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Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

follows second. The third step is a spatial interpolation technique, using a Digital Elevation Model (DEM) of the study region to find upper and lower snowlines. Shuttle Radar Topography Mission (SRTM) data was used as data source for the DEM [66]. The method was developed by [67]. The upper snow line represents the elevation where all cloud-free pixels are classified as snow. Above this altitude, all cloud-covered pixels can also be assumed snow-covered—given a certain overall cloud cover percentage is not reached. The lower snowline on the other hand identifies the altitude below which all cloud-free pixels are also snow-free. Once determined, all cloud-covered pixels below the lower snowline can be assumed snow-free. The snowline method exploits the fact that snow cover increases with elevation: In mountainous regions, the mean snow cover duration increases by ~4 days per 100 m altitude [14,67,68]. For the presented study, only scenes with less than 30% overall cloud coverage are processed by this step. Finally, a third temporal interpolation step is added, involving the complete time series to remove the remaining cloud flags: If a pixel is covered by clouds, the algorithm is jumping back and forth in time until a cloud free condition is found. Figure 5 summarizes all processing steps applied to the time series of daily snow cover data.
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Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps - An Earth Observation-Based Analysis

Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps - An Earth Observation-Based Analysis

Received: 1 September 2018; Accepted: 4 November 2018; Published: 7 November 2018   Abstract: Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables.
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Snow Cover changes in Central Asia derived from long term
time series analysis of medium resolution remote sensing data

Snow Cover changes in Central Asia derived from long term time series analysis of medium resolution remote sensing data

Figure 2 illustrates the mean snow cover duration between 2000 and 2017. ~120.000 MODIS data sets have been processed for this figure. A trend analysis including all years between 1986 and 2017 revealed that the overall snow cover duration is not changing significantly in most regions.

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GLOBAL SNOWPACK - Global Snow Cover characteristics derived from medium resolution remote sensing data

GLOBAL SNOWPACK - Global Snow Cover characteristics derived from medium resolution remote sensing data

The Global SnowPack is a set of snow cover products derived from medium resolution remote sensing data. Daily snow cover maps are combined and analysed to produce information about early season (SCD ES ), late season (SCD LS ), and overall snow cover duration (SCD) within a hydrological year.

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Processing and analysis of Global snow cover time series for climate change assessment

Processing and analysis of Global snow cover time series for climate change assessment

The Global SnowPack is a set of snow cover products derived from medium resolution remote sensing data. Daily snow cover maps are combined and analysed to produce information about early season (SCD ES ), late season (SCD LS ), and overall snow cover duration (SCD) within a hydrological year.

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Remote Sensing of Snow Cover in the Alps. An Overview of Opportunities and Constraints.

Remote Sensing of Snow Cover in the Alps. An Overview of Opportunities and Constraints.

Snow cover in the Alps is changing due to climate change, and in order to quantify this change and the subsequent processes, continuous observation over long time series is necessary. Medium resolution data (500 – 1000 m spatial resolution) is often too coarse to account for the processes occurring in more complex terrain. Higher resolution Landsat or Sentinel data (10 – 30 m) on the other hand lack the temporal resolution which is required to identify possible changes/trends in snow cover duration, onset, or melting of snow. Even though a combination of optical and Radar data from high resolution sensors reduces the data gaps caused by cloud cover, daily information about the snow cover extent and condition is not possible.
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Snow cover development in Central Asia derived from daily, medium resolution remote sensing data between 1986 and 2012

Snow cover development in Central Asia derived from daily, medium resolution remote sensing data between 1986 and 2012

Central Asia comprises an area of ~ 4,000,000 km², containing Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan (Figure 1). The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring during winter and early spring (Klein et al., 2012, Lioubimtseva & Henebry, 2009). Amu Darya and Syr Darya Rivers originate from the mountainous regions of Tian Shan and Pamir in the South and South-East of Central Asia and constitute the major source of fresh water for the region (V. B. Aizen et al., 1995, Glantz, 2005). Increasing demands for hydropower generation, irrigation, and the growing population are opposed to the limited and most valuable water resources, meaning that possible effects of climate change may have serious impacts on the region (Vladimir B. Aizen et al., 1997, Mokhov et al., 2006). The aim of the study was therefore to analyze long-term trends of Snow Cover Duration (SCD), Early Season SCD (SCD ES ), and Late Season SCD (SCD LS ), as
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The Optimal Duration of Contracts

The Optimal Duration of Contracts

Empirical evidence on the drivers of contract duration is scarce. Wallace (2001) shows that the relationship between uncertainty and contract length is inconclusive. Joskow (1987), Crocker and Masten (1988), Brickley et al. (2006), and Lin and Yang (2016), which study, respectively, the contract du- ration for coal, gas, franchises and baseball players, find that contract length is determined by the rents from the contract, the incentive concerns for non- observable effort and the need for flexibility. These findings are corroborated by Bandiera (2007), which seems to be the first empirical study that ana- lyzes both contract duration and the compensation scheme (for land tenancy contracts in Italy); the latter is, however, only captured by the distinction between fixed-rent and share-cropping contracts. For professional football players, Tang (2013) finds that contract length is longer the higher is the expected rent from a player for the team owner; the study also reports that first contracts for a player are typically shorter and less well-paid than second ones.
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Sleep duration and life satisfaction

