Advancing Hydrologic Ensemble Forecasting using Distributed Watershed Models

1. Advancing Hydrologic Ensemble Forecasting using Distributed Watershed Models NWS Talk May 2006 Thorsten Wagener, Chris Duffy, Patrick Reed, Yong Tang, Katie Goodwin and Maitreya Yadav http://www.engr.psu.edu/ce/Divisions/Hydro/hydro.html

2. Overall Objective To provide reliable forecasts of hydrologic variables for different water resources tasks, at gauged and ungauged locations, including estimates of uncertainty. Understanding how to build and work with a new generation of more complex distributed hydrologic models.

3. Outline • Background • Model Building • Calibration • Observations/Hydrologic Theory • Simulation

4. FORECASTING STREAMFLOW INUNDATED AREAS WATER QUALITY INPUT STATE OUTPUT SIMULATION DATA ASSIMILATION & MODEL CALIBRATION DYNAMIC RESPONSE BEHAVIOR MODEL CONCEPTUAL STRUCTURE FUNCTIONAL FORM PARAMETER VALUES OBSERVATIONS & HYDROLOGIC THEORY MODEL BUILDING WATERSHED WATERSHED CHARACT. SYSTEM INVARIANTS A PRIORI KNOWLEDGE UNCERTAINTY

5. FORECASTING DYNAMIC RESPONSE BEHAVIOR MODEL CONCEPTUAL STRUCTURE FUNCTIONAL FORM PARAMETER VALUES MODEL BUILDING WATERSHED UNCERTAINTY WATERSHED CHARACT. SYSTEM INVARIANTS A PRIORI KNOWLEDGE

6. Model Building Questions • How to build distributed watershed models? • What is the necessary degree of coupling of processes? • What are appropriate levels of complexity for different water resources tasks? • What are efficient ways of domain decomposition?

7. Duffy et al. Approach To develop physically-based, multi-scale model for water, solute, sediment, and energy budgets in complex large-scale hydrologic systems MOTIVATION: • to simplify complex, large-scale spatio-temporal models • to study or uncover new and emergent physical phenomena in coupled hydrologic systems • to provide reliable water, solute and energy budgets • to estimate recharge, bank storage, ephemeral stream losses, climate and landuse effects across river basins • to provide predictive tools for water resource forecasting

8. Integrated Hydrologic Model (PIHM)

9.  Flexibility in fitting a complex-shaped domain Ability to grade from small to large elements over a relatively short distance Decrease in number of nodes Unstructured Grid - TINs

10. Nested Triangulation  Seamless assimilation of forcings and parameters at different resolutions • Combinelarge-scale simulations with nested mesoscale forecasts Weber River Watershed

11. General System

12. Modular Modeling System (MMS) • Background: In 1992, The USGS (George Leavesley) released a Unix-based Modular Modeling System (MMS) that incorporated their Performance and Results Measurement System (PRMS) surface runoff model. • A more generic framework, (Leavesley et al., 1996) where different modules and model structures can be selectively combined to form an ‘optimal’ integrated model for environmental and water resources analysis.

13. Major Components of MMS • pre-process component • tools to build and analyzes the input data. • model component • tools to apply the different models. • post-process component • tools to analyze the output statistically and graphically and pass the output to the decision support system or other software.

14. Pre-Process Model Post-Process GUI GUI GUI GIS Weasel Modular Model Data Collection XmBuild DMI Module Library DMI DMI DMI Data Storage Visualization Statistics DSS GIS Weasel Set-up A schematic showing the different conceptual components of the Modular Modeling System (MMS) (Adapted from Leavesley et al. 1983)

15. Susquehanna River Basin GeoDataBase • Climate • Temperate (controlled by polar front, prevailing westerlies & Atlantic) • Orographic effects (P:35-45”, ET:15-50”) • Drainage • 71,410 km2 • Main channel: 714 km • Headwaters: Finger lake uplift and Appalachian mountain and plateau • Mouth: Chesapeake Bay, MD • Physiography • Appalachian plateau • Ridge & Valley • Piedmont • Hydrogeology • Flat/folded sandstone and shale • Some carbonate valleys • Some igneous dikes, sills, and fractures

16. Conceptual Hydrologic Model: Susquehanna River

17. Initial Results

18. Weak or Strong Coupling? • Interception • Snowmelt • Evapotranspiration • Overland flow • Subsurface • Channel routing

19. FORECASTING DATA ASSIMILATION & MODEL CALIBRATION INPUT STATE OUTPUT DYNAMIC RESPONSE BEHAVIOR MODEL CONCEPTUAL STRUCTURE FUNCTIONAL FORM PARAMETER VALUES WATERSHED UNCERTAINTY

20. uttrue Real World zttrue Courtesy of S. Pinker utobs ztobs Model f ( ) ztcomp u : input z : output  : parameters Q x : state variables  xo : uncertainty time Background

21. Model Calibration/D.A. Questions • What are efficient optimization algorithms for highly complex models? • How can parallel computing frameworks be used for efficient model calibration? • What are appropriate calibration strategies for distributed watershed models?

