1. Hydrologic sensitivity of the Lower Nelson River Basin to lakes, wetlands and frozen ground
Rajtantra Lilhare1,*, Stephen J. Déry1, Tricia A. Stadnyk2, and Kristina Koenig3
11-15 December 2017
Arctic Change Annual Scientific Meeting
Québec City Convention Centre, Québec, Canada
*Contact: 1 2 3

2. Research Objectives and Questions
Outline
Introduction
Data and Methods
Research Objectives and Questions
Outcomes
Summary

3. Introduction: Background
Hydrological simulations for a cold region and related water balance estimations depend critically on the input climate forcing datasets, particularly the precipitation and air temperature [Reed et al., 2004; Mote et al., 2005; Tobin et al., 2011].
Hence, to examine the reliability of various input forcings along with the hydrological model and its experimental setup are a prominent source of uncertainty assessment in hydrological variables, which are essential for water resource management.
The presence of permafrost, natural lakes, and wetlands, which are prevalent in much of the Arctic region, affect the regional water budget in hydrological simulations [Smith et al., 2007, Bowling and Lettenmaier, 2010].
Effect of drainage basin physical characteristics (i.e. frozen soil, lake, and wetland) to its surface hydrologic response and water balance are not thoroughly analyzed [Cherkauer and Lettenmaier, 1999, 2003; Bowling and Lettenmaier, 2010], particularly over the Lower Nelson River Basin (LNRB).

4. Introduction: Study Area
The Nelson-Churchill River system geographically extends between ~45.5°-59.5°N and ~90°-117.5°W.
Elevation ranges from 3,200 m to 0 m (sea level) at the river outlets of Hudson Bay.
Five major urban centres: Calgary, Edmonton, Winnipeg, Saskatoon, and Regina.
Total population of these metropolitan areas were 4,236,183 in 2016 [Statistics Canada, 2016].

5. Study Area: The Lower Nelson River Basin (LNRB)
Receiving major outflow from the Nelson River Basin (~970,000 km2)
The LNRB area: ~90,580 km2
Churchill River Diversion started in 1977, operated by Manitoba Hydro
Managed at Notigi Control Structure on the Rat River

6. Research Objectives and Questions
O1. To compare and identify the most reliable available gridded climate forcing datasets for hydrological simulations over the LNRB.
O2. To examine the sensitivity of water balance components to its physical characteristics (i.e. frozen soil, lake and wetland distributions).
Q1. Which is the best suitable gridded climate forcing dataset available for LNRB’s hydrological modeling?
Q2. How are water balance components sensitive to its physical characteristics (i.e. frozen soil, lake and wetland distributions)?

7. VIC input forcings (10 km)
Data
VIC input forcings (10 km)
ANUSPLIN
NARR
IDW
ERA-Interim
WATCH ERA-Interim
Observation-based interpolated daily gridded datasets (10 km), i.e. the Australian National University Spline Interpolation (ANUSPLIN) developed by NRCan.
Precipitation
Tmax
Tmin
North American Regional Reanalysis (NARR) daily datasets (32 km), regridded using bilinear interpolation technique at 10 km.
10 km daily gridded data generated from WATFLOOD hydrological model using 14 ECCC meteorological stations. It uses Inverse Distance Weighted (IDW) method for interpolation.
Improved global reanalysis product developed by European Centre for Medium-Range Weather Forecasts (ECMWF) at 0.125°, regridded using bilinear interpolation technique at 10 km.
Wind speed
Incorporates in situ observations in reanalysis datasets (Weedon et al., 2011), available at 0.50° and regridded using bilinear interpolation technique at 10 km.
Wind speed (NARR)

8. Methods: The Variable Infiltration Capacity Model
Source:

9. VIC Calibration and Validation:
Methods
VIC Calibration and Validation:
10 hydrometric locations with unregulated flows and the observed streamflow datasets obtained from Water Survey of Canada and Manitoba Hydro.
10-year period for calibration and 10-year period for evaluation.
Daily and monthly calibration using the University of Arizona multiobjective complex evolution (MOCOM-UA) optimizer.
Parameter [units]
Definition
Range
binf [fraction]
Parameter used to describe the variable infiltration curve
> 0 to 0.40
Ds [fraction]
Fraction of the Dsmax parameter at which nonlinear base flow occurs
> 0 to 1
Ws [fraction]
Fraction of maximum soil moisture where nonlinear base flow occurs
D2 [m]
Depth of second soil layer
0.3 to 1.5
D3 [m]
Depth of third soil layer
Dsmax [mm day-1]
Maximum velocity of base flow for each grid cell
> 0 to 30

10. 2. Sensitivity Analysis:
Methods
2. Sensitivity Analysis:
Calibrated and validated VIC model ( ) to quantify the possible impacts of frozen soil, lakes and wetlands on the Nelson River subwatersheds water balance estimation [Cherkauer and Lettenmaier, 1999, 2003].
The VIC model’s frozen soil [Cherkauer and Lettenmaier, 1999] and lake and wetland [Bowling and Lettenmaier, 2010] algorithm are used.
Frozen soil, two scenarios: 1) with the frozen soil module (FROST), and 2) without the frozen soil module (NO-FROST).
Lake and wetland, two scenarios: 1) with natural lakes and wetlands representation (LAKE), and 2) without natural lakes and wetlands representation (NO-LAKE).

