Impacts of Temperature Data Sets on Macroscale Snowmelt Simulations in the Missouri River Basin
Publication: Journal of Cold Regions Engineering
Volume 37, Issue 2
Abstract
The objective of this study is to evaluate the impacts of two commonly used temperature databases, Parameter-elevation Relationships on Independent Slopes Model (PRISM) and Topography Weather (TopoWx), on the quantity and distribution of snowmelt in the Missouri River Basin simulated by a new macroscale grid-based model for two representative flood and drought years. The model incorporates a unique LEGO-fashion framework to account for within-grid heterogeneity. The snowmelt simulations were compared with the SNOw Data Assimilation System (SNODAS) estimates, indicating that both data sets provided comparable snowmelt with the SNODAS data (R2 > 0.91). Comparison of the modeling results revealed that both data sets provided comparable magnitude and distribution of the average monthly snowmelt. However, the average daily snowmelt varied up to 16.9% and the snowmelt variations were more pronounced in the areas with complex topography. The simulations suggested that even nuances in the snowmelt coverage led to significant changes in the simulated snowmelt quantity.
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Acknowledgments
This material is based on work supported by the National Science Foundation under Grant NSF EPSCoR Award IIA-1355466. The North Dakota Water Resources Research Institute also provided partial financial support in the form of a graduate fellowship for the first author.
References
Alcamo, J., P. Döll, T. Henrichs, F. Kaspar, B. Lehner, T. Rösch, and S. Siebert. 2003. “Development and testing of the WaterGAP 2 global model of water use and availability.” Hydrol. Sci. J. 48 (3): 317–337. https://doi.org/10.1623/hysj.48.3.317.45290.
Anderson, E. 2002. Calibration of conceptual hydrologic models for use in river forecasting. Silver Spring, MD: National Oceanic and Atmospheric Administration, National Weather Service Hydrology Laboratory.
Anderson, E. 2006. Snow accumulation and ablation model—SNOW-17. Silver Spring, MD: National Oceanic and Atmospheric Administration, National Weather Service Hydrology Laboratory.
Anderson, E. A. 1968. “Development and testing of snow pack energy balance equations.” Water Resour. Res. 4 (1): 19–37. https://doi.org/10.1029/WR004i001p00019.
Badger, A. M., B. Livneh, M. P. Hoerling, and J. K. Eischeid. 2018. “Understanding the 2011 Upper Missouri River Basin floods in the context of a changing climate.” J. Hydrol.: Reg. Stud. 19: 110–123. https://doi.org/10.1016/j.ejrh.2018.08.004.
Behnke, R., S. Vavrus, A. Allstadt, T. Albright, W. E. Thogmartin, and V. C. Radeloff. 2016. “Evaluation of downscaled, gridded climate data for the conterminous United States.” Ecol. Appl. 26 (5): 1338–1351. https://doi.org/10.1002/15-1061.
Benjamin, S. G., et al. 2016. “A North American hourly assimilation and model forecast cycle: The rapid refresh.” Mon. Weather Rev. 144 (4): 1669–1694. https://doi.org/10.1175/MWR-D-15-0242.1.
Bergström, S. 1992. The HBV model: Its structure and applications. Norrköping, Sweden: Swedish Meteorological and Hydrological Institute.
Boryan, C., Z. Yang, R. Mueller, and M. Craig. 2011. “Monitoring US agriculture: The US department of agriculture, national agricultural statistics service, cropland data layer program.” Geocarto Int. 26 (5): 341–358. https://doi.org/10.1080/10106049.2011.562309.
Chu, X., Z. Lin, M. Tahmasebi Nasab, L. Zeng, K. Grimm, M. H. Bazrkar, N. Wang, X. Liu, X. Zhang, and H. Zheng. 2019. “Macro-scale grid-based and subbasin-based hydrologic modeling: Joint simulation and cross-calibration.” J. Hydroinf. 21 (1): 77–91. https://doi.org/10.2166/hydro.2018.026.
Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris. 2008. “Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States.” Int. J. Climatol. 28 (15): 2031–2064. https://doi.org/10.1002/joc.1688.
Daly, C., R. P. Neilson, and D. L. Phillips. 1994. “A statistical-topographic model for mapping climatological precipitation over mountainous terrain.” J. Appl. Meteorol. 33 (2): 140–158. https://doi.org/10.1175/1520-0450(1994)033%3C0140:ASTMFM%3E2.0.CO;2.
Debele, B., R. Srinivasan, and A. K. Gosain. 2010. “Comparison of process-based and temperature-index snowmelt modeling in SWAT.” Water Resour. Manage. 24 (6): 1065–1088. https://doi.org/10.1007/s11269-009-9486-2.
Fontaine, T. A., T. S. Cruickshank, J. G. Arnold, and R. H. Hotchkiss. 2002. “Development of a snowfall–snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT).” J. Hydrol. 262 (1–4): 209–223. https://doi.org/10.1016/S0022-1694(02)00029-X.
Hedstrom, N. R., and J. W. Pomeroy. 1998. “Measurements and modelling of snow interception in the boreal forest.” Hydrol. Processes 12 (10–11): 1611–1625. https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11%3C1611::AID-HYP684%3E3.0.CO;2-4.
Hock, R. 2003. “Temperature index melt modelling in mountain areas.” J. Hydrol. 282 (1–4): 104–115. https://doi.org/10.1016/S0022-1694(03)00257-9.
Jenks, G. 1967. “The data model concept in statistical mapping.” Int. Yearb. Cartography 7: 186–190.
Kienzle, S. W. 2008. “A new temperature based method to separate rain and snow.” Hydrol. Processes 22 (26): 5067–5085. https://doi.org/10.1002/hyp.7131.
Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges. 1994. “A simple hydrologically based model of land surface water and energy fluxes for general circulation models.” J. Geophys. Res. 99 (D7): 14415. https://doi.org/10.1029/94JD00483.
Lindström, G., C. Pers, J. Rosberg, J. Strömqvist, and B. Arheimer. 2010. “Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales.” Hydrol. Res. 41 (3–4).
Liston, G. E., and K. Elder. 2006. “A distributed snow-evolution modeling system (SnowModel).” J. Hydrometeorol. 7 (6): 1259–1276. https://doi.org/10.1175/JHM548.1.
Livneh, B., M. Hoerling, A. Badger, and J. Eischeid. 2016. Causes for hydrologic extremes in the upper Missouri River basin. Washington, DC: National Oceanic and Atmospheric Administration.
Martinec, J. 1960. “The degree-day factor for snowmelt runoff forecasting.” In IUGG general assembly of Helsinki, IAHS commission of surface waters, 468–477. Wallingford, UK: IAHS Press. Publ. 51.
Massmann, C. 2019. “Modelling snowmelt in ungauged catchments.” Water 11 (2): 301. https://doi.org/10.3390/w11020301.
Mehta, V. M., et al. 2013. “Decadal climate information needs of stakeholders for decision support in water and agriculture production sectors: A case study in the Missouri River Basin.” Weather Clim. Soc. 5 (1): 27–42. https://doi.org/10.1175/WCAS-D-11-00063.1.
Menne, M. J., and C. N. Williams Jr. 2009. “Homogenization of temperature series via pairwise comparisons.” J. Clim. 22 (7): 1700–1717. https://doi.org/10.1175/2008JCLI2263.1.
Mesinger, F., et al. 2006. “North American regional reanalysis.” Bull. Am. Meteorol. Soc. 87 (3): 343–360. https://doi.org/10.1175/BAMS-87-3-343.
Muñoz-Sabater, J., et al. 2021. “ERA5-Land: A state-of-the-art global reanalysis data set for land applications.” Earth Syst. Sci. Data 13 (9): 4349–4383. https://doi.org/10.5194/essd-13-4349-2021.
