Spatially-Distributed Simulation of the Snowpack within the GSSHA Hydrologic Model for Areas with Limited Data

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Jason Lamb
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1 Spatially-Distributed Simulation of the Snowpack within the GSSHA Hydrologic Model for Areas with Limited Data 2016 AWRA Specialty Conference Sacramento, CA 13 July 2016 Michael L. Follum*, Charles W. Downer, Jeffrey D. Niemann *

2 Outline Three Focus Areas of CHL Why is a Lab in Mississippi concerned about snow? GSSHA Hydrologic Model Snow Models within GSSHA Development of the RTI Snow Model Test Sites and Results Future Research Conclusions

3 CHL: Three Focus Areas Navigation Flood and Coastal Storm Damage Reduction Military Hydrology CHL deploys/develops physical and numerical models to answer questions in these areas

4 GSSHA Model GSSHA Gridded Surface Subsurface Hydrologic Analysis Model

Example GSSHA Model: Helmand River Basin Details: Drainage Area ~ 50,000 km2 Snowmelt driven spring floods Operational flow forecast used to answer several

5 Example GSSHA Model: Helmand River Basin Details: Drainage Area ~ 50,000 km2 Snowmelt driven spring floods Operational flow forecast used to answer several Military RFI s. Hydrometeorological (HMET) data from Global Climate Models. Observed snow cover area (SCA) from satellites and limited flow data available. BUILDING STRONG

percolation through the snowpack Lateral flow through the snowpack Frozen Ground

6 Cold Region Hydrology (GSSHA) Snow Simulation Energy Balance (EB) (Downer et al., 2004) Temperature Index (TI) (SNOW-17; Anderson 2006) Flow Processes Vertical percolation through the snowpack Lateral flow through the snowpack Frozen Ground Geophysical Institute Permafrost Lab Physics based approach Continuous Frozen Ground Index Empirical approach

7 Snow is Spatially Heterogeneous Snow Heterogeneity Air Temperature (function of elevation; lapse rate) Topography (Blöschl et al., 1991; Kirnbauer et al., 1994) Cloud Cover (Aguado, 1985; Sicart et al., 2006) Vegetation (Jost et al., 2007) Energy Balance (Downer et al., 2004) Robust and can capture spatial heterogeneity Require high-quality spatiotemporal forcing data (e.g. temperature, pressure, relative humidity, shortwave radiation, etc.) (Franz et al., 2008). Temperature Index (SNOW-17; Anderson 2006) Widely used due to limited data requirements and tunable parameters. High number of parameters and accounts for spatial heterogeneity based only on elevation-temperature relationship.

8 Radiation-Derived Temperature Index (RTI) Model Start with commonly used SNOW-17 model; Melt=M f *T a Replace T a with a better representation of the energy available to melt the snowpack. We call this variable T rad Estimate T rad with a simple radiation balance: T rad = f(sw and LW ) Reduces number of calibration parameters Requires cloud cover data (from satellites or most airports) Accounts for topography, vegetation, cloud cover, and snow albedo SW = f(cloud cover, topography, vegetation, lat/long) LW = f(air temperature, cloud cover, vegetation) 8

9 Test Site: Senator Beck Basin, CO Highlights: Showed accurate snow simulation in an Alpine terrain (Follum et al., 2015) RTI model more accurate than TI and EB models

10 Test Site: Senator Beck Basin, CO 10

11 Test Site: Hubbard Brook, NH

12 Future Work: Frozen Ground Why do we care about Frozen Ground: It s effect at the watershed-scale is still not very well understood Flooding (e.g. Red River of the North) Contributes to most of the severe flooding and erosion events in the Pacific Northwest (Johnson & McArthur 1973) Increased erosion (Seyfried & Flerchinger 1994) and water quality concerns It s Prevalent; 50% of exposed lands in Northern Hemisphere (Zhang et al., 2003) What influences presence of Frozen Ground: Vegetation (Diebold 1938; Kienholz, 1940; Shanley and Chalmers, 1999) Elevation / T a (Seyfried & Wilcox, 1995) Topography (Flerchinger et al., 1990) Snow Depth (Willis et al., 1961) Soil Moisture (Willis et al., 1961) Spatial Processes GSSHA Frozen Ground Physics-Based Approach Requires snow depth, T a, and soil thermal conductivity. Soil Moisture calculated within the model. Empirical Approach Requires T a and calibration We propose to replace T a with T rad and then test approach against observed data Average depth to which soil freezes in the winter (Davis 2001) 12

13 Conclusions The RTI model appears to outperform both TI and EB models at the few locations tested (Colorado and New Hampshire). The RTI model incorporates topography and vegetation into the simulation, improving SCA estimates. Compared to the TI model, the RTI model has fewer calibration parameters (depending on number of vegetation classes), and only adds cloud cover as a required input. Approach is transferable to other TI models that use a distributed domain. Similar approach may be applicable to Frozen Ground.

14 Questions

Flood Forecasting Tools for Ungauged Streams in Alberta: Status and Lessons from the Flood of 2013

Flood Forecasting Tools for Ungauged Streams in Alberta: Status and Lessons from the Flood of 2013 John Pomeroy, Xing Fang, Kevin Shook, Tom Brown Centre for Hydrology, University of Saskatchewan, Saskatoon