Derivation of climate-indices and establishment of hazard-development-corridors along the ÖBB rail network

2020 ◽  
Author(s):  
Christian Wally ◽  
Sebastian Lehner ◽  
Christoph Matulla ◽  
Katharina Enigl ◽  
Helene Müller ◽  
...  
Keyword(s):  
Natural Hazards ◽  
Meteorological Data ◽  
Principal Component ◽  
Railway Track ◽  
Extreme Weather Events ◽  
Climate Indices ◽  
Multivariate Statistic ◽  
Rail Network ◽  
Railway Traffic ◽  
Recent Climate

<p>The Austrian Federal Railways (ÖBB) are operating about 4800 kilometers of railway track in all regions of Austria. Most parts of this infrastructure are exposed to various natural hazards like landslides, debris flows, rockfalls, floodings and avalanches but also extreme weather events like strong winds or extreme heat can disrupt railway traffic. The frequency of their occurrence is changing due to recent climate change.</p><p><span>We u</span><span>se </span><span>over 2000 events from 1990 to 2018 and </span><span>a principal component approach to create an event space which </span><span>lets us</span> <span>combine events and meteorological data on a fine spatial grid. This is necessary to </span><span>detect characteristic </span><span>climate-indices </span><span>(</span><span>CIs)</span><span> in temporal series of meteorological parameters, like temperature or precipitation, </span><span>that have </span><span>negative</span><span> effect</span><span>s</span><span> on railway operation or trigger natural hazards that do so. </span><span>The results are evaluated using various multivariate statistic </span><span>methods to quantify the quality of the found CIs.</span></p><p><span>After these steps we can e</span><span>stimate</span><span> the frequency of CI occurrence </span><span>in </span><span>near (2036-2065) and remote future (2071-</span><span>2100) </span><span>by analyzing ensembles of downscaled GCM projections for different climate scenarios. The result </span><span>are hazard-development-corridors that are</span><span> a relative measure for the number of predicted hazard events during the two periods of time along the considered railway tracks. </span></p>

scholarly journals Analysis and impact of recent climate trends on grape composition in north-east Italy

2019 ◽  
Vol 13 ◽  
pp. 04014 ◽  
Author(s):  
Alessia Cogato ◽  
Franco Meggio ◽  
Francesco Pirotti ◽  
Alberto Cristante ◽  
Francesco Marinello
Keyword(s):  
Sugar Content ◽  
Climatic Conditions ◽  
Extreme Weather Events ◽  
Climate Indices ◽  
Climate Trends ◽  
Thermal Indices ◽  
High Positive Correlation ◽  
Climate Conditions ◽  
North East ◽  
Recent Climate

Climate is the most relevant factor influencing the ripening of high quality grapes to produce a given wine style. This notion should be taken into account, given the increase of extreme weather events (EWE) related to climate change. Under this evolving climate scenario, North-East Italian wine regions have seen a recent expansion, potentially disregarding optimal planting choices. The use of marginal land, indeed, could lead to the establishment of vineyards in areas where it is not possible to take advantage of the best row orientation, slope and aspect. Under these conditions, the consequences of some EWE may be more severe. The objective of this study is to verify whether planting options in combination with climate conditions, may affect yield and fruit quality. An area localised in Northern Italy was analysed for row orientation and slope, taking advantage of QGIS tools. The area was also examined for climate conditions, using weather conditions and climate indices. Such variables were combined with 10-year yield and must composition of four varieties (Chardonnay, Pinot Gris, Merlot and Glera) by using linear regression. The paper reports the most significant relationships between climatic conditions and grapevine composition. The results showed high positive correlation between sugar concentration and the number of frost days during the year in three varieties. The sugar content was positively correlated with the relative humidity in June in three varieties and negatively correlated with the number of days with a temperature >25°C during the month of June in two varieties. The content of tartaric acid showed high correlations with thermal indices of May in all varieties.

scholarly journals Assessing social capacity and vulnerability of private households to natural hazards – integrating psychological and governance factors

2013 ◽  
Vol 13 (6) ◽  
pp. 1613-1628 ◽  
Author(s):  
J. Werg ◽  
T. Grothmann ◽  
P. Schmidt
Keyword(s):  
Natural Hazards ◽  
Social Vulnerability ◽  
Extreme Weather Events ◽  
Minority Status ◽  
Industrialized Countries ◽  
Medium Term ◽  
Private Households ◽  
Weather Events ◽  
Socio Demographic Factors ◽  
Financial Losses

