RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE HYDROLOGICAL MODELING

RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE

HYDROLOGICAL MODELING Dennis P. Lettenmaier

Department of Civil and Environmental Engineering

University of Washington

presentation for

Hydrological Sciences ReviewRoyal Netherlands Academy of Arts and Sciences

May 21, 2003

Outline of this talk

1) Macroscale modeling approach a) Strategy

b) Testing and evaluationc) Implementation

2) Examples a) Derived data sets b) S/I streamflow forecasting c) Hydrologic effects of climate change

3) Weak links and research opportunities

1. Macroscale modeling approach

a) Strategy

Traditional “bottom up” hydrologic modeling approach (subbasin by subbasin)

Flood of record

• Principal calibration locations were the Skykomish at Gold Bar and the Snoqualmie at Carnation

Snoqualmie River at Carnation, WA

Macroscale modeling approach (“top down”)

1 Northwest 5 Rio Grande 10 Upper Mississippi2 California 6 Missouri 11 Lower Mississippi3 Great Basin 7 Arkansas-Red 12 Ohio4 Colorado 8 Gulf 13 East Coast

9 Great Lakes

1. Macroscale modeling approach b) Testing and evaluation

Investigation of forest canopy effects on snow accumulation and melt

Measurement of Canopy Processes via two 25 m2 weighing lysimeters (shown here) and additional lysimeters in an adjacent clear-cut.

Direct measurement of snow interception

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350

11/1/96 12/1/96 1/1/97 2/1/97 3/1/97 4/1/97 5/1/97

SW

E (

mm

)ObservedPredicted

Below-canopy

Shelterwood

Tmin = 0.4 C Zo shelterwood = 7 mmTmax = 0.5 C Zo below-canopy = 20 cm

Albedo based onexponential decaywith age; fitted tospot observationsof albedo

Calibration of an energy balance model of canopy effects on snow accumulation and melt to the weighing lysimeter data. (Model was tested against two additional years of data)

Summer 1994 - Mean Diurnal Cycle

Point Evaluation of a Surface Hydrology Model for BOREAS

Flu

x (W

/m2)

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300 Rnet

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H

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120LE

0 3 6 9 12 15 18 21 24

SSA Mature Black Spruce

Rnet

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SSA Mature Jack Pine

Rnet

H

LE

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Local time (hours)

NSA Mature Black Spruce

Observed Fluxes

Simulated Fluxes

Rnet Net Radiation

H Sensible Heat Flux

LE Latent Heat Flux

Range in Snow Cover ExtentObserved and Simulated

Eurasia North America

J F M A M J J A S O N D JMonth

Observed Simulated

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sno

w c

ove

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ten

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06 km

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J F M A M J J A S O N D JMonth

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June 18th-July 20th, 1997

UPPER LAYER SOIL MOISTURE

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OIS

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TOPLATS regionalESTAR distributedTOPLATS distributed

11:00 CST JULY 12 1997

ESTAR TOPLATS

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ESTAR TOPLATS

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11:00 CST JUNE 20, 1997

Illinois soil moisture comparison

Mean Normalized Observed and Simulated Soil MoistureCentral Eurasia, 1980-1985

20°E 30°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 110°E 120°E 130°E 140°E

40°N 40°N

50°N 50°N

60°N 60°N

A

BC

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oil M

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(mm

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l Moi

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Observed Simulated

Cold Season Parameterization -- Frozen Soils

Key

Observed

Simulated

5-100 cm layer

0-5 cm layer

1. Macroscale modeling approach c) Implementation

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Calibration at Global Scales

Selected 26 river basins, which were divided into calibration (red)

and validation (green).

•Only a limited number of model parameters were identified as calibration parameters. The remaining model parameters were determined independently and were not modified.

•Calibration parameters:•Infiltration capacity shape parameter bi

•Depth of second soil layer•Saturated hydraulic conductivity•Exponent for unsaturated hydraulic

conductivity.

•Calibration was performed for nine out of 26 river basins.

•Simulated from 1980-1993 and compared to observed discharge.

Seasonal Evapotranspiration (1980-1993)Uncalibrated (base case) simulation

Global Mean Annual Runoff Ratio (1980-1993)Uncalibrated (base case) simulation

2. Examples a) Derived data sets

LDAS Long-Term Retrospective Data Set, 1950-2000

Ed Maurer

Dennis LettenmaierUniversity of Washington

Department of Civil and

Environmental Engineering

Motivation

Baseline forcing data – for water and energy balance studies (e.g., GEWEX WEBS).

