Impact of climate variability and change on the water balance

Mike Raupach, Peter Briggs, Vanessa HaverdMatt Paget, Kirien Whan

Project 1.2

• Progress– Excellent on most fronts, reasonable on others

• Challenges– Unforeseen Raupach ‘National Service’ commitments, recovery from surgery

(several months of effective downtime)• PMSEIC (now concluded)• Recent emergency contract from DCCEE• AAS Science of Climate Change: Questions and Answers• AGU Water in the Murray-Darling Basin -- the finite-planet challenge in microcosm (will submit as a SEACI

publication)

– Briggs long-service leave in Nepal (one month Oct-Nov)– Haverd CABLE-SLI / CASA-CNP / HPCC challenges– Paget, King, Briggs (WRON server instability issue)

• Impact on deliverables– Delays to some publications (Raupach et al. general AWAP paper; Haverd et al.

CABLE-SLI paper)• Contentious issues

– Policy: nothing– Scientific: temperature sensitivity of water balance components? to be discussed

with 2.2• Major changes in research direction

– None, though we may re-evaluate the approaches to linear modelling if required (and have switched modelling platform to R to facilitate this)

Project 1.2 Adminstrative Summary

GRACE

AWAP

Garcia et al. 2011 (in press)

GRACE/AWAP/GLDAS intercomparison

“Remarkable agreement”

Munier et al. 2011(in review GRL)

GRACE/AWAP

Water storage variations over the Canning Basin

“Very promising”

Webb et al. 2011(in preparation)

“AWAP soil moisture explains timing of grape maturation better than

precip”

Activities1. Maintain and enhance the AWAP hydromet data

stream

2. Produce regionally-averaged time series of AWAP hydromet

3. Apply a general statistical model to relate water balance responses to a set of climate indices

4. Determine parameters in the statistical model over the whole of Australia (including SE Australia)

5. Identify and explain the different sensitivities of hydrological responses (soil moisture, runoff and evaporation) to the drivers (rainfall, temperature). Particularly: what determines the gain of the rainfall-runoff amplifier?

Data generation

Statistical modelling

Physical model analysis

Data generation : Progress (Briggs, Paget)

• Incorporation of two further updates to BoM Version 3 meteorology• Reanalysis of AWAP Historical Series and public release of model

run 26c (1900-2009) • Update to Feb 2011 completed and mounted, public announcement

next week• Creation of regionalised AWAP time series for 245 ANRA drainage

basins, major drainage divisions• Public release of ‘usefulised’ metadata documents at

www.csiro.au/awap– AWAP 26c Data Announcement: Your Questions Answered– Spatial Soil and Vegetation Parameters for AWAP Modelling – AWAP 26c ReadMe file update

Data generation: Key Finding

• Characterising the Southeast Australian water balance Jan 1997 to Feb 2011: – Maps: Percentile rank maps of SE Australia,

monthly thumbnail series– Line plots: Regionally averaged monthly time

series for the MDB dry, agricultural, and wet subdivisions

– Other regions?

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• Mock-up of sample percentile rank monthly thumbnail series (ranks wrt 1961-1990 distribution for the same month)

• Real versions will be SEACI-region only; separate series for each major component

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Data generation: Reporting

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Wet, Agricultural, Dry areas of the MDBRegionally-Averaged Monthly AWAP Time Series: 1997 to Feb 2011

Statistical modelling: ProgressNon-linear (Whan, Raupach)• Excellent progress with CART (Classification and Regression Tree),

important results• Key finding: With appropriate use, forecasts of wet, medium or dry

conditions for the MDB can be made 6 months out with 70% skill, comparable to nowcasting.

Linear (Raupach, Briggs)• Good progress with methods; analysis somewhat delayed.• Model developed in Matlab, implemented in F90, re-implemented in R

to improve flexibility in choice of methods• Post-processing routines developed to map continent-wide correlation

matrices by ANRA drainage basin• Preliminary results suggest skills that are not comparable to CART, but

we have not explored the analysis space very far yet.