Abstract. People are unequally affected by extreme weather events in terms of mortality, morbidity and financial losses; this is the case not only for developing, but also for industrialized countries. Previous research has established indicators for identifying who is particularly vulnerable and why, focusing on socio-demographic factors such as income, age, gender, health and minority status. However, these factors can only partly explain the large disparities in the extent to which people are affected by natural hazards. Moreover, these factors are usually not alterable in the short to medium term, which limits their usefulness for strategies of reducing social vulnerability and building social capacity. Based on a literature review and an expert survey, we propose an approach for refining assessments of social vulnerability and building social capacity by integrating psychological and governance factors.

scholarly journals Multivariate clustering of reindeer herding districts in Sweden according to range prerequisites for reindeer husbandry

Rangifer ◽  
2009 ◽  
Vol 27 (2) ◽  
pp. 107-119
Author(s):  
Henrik Lundqvist ◽  
Öje Danell
Keyword(s):  
Geographical Information Systems ◽  
Large Scale ◽  
Global Climate ◽  
Meteorological Data ◽  
Principal Component ◽  
Geographical Information ◽  
Reindeer Herding ◽  
Factors Affecting ◽  
Decision Tools ◽  
Forage Value

The 51 reindeer herding districts in Sweden vary in productivity and prerequisites for reindeer herding. In this study we characterize and group reindeer herding districts based on relevant factors affecting reindeer productivity, i.e. topography, vegetation, forage value, habitat fragmentation and reachability, as well as season lengths, snow fall, ice-crust probability, and insect harassment, totally quantified in 15 variables. The herding districts were grouped into seven main groups and three single outliers through cluster analyses. The largest group, consisting of 14 herding districts, was further divided into four subgroups. The range properties of herding districts and groups of districts were characterized through principal component analyses. By comparisons of the suggested grouping of herding districts with existing administrative divisions, these appeared not to coincide. A new division of herding districts into six administrative sets of districts was suggested in order to improve administrative planning and management of the reindeer herding industry. The results also give possibilities for projections of alterations caused by an upcoming global climate change. Large scale investigations using geographical information systems (GIS) and meteorological data would be helpful for administrative purposes, both nationally and internationally, as science-based decision tools in legislative, economical, ecological and structural assessments. Abstract in Swedish / Sammanfattning: Multivariat gruppering av svenska samebyar baserat på renbetesmarkernas grundförutsettningar Svenska renskötselområdet består av 51 samebyar som varierar i produktivitet och förutsättningar för renskötsel. Vi analyserade variationen mellan samebyar med avseende på 15 variabler som beskriver topografi, vegetation, betesvärde, fragmentering av betesmarker, klimat, skareförekomst och aktivitet av parasiterande insekter och vi föreslår en indelning av samebyar i tio grupper. Den största gruppen, som bestod av 14 samebyar, delades vidare in i 4 undergrupper. Klusteranalyser med 4 olika linkage-varianter användes till att gruppera samebyarna. Principalkomponentsanalys användes för att kartlägga undersökta variabler och de resulterande samebygruppernas karaktär. Samebygrupperna följde inte länsgränser och tre samebyar föll ut som enskilda grupper. Denna undersökning ger underlag för jämförelser mellan samebyar med beaktande av likheter och olikheter i fråga om produktivitet och funktionella särdrag istället för länsgränser och historik. Vi föreslår en ny administrativ indelning i sex områden som skulle kunna fungera som ett alternativt underlag för planering och beslut som rör produktionsaspekter i rennäringen. Resultaten ger också underlag för förutsägelser av förändringar i samebyars produktionsförutsättningar till följd av klimatförändringar.

scholarly journals Summarizing the state of the terrestrial biosphere in few dimensions

2019 ◽  
Author(s):  
Guido Kraemer ◽  
Gustau Camps-Valls ◽  
Markus Reichstein ◽  
Miguel D. Mahecha
Keyword(s):  
Data Streams ◽  
Land Surface ◽  
Principal Component ◽  
The State ◽  
Climate Indices ◽  
Surface Dynamics ◽  
Terrestrial Biosphere ◽  
Explanatory Variables ◽  
Arctic Regions ◽  
Agricultural Areas

Abstract. In times of global change, we must closely monitor the state of the planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems – i.e. the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73 % of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We assume that this generic approach has great potential for the analysis of land-surface dynamics from observational or model data.