Derived “Pseudo-Observations” – for variables not widely measured (e.g., soil moisture) – analogous to reanalysis.

Climate variability and change - characterizing variability and change in variables not directly observed.

Implementation Strategy

VIC model implemented for 15 sub-regions, with consistent forcings.

Surface forcing data:

Daily precipitation; maximum and minimum temperatures (from gauge measurements)

Radiation, humidity parameterized from Tmax and Tmin

Wind (from NCEP/NCAR reanalysis)

Soil parameters: derived from Penn State State STATSGO in the U.S., FAO global soil map elsewhere.

Vegetation coverage from the University of Maryland 1-km Global Land Cover product (derived from AVHRR)

100šW

100šW

98šW

98šW

96šW

96šW

40šN 40šN

42šN 42šN

Missouri River Basin

Precipitation Station Locations

Temperature and Precipitation Data

Within the U.S.:•Precipitation adjusted for time-of-observation

•Precipitation re-scaled to match PRISM mean for 1961-90 (especially important in western U.S.

Precipitation and Temperature from gauge observations gridded to 1/8o

Avg. Station density:Area Km2/station

U.S. 700-1000

Canada 2500

Mexico 6000

Validation with Observed Runoff

Hydrographs of routed runoff show good correspondence with observed and naturalized flows.

Comparisons with Illinois Soil Moisture

19 observing stations are compared to the 17 1/8º modeled grid cells that contain the observation points.

Persistence

Moisture Level

Moisture Flux

Variability

Evaluation of Energy Forcings

Comparison with 4 SURFRAD Sites

• 3-minute observations aggregated to 3-hour

• Average Diurnal Cycle is for June, July, August 1996-99

• Peak underestimated 3-15% at each site (avg. 10% for all sites)

• Daily average within 10%, (avg. 2%)

Seasonal Soil Moisture Variation

•Shown is seasonal variation of soil moisture.•Top plot is scaled by the total soil pore volume.•Bottom plot is scaled by its dynamic range for 50-years.

Soil Moisture - Active Range

50-Year Soil Moisture Range Scaled by Annual Precipitation

Scale indicates level of hydrologic interaction of soil

column

Soil Moisture - Persistence

Persistence of soil moisture anomalies, based on the full 50+ year timeseries at each grid cell.

Persistence is generally seen where soil moisture interaction is high.

Variables

Data Availability

Monthly Average VariablesCurrently downloadable from www.hydro.washington.edu

Each file contains monthly data for 1950-2000

Avg. File Size (compressed netCDF): 120 MB

3-Hourly VariablesArchived at San Diego Supercomputer Center (link from www.hydro.washington.edu)

Each file contains 3-hourly data for one year

Avg. File Size (compressed netCDF): 200-450 MB

Daily VariablesArchived at San Diego Supercomputer center (link from www.hydro.washington.edu)

Each file contains daily average data for one year

Avg. File Size (compressed netCDF): 20-100 MB

Example Application 1: Hydrologic predictability over the Missouri River basin

Methods for Determining Runoff Predictability

• Indices Characterizing Sources of Predictability:

SOI – An index identifying ENSO phaseAO – An index of phase of the Arctic OscillationSM – Soil moistureSWE – Snow water equivalent

• Varying Lead Times between Initial Conditions (IC) and Forecast Runoff

• Only Use Indices in Persistence Mode

ForecastSeason

DJF

Initialization Dates for DJF Forecast

Dec 1Dec 1 Mar 1 Jun 1 Sep 1

Lead-0Lead-4 Lead-3 Lead -2 Lead 1

D J F M A M J J A S O N

Climate

Land

Methods 2

• Multiple linear regression used between IC and runoff

• Variance explained (r2) indicates level of predictability

• Variables introduced in order of how well indices represent current knowledge of state:

1. SOI/AO2. SWE3. SM

• Incremental predictability

r2SOI/AO

r2SWE

Runoff

SOI/AO SWE

Methods 3Test for Significant Predictability (r2) in 2 steps

Local Significance:

