Wednesday, 15 January 2020: 11:15 AM
An accurate estimation of snow water equivalent (SWE) depends primarily on its accumulations over the temporal domain as well as its spatial distribution over the space as the processes govern making snowpacks and melting are non-local both in time and space.
The predicting summer streamflow runoff using model simulations, on basins that are dominated by snowmelt process, depends on how much SWE remains in a watershed at the onset of melting period early summer.
Numerical simulations of snow water equivalent (SWE) in snow dominant basins can be substantially different and has not properly validated as few SWE observations have existed over large areas coherently until the recent time (~2015). The new approaches like NASA JPL’s Airborne Snow Observatory (ASO) has created a new dimension in SWE model data validations processes because of their high-quality availability in space and reduced availability over the snowmelt season as well.
In this work, we use WRFHydro, VIC, SUMMA, LIS, and LISHydro model environments to simulate the SWE throughout 2015 to 2018 for the Tuolumne basin in California, and demonstrate the different land surface model frameworks provide substantially different SWE values. The discrepancies of the simulated SWE can be varied between 30 to 60 percent over the study period between different models.
We also routed the streamflow generated from above land-surface model using 250 m digital elevation model data and computes the streamflow hydrographs. We further document that the discrepancies in the SWE at the early summer in different model frameworks create substantial differences in streamflow in the early/mid-summer.
Furthermoce, we noticed that the NoahMP land surface model used in the different model frameworks could provide substantially different SWE so as the streamflow hydrographs.
Finally, we used the ASO SWE observations for the period of 2015-2017, and demonstrate that basin-scale streamflow hydrographs can be substantially improved using SWE data assimilations process.
The predicting summer streamflow runoff using model simulations, on basins that are dominated by snowmelt process, depends on how much SWE remains in a watershed at the onset of melting period early summer.
Numerical simulations of snow water equivalent (SWE) in snow dominant basins can be substantially different and has not properly validated as few SWE observations have existed over large areas coherently until the recent time (~2015). The new approaches like NASA JPL’s Airborne Snow Observatory (ASO) has created a new dimension in SWE model data validations processes because of their high-quality availability in space and reduced availability over the snowmelt season as well.
In this work, we use WRFHydro, VIC, SUMMA, LIS, and LISHydro model environments to simulate the SWE throughout 2015 to 2018 for the Tuolumne basin in California, and demonstrate the different land surface model frameworks provide substantially different SWE values. The discrepancies of the simulated SWE can be varied between 30 to 60 percent over the study period between different models.
We also routed the streamflow generated from above land-surface model using 250 m digital elevation model data and computes the streamflow hydrographs. We further document that the discrepancies in the SWE at the early summer in different model frameworks create substantial differences in streamflow in the early/mid-summer.
Furthermoce, we noticed that the NoahMP land surface model used in the different model frameworks could provide substantially different SWE so as the streamflow hydrographs.
Finally, we used the ASO SWE observations for the period of 2015-2017, and demonstrate that basin-scale streamflow hydrographs can be substantially improved using SWE data assimilations process.
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