2. Remote Sensing, Land Surface Modelling and Data Assimilation Christoph Rüdiger, Jeffrey Walker The University of Melbourne Jetse Kalma The University of Newcastle Garry Willgoose The university of Leeds

3. Christoph Rüdiger & Jeffrey Walker Overview Remote Sensing Data Assimilation Land Surface Modelling Combining the Options

4. Christoph Rüdiger & Jeffrey Walker Remote Sensing

5. Christoph Rüdiger & Jeffrey Walker Remote Sensing Remote Sensing defined: Measurement of energy reflections or emissions of different spectra from a distance Modes of Remote Sensing in Hydrology: Ground-based air-borne space-borne platforms

6. Christoph Rüdiger & Jeffrey Walker Visual Band (~400nm – 700nm) Infrared Band (~0.7μm – 1000μm) Microwave Band (~1cm – 30cm) Radio Band (>30cm) Gravitational Measurements Observed Wavelengths - Spectral Resolution -

7. Christoph Rüdiger & Jeffrey Walker What Can Be Measured (some examples) Subsurface Surface soil moisture, soil temperature, gravitational effects Surface Vegetation cover, vegetation density, evapotranspiration, temperature, sea level, elevation, fires Atmosphere Cloud cover, aerosols, wind, temperature

8. Christoph Rüdiger & Jeffrey Walker Remote Sensing - Spatial Resolution - Study Catchment

9. Christoph Rüdiger & Jeffrey Walker Current Missions Visual Band (~400nm – 700nm) Modis, Landsat … Infrared Band (~0.7μm – 1000μm) Landsat, GOES Microwave Band (~1cm – 30cm) TRMM, AMSR-E Radio Band (>30cm) TRMM Gravitational Measurements Grace

10. Christoph Rüdiger & Jeffrey Walker Limitations of Individual Bands Atmospheric interference (infrared). Radio interference (microwave). Surface conditions, vegetation, cloud and aerosol effects (all). Penetration depth (all). Other effects? Rüdiger et al., in review

11. Christoph Rüdiger & Jeffrey Walker Summary of Remote Sensing Advantages: Observation of large areas Observations of remote areas Large quantity of environmental states can be observed Limitations: Either low resolution or low rate of repeat overpasses Influence of surface and atmospheric conditions have to be filtered Average values of observed states, need for downscaling

12. Christoph Rüdiger & Jeffrey Walker Data Assimilation

13. Christoph Rüdiger & Jeffrey Walker Data Assimilation Defined Definition 1: using data to force a model ie. precipitation and evapotranspiration to force a LSM Analogy: passenger giving instructions to a blindfolded driver on the autostrada at peak hour

14. Christoph Rüdiger & Jeffrey Walker Analogy 1 Initial state Update

15. Christoph Rüdiger & Jeffrey Walker Data Assimilation Defined Definition 1: using data to force a model ie. precipitation and evapotranspiration to force a LSM Definition 2: using state observations to make a correction to the forecast model state ie. surface soil moisture obs. to correct forecasts Analogy: driver can see through his blindfold for 1/10 th second every 30 seconds Analogy: passenger giving instructions to a blindfolded driver on the autostrada at peak hour

16. Christoph Rüdiger & Jeffrey Walker Analogy 2 Initial state Avail. Info Forecast Avail. Info Forecast Avail. Info

17. Christoph Rüdiger & Jeffrey Walker What is the Usefulness of Data Assimilation Organises data (model acts as interpolator) Complements data (fills in unobserved regions) Supplments data (provides unobserved quantities) Quality controls data Calibrates data

18. Christoph Rüdiger & Jeffrey Walker Some Methods of Data Assimilation 1.Direct Insertion 2.Statistical Correction 3.Optimal Interpolation (OI) 4.Variational over Space and Time (4DVAR) 5.Sequential Data Assimilation (eg. Kalman Filter)

19. Christoph Rüdiger & Jeffrey Walker Continuous or Sequential DA? Continuous (ie. variational) Regression schemes Adjoint derivation In general: Minimisation of objective function Time State Value Window 1Window 2

20. Christoph Rüdiger & Jeffrey Walker Continuous or Sequential DA? Sequential (ie. Kalman filter) Predict: Observe: Correct: Time State Value

21. Christoph Rüdiger & Jeffrey Walker Extended or Ensemble KF? Time State Value EKF Covariance Time State Value EnKF Covariance

22. Christoph Rüdiger & Jeffrey Walker DA as a Spatial Interpolator Soil Moisture Houser et al., WRR 1998

23. Christoph Rüdiger & Jeffrey Walker Summary of Data Assimilation Advantages Variational: Computationally inexpensive Does not need prior knowledge of system states or errors No linearisation of model needed Can obtain model sensitivity values Sequential: Update of states at every observation point Model size depends on computer not mathematics Advantage over variational schemes for distributed models

