA
ALABAMA
SOUTHCAROLINA
NORTH CAROLINA
TENNESSEE
PENNSYLVANIA
MARYLAND
NEW JERSEY
NEW YORK
VERMONT
MAINE
DELAWARE
LOUISIANA
MICHIGAN
MASSACHUSETTSNEW HAMPSHIRE
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Multi-Objective Approaches
M(θ)
Model
Radiation
Inputs Outputs
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Multi-Objective Optimization Problem
This image cannot currently be displayed.
MOCOM Algorithm:
Does NOT require conversion to a sequence of single optimization problems
Simultaneously finds several Pareto Solutions in a Single Optimization
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
ARM-CART SGP Site
100 km
Grid: ~100,000 km2
Luis A. Bastidas Z. ([email protected])
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
{H, Tg, Sw} {λE} {Tg} {Sw}
Single- & Multi-Flux Calibrations {H}
λE
Tg
H
Sw
Computed
Obs
erve
d
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Large-Scale Irrigation and Incorporation in Models
Impact of Irrigation
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
Irrigation over central California Irrigation areas
122W 120W
39N
36N
CIMIS stations
• Meteorological conditions are the key factors to decide when and how much water to apply, • Californian Irrigation Management Information System (CIMIS) with more than 200 stations (nearly 150 active) provides the information to farmers. • So far, not many researchers have use this dataset to evaluate the climate variability and climate model output in this region
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
1- “Control” run: without modifying anything (MM5-C). Model makes no special consideration for irrigation ( no water added) 2- “Field Capacity” run: setting up root-zone soil moisture at Field Capacity at each time step (MM5-F) 3- “Recommended” run: setting up root-zone soil moisture based Hanson et al (2004) method (MM5-R).
• Irrigation starts when the root zone’s relative available Soil-
Water (SW) content is less than maximum allowable water depletion of soil (i.e. adding water into model soil);
• Irrigation ends when soil moisture reaches field capacity (i.e.
stop adding water).
Modeling Study Setup Using MM5
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
• MODIS ET: 0.05-degree, monthly (Tang et al. 2009) http://ftp.hydro.washington.edu/pub/qiuhong/usa/ • MODIS skin temperature https://lpdaac.usgs.gov/lpdaac/get_data/ • GLDAS-1 ET: 0.25-degree, monthly http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings • Ameriflux at Blodgett Forest site http://public.ornl.gov/ameriflux/ • California Irrigation Management Information system (CIMIS) data http://www.cimis.water.ca.gov/cimis/data.jsp
Data Sources:
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
MM5-R
122W 120W
Mean skin surface temp. at daytime in June, July and August, 2007.
MODIS
39N
36N
122W 120W
Skin Temp. (oC)
MM5-C NARR
122W 120W 122W 120W
Adding irrigation into RCM (MM5), Improves the model’s ability to simulate the temperature patterns observed by MODIS
Sorooshian et al, (JGR 2011)
Center for Hydrometeorology and Remote Sensing, University of California, Irvine
GLDAS MM5-C MM5-R MODIS
Monthly ET (mm/month)
Actual ET comparison-spatial distribution – July 2007
- with more realistic irrigation scheme, significant improvement in capturing ET over irrigated Central Valley in California: