Applications of Remote Sensing Dataset on Studies of Biases in Land Surface Models (LSM)

Min-Hui Lo, Wen-Ying Wu, Ren-Jie Wu

Department of Atmospheric Sciences, NTU

2015/06/03 @ CWB

(1) Simulations of Land Water Storage in Australia(2) Simulations of Latent Heat Fluxes in California

What is GRACE – global water cycle

Snow No Snow

GRACE is like a giant scale in the sky that tells you how much weight you’ve gained or lost each month

Implications for terrestrial and global hydrologyMass variations in Earth’s global water reservoirs

SGLOBAL = SOCEAN + SLAND + SICE = 0

We can now track ocean, land and ice changes using a single

observing system

Global Water Storage

Courtesy of JT Reager

Total Oceans

Total Land

Total Oceans(seasonal cycle removed)

IPCC AR5

Changes in Land water Storage

• A transition from El Nino to La Nina->more P on the land

• Persistence time of TWS over AUS is longer.

Extra Water

AUS

NA

SA

Australia includes expansive arheic and endorheic basins that impede runoff to ocean.(Fasullo et al.,2013)

A unique endorheic (inland) river system in Australia

http://webworld.unesco.org/water/ihp/publications/waterway/webpc/pag13.html

Can LSM simulate such changes?

Models vs Observations

• GLDAS included results from three land models: NOAH, Mosaic , Vic, CLM2• Land Water Storage in GLDAS: canopy water, snow, soil moisture

Differences between models and

observations:

• Amplitude of Annual cycle

• Interannual varibility• Long term trend• Flood/Drought Event

(Flooding/decaying period)

Factor:• Interaction between

storage and transport• Water capacity

(Ground Water component, depth)

Simulations in CLM4 with groundwater components

Comparisons of GLDAS, CLM4, GRACE

However, there still are some discrepancies

Short Summary I

• Global TWS is able to cause the temporary seal level variability; while the hydrologic process plays a crucial role.

• GRACE provided a new method to estimate the variability of TWS, which is available for examination the water storages simulated in land surface models.

• The component of groundwater should be into the land model with realistic mechanism.

• The mechanisms whereby water transport influences TWS might impact the climate?

Simulations of Latent Heat Fluxes (Evapotranspiration) in California

Irrigation intensity map (FAO)

modified from Faunt et al., 2009

Water budget in heavily irrigated system

Central Valley Aquifer

Central Valley Surface Processes

GWR (184)

precipitation(374)

E(607)

surface water deliveries (241)

groundwaterwithdrawal

(203)storage lose (33)

(unit: mm/year)

California’s Central Valley(52,000 km2)

• Is one of the most productive agricultural regions in the world

• Produces 1/4 of the food in the U.S.

1) CLM 2) MOSAIC3) NOAH4) VIC

How about the current LSMs?

Compare the ET of 4 different LSMs to observations

2003 2004 2005 2006 2007 2008 20090

100

200

300

400

500

Time

mm

/year

Average Preciptation over CV

Observational-based estimated ET from Anderson et al., 2012

from Anderson et al., 2015

How to solve this ET discrepancy?

Representation of water withdrawals in the model

dS

dt= In -Out

Inverse approach

Obs: 700 mm/yr

Model: 350 mm/yr

GRACE ( from 2003~2010) dS

dt

60 mm/yr of decline

dS

dt= In -Out

Surface water and groundwater withdrawals

160 mm/yr

190 mm/yr

OBS

Results

from Anderson et al., 2015

0 2 4 6 8 10 120

10

20

30

40

50

60

70

80

90

100

Month

Evap

otr

an

sp

ira

tion

, m

m/m

on

thClimatological Evapotranspiration, Averaged in Central Valley

obs

CMIP5

from Anderson et al., 2015

Simulations of Evapotranspiration in CMIP5

Caveat:

• Constant irrigated water?

• GW withdrawal from confined and unconfined aquifers globally?

Future work:

• Apply this approach globally

• Couple to GCM to see human fingerprint on the climate

Summary

• How to best utilize the GRACE data in climate model? at the seasonal forecast?

• Integrate GRACE information into the LSM

Thanks!

ET estimates by MODIS

Using satellite data to quantify anthropogenic fluxes globally

Total water storage variationsfrom GRACE

Total Water Storage Changes in the Combined Sacramento-San Joaquin River BasinsFrom the NASA GRACE Satellite Mission for March 2002- December 2013

Data courtesy of UC Irvine, National Center for Atmospheric Research, University of Texas, NASA

Courtesy of Jay Famiglietti

Long-term

GRACEpereiod

(CLM4.0)

Precipitation