1. How does the choice/configuration of hydrologic models affect the portrayal of climate change impacts? Pablo Mendoza 1

2. Subjectivity in model selection: How does the choice of model equations impact simulations of hydrologic processes? Missing processes, inappropriate parameterizations? Subjectivity in selecting/applying models Define a-priori values for model parameters Decide what model parameters we adjust, if any Decide what calibration strategy we implement, if any  Choice of objective function  Choice of forcing data and calibration period Model parameters Decide which processes to include Define parameterizations for individual processes Define how individual processes combine to produce the system-scale response Solve model equations Model structure Subjectivity in parameter identification: How does our choice of model parameters impact simulations of hydrologic processes? Compensatory effects of model parameters (right answers for the wrong reasons)? Climate change studies commonly involve several methodological choices that might impact the hydrologic sensitivities obtained. In particular:

3. Study area 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. 3

4. Approach How do our methodological choices impact the results that we obtain when we evaluate hydrologic sensitivities to climate change? Master question! Impact of hydrologic model choice and parameters Key points -Different hydrologic model structures. -Uncalibrated vs. calibrated model sensitivities. -Model structures vs. parameters. Impact of spatial forcing resolution on hydrologic sensitivities Key points -Use of dynamical downscaling outputs at different spatial scales, generated with the same methodology. -Impact of the spatial aggregation of a 4 km gridded dataset on hydrologic sensitivities. 4

5. Model structure selection Differences in both model architecture and model parameterizations 5

6. Part I: Impact of hydrologic model choice and parameters 6

7. Research plan: impact of model choice and parameters Why do different models have different sensitivities to climate change? Is it due to differences in model structure rather than parameter values? KEY QUESTIONS Evaluation of uncalibrated model performance and sensitivities to climate change (done) Model calibration (almost done) Calculation of calibrated model sensitivities and comparison with uncalibrated (ongoing) Assessment of differences on hydrologic sensitivities among feasible parameter sets (ongoing) Task 1 Task 2 Task 3 Task 4 APPROACH 7

8. Status 8

9. Uncalibrated model simulations (WRF@4km resolution) How does model performance change with model calibration? 9 Calibrated model simulations (WRF@4km resolution)  Calibration process substantially improves streamflow simulation. Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

10. How are observed hydrologic signature measures reproduced by different models? Raw values 10  The choice of a particular objective function (e.g. RMSE) for model calibration does not necessarily improve simulated signature measures! (example: FMS) Calibrated Uncalibrated Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

11. How does hydrologic model choice affect the partitioning of precipitation into ET and runoff? 11 Uncalibrated model simulations (WRF@4km resolution) Calibrated model simulations (WRF@4km resolution)  Uncalibrated models: Climate change signal in Noah (↑ET and ↑Runoff) differs from the rest of models (↑ET and ↓Runoff).  Inter-model differences are larger that climate change, even after calibration process. Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

12. Changes in signature measures 12 Uncalibrated Calibrated  Inter-model differences in signature measure sensitivities don’t necessarily decrease after calibration! (e.g. seasonality and flashiness, especially at East and Yampa). Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

13. How does the impact of model parameters compare with that of model choice? 13 Optimal parameter set for NSE (raw space) Optimal parameter set for objective function Parameter sets selected Approach (analysis restricted to VIC): Randomly select 2 points in the parameter space located in the area of maximum values of the objective function (ie. 8 parameter sets in total)  The optimal parameter set may change significantly with the choice of objective function. Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

14. 14 Impact of parameters (VIC)Impact of model choice (after calibration)  Inter-parameters differences (VIC) have similar magnitudes than inter-model differences when we look at monthly runoff. Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

15. 15 Impact of parameters (VIC) Impact of model choice (after calibration)  Uncertainty in monthly sensitivities of internal states and fluxes is still substantial, even when evaluating a limited set of model parameters. Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

16. 16 Impact of parameters on the change in hydrologic signature measures (VIC)Impact of model choice on the change in hydrologic signature measures  Model parameters: larger impact on changes in runoff ratio.  Model choice: larger impact on changes in runoff seasonality and flashiness. Results Model performance Impact of model structure on climate sensitivity Impact of model parameters on climate sensitivity