NCEI (National Centers for Environmental Information). 2018. “Land-based station data.” Accessed December 6, 2018. https://www.ncdc.noaa.gov/data-access/land-based-station-data.
Neitsch, S., J. Arnold, J. Kiniry, and J. Williams. 2011. Soil and Water Assessment Tool (SWAT) theoretical documentation version 2009. College Station, TX: Texas Water Resources Institute.
Newman, A. J., et al. 2015. “Gridded ensemble precipitation and temperature estimates for the contiguous United States.” J. Hydrometeorol. 16 (6): 2481–2500. https://doi.org/10.1175/JHM-D-15-0026.1.
NOHRSC (National Operational Hydrologic Remote Sensing Center). 2004. “Snow Data Assimilation System (SNODAS) Data products at NSIDC, version 1.” National Snow and Ice Data Center. Accessed June 20, 2019. https://nsidc.org/data/G02158.
Norton, P. A., M. T. Anderson, and J. F. Stamm. 2014. Trends in annual, seasonal, and monthly streamflow characteristics at 227 streamgages in the Missouri River watershed, water years 1960–2011. Reston, VA: USGS.
NWS (National Weather Service). 2012. The Missouri/Souris river floods of May-August 2011. Kansas City, MO: NWS.
Ohmura, A. 2001. “Physical basis for the temperature-based melt-index method.” J. Appl. Meteorol. 40 (4): 753–761. https://doi.org/10.1175/1520-0450(2001)040%3C0753:PBFTTB%3E2.0.CO;2.
Oyler, J. W., A. Ballantyne, K. Jencso, M. Sweet, and S. W. Running. 2015. “Creating a topoclimatic daily air temperature data set for the conterminous United States using homogenized station data and remotely sensed land skin temperature.” Int. J. Climatol. 35 (9): 2258–2279. https://doi.org/10.1002/joc.4127.
Pomeroy, J. W., J. Parviainen, N. Hedstrom, and D. M. Gray. 1998. “Coupled modelling of forest snow interception and sublimation.” Hydrol. Processes 12 (15): 2317–2337. https://doi.org/10.1002/(SICI)1099-1085(199812)12:15%3C2317::AID-HYP799%3E3.0.CO;2-X.
Qi, J., S. Li, R. Jamieson, D. Hebb, Z. Xing, and F.-R. Meng. 2017. “Modifying SWAT with an energy balance module to simulate snowmelt for maritime regions.” Environ. Modell. Software 93: 146–160. https://doi.org/10.1016/j.envsoft.2017.03.007.
Qiao, L., Z. Pan, R. B. Herrmann, and Y. Hong. 2014. “Hydrological variability and uncertainty of lower Missouri River Basin under changing climate.” JAWRA J. Am. Water Resour. Assoc. 50 (1): 246–260. https://doi.org/10.1111/jawr.12126.
Quick, M. C., and A. Pipes. 2009. “UBC watershed model.” Hydrol. Sci. J. 22 (1): 153–161. https://doi.org/10.1080/02626667709491701.
Raleigh, M. S., and M. P. Clark. 2014. “Are temperature-index models appropriate for assessing climate change impacts on snowmelt?” In Proc., of the Western Snow Conf. Durango, CO: 82nd Annual Western Snow Conference.
Rippey, B. R. 2015. “The U.S. drought of 2012.” Weather Clim. Extremes 10: 57–64. https://doi.org/10.1016/j.wace.2015.10.004.
Schmidt, R. A., and D. R. Gluns. 1991. “Snowfall interception on branches of three conifer species.” Can. J. For. Res. 21 (8): 1262–1269. https://doi.org/10.1139/x91-176.
Stewart, I. T. 2009. “Changes in snowpack and snowmelt runoff for key mountain regions.” Hydrol. Processes 23 (1): 78–94. https://doi.org/10.1002/hyp.7128.