•Tested at each grid cell

•Accounts for temporal autocorrelation

•95% confidence level estimated

Field Significance (Livezey and Chen, 1983):

•Tests area showing local significance over entire basin

•Accounts for limited sample size, spatial correlation in both predictors and predictand

•95% confidence for field significance

Total Runoff Predictability

1.5

4.5

7.5

10.5

13.5

Lead,months

• Uses all 4 indices to predict runoff

• “X” no field significance

• Field significance is domain-wide measure

Predictability due to Climate Signals

• Predictors currently available

• Moderate levels of r2

• Greater influence in winter, in area and lead time

• Difficulty in long-lead persistence prediction with climate signals

Predictability due to Soil Moisture

•Widespread predictability at 0 lead (1½ month)

•Winter Runoff: little predictability where runoff is high

•Summer Runoff: limited predictability to 3 seasons

Example Application 2: Hydrologic predictability over the North American

Monsoon (NAMS) region

Exploratory Work on Teleconnections between SST and Soil Moisture

Sea surface temperature: Extended Reconstruction of Global Sea Surface Temperature data set based on COADS data. (1847-1997) developed by T.M. Smith and R.W. Reynolds, NCDC. The original data resolution is 2ºlongitude, 2 º latitude. It was interpolated into 0.5 º resolution (The ocean domain is chosen according to the Bin Yu and J.M. Wallace’s paper, 2000, J. Climate, 13, 2794-2800)

Soil Moisture: VIC retrospective land surface dataset (1950-1997). The original data with 1/8 degree resolution is aggregated into 0.5 º resolution.

Study Domain and Datasets

Maximum Positive Correlation Coefficient

• SST has significant positive correlation coefficient with soil moisture in most areas even with lead time more than 9 months.

• Southwestern United States shows higher correlation Coefficient (greater than 0.6) with SST than Mexico region.

• June shows larger area with higher coefficient than other months.

Predictability of Soil Moisture by SST First and Second PC

Southwestern US area shows highest predictability (the highest variance explained is about 0.45)

Predictability of Soil Moisture by Persistence

• Soil moisture shows significant persistence even in at 6-month lead time especially for June soil moisture.

• Mexican part of the domain also shows high persistence for June soil moisture

June Soil Moisture Predictability by Persistence and SST PCs

The highest variance explained is more than 90%. For June, over 40% of the variance is explained over most of the study domain, including Mexico.

June soil moisture predicted by Persistence and SST PCs

• Last December can explain 66.7% June soil moisture

LDAS Data

Predicted

Introducing SST PCs benefits long-time lead predictability (of June soil moisture), but no significant benefits for less than 6-month lead

time predictability.

SST and Persistence

Persistence

Region with 90% significant correlation between last winter’s JFM precipitation and JJAS precipitation (1965-1999) in given

Monsoon Region

JJAS monsoon precipitation with statistically significant correlation to previous winter’s (JFM) precipitation

by region

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1950 1960 1970 1980 1990

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1950 1960 1970 1980 1990

Year

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(m

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Monsoon West Monsoon North

The significant period is around

1970-1999.

15-year moving correlation between last winter JFM precipitation index and JJAS Monsoon precipitation

Monsoon West Monsoon North

Hypotheses

• For Monsoon West, the region for which monsoon precipitation is (negatively correlated with) the previous winter’s precipitation is the Southwestern U.S. (at least for the period 1965-1999).

• For Monsoon North, the region for which monsoon precipitation is (negatively correlated with) the previous winter’s precipitation is the Midwest and Southwestern U.S. (for the period 1970-1999).

• The land surface feedback mechanism could be: anomalous winter precipitation leads to anomalous spring soil moisture, hence lower early summer surface temperature, and weaker monsoon precipitation.

2. Examples b) Seasonal to interannual streamflow forecasting

climate model forecastmeteorological outputs

• ~1.9 degree resolution (T62)• monthly total P, avg T

Use 3 step approach: 1) statistical bias correction 2) downscaling3) hydrologic simulation

General Approach

hydrologic model inputs

streamflow, soil moisture,snowpack,runoff• 1/8-1/4 degree resolution

• daily P, Tmin, Tmax

Models: Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC

• forecast ensembles available near beginning of each month, extend 6 months beginning in following month

• each month:• 210 ensemble members define GSM climatology for

monthly Ptot & Tavg• 20 ensemble members define GSM forecast

Models: VIC Hydrologic Model

domain slide

Example Flow Routing Network

One Way Coupling of GSM and VIC models

a) bias correction: climate model climatology observed climatologyb) spatial interpolation:

GSM (1.8-1.9 deg.) VIC (1/8 deg)c) temporal disaggregation (via resampling of observed patterns):

monthly daily

a. b. c.