Development of a Non-Linear Integrated Drought Index (NDI) for Managing Drought and Water Resources Forecasting in the Upper Tana River Basin-Kenya

2020 ◽  
Vol 11 (1) ◽  
pp. 15-33
Author(s):  
Raphael Muli Wambua
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Lead Time ◽  
Drought Index ◽  
Meteorological Data ◽  
Principal Component ◽  
Drought Forecasting ◽  
Tropical River ◽  
Tana River ◽  
Non Linear

This article uses the non-linear integrated drought index (NDI) for managing drought and water resources forecasting in a tropical river basin. The NDI was formulated using principal component analysis (PCA). The NDI used hydro-meteorological data and forecasted using recursive multi-step neural networks. In this article, drought forecasting and projection is adopted for planning ahead for mitigation and for the adaptation of adverse effects of droughts and food insecurity in the river basin. Results that forecasting ability of NDI model using ANNs decreased with increase in lead time. The formulated NDI as a tool for projecting into the future.

scholarly journals Analysis of the Effects of High Precipitation in Texas on Rainfed Sorghum Yields

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1920 ◽  
Author(s):  
Sharma ◽  
Kannan ◽  
Cook ◽  
Pokhrel ◽  
McKenzie
Keyword(s):  
Crop Yield ◽  
Missing Values ◽  
Crop Yields ◽  
Principal Component ◽  
Growing Season ◽  
Grain Sorghum ◽  
Extreme Weather Events ◽  
Total Precipitation ◽  
Independent Variables ◽  
Two Parameters

Most of the recent studies on the consequences of extreme weather events on crop yields are focused on droughts and warming climate. The knowledge of the consequences of excess precipitation on the crop yield is lacking. We attempted to fill this gap by estimating reductions in rainfed grain sorghum yields for excess precipitation. The historical grain sorghum yield and corresponding historical precipitation data are collected by county. These data are sorted based on length of the record and missing values and arranged for the period 1973–2003. Grain sorghum growing periods in the different parts of Texas is estimated based on the east-west precipitation gradient, north-south temperature gradient, and typical planting and harvesting dates in Texas. We estimated the growing season total precipitation and maximum 4-day total precipitation for each county growing rainfed grain sorghum. These two parameters were used as independent variables, and crop yields of sorghum was used as the dependent variable. We tried to find the relationships between excess precipitation and decreases in crop yields using both graphical and mathematical relationships. The result were analyzed in four different levels; 1. Storm by storm consequences on the crop yield; 2. Growing season total precipitation and crop yield; 3. Maximum 4-day precipitation and crop yield; and 4. Multiple linear regression of independent variables with and without a principal component analysis (to remove the correlations between independent variables) and the dependent variable. The graphical and mathematical results show decreases in rainfed sorghum yields in Texas for excess precipitation could be between 18% and 38%.

scholarly journals Summarizing the state of the terrestrial biosphere in few dimensions

2020 ◽  
Vol 17 (9) ◽  
pp. 2397-2424 ◽  
Author(s):  
Guido Kraemer ◽  
Gustau Camps-Valls ◽  
Markus Reichstein ◽  
Miguel D. Mahecha
Keyword(s):  
Data Streams ◽  
Land Surface ◽  
Principal Component ◽  
The State ◽  
Climate Indices ◽  
Surface Dynamics ◽  
Terrestrial Biosphere ◽  
Explanatory Variables ◽  
Arctic Regions ◽  
Agricultural Areas

Abstract. In times of global change, we must closely monitor the state of the planet in order to understand the full complexity of these changes. In fact, each of the Earth's subsystems – i.e., the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create global indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical state of ecosystems and land–atmosphere fluxes of water, energy, and carbon fluxes. We find that three indicators account for 82 % of the variance of the selected biosphere variables in space and time across the globe. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. The third indicator represents mostly changes in surface albedo. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heat wave (2010). The anomalies also allow us to interpret the impacts of these events. The indicators can also be used to detect and quantify changes in seasonal dynamics. Here we report, for instance, increasing seasonal amplitudes of productivity in agricultural areas and arctic regions. We assume that this generic approach has great potential for the analysis of land surface dynamics from observational or model data.

scholarly journals Possible Effects of Climate Change on Ixodid Ticks and the Pathogens They Transmit: Predictions and Observations

2020 ◽  
Author(s):  
Nicholas H Ogden ◽  
C Ben Beard ◽  
Howard S Ginsberg ◽  
Jean I Tsao
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Animal Health ◽  
Life Cycles ◽  
Pathogen Transmission ◽  
Ixodid Ticks ◽  
Extreme Weather Events ◽  
Mountainous Regions ◽  
Recent Climate ◽  
Tick Survival