Sleep duration and life satisfaction

The sample contains five waves of the SOEP from 2008 to 2012. The dependent variable comes from individual responses to the following question ‘we would like to ask you about your satisfaction with your life in general", which is coded on a scale from 0 (completely dissatisfed) to 10 (completely satisfied). The medical literature, some of which was briefly discussed above, finds that both too much and too little sleep are associated with health problems, thus it is likely to be more appropriate to model the relationship between happiness and amount of sleep as a curvilinear relationship rather than a linear one. This seems more appropriate on a priori grounds too: it seems unreasonable to expect a constant impact on well-being of an extra hour of sleep with maximum well-being associated with either zero or twenty-four hours. 5 Thus coefficients for sleep duration and sleep duration squared are used to find the turning point for the amount of sleep associated with maximum life satisfaction. This is akin to those studies investigating the relationship between age and well-being, which use the coefficients for age and age squared to find the turning point for the age associated with minimum life satisfaction (for example, Blanchflower and Oswald 2008). Multivariate regressions are run, starting with pooled OLS before moving on to fixed effects. The latter is preferred because of its well-known ability to control for individual heterogeneity. This can help to control for unusual shift patterns somewhat. If an individual works shifts, and does not change his job, this shiftwork can be said to be controlled for with FE estimates. The SOEP does have data about shiftwork and other unusual working patterns, however it is not in the same waves as those that contain the questions about sleep duration. 6 As the introduction discusses, individuals can exhibit quite a bit of heterogeneity in sleep patterns and sleep needs. Pooled OLS is included
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Policy discontinuity and duration outcomes

Policy discontinuity and duration outcomes

1098 1228 1571 Notes: Estimates, standard errors and numbers of observations are in the first, second and third line, respectively. Estimates in bold are statistically significant at the 5% level. information about the NDYP is released – a bias towards zero if the treated and comparison groups react similarly to the disclose of information. We therefore right-censor spells for the comparison group when they cross April 1, 1998. Likewise, treatment spells are right-censored at 6 months. We show in Subsection 5.5 that the causal effect of participation kicks in at an elapsed duration of 189 days, so we use this as the right-censoring value. We then use the resulting possibly right-censored data to estimate the effects of information arrival at elapsed durations 4 to 5 months in continuous time. Given the high data demands of this procedure, which requires a sufficiently large number of informative spells at each elapsed duration, we restrict the continuous-time analysis to durations 123 to 181 days.
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A Novel Data Fusion Technique for Snow Cover Retrieval

A Novel Data Fusion Technique for Snow Cover Retrieval

Fig. 11 shows two examples of snow maps at the end (on April 17th, 2014) of the winter season 2013-2014 and at the start (on November 23th, 2014) of winter season 2014-2015. The right figures show the images with clouds, whereas the left ones show the corrected images, as above explained. The colors highlight the different behaviors of the fused product: green and white represent the pixels where AMUNDSEN and MODIS agree and, therefore, where the data fusion approach is not applied and consequently has the same value of the two single sources; the dark and light blue are the pixels where the fused snow map has the same value of the MODIS map; finally, the dark and light pink indicate the pixel where the fused snow map follows the behavior of the AMUNDSEN simulation. In the winter image, most of the pixels classified as snow by AMUNDSEN as well as by the fused product (dark pink) are located on the northern exposure. This behavior may be ascribed to the MODIS underestimation in low- light conditions which frequently happen during winter time as reported in [32]. The cyan color indicates that the fused product follows the MODIS product behavior in detecting the snow absence. Most of these areas are located in forest: as highlighted in Fig. 9, in forested area the fused product results follow the AMUNDSEN behavior because of the well-known limitation of optical satellites to detect snow under the canopy. In this context, it is worthwhile mentioning that snow detection in forest is very complex and it depends on many factors such as the location of the forest (north/south), the density of the forest, the type of the forest (broadleaf or conifer). It is found that normally MODIS product tends to underestimate the snow cover in forested areas [47], [48]. At the same time, at the beginning and end of the season, it can be supposed that AMUNDSEN model may simulate low values of SWE in these transient periods, so that SVM classifier can give in some cases, as shown in fig.10, the priority to the MODIS product. This behavior highlights the importance in the selection of the feature to be used in the data fusion approach, both the inputs and the related quality measures. These measures shall provide both an evaluation of the quality of the inputs and try as well to cover the different spatial and temporal variability, which the snow has in mountain areas. As a future step, different quality measures will be evaluated in order to understand their impact on the final products and how they can tackle the heterogeneity of snow cover in complex terrain.
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Job duration and inequality

