1. Hydrologic data assimilation NSF workshop Oklahoma Dr Damian Barrett CSIRO Land & Water 23 October 2007

2. CSIRO. Hydrologic Forecasting Drought in south-eastern Australia Murray-Darling Basin Murrumbidgee catchment Murray-Darling Basin: Australia’s ‘food bowl’: 10 6 km 2 75% of irrigated crops & pastures 40% of national ag income (AUD$2-3B) Drought 2001  ? Long term mean inflow: 11,200 GL yr -1 Mean inflow drought: 5000 – 7000 GL yr -1 2006 inflow: 1000 GL yr -1 2007 inflow: 1550 GL yr -1 (Sept) Zero water allocations 07-08 season? Focusing attention on developing new approaches to forecasting water availability on days-seasons timescales

3. CSIRO. Hydrologic Forecasting Hydrologic forecasting A Definition: The prediction of hydrologic state variables (rainfall, ET, SMC, runoff, drainage, stream flow…) at future time based on the evolution of those variables in time (model) and conditioning those variables with observations while considering the relative uncertainties of model and observations MODEL STATES ANALYSIS STATES FORECAST OBSERVATIONS

4. CSIRO. Hydrologic Forecasting Role of satellite observations Polar orbiting moderate resolution satellite sensors: Whole earth coverage at high temporal frequency Spatial infilling (gaps between in situ gauges & instruments) Multiple sensors: Different & independent ‘viewpoint’ in space/time/wavelength Remote Sensing: information on radiometric properties of surface Two challenges: Relate observations to hydrologic state variables while quantifying errors and removing biases ‘Synthesise’ data from multiple sensors to yield optimal estimates of relevant state variables

5. CSIRO. Hydrologic Forecasting Model data assimilation: schema Forecast L zz Water budget zz day i day i+1 day i-1 Observations Forcing Forward model State variables Observation model Modeled observations ForcingForward model PPT I/R R ET T eaea RnRn

6. CSIRO. Hydrologic Forecasting Model data assimilation: schema Op RT -1 AnalysisForecast J L zz Water budget zz day i day i+1 day i-1 Observations Forcing Forward model State variables Observation model Modeled observations Forecast ForcingObservationsForward model3D Variational Assimilation TSTS PPT I/R R ET T eaea RnRn SEB Microwave RT TBTB J TVTV TSTS Sampling/interpolation

7. CSIRO. Hydrologic Forecasting 3D variational assimilation Cost-function: a metric of ‘distance’ between model and observations in state space R = covariance matrix of observation errors B = covariance matrix of model errors x a = analysis state vector x b = ‘background’ vector of model states ‘Observation operator’ Benefits: gradient search (no inversions) and sequential (imagery) Expensive: requires re-evaluation of H every iteration

8. CSIRO. Hydrologic Forecasting 3D variational assimilation H is the ‘tangent linear operator’: fixed providing x b – x a is ‘small’ Requires evaluation of H and construction of  once only Taylor expansion of observation model

9. CSIRO. Hydrologic Forecasting Model output: spatial data D 050mm00.40 270310 o K-0.10.1 rainfall 29/09/05 (272) 04/10/05 (277)

10. CSIRO. Hydrologic Forecasting Model output: comparison with stream flow Observed  peak flow (ML/day) Modelled  peak flow (ML/day) r 2 = 0.85 48 47 61 57 38 33 44 Flow (ML/day) Gauge #33 Model Obs

11. CSIRO. Hydrologic Forecasting Challenges Models Coupling models operating at different scales while dealing with non-linearities and heterogeneity Inadequate physics in forward and observation models Quantifying errors in model Efficient optimisation of massive problems Modelling at a scale relevant to decision making Observations Gaps in observations (drift by model) Key data sets and improve QA Quantifying errors in observations Representivity, scaling and aggregation errors: matching observations and model variables that differ in time/space scale

12. Contact Us Phone: 1300 363 400 or +61 3 9545 2176 Email: enquiries@csiro.au Web: www.csiro.au Thank you CSIRO Land and Water Dr Damian Barrett Research Group Leader – Remote sensing Phone: 02 6246 5558 Email: Damian.Barrettt@csiro.au Web: www.csiro.au/group