Abstract The global climate has been changing over the last century due to greenhouse gas emissions and will continue to change over this century, accelerating without effective global efforts to reduce emissions. Ticks and tick-borne diseases (TTBDs) are inherently climate-sensitive due to the sensitivity of tick lifecycles to climate. Key direct climate and weather sensitivities include survival of individual ticks, and the duration of development and host-seeking activity of ticks. These sensitivities mean that in some regions a warming climate may increase tick survival, shorten life-cycles and lengthen the duration of tick activity seasons. Indirect effects of climate change on host communities may, with changes in tick abundance, facilitate enhanced transmission of tick-borne pathogens. High temperatures, and extreme weather events (heat, cold, and flooding) are anticipated with climate change, and these may reduce tick survival and pathogen transmission in some locations. Studies of the possible effects of climate change on TTBDs to date generally project poleward range expansion of geographical ranges (with possible contraction of ranges away from the increasingly hot tropics), upslope elevational range spread in mountainous regions, and increased abundance of ticks in many current endemic regions. However, relatively few studies, using long-term (multi-decade) observations, provide evidence of recent range changes of tick populations that could be attributed to recent climate change. Further integrated ‘One Health’ observational and modeling studies are needed to detect changes in TTBD occurrence, attribute them to climate change, and to develop predictive models of public- and animal-health needs to plan for TTBD emergence.

scholarly journals Spatial and Temporal Analysis of Drought in Nepal using Standardized Precipitation Index and its Relationship with Climate Indices

1970 ◽  
Vol 7 (1) ◽  
pp. 59-74 ◽  
Author(s):  
M Sigdel ◽  
M Ikeda
Keyword(s):  
Principal Component Analysis ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Index ◽  
Climate Indices ◽  
Indian Ocean Dipole Mode ◽  
Time Space

Drought over Nepal is studied on the basis of precipitation as a key parameter. Using monthly mean precipitation data for a period of 33 years, Standardized Precipitation Index (SPI) is produced for the drought analysis with the time scale of 3 months (SPI-3) and 12 months (SPI-12) as they are applicable for agriculture and hydrological aspects, respectively. Time-space variability is explored based on Principal Component Analysis (PCA) along with Rotated PCA (RPCA). Four rotated components were explored for both SPI-3 and SPI-12 representing climatic variability with cores over eastern, central and western Nepal separately. Droughts associated with SPI-3 occurred almost evenly over these regions. Droughts associated with SPI-12 were consistent with SPI-3 for summer, since summer precipitation dominates annual precipitation. Connection between SPI and the climate indices such as Southern Oscillation Index (SOI) and Indian Ocean Dipole Mode Index (DMI) was studied, suggesting that one of the causes for summer droughts is El Nino, while the winter droughts could be related with positive DMI. Keywords: Standardized Precipitation Index; Nepal; Principal component analysis; Drought DOI: http://dx.doi.org/10.3126/jhm.v7i1.5617 JHM 2010; 7(1): 59-74

scholarly journals Multivariate statistic and time series analyses of grain-size data in quaternary sediments of Lake El'gygytgyn, NE Russia

2013 ◽  
Vol 9 (6) ◽  
pp. 2459-2470 ◽  
Author(s):  
A. Francke ◽  
V. Wennrich ◽  
M. Sauerbrey ◽  
O. Juschus ◽  
M. Melles ◽  
...  
Keyword(s):  
Time Series ◽  
Grain Size ◽  
Far East ◽  
Principal Component ◽  
Sediment Supply ◽  
Multivariate Statistic ◽  
Lake El’Gygytgyn ◽  
Lake Center ◽  
The Impact ◽  
Size Data

Abstract. Lake El'gygytgyn, located in the Far East Russian Arctic, was formed by a meteorite impact about 3.58 Ma ago. In 2009, the International Continental Scientific Drilling Program (ICDP) at Lake El'gygytgyn obtained a continuous sediment sequence of the lacustrine deposits and the upper part of the impact breccia. Here, we present grain-size data of the past 2.6 Ma. General downcore grain-size variations yield coarser sediments during warm periods and finer ones during cold periods. According to principal component analysis (PCA), the climate-dependent variations in grain-size distributions mainly occur in the coarse silt and very fine silt fraction. During interglacial periods, accumulation of coarser material in the lake center is caused by redistribution of clastic material by a wind-induced current pattern during the ice-free period. Sediment supply to the lake is triggered by the thickness of the active layer in the catchment and the availability of water as a transport medium. During glacial periods, sedimentation at Lake El'gygytgyn is hampered by the occurrence of a perennial ice cover, with sedimentation being restricted to seasonal moats and vertical conduits through the ice. Thus, the summer temperature predominantly triggers transport of coarse material into the lake center. Time series analysis that was carried out to gain insight into the frequency of the grain-size data showed variations predominately on 98.5, 40.6, and 22.9 kyr oscillations, which correspond to Milankovitch's eccentricity, obliquity and precession bands. Variations in the relative power of these three oscillation bands during the Quaternary suggest that sedimentation processes at Lake El'gygytgyn are dominated by environmental variations caused by global glacial–interglacial variations (eccentricity, obliquity), and local insolation forcing and/or latitudinal teleconnections (precession), respectively.

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