Tahmasebi Nasab, M., and X. Chu. 2020. “Macro-HyProS: A new macro-scale hydrologic processes simulator for depression-dominated cold climate regions.” J. Hydrol. 580: 124366. https://doi.org/10.1016/j.jhydrol.2019.124366.
Tahmasebi Nasab, M., and X. Chu. 2021. “Do sub-daily temperature fluctuations around the freezing temperature alter macro-scale snowmelt simulations?” J. Hydrol. 596: 125683. https://doi.org/10.1016/j.jhydrol.2020.125683.
Terink, W., A. F. Lutz, G. W. H. Simons, W. W. Immerzeel, and P. Droogers. 2015. “SPHY v2.0: Spatial processes in HYdrology.” Geosci. Model Dev. 8 (7): 2009–2034. https://doi.org/10.5194/gmd-8-2009-2015.
Thornton, P. E., R. Shrestha, M. Thornton, S.-C. Kao, Y. Wei, and B. E. Wilson. 2021. “Gridded daily weather data for North America with comprehensive uncertainty quantification.” Sci. Data 8 (1): 1–17. https://doi.org/10.1038/s41597-021-00973-0.
USACE (US Army Corps of Engineers). 2012. Post 2011 flood event analysis of Missouri River mainstem flood control storage. Omaha, NE: USACE.
USBR (US Bureau of Reclamation). 2016. SECURE water Act Section 9503(c)-reclamation climate change and water. Denver: USBR.
USGAO (US Government Accountability Office). 2014. Missouri River flood and drought. Washington, DC: USGAO.
USGCRP (US Global Change Research Program). 2018. In Impacts, risks, and adaptation in the United States: The fourth national climate assessment, volume II, edited by D. R. Reidmiller, C. W. Avery, D. R. Easterling, K. E. Kunkel, K. L. M. Lewis, T. K. Maycock, and B. C. Stewart. Washington, DC: USGCRP.
USGS (United States Geological Survey). 2022. “USGS water data for the nation.” Accessed May 12, 2022. https://waterdata.usgs.gov/nwis.
van der Wiel, K., et al. 2018. “100-Year lower Mississippi floods in a global climate model: Characteristics and future changes.” J. Hydrometeorol. 19 (10): 1547–1563. https://doi.org/10.1175/JHM-D-18-0018.1.
Walton, D., and A. Hall. 2018. “An assessment of high-resolution gridded temperature data sets over california.” J. Clim. 31 (10): 3789–3810. https://doi.org/10.1175/JCLI-D-17-0410.1.
Wan, Z. 2008. “New refinements and validation of the MODIS land-surface temperature/emissivity products.” Remote Sens. Environ. 112 (1): 59–74. https://doi.org/10.1016/j.rse.2006.06.026.
WMO (World Meteorological Organization). 1986. Intercomparison of models of snowmelt-runoff. Operational Hydrology, Rep. No. 23. Geneva: WMO.
Xia, Y., et al. 2012. “Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products.” J. Geophys. Res.: Atmos. 117 (D3): 109. https://doi.org/10.1029/2011JD016048.
Zeng, X., P. Broxton, and N. Dawson. 2018. “Snowpack change from 1982 to 2016 over conterminous United States.” Geophys. Res. Lett. 45 (23): 12940–12947. https://doi.org/10.1029/2018GL079621.
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Published In
Journal of Cold Regions Engineering
Volume 37 • Issue 2 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 18, 2021
Accepted: Nov 6, 2022
Published online: Jan 24, 2023
Published in print: Jun 1, 2023
Discussion open until: Jun 24, 2023
ASCE Technical Topics:
- Basins
- Bodies of water (by type)
- Climates
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Geometry
- Hydrologic data
- Hydrologic engineering
- Hydrology
- Information management
- Mathematics
- Meteorology
- Models (by type)
- Precipitation
- Prism
- River engineering
- Rivers and streams
- Simulation models
- Snow
- Snowmelt
- Temperature (by type)
- Temperature distribution
- Thermal properties
- Thermodynamics
- Water and water resources
- Water management
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