17. 1.Calibration of hydrologic models improves streamflow simulation, but does not necessarily: i.Improve representation of hydrological processes. ii.Decrease inter-model differences in signature measures change (PGW - CTRL). 2. Inter-model differences in hydrologic sensitivities to climate change: i.Are less pronounced for calibrated models rather than uncalibrated models. ii.May be larger than climate change signals even after calibration. 3. Regarding the role of parameters: i.Model choice (after calibration) and parameter selection from “optimal zones” provide similar uncertainty in impact of climate change on monthly runoff. ii.Preliminary analysis suggests that uncertainty in monthly variations of specific fluxes and states (e.g. ET, Soil moisture, SWE) is model-dependent rather than parameter-dependent. Conclusions: Part I 17

18. Part II: Impact of spatial forcing resolution on hydrologic sensitivities 18

19. Research plan: impact of forcing resolution What is the impact of forcing spatial resolution on signature measures? How does forcing spatial resolution affect climate change impact results? KEY QUESTIONS Generate forcing datasets for all models: -Raw WRF @4km, 12km and 36km (done). -WRF-4 km data aggregated to 12km and 36 km (done). Experiment 1: evaluate climate change impact using raw WRF outputs - Uncalibrated model simulations (done). - Calibrated model simulations (ongoing). Task 1 Task 2 Task 3 APPROACH Experiment 2: evaluate climate change impact using aggregated WRF-4km outputs. - Uncalibrated model simulations (done). - Calibrated model simulations (ongoing). 19

20. Status 20

21. 7-year average cool-season precipitation : 1 October – 31 May 36 km 4 kmOBSERVATIONS 1000 900 800 700 600 500 400 300 200 100 0 Precipitation (mm) 12 km Fig. Kyoko Ikeda Results: previous studies

22. 7-year average warm-season precipitation: 1 June – 30 September 36 km4 kmOBSERVATIONS 700 600 500 400 300 200 100 0 Precipitation (mm) SNOTEL GHCN 12 km Fig. Kyoko Ikeda Results: previous studies

23. Results 23 How does forcing resolution affect signature measures of hydrologic behavior? RR: Runoff Ratio  Impact of forcing is model dependent (Noah is much more sensitive)  Impact of forcing resolution on runoff ratio is also basin dependent. Calibrated Impact of forcing resolution on signature measures (historical) Impact of forcing resolution on climate sensitivity Y: Yampa River Basin; E: East River Basin; A: Animas River Basin Uncalibrated

24. 24 How does forcing resolution affect signature measures of hydrologic behavior? CTR: Runoff Seasonality Calibrated  36km resolution datasets tend to produce earlier runoff (less clear for 12km).  Aggregation reduces resolution differences. Results depend on basin/model. Impact of forcing resolution on signature measures (historical) Impact of forcing resolution on climate sensitivity Results Uncalibrated Y: Yampa River Basin; E: East River Basin; A: Animas River Basin

25. Experiment 1: raw WRF output 25  Raw WRF outputs at 12km and 36km change direction of signal (↓Runoff and ↑ET), even after model calibration, to ↑Runoff and ↑ET. Impact of forcing resolution on signature measures (historical) Impact of forcing resolution on climate sensitivity Results

26. Experiment 2: aggregated WRF output 26  Aggregated WRF-4km outputs at 12km and 36km don’t change signals significantly, but may affect the amplitude (e.g. calibrated Noah). Impact of forcing resolution on signature measures (historical) Impact of forcing resolution on climate sensitivity Results

27. 27 How will signature measures change across models and forcing resolutions? RR: Runoff Ratio  RR clearly depends on forcing resolution! General decrease of RR in all cases. Y: Yampa River Basin E: East River Basin A: Animas River Basin Calibrated Impact of forcing resolution on signature measures (historical) Impact of forcing resolution on climate sensitivity Results Uncalibrated

28. 28 How will signature measures change across models and forcing resolutions? CTR: Runoff Seasonality  Shift to earlier runoff in all cases (answer does not depend on forcing) Y: Yampa River Basin E: East River Basin A: Animas River Basin Calibrated Impact of forcing resolution on signature measures (historical) Impact of forcing resolution on climate sensitivity Results Uncalibrated

29. 1.The impact of forcing resolutions on signature measures for historical simulations is reduced when we use spatially aggregated WRF-4km outputs. This implies that physics options in each WRF configuration (4km, 12km and 36 km) dominates hydrological responses. 2.Regarding climate change signal: Raw WRF outputs at 12km and 36km change direction of signal, even after model calibration, to ↑Runoff and ↑ET. Aggregated WRF-4km outputs at 12km and 36km don’t change signals significantly, but may affect the amplitude (e.g. calibrated Noah). 3. Under a future climate scenario, earlier runoff volumes and a general decrease in runoff ratios is obtained with all forcing datasets. However, results are still model dependent. Conclusions: Part II 29