SST Climate Index Analysis Regions ‘Indo-Pacific large-scale modes of climate variability’

DMI = IODW – IODE

EMI = ModokiC – ½ ModokiW – ½ ModokiETripole = TripoleC – ½ TripoleW – ½ TripoleE

NichollsIOD = NichollsW – NichollsE

Derived

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Reporting: Sample CART analysis

• Spring rain forecast from winter climate indices (Nino3, Tripole, Tripole Region C)

• Distribution of 110 cases (years) into 4, then 3 statistically significant clusters.

Reporting: Linear statistical modelling

Precip vs Nino3Basin-mapped correlation matrix

Background: Physical model analysis (Haverd, Raupach, Briggs)

Temperature sensitivity of the Murray Basin water balance: an assessment using the CABLE-SLI land surface scheme (Haverd)

• Motivation– Defensible evaluation of sensitivity of SEOz water balance to meteorological inputs,

particularly sensitivity of runoff to air temperature– Attribution of met-sensitivity to land surface processes

• Approach– Application of detailed land surface model: CABLE-SLI, which accounts for coupled

water, energy and carbon stores fluxes in soil and vegetation.– CABLE-SLI embedded in AWAP framework– NB. CABLE process description embodies more of our unsterstanding of T-sens of

land surface processes than WaterDyn: leaf and soil temperatures are calculated and used to drive T-dep processes including soil evap, wet canopy evap, transpiration and photosynthesis.

– Model data fusion using multiple data types (soil moisture, streamflow, flux data, long-term NPP observations)

– Model runs with and without met perturbations

Progress: Physical model analysis

Temperature sensitivity of the Murray Basin water balance: an assessment using the CABLE-SLI land surface scheme (Haverd)

• Achievements– Leaf area partitioned between woody and herbaceous components– Tiled model with woody/grassy tiles leading to partition of fluxes between woody

and grassy components– Merging with CASA-CNP biogeochemical model to enable estimation of carbon

fluxes and stores, and hence model evaluation against carbon observations.– Merging simplified TOPMODEL with existing soil module to enable partition

between saturated and unsaturated zones and hence improved prediction of stream-flow dynamics.

• Outcomes– Prediction of more observables, particularly Carbon fluxes and stores, and

hence the opportunity to more tightly constrain model predictions (including WB predictions) using corresponding observations.

– A more tightly constrained model and hence enhanced confidence in predictions of met sensitivity of the WB, as well as the ability to attribute met sensitivities to processes.

Key Finding: Continental NPP consists of equal contributions from woody and grassy vegetation

Total NPP: 234 gCm-2

Woody NPP: 119 gCm-2 Grassy NPP: 115 gCm-2

Reasons for distinguishing woody and grassy veg:

• Different root density distributions: deep soil moisture more accessible to woody veg

• Different biomass turnover times: important when using biomass observations to constrain model predictions

Precip: 450 mm y-1 Soil evap: 263 mm y-1 Transp: 129 mm y-1

Wet canopy evap: 40 mm y-1 Discharge: 31 mm y-1

Key Finding: Continental Water Balance from CABLE-SLI

Key Finding: Preliminary estimate of Temperature sensitivity: Murray Basin 2000-2008

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Coming soon for the annual report:

• Updates to these numbers using the new CABLE-SLI + CASA-CNP + TopModel

Forward Planning 2011-12

Data Generation and Maintenance• Process improvement

– Automated daily mirroring of BoM data archive at CSIRO– Automated dynamic updating of model results, and web serving– Outcomes: merging of historical and operational streams to single, automated,

dynamically updated modelling system leading to reduced staff time on this activity• Operationalisation of AWAP WaterDyn at BoM• Delivery of AWAP overview paper, minor metadata items as appropriate

Statistical Modelling• Non-linear: Further questions for CART analysis of the MDB:

– Why is it so skillful?– Where do we lose skill?– Is the whole MDB too coarse an analysis region—what happens as you refine the

scale?• Linear:

– Further exploration of the modes-of-climate-variability space using the current R infrastructure, with consideration of method changes as required.

Physical model analysis• Subject to discussion shortly