1. Update 2.2: Uncertainty in Projected Flow Simulations
Created by: Scott Pokorny
In Association with: University of Manitoba, Manitoba Hydro

2. Presentation Outline Review of project goals Hec-HMS model updates
Dew point temperature estimation
Forcing data standardization updates

3. Review of project goals
To assess and quantify the uncertainty in projected flow simulations
3 models for the Lower Nelson River Basin (LNRB) were selected
Hec-HMS, Watflood, and VIC
Uncertainty is to be compared between models to assess how the input, model structure, parameter choice and output uncertainties relate
Forcing data inputs are to be standardized to focus the uncertainty analysis on the models

4. Hec-HMS Updates
Model updated from version 3.5 to version for more evaporation method options
Ungauged sub basins spatially close to each other have been grouped
Total number of junctions and sub basins reduced

5. Hec-HMS Updates 4.2.1 simple vs 4.2.1 4.2.1 simple vs 3.5 4.2.1 vs 3.5
4.2.1 simple vs 4.2.1
4.2.1 simple vs 3.5
4.2.1 vs 3.5
Mean abs difference (m3/s) =
8.100
24.654
22.312
Max abs difference (m3/s) =
66.200
Correlation
1.000
0.998
The version 3.5 takes ~5 minutes to run
The version takes ~1 minute to run
The version simplified takes ~40 seconds to run
Roughly 70 parameters are expected to be included in the GLUE runs after updates are finished

6. Dew Point Temperature Estimation
Before the update, Hec-HMS handled evaporation with monthly average evaporation values
The Priestly Taylor evaporation method will now be used but requires a dew point temperature time series as input
This will need to be estimated for the climate projections from the available GCM data
Hubbard, K. G., Mahmood, R., & Carlson, C. (2003). Estimating daily dew point temperature for the northern Great Plains using maximum and minimum temperature. Agronomy Journal, 95(2),

7. Dew Point Temperature Estimation
Coefficients have been estimated with regression at 13 stations nearby the LNRB
This assumes that the regression constants will remain constant into the future
To evaluate this assumption, coefficients will be regressed over a moving 10 year window where data is available
There is less data available for regression when precipitation data is required
Therefore, method 3 regression will be done twice, once with all available temperature data and once only using days where precipitation is also available

8. Dew Point Temperature Estimation

9. Dew Point Temperature Estimation
Stn Name:
FLIN_FLON
Years
Temp only
available data (%)
79.36
77.93
81.22
79.85
78.86
75.14
77.01
75.17
75.60
77.08
78.84
78.89
80.34
Precip required
44.60
57.04
58.58
54.75
54.57
50.55
48.48
41.94
35.68
34.06
34.16
33.34
29.98

10. Dew Point Temperature Estimation
Method E
MAE
d-index r2
RMSE
Lit Values
Lit Values (Pr clip)
Method 3 Values
Method 4 Values
Method 3 Values (Pr clip)

11. Forcing Data Standardization Updates

12. Forcing Data Standardization Updates
Watch ERA Interim
Watch ERA Precipitation
Kendall Corr
Spearman Corr
Mean Yearly Sum Abs Diff
max
0.45
0.53
97.87
mean
0.27
0.31
38.81
min
0.16
0.18
5.16
stdev
0.05
0.06
15.77

13. Forcing Data Standardization Updates
North American Regional Reanalysis (NARR)
NARR
Precipitation
Kendall Corr
Spearman Corr
Mean Yearly Sum Abs Diff
max
0.49
0.62
245.78
mean
0.36
0.46
80.68
min
0.24
0.31
33.49
stdev
0.05
0.06
38.58

14. Forcing Data Standardization Updates
Watflood Output
Watflood Precipitation
Kendall Corr
Spearman Corr
Mean Yearly Sum Abs Diff
PBIAS (%)
max
1.00
107.73
15.09
mean
0.67
0.77
29.95
-0.80
min
0.51
0.60
0.00
-34.90
stdev
0.08
0.07
16.20
6.81

15. Forcing Data Standardization Updates
The most appealing data set is ANUSPLIN
Already gridded at 10km resolution
Gap filled data at station locations
Watflood data requires a radius of influence and smoothing distance
High performing values for these variables will be based station location and spacing
Calibration would be needed to find the most acceptable values

16. Questions?