24. Christoph Rüdiger & Jeffrey Walker Summary of Data Assimilation Limitations Variational: Regression scheme can become unstable Adjoint derivation can be a complex problem Long-term forecasts become inaccurate Sequential: Models have to be linearised to certain extent Can be computationally infeasible Error estimates can cause problems

25. Christoph Rüdiger & Jeffrey Walker Hydrological Modelling

26. Christoph Rüdiger & Jeffrey Walker Hydrological Modelling Different models available Soil moisture models Land surface models Atmospheric models Land surface – atmosphere models General Circulation models Different approaches for modelling: Lumped Distributed Semi-distributed

27. Christoph Rüdiger & Jeffrey Walker Difference between model approaches distributed semi-distributed or lumped

28. Christoph Rüdiger & Jeffrey Walker Semi-distributed model Kalma et al., 1995

29. Christoph Rüdiger & Jeffrey Walker Two Models Liang et al., 1998 Koster et al., 2000

30. Christoph Rüdiger & Jeffrey Walker Drought monitoring Flood prediction Irrigation policies Weather forecasting Importance of Land Surface States (soil moisture, soil temperature, snow)

31. Christoph Rüdiger & Jeffrey Walker Importance of Land Surface States (soil moisture, soil temperature, snow) Early warning systems Flood prediction – infiltration, snow melt Socio-economic activities Agriculture – yield forecasting, management (pesticides etc), sediment transport Water management – irrigation Policy planning Drought relief Global change Weather and climate Evapotranspiration – latent and sensible heat Albedo

32. Christoph Rüdiger & Jeffrey Walker Soil Moisture vs Sea Surface Temp Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST). Koster et al., JHM 2000

33. Christoph Rüdiger & Jeffrey Walker Importance of Soil Moisture Koster et al., JHM 2000 (JJA)

34. Christoph Rüdiger & Jeffrey Walker Combining the Efforts

35. Christoph Rüdiger & Jeffrey Walker The Situation

36. Christoph Rüdiger & Jeffrey Walker The Problem With LSMs Same forcing and initial conditions but different predictions of soil moisture! Houser et al., GEWEX NEWS 2001

37. Christoph Rüdiger & Jeffrey Walker Why do we need improvement? Koster et al., JHM, 2000

38. Christoph Rüdiger & Jeffrey Walker How Do We Measure Soil Moisture

39. Christoph Rüdiger & Jeffrey Walker Case Study – Variational DA Assimilation of Streamflow and Surface Soil Moisture Observations

40. Christoph Rüdiger & Jeffrey Walker Bayesian Regression Kuczera, 1982

41. Christoph Rüdiger & Jeffrey Walker Results “Experiment 1” DischargeSoil Moisture

42. Christoph Rüdiger & Jeffrey Walker Results “Experiment 1”

43. Christoph Rüdiger & Jeffrey Walker Results “Experiment 2” DischargeSoil Moisture

44. Christoph Rüdiger & Jeffrey Walker Results Experiment 2 cont’d Root Zone Soil MoistureSurface Soil Moisture

45. Christoph Rüdiger & Jeffrey Walker Summary of Variational Approach Retrieval of initial states possible to high accuracy. Only few iterations necessary. Limitations when additional errors are involved. Long forecasting window will lead to less accurate results. First estimate of initial states can be important

46. Christoph Rüdiger & Jeffrey Walker Case Study – Sequential DA Assimilation of Surface Soil Moisture

47. Christoph Rüdiger & Jeffrey Walker Direct Insertion Every Hour

48. Christoph Rüdiger & Jeffrey Walker Kalman Filter Update Every Hour

49. Christoph Rüdiger & Jeffrey Walker Effects of Extreme Events

50. Christoph Rüdiger & Jeffrey Walker Number of Observations All observations Single Observation

51. Christoph Rüdiger & Jeffrey Walker Summary of Sequential DA Require a statistical assimilation scheme (ie. a scheme which can potentially alter the entire profile). Simulation results may be degraded slightly if simulation and observation values are already close. The updating interval is relatively unimportant when using a calibrated model with accurate forcing.

52. Christoph Rüdiger & Jeffrey Walker Final Words Other assimilation work Complete the global SMMR assimilation – Ni et al. SMMR/AMSR assimilation Australia – Walker et al. Continental snow assimilation – Sun et al. TRMM assimilation – Entin et al. G-LDAS – Rodell et al. Runoff assimilation – Rüdiger et al. Evapotranspiration assimilation – Pipunic et al.

53. Christoph Rüdiger & Jeffrey Walker Ciao di Rocco

54. Thankyou!