1. Regional water cycle studies: Current activities and future plans Water System Retreat, NCAR 14 January 2015 Martyn Clark, Naoki Mizukami, Andy Newman, Pablo Mendoza, Andy Wood (NCAR) Luis Samaniego (UFZ) Bart Nijssen (UW)

2. Outline Motivation ▫Large inter-model differences in representation of the land component of the water cycle ▫Opportunities to improve both fidelity of process representations and characterization of model uncertainty Model development activities ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing ▫Routing Continental-scale model benchmarks ▫Data, information, knowledge and wisdom: Can complex process-based models make adequate use of the data on meteorology, vegetation, soils and topography? ▫Use of simple models (statistical, bucket) as benchmarks Summary

3. Basins of interest for this study The Colorado Headwaters Region offers a major renewable water supply in the southwestern United States, with approximately 85 % of the streamflow coming from snowmelt. Hence, we conduct this research over three basins located in this area: -Yampa at Steamboat Springs -East at Almont -Animas at Durango Simulations in the Colorado Headwaters

4. How does hydrologic model choice affect the magnitude and direction of climate change signal? 4 Uncalibrated model simulationsCalibrated model simulations  Uncalibrated models: Climate change signal in Noah ( ↑ ET and ↑ Runoff) differs from the rest of models ( ↑ ET and ↓ Runoff).  After calibration, signal direction from Noah-LSM switches to ↑ ET and ↓ Runoff.  Inter-model agreement does not necessarily improve in terms of magnitude and direction. Results: hydrology

5. CONUS-scale simulations interplay between downscaling methodology and hydrology simulations UCO MR AR GB CA LCO RIO NLDAS 12km domain- 205 x 462 grid boxes

6. Understanding different sources of uncertainty GCM initial conditions Emissions Scenario(s) Global Climate Model(s) Downscaling method (s) Hydrologic Model Structure(s) Hydrologic Model Parameter(s) projection Combined uncertainty projection 6 6

7.  Inter-model difference in canopy evaporationSubmitted to JHM Impact on Annual water balance – statistical downscaling methods and models

8. 8 Extreme runoff – inter-forcing difference High flow 20yr Daily Max. flow [mm/day] Low flow 7Q10 [mm/day]

9. 9 Extreme runoff – Inter-model difference Low flow estimate is more dependent on models High flow 20yr Daily Max. flow [mm/day] Low flow 7Q10 [mm/day]

10. 10 SWE – Inter-model difference vs. inter-forcing difference Inter-model differences are larger than inter-forcing Inter-model comparison in peak SWE [mm] Inter-forcing comparison in peak SWE [mm]

11. Outline Motivation ▫Large inter-model differences in representation of the land component of the water cycle ▫Opportunities to improve both fidelity of process representations and characterization of model uncertainty Model development activities ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing ▫Routing Continental-scale model benchmarks ▫Data, information, knowledge and wisdom: Can complex process-based models make adequate use of the data on meteorology, vegetation, soils and topography? ▫Use of simple models (statistical, bucket) as benchmarks Summary

12. What are the key issues that constrain progress in model development? Unsatisfactory process representation ▫Missing processes (e.g., spatial heterogeneity, groundwater) ▫Dated/simplistic representation of some processes Limited capabilities to isolate and evaluate competing model hypotheses ▫The failure of MIPs and the need for a controlled approach to model evaluation Insufficient recognition of the interplay between different modeling decisions ▫The interplay between model parameters and process parameterizations ▫Interactions among different model components Inadequate attention to model implementation ▫Impact of operator-splitting approximations in complex models ▫Bad behavior of conceptual hydrology models Ignorance of uncertainty in models and data ▫To what extent does data uncertainty constrain our capabilities to effectively discriminate among competing modeling approaches? ▫Are we so “over-confident” in some parts of our model that we may reject modeling advances in another part of the model?

13. Modeling approach Propositions: 1.Most hydrologic modelers share a common understanding of how the dominant fluxes of water and energy affect the time evolution of thermodynamic and hydrologic states ▫The collective understanding of the connectivity of state variables and fluxes allows us to formulate general governing model equations in different sub- domains ▫The governing equations are scale-invariant 2.Differences among models relate to a)the spatial discretization of the model domain; b)the approaches used to parameterize individual fluxes (including model parameter values); and c)the methods used to solve the governing model equations. General schematic of the terrestrial water cycle, showing dominant fluxes of water and energy Given these propositions, it is possible to develop a unifying model framework For example, by defining a single set of governing equations, with the capability to use different spatial discretizations (e.g., multi-scale grids, HRUs; connected or disconnected), different flux parameterizations and model parameters, and different time stepping schemes Clark et al. (WRR 2011); Clark et al. (under review)

14. soil aquifer soil aquifer soil c) Column organization a) GRUs b) HRUs i) lumpii) grid iii) polygon

