Local-scale variability of seasonal mean and extreme values  of in situ snow depth and snowfall measurements

Abstract. Daily measurements of snow depth and snowfall can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are usually located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall, and snow onset and disappearance dates, which are calculated for various seasons (December to February (DJF), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential variability. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA, which show median variations of less than 5 % for all indicators. Local-scale variability ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. The highest percentage (90 %) of station pairs with variability of less than 15 % is observed for days with snow on the ground. The lowest percentage (30 %) of station pairs with variability of less than 15 % is observed for average snow depth. Median differences of snow disappearance dates are rather small (3 d) and similar to the ones found for snow onset dates (2 d). An analysis of potential sunshine duration could not explain the higher variabilities in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3 d new snow sum, which are often used for avalanche prevention measures. The found return levels are within each other's 95 % confidence intervals for all (but three) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of variabilities of commonly used snow indicators and extremal indices. Knowledge about such variabilities in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

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Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements

10.5194/tc-2021-125 ◽

2021 ◽

Author(s):

Moritz Buchmann ◽

Michael Begert ◽

Stefan Brönnimann ◽

Christoph Marty

Keyword(s):

Extreme Events ◽

Snow Depth ◽

Extreme Values ◽

Local Scale ◽

Swiss Alps ◽

Scale Uncertainty ◽

Return Levels ◽

Maximum Snow Depth ◽

Climate Indicators ◽

The Impact

Abstract. Measurements of snow depth and snowfall on the daily scale can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are mostly located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall as well as snow onset and disappearance dates, which are calculated for various seasons (December to February (DFJ), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential uncertainty. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA which show median uncertainties of less than 5 % for all indicators. Local-scale uncertainty ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. Highest (lowest) percentage of station pairs with uncertainty less than 15 % is observed for days with snow on the ground with 90 % (average snow depth, 30 %). Median differences of snow disappearance dates are rather small (three days) and similar to the ones found for snow onset dates (two days). An analysis of potential sunshine duration could not explain the higher uncertainties in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3-day new snow sum, which are often used for prevention measures. The found return levels are within each other’s 95 % confidence intervals for all (but two) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and extremal indices. Knowledge about such uncertainties in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

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Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements

10.5194/egusphere-egu21-918 ◽

2021 ◽

Author(s):

Moritz Buchmann ◽

Michael Begert ◽

Stefan Brönnimann ◽

Christoph Marty

Keyword(s):

Snow Depth ◽

Extreme Values ◽

Sunshine Duration ◽

Local Scale ◽

Swiss Alps ◽

Prevention Measures ◽

Climate Indicators ◽

Close Proximity ◽

Relative Differences ◽

The Impact

Measurements of snow depth and snowfall can vary dramatically over small distances. However, it is not clear if this applies to all derived variables and is the same for all seasons.To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements for the Swiss Alps consisting of 30 station pairs with up to 50 years of parallel data. Station pairs are mostly located in the same villages or within close proximity (less than 3km horizontally and 100m vertically).We calculated a series of snow climate indicators as derived values from the daily snow depth and snowfall measurements for various seasons (DJF, MA, and November-April). Snow climate indicators include average snow depth, max. snow depth, sum of new snow as well as snow onset and disappearance dates. Further, we quantified the return levels of a 10- and 50-year event for max. snow depth and the 3-day new snow sum to investigate the impact of local-scale variations on the estimation of extreme events, which are often used for prevention measures. We computed the relative differences for all these indicators at each station pair to demonstrate the potential uncertainty. To address the local-scale variations of the measurement sites, we calculated the potential sunshine duration for each known location using GIS and a DEM. However, information from metadata (including the exact coordinates) has to be treated with caution as it can be correct, incomplete, incorrect or simply missing at all.We found the largest differences for all indicators in spring and the smallest in DJF and Nov-Apr. Furthermore, there is hardly any difference between DJF and Nov-Apr. Surprisingly, median differences of snow disappearance dates are rather small (three days) and similar to the ones found for snow onset dates (two days). We tried to explain the variations of snow disappearance dates with accumulated potential sunshine duration during March and April, however, no clear relationship could be found. This suggests that the potential sunshine duration is not an appropriate proxy for local-scale variations, mainly because vegetation, buildings and the like are not available in a DEM.

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Evaluation of break detection methods for snow data series

10.5194/ems2021-47 ◽

2021 ◽

Author(s):

Moritz Buchmann ◽

Michael Begert ◽

Stefan Brönnimann ◽

Gernot Resch ◽

Christoph Marty

Keyword(s):

