Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

doi:10.1007/s11707-018-0719-7

Front. Earth Sci.

2018, Vol. 12

Issue (4) : 693-710 https://doi.org/10.1007/s11707-018-0719-7

RESEARCH ARTICLE

Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

Donal O’Leary III^1,²(

), Dorothy Hall^3,⁴, Michael Medler², Aquila Flower²

¹. University of Maryland, Department of Geographical Sciences, College Park, MD 20740, USA
². Western Washington University, Department of Environmental Studies, Bellingham, WA 98225, USA
³. National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD 20771, USA
⁴. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA

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Abstract

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from “snow-covered” to “no snow” during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 m MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001–2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001–2015 average, with 25% of its land area melting>65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

Keywords Cascade Mountains snowmelt spring phenology MODIS remote sensing

Corresponding Author(s): Donal O’Leary III

Just Accepted Date: 21 June 2018 Online First Date: 03 August 2018 Issue Date: 20 November 2018

Cite this article:

Donal O’Leary III,Dorothy Hall,Michael Medler, et al. Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps[J]. Front. Earth Sci., 2018, 12(4): 693-710.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0719-7
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/693

Fig.1 Overview of study area elevation including Washington, Oregon, and California state boundaries (thick grey lines), the Cascade Mountains Ecoregion, and park unit polygons (thick black lines, left), and close-up views of Mt. Rainier (MORA), Crater Lake (CRLA) (with thin line of Cascade Mountains boundary visible crossing SE corner of the park), and Lassen Volcanic (LAVO) National Parks (right). SNOTEL stations are indicated by white circles with black outlines. Note that water bodies (including Crater Lake itself) are not differentiated on the elevation surface. Elevation data (30 m) resampled to resolution and registration of the STM data (500 m).

Tab.1 Explanation of snowmelt and cloud interference logic for each pixel. Case describes the series of consecutive MOD10A2 image values. Snowmelt DOY is the resulting value in the STM. All DOY values are relative to the DOY of the no-snow MODIS image. Cloud interference values represent the number of temporally-interpolated images+1, to avoid values of zero which may be confused as ‘No Data’ values in the STM dataset. For example, case 5 (bottom) shows a snow-covered composite image and a snow-free composite image separated by 4 consecutive images of no-snow. The resulting snowmelt value is interpolated to be the DOY of the snow-free image minus 16 (i.e., 32 days of separation between the snow- and no-snow images, divided by two), with a cloud interference value of 5 (4 cloud images, plus 1)

Tab.2 Pixel population sizes and elevation quantile limits for areas of interest (Q1= lowest elevation, Q3= highest)

Fig.2 Overview of STMs for the Cascade Mountains including outline of the Cascade Mountains Ecoregion (thick black line) and state boundaries (grey lines). Mean STM from 2001–2015, 2015 alone, and 2015-Mean difference are shown from left to right. White regions indicate areas with Count values less than 8 (mean) or no snow snowmelt detected (2015). Elevation quantile contour lines are omitted for clarity.

Fig.3 2015 snowmelt anomaly distributions by study area (plot) and elevation quantile (color). Count describes the number of pixels with side length of ~500 meters. See Tables 2 and 3 for detailed statistics. Note how the Cascade Mountains as a whole experienced greater anomalies in the highest quantile (blue), while Mt. Rainier (MORA), Crater Lake (CRLA), and Lassen Volcanic (LAVO) National Parks experienced a greater anomaly in the lowest quantile (red).

Fig.4 Maps of Mt. Rainier (MORA), Crater Lake (CRLA), and Lassen Volcanic (LAVO) National Parks (top to bottom), showing 2001?2015 mean STM, 2015 STM, and 2015-mean difference (left to right). In the difference panels, the thin solid black line indicates the lower bound of the middle elevation quantile, and the dashed line indicates the lower bound of the upper elevation quantile (Table 2). The year 2015 shows early snowmelt for most of each park, however the lower-elevation areas surrounding the volcanoes show a greater difference than the high-elevation areas within the parks. White regions indicate areas with Count values less than 8 (mean) or no snow snowmelt detected for the year 2015, primarily located in the glaciers of MORA, and Crater Lake itself.

Tab.3 Descriptive statistics for distributions of anomalies by study area and elevation quantile (Q1= lowest elevation, Q3= highest). Mean snowmelt timing includes years 2001?2015 and excludes points with fewer than 8 years of coverage. 2015 anomaly is calculated on a per-pixel basis by subtracting the mean from the year 2015

Tab.4 Snowmelt timing quantiles for the Cascade Mountains and Mount Rainier, Crater Lake, and Lassen Volcanic National Parks. The values shown here describe 2015 snowmelt timing anomaly experienced by a given percentage of land area for each study area in days. For example, 10% of the land area for the Cascade Mountains had a snowmelt timing anomaly of ≥83.73 days earlier than the 2001?2015 average

Fig.5 Cloud Interference and Count maps for the Cascade Mountains and national parks of interest. Mean cloud cover maps show the mean cloud interference values per pixel from 2001?2015, as described in Table 1. Count maps show the number of years of STM coverage, with mountainous regions experiencing accumulation and observable snowmelt for most, if not all, of the 15 years on record (darkest blue, ‘Count’ images). Note that mean cloud interference values disregard null values (years without STM values) and may therefore overestimate mean cloud interference for regions with low Count values. White areas (MORA glaciers, Crater Lake itself, Northern California valleys) have no snow observations for the period of record.

Fig.6 Snow-cover depletion curves (SCDCs) for the Cascade Mountains, MORA, CRLA, and LAVO. Mean SCDC is calculated as the mean (black) of all SCDCs from 2001?2015, not from the mean STM image. The year 2015 (red) is clearly the earliest melt of all years for all regions, with an earlier initial melt and earlier complete melt than all other years. Note that each X-axis is focused on melt season for each region.

Table A1 Examples of the differences in the STM and SNODOY results. In ideal, clear sky conditions the STM reports snowmelt within 8-day ranges (001, 009, 017, 025, etc.), whereas the SNOTEL stations allow for a daily time step

Fig. A1 Histograms illustrating the errors between the Snowmelt Timing Maps (STMs) and SNOTEL stations (days, x axis; frequency, y-axis). Errors are calculated by subtracting the SNOTEL melt Day Of Year (SNODOY) from the STM DOY. Errors are non-normally distributed (α = 0.10) about means ranging from 0.81 (2008) to 7.40 (2015), except for 2004, which is normally distributed.

Fig. A2 Histograms describing the errors between the Snowmelt Timing Maps (STMs) and SNOTEL stations (days, x axis; frequency, y-axis) for all SNOTEL locations within the Cascade Mountains Ecoregion for years 2001?2015. Years marked with an asterisk are normally distributed (α = 0.10). Means range from 17.05 (2008) to+5.03 (2001).

Fig. A3 Histograms of errors between the Snowmelt Timing Maps (STMs) and SNOTEL stations (days, x axis; frequency, y-axis) for the entire period of record for all SNOTEL sites, and those within the Cascade Mountains Ecoregion, calculated as STM-SNODOY+3.5. Colors indicate cloud interference value (1= no cloud interference, 5= four consecutive 8-day composite images of clouds). Both populations are centered about means of 4.31 and ?4.76 with standard deviations of 17.53 and 21.70, respectively. Clouds interfered with 10.67% of readings for all SNOTEL stations, and 11.92% of those within the Cascade Mountains Ecoregion.

Table A2 Description of the population of validation errors by cloud interference value

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