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TGSM

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GSM Regional Bias:a spatial example

Bias is removed at the monthly GSM-scale from the meteorological forecasts

(so 3rd column ~= 1st column)

GSM Regional Bias:

one cell example

For sample cell located over Ohio River basin, biases in monthly Ptot & Tavg are significant!

GSM Regional Bias:

one cell example

Bias: Developing a Correction

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20 member forecast ensemble

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from 1979 SSTsfrom 1980 SSTs

from 1981 SSTs

from 1999 SSTs

from current SSTs

(21 sets)10 member climatology ensembles

Bias: Developing a Correction

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percentile (wrt 1979-99)

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GSM

Observed

July Tavg, for 1 GSM cell

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1979 SSTsetc.

from 1999SSTs

10 member climatology ens.

* for each month, each GSM grid cell and variable

*

Downscaling Test

1. Start with GSM-scale monthly observed met data for 21 years

2. Downscale into a daily VIC-scale time series

3. Force hydrology model to produce streamflow

4. Is observed streamflow reproduced?

Simulations

Forecast Productsstreamflow soil moisture

runoffsnowpack

VIC model spin-upVIC forecast ensemble

climate forecast

information (from GSM)

VIC climatology ensemble

1-2 years back start of month 0 end of month 6

NCDC met. station obs. up to 2-4 months from

current

LDAS/other met. forcings

for remaining

spin-up

data sources

A B C

Columbia River Application

CRB

Initial Conditions

late-May SWE &water balance

CRB

Initial Conditions

(percentiles)

CRB: May forecast

hindcast“observed”

forecast

forecast medians

CRB May forecast

basin avg. soil moisture

CRB May Forecast

Streamflow

CRBMay Forecast

cumulative flow averages

forecastmedians

2. Examples c) Hydrologic effects of climate change

Climate Scenarios

Global climate simulations, next ~100 yrs

Downscaling

Delta Precip,Temp

HydrologicModel (VIC)

Natural Streamflow

ReservoirModel

DamReleases,Regulated

Streamflow

PerformanceMeasures

Reliability of System Objectives

Overview of ColSim Reservoir Model

Physical Systemof Damsand Reservoirs

Reservoir Operating Policies

Reservoir StorageRegulated StreamflowFlood ControlEnergy ProductionIrrigation ConsumptionStreamflow Augmentation

0100000200000300000400000500000600000700000800000900000

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

Flow

(cfs

)

Streamflow Time Series

Dam Operations in ColSim

Storage Dams

Run-of-River Dams

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200000

300000

400000

500000

600000

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Month

Avg

Str

eam

flow

(cf

s)

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200000

300000

400000

500000

600000

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Month

Avg

Str

eam

flow

(cf

s) Virgin Regulated

Flow In=Flow out + Energy

H

GCM grid mesh over western U.S. (NCAR/DOE Parallel Climate Model at ~ 2.8 degrees lat-long)

ACPI: PCM-climate change scenarios, historic simulation v air temperature observations

ACPI: PCM-climate change scenarios, historic simulation v precipitation observations

Climate Change Scenarios

Historical B06.22 (greenhouse CO2+aerosols forcing) 1870-2000

Climate Control B06.45 (CO2+aerosols at 1995 levels) 1995-2048

Climate Change B06.44 (BAU6, future scenario forcing) 1995-2099 Climate Change B06.46 (BAU6, future scenario forcing) 1995-2099 Climate Change B06.47 (BAU6, future scenario forcing) 1995-2099

Climate Control B06.45 derived-subset 1995-2015

Climate Change B06.44 derived-subset 2040-2060

PCM Simulations (~ 3 degrees lat-long)

PNNL Regional Climate Model (RCM) Simulations (~ ¾ degree lat-long)

Future streamflows

• 3 ensembles averaged

• summarized into 3 periods;» Period 1 2010 - 2039

» Period 2 2040 - 2070

» Period 3 2070 - 2098

Bias Correction and Downscaling Approach

climate model scenariometeorological outputs

hydrologic model inputs

snowpackrunoffstreamflow

• 1/8-1/4 degree resolution• daily P, Tmin, Tmax

•2.8 (T42)/0.5 degree resolution•monthly total P, avg. T

Bias Correction

from NCDC observations

from PCM historical runraw climate scenario

bias-corrected climate scenario

month mmonth m

Note: future scenario temperature trend (relative to control run) removed before, and replaced after, bias-correction step.