15. The Structure for Unifying Multiple Modeling Alternatives (SUMMA)

16. Example simulations Impact of model parameters, process parameterizations and model architecture on simulations of transpiration Stomatal resistance parameterizations Rooting profiles Subsurface flow among soil columns

17. Outline Motivation ▫Large inter-model differences in representation of the land component of the water cycle ▫Opportunities to improve both fidelity of process representations and characterization of model uncertainty Model development activities ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing ▫Routing Continental-scale model benchmarks ▫Data, information, knowledge and wisdom: Can complex process-based models make adequate use of the data on meteorology, vegetation, soils and topography? ▫Use of simple models (statistical, bucket) as benchmarks Summary

18. The parameter estimation problem… Many CONUS-scale applications based on very uncertain a-priori model parameters Basin-by-basin calibration efforts provide patchwork-quilt of model parameters (no physical realism) Traditional model calibration leads to the “right answers for the wrong reasons” (compensatory effects) Solutions?

19. Initial computational infrastructure

20. Continental-scale parameter estimation 20 Soil Data (e.g., STATSGO space) βiβi Adjust TF coefficients Model Layers (e.g., 3 Layers) Horisontal upscaling Model Params (e.g., 3 Layers) Vertical upscaling Model Params (e.g., STATSGO space) PiPi (Pedo-) transfer function simulations

21. Individual basins; donor catchments 21 A priori parameter NLDAS Calibrated parameters single basin Max Soil Moisture Storage in bottom layer Calibrated parameters – region Nearest Neighbor Calibrated multiplier C ρbulk (basin i ) C d1 (basin i ) C d2 (basin i ) C ztot (basin i ) i = 1,…15

22. Estimation with default TF coefficientsEstimation with calibrated TF coefficients Max Soil Moisture Storage in bottom layer Calibrated TF coef. a ρbulk a d1 a d2 a ztot Transfer function calibration

23. Outline Motivation ▫Large inter-model differences in representation of the land component of the water cycle ▫Opportunities to improve both fidelity of process representations and characterization of model uncertainty Model development activities ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing ▫Routing Continental-scale model benchmarks ▫Data, information, knowledge and wisdom: Can complex process-based models make adequate use of the data on meteorology, vegetation, soils and topography? ▫Use of simple models (statistical, bucket) as benchmarks Summary

24. Uncertainties in model forcing data N-LDAS vs. Maurer ▫Gridded meteorological forcing fields (12- km grid) across the CONUS, 1979-present Opportunities to improve these products ▫Make more extensive use of data from stations (additional networks) and NWP models (finer spatial resolution) in a formal data fusion framework ▫Provide quantitative estimates of data uncertainty (ensemble forcing) CLM simulations over the Upper Colorado River basin for three elevation bands, using two different meteorological forcing datasets Mizukami et al. (JHM, 2013) See Andy Newman’s presentation on ensemble forcing (next)

25. Routing CLM simulations coupled with network-based routing model configured for the USGS geospatial fabric

26. Outline Motivation ▫Large inter-model differences in representation of the land component of the water cycle ▫Opportunities to improve both fidelity of process representations and characterization of model uncertainty Model development activities ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing ▫Routing Continental-scale model benchmarks ▫Data, information, knowledge and wisdom: Can complex process-based models make adequate use of the data on meteorology, vegetation, soils and topography? ▫Use of simple models (statistical, bucket) as benchmarks Summary

27. Simple models as benchmarks The NERD approach (statistical models as benchmarks) Bucket-style models as a statistical model Can more complex models extract the same information content from the available data on meteorology, vegetation, soils and topography? If not, why not? What work do we need to do in order to ensure that physically realistic models perform better than models with inadequate process representations? Newman et al., HESS (in press)

28. Model constraints? Hard coded parameters are the most sensitive ones

29. Outline Motivation ▫Large inter-model differences in representation of the land component of the water cycle ▫Opportunities to improve both fidelity of process representations and characterization of model uncertainty Model development activities ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing ▫Routing Continental-scale model benchmarks ▫Data, information, knowledge and wisdom: Can complex process-based models make adequate use of the data on meteorology, vegetation, soils and topography? ▫Use of simple models (statistical, bucket) as benchmarks Summary

30. Summary and future plans Work underway for a continental-scale implementation of the flexible hydrologic modeling approach, improving continental-scale parameter estimates and improving characterization of forcing uncertainty ▫Applications: Improved representation of hydrologic processes in climate risk assessments and in streamflow prediction systems Work started on improving representation of hydrologic processes in CLM ▫Collaboration with CUAHSI Collaboration with the Canadians (University of Saskaskewan) ▫Cold season hydrologic processes; interest in WRF-Hydro New focal areas: Alaska and Hawaii ▫Extend methods developed in the CONUS to “more challenging” modeling environments Funding from Bureau of Reclamation, US Army Corps of Engineers, NASA, NOAA, NSF, and (hopefully) DOE