22. Comparison of 3 Multi-objective Population-based Search Algorithms • Epsilon Nondominated Sorted Genetic Algorithm-II • Developed by Kollat and Reed (2005) • Extension of Deb et al. (2002) • Multiobjective Shuffled Complex Evolution Metropolis • Developed by Vrugt et al. (2004) • Extension of Yapo et al. (1998) • Strength Pareto Evolutionary Algorithm-II • Developed by Zitzler et al. (2001) • Extension of Zitzler & Thiele (1999)

23. What are efficient optimization algorithms for highly complex models? Testing of the efficiency and effectiveness of different multi-objective optimization algorithms resulted in improved understanding of how complex a problem can be solved in what time and with what reliability. Test case: Sacramento model with Leaf River Data RMSE(T): Box-Cox Transformed RMSE RMSE(R): RMSE of raw data

24. Binary metric top ranking ratios SPEA2 has the highest binary metric top ranking ratio (i.e., it is the most reliable algorithm) Reliability?

25. Dynamic unary metrics (best runs) ε-NSGAII’s best trial run is superior to those of SPEA2 and MOSCEM-UA Convergence?

26. How can parallel computing frameworks be used for efficient model calibration?

27. What are appropriate calibration strategies for distributed watershed models? Main problem: The ‘open’ calibration of complex distributed hydrologic models is too complex. How can the calibration problem be simplified? • For the Sacramento/HLRMS framework. • For PIHM.

28. Juniata River Basin

29. Sacramento/HLRMS Model Scenarios

30. Sensitivity Analysis Which parameters dominate the model response?

31. SensitivityAnalysis Sacramento Model at Saxton

32. Hierarchical Calibration ANNUAL COARSE DATA MODEL HOURLY DETAILED

33. FORECASTING INPUT STATE OUTPUT DYNAMIC RESPONSE BEHAVIOR MODEL OBSERVATIONS & HYDROLOGIC THEORY WATERSHED UNCERTAINTY WATERSHED CHARACT. SYSTEM INVARIANTS A PRIORI KNOWLEDGE

34. Observations/Hydrol. Theory Questions • How can our understanding about the link between watershed structure and watershed behavior be used to constrain hydrologic predictions? • A new approach to the ungauged basins problem? • How can we use our understanding about watershed function to decompose the model domain?

35. Predictions in Ungauged Basins – October 1970

37. Regionalizing Watershed Behavior

39. Ensemble Evaluation? RELIABILITY: How much of the observations are contained by the ensemble? SHARPNESS: How wide are the ensemble prediction ranges?

40. Reliability and Sharpness Coquet@Morwick

41. 80 – 85% 11 – 18.5% 85 – 90%, 18.5 – 26% 90 – 95% 26 – 33.5% 95 – 100% 33.5 – 41% Reliability and Sharpness per Flow Percentile Sharpness Values Reliability Values

42. Multiple Response Characteristics as Constraints Reliability = 80% Sharpness = 75% Behavioral = 1% Dove@Kirkby Mills All 19 indices used as constraint on ensemble predictions

43. P/PE – Max Feb, Max Nov, Mean, Runoff ratio Low Flows DPSBAR (Slope) – Median, Runoff ratio Low Flows BFIHOST – High flow Discharge, skewness and variability in flow, High pulse count Low and Mid Flows Tillingbourne@Shalford Rainfall Elevation Hydrogeology

44. Next Step: Using U.S. MOPEX Data

45. FORECASTING STREAMFLOW INUNDATED AREAS WATER QUALITY SIMULATION DYNAMIC RESPONSE BEHAVIOR MODEL CONCEPTUAL STRUCTURE FUNCTIONAL FORM PARAMETER VALUES WATERSHED UNCERTAINTY

46. Simulation Questions • How can we estimate the reliability of forecasts? • How can we create ensemble (probabilistic) predictions of inundated areas? • What is needed for long term simulations incl. climate change impacts (droughts & floods)?

47. Probabilistic Inundation and Hazard Maps

48. Acknowledgements Partial support for this work was provided by SAHRA under NSF- STC grant EAR-9876800, and the National Weather Service Office of Hydrology under grant numbers NOAA/NA04NWS4620012,UCAR/NOAA/COMET/S0344674, NOAA/DG 133W-03-SE-0916. We thank The British Atmospheric Data Center for providing the temperature data (http://badc.nerc.ac.uk/home/index.html).

49. References Yang, T., Reed, P. and Wagener, T. 2006. How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrology and Earth System Sciences. In Press. Wagener, T. and Gupta, H.V. 2005. Model identification for hydrological forecasting under uncertainty. Stochastic Environmental Research and Risk Assessment. DOI 10.1007/s00477-005-0006-5. Ajami, N.K., Gupta, H.V., Wagener, T. and Sorooshian, S. 2004. Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology, 298(1-4), 112-135. Yang, T., Reed, P. and Kollat, J. 2006. … . Advances in Water Resources, in Review. Yadav, M., Wagener, T. and Gupta, H.V. 2006. Regionalization of constraints on hydrologic watershed behavior . Advances in Water Resources, in Preparation. Wagener, T. and Kollat, J. Visual and numerical evaluation of hydrologic and environmental models using the Monte Carlo Analysis Toolbox (MCAT). Environmental Modeling and Software, in Press pending minor Revisions. Vrugt, J.A., Gupta, H.V., Dekker, S.C., Sorooshian, S., Wagener, T. and Bouten, W. 2006. Confronting parameter uncertainty in hydrologic modeling: Application of the SCEM-UA algorithm to the Sacramento Soil Moisture Accounting model. Journal of Hydrology, In Press. Wagener, T. and Wheater, H.S. 2006. Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty. Journal of Hydrology, 320(1-2), 132-154.