1. Development of hyper-resolution large-ensemble continental-scale hydrologic model simulations AGU, San Francisco, CA 14 December 2014 Martyn Clark, Naoki Mizukami, Andy Newman, Pablo Mendoza (NCAR) Bart Nijssen (UW)

2. Outline Motivation ▫Improve operational applicability of process-based models, while accounting for model/data uncertainty ▫Improve information content in probabilistic forecasts Model development ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing 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

3. Motivation: 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?

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

5. Outline Motivation ▫Improve operational applicability of process-based models, while accounting for model/data uncertainty ▫Improve information content in probabilistic forecasts Model development ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing 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

6. Improving model physics It is difficult to adequately represent model uncertainty using multi- model and multi-physics approaches ▫Wrong results for the same reasons ▫A small collection of model provides poor coverage of the hypothesis space It is difficult to understand the importance of individual sources of model uncertainty through the analysis of total (integrated) model errors ▫Model-observation differences emerge through complex compensations among different sources of model error ▫Therefore…it is very difficult to attribute model-observation differences to individual model components A controlled and systematic approach to model development is needed to understand how individual sources of model uncertainty affect integrated model predictions

7. 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)

8. The unified approach to hydrologic modeling

9. soil aquifer soil aquifer soil b) Column organization a) GRUs and HRUs

10. Example simulations Martyn Clark (left) and Chris Landry (right) at the upper site tower in the Senator Beck basin The sheltered site at Reynolds Mountain East Reynolds Creek, Idaho, USA Senator Beck, Colorado, USA Reynolds Creek Senator Beck

11. Different model parameterizations do not account for local site characteristics (dust-on- snow in Senator Beck) Model fidelity and characterization of uncertainty can be improved through parameter perturbations Reynolds Creek Senator Beck Example application: Simulations of snow in open clearings

12. Example application: Interception of snow on the vegetation canopy Can reproduce observations but rather uncertain about temperature sensitivity Different interception formulations Simulations of canopy interception (Umpqua)

13. Example Application: Importance of model architecture (spatial variability and hydrologic connectivity)  1-D Richards’ equation somewhat erratic  Lumped baseflow parameterization produces ephemeral behavior  Distributed (connected) baseflow provides a better representation of runoff

14. Outline Motivation ▫Improve operational applicability of process-based models, while accounting for model/data uncertainty ▫Improve information content in probabilistic forecasts Model development ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing 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

15. Continental-scale parameter estimation 15 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

16. Individual basins; donor catchments 16 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

17. 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

18. Outline Motivation ▫Improve operational applicability of process-based models, while accounting for model/data uncertainty ▫Improve information content in probabilistic forecasts Model development ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing 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

19. 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)

20. 12,000+ stations with serially complete data Probabilistic quantitative precipitation estimation

21. Central US Flood of 1993 June 1993 total precipitation Example Output

22. Outline Motivation ▫Improve operational applicability of process-based models, while accounting for model/data uncertainty ▫Improve information content in probabilistic forecasts Model development ▫Model architecture and process parameterizations ▫Continental-scale parameter estimation ▫Ensemble forcing 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

23. 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)

24. Summary: Improve model fidelity and characterization of model uncertainty There are serious shortcomings in popular multi-physics and multi-model approaches to characterize uncertainty ▫Competing modeling approaches can provide the wrong results for the same reasons (albedo example) ▫A small collection of models provides poor coverage of the hypothesis space ▫Difficult to provide much insight from analysis of total model error The unified approach to hydrologic modeling can enable a physically- based characterization of uncertainty ▫Understand how individual sources of uncertainty (parameters and structure) propagate to the integrated system response ▫Rigorous test of the suitability of different process parameterizations and model parameters in different regions and different spatial scales Work underway for a continental-scale implementation of the flexible modeling approach, improving continental-scale parameter estimates and improving characterization of forcing uncertainty