11. Pearson’s Correlation (r, p-value < 0.05)
Results:
Data
Pearson’s Correlation (r, p-value < 0.05)
Winter
Spring
Summer
Fall
ANUSPLIN
0.90
0.96
0.93
0.95
ERA-I
0.35
0.53
0.48
0.63
NARR
0.70
0.82
0.62
0.84
WFDEI
0.72
0.68
0.81
In preparation Lilhare et al., (2017)

12. Spatial differences of mean seasonal precipitation (1979-2009)
Results:
Spatial differences of mean seasonal precipitation ( )
ANUSPLIN consistently shows dry bias ( ̴10 to 60 mm month-1) in all seasons.
NARR precipitation underestimates, over the most part of the LNRB, specially for summer season.
ERA-I and WFDEI show wet bias for all seasons.
In preparation Lilhare et al., (2017)

13. Pearson’s Correlation (r, p-value < 0.05)
Results:
Data
Pearson’s Correlation (r, p-value < 0.05)
Winter
Spring
Summer
Fall
ANUSPLIN
0.99
ERA-I
0.95
0.97
NARR
0.96
0.98
WFDEI
In preparation Lilhare et al., (2017)

14. Spatial differences of mean seasonal air temperature (1979-2009)
Results:
Spatial differences of mean seasonal air temperature ( )
ANUSPLIN temperature is colder (0 °C to 5 °C) than IDW in all seasons.
Minimum differences (±1 °C) in the NARR temperature.
ERA-I shows cold bias in spring, summer, and fall seasons.
WFDEI temperature is quite colder than IDW in all seasons.
In preparation Lilhare et al., (2017)

15. Results:
VIC model monthly calibration ( ) with different forcing datasets
NARR-VIC and ENSEMBLE-VIC, among all other datasets, show better performances (NSE, KGE>0.50) for seven selected subwatersheds.
Poor performances by ANUSPLIN- VIC and WFDEI-VIC (NSE, KGE<0.50) for more than four rivers.
ERA-I-VIC and IDW-VIC show satisfactory results for most of the rivers.
ERA-I-VIC shows positive PBIAS for all selected rivers.
In preparation Lilhare et al., (2017)

16. Results:
Highly consistent model performance for NARR-VIC and ENSEMBLE-VIC.
The runoff is considerably lower for the ANUSPLIN-VIC and IDW-VIC simulations and runoff timing is shifted (~20 days) in IDW-VIC.
ERA-I-VIC and WFDEI-VIC yield more spring, summer, and fall runoff.
Variability in the NARR-VIC simulation is similar to observations for late spring, summer and early fall.
All simulations show high coefficient of variation (CV >2) for late winter season.
In preparation Lilhare et al., (2017)

17. Preliminary Results: Sensitivity Analysis
Evapotranspiration (ET) increases in LAKE & WETLAND scenario and decreases in NO-LAKE & WETLAND scenario.
The absence of natural lakes and wetlands over the Odei River sub-watershed yield higher surface runoff and baseflow.

18. Preliminary Results: Sensitivity Analysis
% Change in water balance estimations ( ) for the Odei River
Preliminary Results: Sensitivity Analysis
WETLAND-NO WETLAND scenario illustrates 9% and 7% increase in mean annual ET and soil moisture (SM) but decrease in total runoff (TR) by 33.5%.
FROST-NO FROST scenario shows 8% and 1% increase in mean annual ET and SM whereas decrease in TR by 34%.

19. Summary
This study shows spatial and temporal differences among the VIC input forcing datasets over the LNRB, which is essential to capture the uncertainties in modelling hydrologic responses.
Overall, the NARR and ENSEMBLE datasets have reliable results for the LNRB hydrology, whereas other datasets show issues with either precipitation or with air temperature.
Runoff and its variability are significantly affected by different VIC model setup and various input forcing datasets.

20. Summary
This work, as well as other previous research, suggests that presence of frozen ground and the role of lakes and wetlands may be more pronounced under changing climate conditions, which will be considered in future work.
The present setup of the VIC model including frozen ground, lakes, and wetlands will also provide the necessary support and useful insights into climate change impacts on the surface water hydrology of the LNRB.

21. Acknowledgements
Natural Sciences and Engineering Research Council (NSERC) through the BaySys project
Water Survey of Canada
Dr. Siraj Ul Islam (Research Associate and Adjunct Professor, UNBC)

22. References
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23. THANK YOU! Any questions?