Downscaling

observed mean fields

(1/8-1/4 degree)

monthly PCManomaly (T42)

VIC-scale monthly simulation

interpolated to VIC scale

Inflow

Run of River Reservoirs (inflow=outflow + energy)

Inflow

Inflow

Inflow

Inflow

Inflow

Storage ReservoirsReleases Depend on:•Storage and Inflow•Rule Curves (streamflow forecasts)•Flood Control Requirements•Energy Requirements•Minimum Flow Requirements•System Flow Requirements

System Checkpoint

Consumptive use

Consumptive use

Inflow +

ColSim

Regional Climate Model (RCM) grid and hydrologic model domains

PCM Business-as-Usual scenarios

Columbia River Basin(Basin Averages)

control (2000-2048)

historical (1950-99)

BAU 3-run average

RCM Business-as-Usual scenarios

Columbia River Basin(Basin Averages)

control (2000-2048)

historical (1950-99)

PCM BAU B06.44

RCM BAU B06.44

PCMBusiness-As-Usual

Mean Monthly

Hydrographs

Columbia River Basin@ The Dalles, OR

1 month 12 1 month 12

CRB Operation Alternative 1 (early refill)

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

40,000,000

45,000,000

50,000,000

55,000,000

O N D J F M A M J J A S

To

tal

En

d o

f M

on

th S

yste

m S

tora

ge

(acr

e-fe

et)

Max Storage

Control

Base Climate Change

Change (Alt. 1)

Dead Pool

CRB Operation Alternative 2 (reduce flood storage by 20%)

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

40,000,000

45,000,000

50,000,000

55,000,000

O N D J F M A M J J A S

En

d o

f M

on

th T

ota

l S

ys

tem

Sto

rag

e (

ac

re-f

ee

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Max Storage

Control

Base Climate Change

Change (Alt. 2)

Dead Pool

Columbia River Basin Water Resource Sensitivity to PCM Climate Change Scenarios

0%

20%

40%

60%

80%

100%

120%

Portland-Vancouver

Spring FloodControl

Reliability

Portland-Vancouver

Winter FloodControl

Reliability

Autumn FirmPower

Reliability(November)

% of ControlHydropower

Revenues

McNaryInstream

TargetReliability

(April-August)

Middle SnakeAgriculturalWithdrawalReliability

Grand CouleeRecreationReliability

Rel

iab

ility

(%

, mo

nth

ly b

ased

)

Control

Period 1

Period 2

Period 3

RCM

A v e r a g e M o n t h ly D e f i c i t a t t h e M c N a r y D a m T a r g e t ( c f s )

M o n th ly R e l i a b i l i t y a t t h e M c N a r y D a m T a r g e t

Per

iod

1

-

20,000

40,000

60,000

80,000

100,000

120,000

Apr May Jun Jul Aug

Control

Current Operations

Refill 2 w eeks earlier

Refill 1 month earlier

Per

iod

1

Per

iod

2

Per

iod

3

3) Weak links and research opportunities

1) How can macroscale hydrology models be evaluated/diagnosed at the spatial scales at which they are designed to be applied? What is the roll of large scale field experiments, and how should the next generation be designed?

2) Are there “interesting” research issues in climate change impact assessment (where most uncertainties are in the climate forcings) vs feedbacks (roll of the land surface in climate)? Planning under uncertainty?

3) Applications issues: a) We don’t have a good pathway for infusing science advances into operational model improvements; and b) Lack of skill score history for hydrologic prediction – no basis for audit

4) What is the role of remote sensing and data assimilation in large scale hydrologic modeling and prediction?

RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE HYDROLOGICAL MODELING

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Transcript of RECENT PROGRESS AND OPPORTUNITIES IN MACROSCALE HYDROLOGICAL MODELING