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Variogram Time Series Analysis Applied to the Spatial Structure of Snow Accumulation.

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Title: Variogram Time Series Analysis Applied to the Spatial Structure of Snow Accumulation.
Authors: Fassnacht, Steven R.1,2,3,4 (AUTHOR) Steven.Fassnacht@colostate.edu, López‐Moreno, Juan Ignacio5 (AUTHOR), Barnard, David M.6 (AUTHOR), Morán‐Tejeda, Enrique7 (AUTHOR), Webb, Ryan W.8 (AUTHOR), Von Thaden, Benjamin C.1 (AUTHOR), Pfohl, Anna K. D.1 (AUTHOR), Collados‐Lara, Antonio‐Juan9,10 (AUTHOR), MacDonald, Marin S.1 (AUTHOR), Flynn, Helen1 (AUTHOR), Tedesche, Molly E.11,12 (AUTHOR)
Source: Water Resources Research. Mar2026, Vol. 62 Issue 3, p1-12. 12p.
Subjects: Variograms, Snow accumulation, Spatial data structures, Hydrological research, Precipitation anomalies, Time series analysis
Geographic Terms: Rocky Mountains, West (U.S.)
Abstract: The correlation of earth system properties is important for assessing monitoring strategies, determining scales of modeling, and improving forecasting capabilities. We present a new method to examine the spatial scale of inter‐annual patterns from time series data. The variability in annual patterns between stations is computed using daily data from a network of stations. This variability is used to compute the semi‐variance for intervals of distance and plotted in the form of a variogram. Variograms are used to identify the correlation distances for a specific process. Here, the method is applied to 90 stations of daily snow water equivalent accumulation and precipitation data over the Southern Rocky Mountains of the Western USA for a 40‐year period (1981–2020). At 5‐, 10‐, or 20‐km lag distances, snow accumulation patterns are very similar to 90 or 100 km. Snow accumulation patterns are less correlated up to about 380 km; beyond there is no quantifiable spatial correlation. Summer precipitation patterns are correlated up to about 60 km while winter precipitation patterns are spatially consistent for 100 km and likely to more than 300 km. Subsets of the accumulation and precipitation data to explore differences due to geographic location, land cover type, and the Oceanic Niño Index yielded similar results. Plain Language Summary: A method is presented that estimates the distance over which hydrological processes are correlated. A network of station data are used to compare patterns in the processes between pairs of stations. The year‐to‐year variability is evaluated versus the distance between each pair of stations. The method is applied to snow accumulation and precipitation data for the Southern Rocky Mountains of the Western United States. Accumulation is consistent up to 100 km, and partially correlated up to 380 km, regardless of location over the area, land cover type or large‐scale weather patterns. Summer rain is correlated up to 60 km, while winter precipitation is consistent up to 100 km and likely to 300 km, with some variability based on large‐scale weather patterns. Key Points: A new method is presented to determine the distance over which hydrological processes are correlatedHydrological data from station pairs are compared to determine annual patterns and year‐to‐year variability versus distanceThe method is applied to daily snow accumulation and precipitation data over the Southern Rocky Mountains over four decades [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The correlation of earth system properties is important for assessing monitoring strategies, determining scales of modeling, and improving forecasting capabilities. We present a new method to examine the spatial scale of inter‐annual patterns from time series data. The variability in annual patterns between stations is computed using daily data from a network of stations. This variability is used to compute the semi‐variance for intervals of distance and plotted in the form of a variogram. Variograms are used to identify the correlation distances for a specific process. Here, the method is applied to 90 stations of daily snow water equivalent accumulation and precipitation data over the Southern Rocky Mountains of the Western USA for a 40‐year period (1981–2020). At 5‐, 10‐, or 20‐km lag distances, snow accumulation patterns are very similar to 90 or 100 km. Snow accumulation patterns are less correlated up to about 380 km; beyond there is no quantifiable spatial correlation. Summer precipitation patterns are correlated up to about 60 km while winter precipitation patterns are spatially consistent for 100 km and likely to more than 300 km. Subsets of the accumulation and precipitation data to explore differences due to geographic location, land cover type, and the Oceanic Niño Index yielded similar results. Plain Language Summary: A method is presented that estimates the distance over which hydrological processes are correlated. A network of station data are used to compare patterns in the processes between pairs of stations. The year‐to‐year variability is evaluated versus the distance between each pair of stations. The method is applied to snow accumulation and precipitation data for the Southern Rocky Mountains of the Western United States. Accumulation is consistent up to 100 km, and partially correlated up to 380 km, regardless of location over the area, land cover type or large‐scale weather patterns. Summer rain is correlated up to 60 km, while winter precipitation is consistent up to 100 km and likely to 300 km, with some variability based on large‐scale weather patterns. Key Points: A new method is presented to determine the distance over which hydrological processes are correlatedHydrological data from station pairs are compared to determine annual patterns and year‐to‐year variability versus distanceThe method is applied to daily snow accumulation and precipitation data over the Southern Rocky Mountains over four decades [ABSTRACT FROM AUTHOR]
ISSN:00431397
DOI:10.1029/2025WR040065