30. Thank you

31. EXTRA 31

32. Useful information: SIGNATURE MEASURES!!! What do they represent? i.RR: overall water balance. ii.FMS: vertical redistribution of soil moisture. iii.FHV: watershed response to large precipitation events. iv.FLV: Long term baseflow. v.FMM: Mid-range flow levels. vi.CTR: runoff seasonality. Casper et al. (2012) EXAMPLES RR: Runoff Ratio (Q/P) FMS: Slope of mid-segment in FDC (0.2 < Pexc < 0.7) FHV: High segment volume in FDC (0 < Pexc < 0.02) FLV: Low segment volume in FDC (0.7 < Pexc < 1) FMM: Median value of simulated streamflow CTR: Centroid of avg. water year daily hydrograph (days since Oct 1) Approach: diagnostic signatures 32

33. Results: impact of model choice and parameters Uncalibrated model simulations (WRF@4km resolution) How does model performance change with model calibration? 33 Calibrated model simulations (WRF@4km resolution)

34. Results: impact of model choice and parameters 34 How are observed hydrologic signature measures reproduced by different models? Raw values Uncalibrated Calibrated

35. Results: impact of model choice and parameters How are observed hydrologic signature measures reproduced by different models? CTRL - Observed 35 Uncalibrated Calibrated

36. Results: impact of model choice and parameters 36 How are observed hydrologic signature measures reproduced by different models? CTRL - Observed Uncalibrated Calibrated

37. Monthly total runoff values for different basins/models Results: impact of model choice and parameters 37 Uncalibrated model simulations (WRF@4km resolution) Calibrated model simulations (WRF@4km resolution)

38. Monthly differences (PGW-CTRL) in mm for specific fluxes/states Results: impact of model choice and parameters 38 Uncalibrated model simulations (WRF@4km resolution) Calibrated model simulations (WRF@4km resolution)

39. Current (CTRL) and Future (PGW) signature measures Results: impact of model choice and parameters 39 Uncalibrated Calibrated

40. Current (CTRL) and Future (PGW) signature measures Results: impact of model choice and parameters 40

41. Changes in signature measures Results: impact of model choice and parameters 41

42. Changes in signature measures (PGW vs. CTRL runs) Results: impact of model choice and parameters 42

43. Changes in signature measures (PGW vs. CTRL runs) Results: impact of model choice and parameters 43

44. Results: impact of model choice and parameters What is the impact of the objective function on the optimal parameter set? 44 Optimal parameter set for NSE (raw space) Optimal parameter set for objective function Kling-Gupta Efficiency (KGE) Nash-Sutcliffe Efficiency (NSE)

45. Results: impact of model choice and parameters So… how will our hydrologic sensitivities change if we arbitrarily select parameter sets within the optimal region? 45 Optimal parameter set for NSE (raw space) Optimal parameter set for objective function Parameter sets selected Approach: Randomly select 2 points in the parameter space located in the area of maximum values of the objective function (ie. 8 parameter sets in total)

46. Results: impact of model choice and parameters 46 Model performance for CTRL simulations (Sep/2002 – Oct/2008)

47. Results: impact of model choice and parameters 47 Model performance for CTRL simulations (Sep/2002 – Oct/2008) East River Basin

48. Results: impact of model choice and parameters 48 How are observed hydrologic signature measures reproduced by different parameter sets? Raw values

49. Results: impact of model choice and parameters 49 How are observed hydrologic signature measures reproduced by different parameter sets? CTRL - Observations

50. Results: impact of model choice and parameters 50 Impact of parameters on partitioning of precipitation into ET and Runoff

51. Results: impact of model choice and parameters 51 How will hydrologic signature measures change in a future climate? PGW vs. CTRL values for 8 different parameter sets (VIC)

52. Results: impact of model choice and parameters 52 Impact of parameters on the change in hydrologic signature measures (VIC) Impact of model choice on the change in hydrologic signature measures

53. Results: impact of model choice and parameters 53 How will hydrologic signature measures change in a future climate? Current and future raw values

54. Results: impact of model choice and parameters 54 How will hydrologic signature measures change in a future climate? Future - Current

55. Results: impact of model choice How are observed hydrologic signature measures reproduced by different models/datasets? Raw values 55 Experiment 1: raw WRF output & uncalibrated models

