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

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.

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

2021 ◽  
Vol 15 (10) ◽  
pp. 4625-4636
Author(s):  
Moritz Buchmann ◽  
Michael Begert ◽  
Stefan Brönnimann ◽  
Christoph Marty
Keyword(s):  
Extreme Events ◽  
Snow Depth ◽  
Extreme Values ◽  
Local Scale ◽  
Swiss Alps ◽  
Detection Methods ◽  
Return Levels ◽  
Maximum Snow Depth ◽  
Climate Indicators ◽  
The Impact

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.

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

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

<p>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.</p><p>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).</p><p>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. <br>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.</p><p>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).<br>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.</p>

scholarly journals Evaluation of break detection methods for snow data series

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

<p>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 “parallel” stations in close proximity are not always available.<br>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). <br>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%) .<br>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.<br>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.</p>

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

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.

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

2010 ◽  
Vol 09 (02) ◽  
pp. 203-217 ◽  
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.

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

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.

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

Water ◽  
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.

scholarly journals Improving the usability of climate indicator visualizations through diagnostic design principles

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.

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

The impact of extreme events on energy price risk

2021 ◽  
pp. 105308
Author(s):  
Jun Wen ◽  
Xin-Xin Zhao ◽  
Chun-Ping Chang
Keyword(s):  
Extreme Events ◽  
Price Risk ◽  
Energy Price ◽  
The Impact
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