Time Series ◽

Snow Depth ◽

Swiss Alps ◽

Detection Methods ◽

Data Series ◽

Detection Algorithms ◽

Climate Indicators ◽

Seasonal Analysis ◽

Almost All ◽

New Applications

Measurements of snow depth and snowfall can vary dramatically over small distances. However, it is not clear if this applies to all derived variables and is the same for all seasons. Almost all meteorological time series incorporate some sort of inhomogeneities. Complete metadata and existing &#8220;parallel&#8221; stations in close proximity are not always available. First, we analyse the impacts of local-scale variations based on a unique set of parallel manual snow measurements for the Swiss Alps consisting of 30 station pairs with up to 70 years of parallel data. Station pairs are mostly located in the same villages (or within 3km horizontal and 150m vertical distances).&#160; Seasonal analysis of derived snow climate indicators such as maximum seasonal snow depth, sum of new snow, or days with snow on the ground shows that largest differences occur in spring and the smallest ones are found in DJF and NDJFMA. Relative inter-pair differences (uncertainties) for days with snow on the ground (average snow depth) are below 15% for 90% (30%) . Second, in view of any homogenization efforts of snow data series, it is paramount to understand the impacts of inhomogeneities. Using state-of-the-art break detection algorithms, we strive to investigate which method works best for detecting breaks in snow data series. The results can then be used on time series with insufficient metadata or no neighbouring stations in order to include them in future homogenization processes. Furthermore, the knowledge about inhomogeneities and breakpoints paves the way for new applications such as the reliable combination of two parallel series into one single series.

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Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

10.5194/tc-2020-277 ◽

2020 ◽

Author(s):

Ahmad Hojatimalekshah ◽

Zach Uhlmann ◽

Nancy F. Glenn ◽

Christopher A. Hiemstra ◽

Christopher J. Tennant ◽

...

Keyword(s):

Snow Depth ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Local Scale ◽

Snow Accumulation ◽

Tree Structure ◽

Scale Model ◽

Fine Scale ◽

Individual Tree ◽

The Impact

Abstract. Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts, and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA, to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We apply the Marker-controlled watershed algorithm for individual tree segmentation and measure the snow depth using the Multi-scale Model to Model Cloud Comparison algorithm. Canopy, topography and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity) along with local scale processes such as wind are highly influential on snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results emphasize the importance of tree species and distribution on snow depth patterns. Fine scale analysis from TLS provides information on local scale controls, and provides an opportunity to be readily coupled with airborne or spaceborne lidar to investigate larger-scale controls on snow depth.

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POWER LAW AND STRETCHED EXPONENTIAL EFFECTS OF EXTREME EVENTS IN CHINESE STOCK MARKETS

Fluctuation and Noise Letters ◽

10.1142/s0219477510000162 ◽

2010 ◽

Vol 09 (02) ◽

pp. 203-217 ◽

Cited By ~ 23

Author(s):

XIAOJUN ZHAO ◽

PENGJIAN SHANG ◽

YULEI PANG

Keyword(s):

Correlation Analysis ◽

Power Law ◽

Extreme Events ◽

Stock Markets ◽

Cross Correlation ◽

Extreme Values ◽

Cumulative Distribution ◽

Stretched Exponential ◽

Cross Correlation Analysis ◽

Chinese Stock Markets

This paper reports the statistics of extreme values and positions of extreme events in Chinese stock markets. An extreme event is defined as the event exceeding a certain threshold of normalized logarithmic return. Extreme values follow a piecewise function or a power law distribution determined by the threshold due to a crossover. Extreme positions are studied by return intervals of extreme events, and it is found that return intervals yield a stretched exponential function. According to correlation analysis, extreme values and return intervals are weakly correlated and the correlation decreases with increasing threshold. No long-term cross-correlation exists by using the detrended cross-correlation analysis (DCCA) method. We successfully introduce a modification specific to the correlation and derive the joint cumulative distribution of extreme values and return intervals at 95% confidence level.

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Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack

Climatic Change ◽

10.1007/s10584-021-03027-x ◽

2021 ◽

Vol 165 (3-4) ◽

Author(s):

Maria Vorkauf ◽

Christoph Marty ◽

Ansgar Kahmen ◽

Erika Hiltbrunner

Keyword(s):

Snow Depth ◽

Growing Season ◽

High Elevation ◽

Alpine Plants ◽

Swiss Alps ◽

Climate Change Scenarios ◽

Freezing Damage ◽

Strong Shift ◽

Air Temperatures ◽

Early Snowmelt

AbstractThe start of the growing season for alpine plants is primarily determined by the date of snowmelt. We analysed time series of snow depth at 23 manually operated and 15 automatic (IMIS) stations between 1055 and 2555 m asl in the Swiss Central Alps. Between 1958 and 2019, snowmelt dates occurred 2.8 ± 1.3 days earlier in the year per decade, with a strong shift towards earlier snowmelt dates during the late 1980s and early 1990s, but non-significant trends thereafter. Snowmelt dates at high-elevation automatic stations strongly correlated with snowmelt dates at lower-elevation manual stations. At all elevations, snowmelt dates strongly depended on spring air temperatures. More specifically, 44% of the variance in snowmelt dates was explained by the first day when a three-week running mean of daily air temperatures passed a 5 °C threshold. The mean winter snow depth accounted for 30% of the variance. We adopted the effects of air temperature and snowpack height to Swiss climate change scenarios to explore likely snowmelt trends throughout the twenty-first century. Under a high-emission scenario (RCP8.5), we simulated snowmelt dates to advance by 6 days per decade by the end of the century. By then, snowmelt dates could occur one month earlier than during the reference periods (1990–2019 and 2000–2019). Such early snowmelt may extend the alpine growing season by one third of its current duration while exposing alpine plants to shorter daylengths and adding a higher risk of freezing damage.

