Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better
Abstract Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied a...
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Wiley
2024-07-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR035982 |
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| author | Theodore B. Barnhart Annie L. Putman Aaron J. Heldmyer David M. Rey John C. Hammond Jessica M. Driscoll Graham A. Sexstone |
| author_facet | Theodore B. Barnhart Annie L. Putman Aaron J. Heldmyer David M. Rey John C. Hammond Jessica M. Driscoll Graham A. Sexstone |
| author_sort | Theodore B. Barnhart |
| collection | DOAJ |
| description | Abstract Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful. |
| format | Article |
| id | doaj-art-e332efcfdec640feb3af48bcb49d2773 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-e332efcfdec640feb3af48bcb49d27732025-08-20T02:36:39ZengWileyWater Resources Research0043-13971944-79732024-07-01607n/an/a10.1029/2023WR035982Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always BetterTheodore B. Barnhart0Annie L. Putman1Aaron J. Heldmyer2David M. Rey3John C. Hammond4Jessica M. Driscoll5Graham A. Sexstone6U.S. Geological Survey Wyoming‐Montana Water Science Center Helena MT USAU.S. Geological Survey Utah Water Science Center West Valley City UT USAU.S. Geological Survey Wyoming‐Montana Water Science Center Cheyenne WY USAU.S. Geological Survey Hydrologic Remote Sensing Branch, Water Resources Mission Area Denver CO USAU.S. Geological Survey Maryland‐Delaware‐D.C. Water Science Center Catonsville MD USAU.S. Geological Survey Denver CO USAU.S. Geological Survey Colorado Water Science Center Denver CO USAAbstract Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful.https://doi.org/10.1029/2023WR035982snow modelingmodel resolutionstreamflowmodelingsnow and icewater supply |
| spellingShingle | Theodore B. Barnhart Annie L. Putman Aaron J. Heldmyer David M. Rey John C. Hammond Jessica M. Driscoll Graham A. Sexstone Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better Water Resources Research snow modeling model resolution streamflow modeling snow and ice water supply |
| title | Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better |
| title_full | Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better |
| title_fullStr | Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better |
| title_full_unstemmed | Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better |
| title_short | Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better |
| title_sort | evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations finer is not always better |
| topic | snow modeling model resolution streamflow modeling snow and ice water supply |
| url | https://doi.org/10.1029/2023WR035982 |
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