56. Results: impact of model choice 56 Experiment 1: raw WRF output & uncalibrated models How are observed hydrologic signature measures reproduced by different models/datasets? Raw values

57. Results: impact of model choice How are observed hydrologic signature measures reproduced by different models/datasets? Raw values 57 Experiment 2: aggregated WRF output & uncalibrated models

58. Results: impact of model choice 58 Experiment 2: aggregated WRF output & uncalibrated models How are observed hydrologic signature measures reproduced by different models/datasets? Raw values

59. Results: impact of model choice 59 How does forcing resolution affect signature measures of hydrologic behavior? FMS: Flashiness of runoff Uncalibrated models Calibrated models Y: Yampa River Basin E: East River Basin A: Animas River Basin

60. Results: impact of model choice 60 How does forcing resolution affect signature measures of hydrologic behavior? FHV: Response to large precipitation events Uncalibrated models Calibrated models Y: Yampa River Basin E: East River Basin A: Animas River Basin

61. Results: impact of model choice 61 How does forcing resolution affect signature measures of hydrologic behavior? FLV: Long-term baseflow Uncalibrated models Calibrated models Y: Yampa River Basin E: East River Basin A: Animas River Basin

62. Results: impact of model choice 62 How does forcing resolution affect signature measures of hydrologic behavior? FMM: Mid-range flow levels Uncalibrated models Calibrated models Y: Yampa River Basin E: East River Basin A: Animas River Basin

63. Results: impact of model choice 63 How will signature measures change across models and forcing resolutions? FMS: Flashiness of runoff Y: Yampa River Basin E: East River Basin A: Animas River Basin Uncalibrated Calibrated

64. Results: impact of model choice 64 How will signature measures change across models and forcing resolutions? FHV: Response to large precipitation events Y: Yampa River Basin E: East River Basin A: Animas River Basin Uncalibrated Calibrated

65. Results: impact of model choice 65 How will signature measures change across models and forcing resolutions? FLV: Long-term baseflow Y: Yampa River Basin E: East River Basin A: Animas River Basin Uncalibrated Calibrated

66. Results: impact of model choice 66 How will signature measures change across models and forcing resolutions? FMM: Mid-range flow levels Y: Yampa River Basin E: East River Basin A: Animas River Basin Uncalibrated Calibrated

67. Results: impact of forcing spatial resolution Experiment 1: raw WRF output 67 Total runoff (uncalibrated) Total runoff (calibrated)

68. Results: impact of forcing spatial resolution Experiment 2: aggregated WRF output 68 Total runoff (uncalibrated) Total runoff (calibrated)

69. Results: impact of forcing spatial resolution Experiment 1: raw WRF output 69 Evapotranspiration (uncalibrated) Evapotranspiration (calibrated)

70. Results: impact of forcing spatial resolution 70 Evapotranspiration (uncalibrated) Evapotranspiration (calibrated) Experiment 2: aggregated WRF output

71. Results: impact of forcing spatial resolution Experiment 1: raw WRF output 71 SWE (uncalibrated) SWE (calibrated)

72. Results: impact of forcing spatial resolution 72 SWE (uncalibrated) SWE (calibrated) Experiment 2: aggregated WRF output

73. Results: impact of forcing spatial resolution Experiment 1: raw WRF output 73 Soil moisture (uncalibrated) Soil moisture (calibrated)

74. Results: impact of forcing spatial resolution 74 Soil moisture (uncalibrated) Soil moisture (calibrated) Experiment 2: aggregated WRF output

75. NSE surfaces: PRMS Nsim = 10,000 (100 x 100 points) East River Basin 75

76. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 76

77. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 77

78. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 78

79. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 79

80. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 80

81. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 81

82. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 82

83. NSE surfaces: PRMS Nsim = 2,500 (50 x 50 points) East River Basin 83

84. NSE surfaces: VIC Nsim = 10,000 (100 x 100 points) East River Basin Is this related to spatial parameterization or to the model? No, because thick2 is spatially constant in the basin 84

85. NSE surfaces: VIC Default Calibration strategy: One multiplier for each parameter (binfilt, Ds, Dsmax, Ws, depth2, depth3) Before discontinuity After discontinuity The conflictive parameter is spatially constant!! 85

86. Results: impact of model choice What is the impact of the objective function on the optimal parameter set? 86

87. NSE surfaces: VIC Nsim = 2,500 (50 x 50 points) East River Basin 87

88. NSE surfaces: VIC Nsim = 2,500 (50 x 50 points) East River Basin 88

89. EXTRA: the VIC experiment 89