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Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽

10.3390/w13030307 ◽

2021 ◽

Vol 13 (3) ◽

pp. 307

Author(s):

Chi Zhang ◽

Naixia Mou ◽

Jiqiang Niu ◽

Lingxian Zhang ◽

Feng Liu

Keyword(s):

Tibetan Plateau ◽

Snow Cover ◽

Snow Depth ◽

Feedback Effect ◽

Effect Of Temperature ◽

The Tibetan Plateau ◽

Reanalysis Dataset ◽

Spatio Temporal ◽

The Impact ◽

Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

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Improving the usability of climate indicator visualizations through diagnostic design principles

Climatic Change ◽

10.1007/s10584-021-03109-w ◽

2021 ◽

Vol 166 (3-4) ◽

Author(s):

Michael D. Gerst ◽

Melissa A. Kenney ◽

Irina Feygina

Keyword(s):

Design Guidelines ◽

Party Affiliation ◽

Abstract Concepts ◽

Design Modification ◽

Climate Indicators ◽

Present Complex ◽

Control Versus ◽

Climate Indicator ◽

Political Party Affiliation ◽

The Impact

AbstractVisual climate indicators have become a popular way to communicate trends in important climate phenomena. Producing accessible visualizations for a general audience is challenging, especially when many are based on graphics designed for scientists, present complex and abstract concepts, and utilize suboptimal design choices. This study tests whether diagnostic visualization guidelines can be used to identify communication shortcomings for climate indicators and to specify effective design modifications. Design guidelines were used to diagnose problems in three hard-to-understand indicators, and to create three improved modifications per indicator. Using online surveys, the efficacy of the modifications was tested in a control versus treatment setup that measured the degree to which respondents understood, found accessible, liked, and trusted the graphics. Furthermore, we assessed whether respondents’ numeracy, climate attitudes, and political party affiliation affected the impact of design improvements. Results showed that simplifying modifications had a large positive effect on understanding, ease of understanding, and liking, but not trust. Better designs improved understanding similarly for people with different degrees of numerical capacity. Moreover, while climate skepticism was associated with less positive subjective responses and greater mistrust toward climate communication, design modification improved understanding equally for people across the climate attitude and ideological spectrum. These findings point to diagnostic design guidelines as a useful tool for creating more accessible, engaging climate graphics for the public.

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A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

Remote Sensing ◽

10.3390/rs13152869 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2869

Author(s):

MohammadAli Hemati ◽

Mahdi Hasanlou ◽

Masoud Mahdianpari ◽

Fariba Mohammadimanesh

Keyword(s):

Time Series ◽

Change Detection ◽

Policy Development ◽

Atmospheric Correction ◽

Open Data ◽

Detection Methods ◽

Landsat 8 ◽

Landsat Images ◽

Detection Analysis ◽

The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Screening for Melanoma and Other Skin Cancer Shows a Higher Early Melanoma Incidence: Social Educational Program “Life Fear-Free”

Dermatopathology ◽

10.3390/dermatopathology8010011 ◽

2021 ◽

Vol 8 (1) ◽

pp. 54-68

Author(s):

Lev Demidov ◽

Igor Samoylenko ◽

Nina Vand ◽

Igor Utyashev ◽

Irina Shubina ◽

...

Keyword(s):

Skin Cancer ◽

Screening Program ◽

Excisional Biopsy ◽

Skin Lesions ◽

Detection Methods ◽

Non Melanoma Skin Cancer ◽

The Social ◽

Educational Campaign ◽

Skin Cancer Detection ◽

The Impact

Background: The screening program Life Fear-Free (LFF) aimed at early diagnosis of cutaneous melanoma (CM) was introduced in Samara, Chelyabinsk, Yekaterinburg, and Krasnodar (Russia) in 2019. Objectives: To analyze the impact of the program on early CM and non-melanoma skin cancer (NMSC) detection. Methods: According to the social educational campaign, people were informed about CM risk factors and symptoms and were invited for skin examination. The program planned to involve 3200 participants in total. Participants with suspicious lesions were invited for excisional biopsy. Results: 3143 participants, including 75.4% women, were examined for skin lesions. The average age of the participants was 43.7 years. Mostly skin phototypes II and III were registered (48.2% and 41.0%, respectively); 3 patients had CM, 15 had basal cell carcinoma, and 1 had Bowen’s disease, which were confirmed histologically. All detected melanomas had Breslow’s thickness of 1 mm. Conclusion: The participants showed high interest in early skin cancer detection programs. The incidence rate of CM and NMSCs among the program participants was higher than in general public. The early disease grade was proven for the detected CMs and NMSCs. The study has shown that it is important to continue such programs.

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