Job duration and inequality

Job duration and inequality Siyan Chen and Saul Desiderio Abstract As suggested by recent empirical evidence, one of the causes behind the widespread rise of inequality experienced by OECD countries in the last few decades may have been the increased flexibility of labor markets. The authors explore this hypothesis through the analysis of a stock-flow consistent agent-based macroeconomic model able to reproduce with good statistical precision several empirical regularities. To this scope they employ three different sensitivity analysis techniques, which indicate that increasing job contract duration (i.e. decreasing flexibility) has the effect of reducing income and wealth inequality. However, the authors also find that this effect is diminished by tight monetary policy and low credit supply. This result suggests that the final outcome of structural reforms aimed at changing labor flexibility can depend on the macroeconomic environment in which these are implemented.
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PolInSAR Signatures of Alpine Snow

PolInSAR Signatures of Alpine Snow

Presently the representation of snow processes in global climate models remains rudimentary, and avail- able data for modelling and forecasting snowmelt run- off is insufficient. Snow extent, water equivalent, wet- ness and metamorphic state are key input parameters for these models. Theory shows that the combination of X-and Ku-band is particularly promising for retriev- ing these physical properties. This work is aimed at understanding the PolInSAR signatures of different snow states and structures, and how the technique might be most usefully applied to this task.
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Job duration and inequality

Job duration and inequality

estimated turning point between 12 and 13). In both cases the estimated models prompt that the relationship between Gini index and job contract duration is decreasing over the considered parameter space, but also that it might be non-monotonic for larger values of D. Different mechanisms link inequality to job contracts. The principal channel through which contracts affect inequality is probably unemployment, because when more workers are employed income distribution becomes less unequal among the poorer classes of the population, that is those who suffer more from the loss of jobs. This is confirmed by Panel (a) of Figure 3, where the Monte Carlo unemployment rate is reported. A glance at this variable, in fact, reveals that its response to parameter D closely follows that of the income Gini index: as D increases, unemployment first increases and then decreases monotonically for D > 2. The reason why this happens is straightforward: when contracts are longer, in fact, firms can fire workers less frequently and therefore they are forced to pay wages even when this is not economically efficient from the individual point of view. On the other hand, however, this microeconomic inefficiency has the unintended consequence of sustaining aggregate demand for the consumption good. In other words, longer contracts increase the likelihood of having macroeconomic coordination between aggregate demand and supply, thus reducing the probability to experience recessions and long periods of high unemployment. However, we will see that this mechanism works specially when monetary policy and the commercial bank’s credit policy are loose.
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Numeracy and Unemployment Duration

Numeracy and Unemployment Duration

ABSTRACT IZA DP No. 12531 AUGUST 2019 Numeracy and Unemployment Duration * Governments are showing an increasing interest in quantitative models that give insights into the determinants of unemployment duration. Yet, these models oftentimes do not explicitly take into account that unemployment prospects are influenced by personality characteristics that are not being fully captured by variables in administrative data. Using German survey data linked with administrative data, we show that numeracy skills are strongly related to unemployment duration, while at the same time we confirm well- established patterns documented in the literature. Low numeracy is strongly related to a longer unemployment duration of workers below median age (33) in our sample, even after including a rich set of controls. We find that unrealistic reservation wages are not the main driver, nor do results seem to be driven by locking-in effects caused by programme participation. On the other hand, the absence of a relationship between numeracy and unemployment duration for older workers might well be driven by a locking-in effect for those with high numeracy, as they tend to commit more often to intensive training programmes. Another tentative explanation, which is supported by the data, is that younger people have fewer signals to send such that their cognitive abilities may have a higher relative signalling value.
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Wealth, Portfolios, and Unemployment Duration

Wealth, Portfolios, and Unemployment Duration

We use administrative data on individual balance sheets in Denmark to document how an individual’s financial position affects job search behavior. We look at the effect of wealth at the entry into unemployment on the exit rate from unemployment as well as the effect on the subsequent match quality. The detailed data allows us not only to distinguish between liquid and illiquid parts, but also to decompose each of them into assets and liabilities. The decomposition of wealth into these four components is key to understanding how wealth affects job finding rates. In particular, we show that liquid assets reduce the probability of becoming re-employed, but we do not see an effect of liquid liabilities or the illiquid wealth components, while interest payments speed up re-employment. The results on subsequent match quality in form of job duration and wages are mixed.
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Monitoring of the alpine snow cover using automatic digital photography: results from the Hohe Tauern range (Central Austrian Alps) / submitted by Matthias Rieckh

Monitoring of the alpine snow cover using automatic digital photography: results from the Hohe Tauern range (Central Austrian Alps) / submitted by Matthias Rieckh

(2.4) Repeated monitoring of the SCA during the ablation season allows the creation of so- called depletion curves, which quantify the gradual diminishment of the areal extent of the seasonal snow cover. Depending on the initial snow reserves, meteorological conditions and intermittent precipitation during the snowmelt season, these curves take a different course each year (Hall & Martinec 1985). However, depletion curves as well as the SCA in general cannot be used as a single index for seasonal runoff forecasts in alpine basins, because the areal extent of snow cover is not unequivocally related to the snow reserves in terms of the water equivalent (Martinec 1980, Seidel & Martinec 2004). Pattern recognition of snow fields represents a method to estimate whether the snow reserves are relatively high or low for a given snow coverage (Good & Martinec 1987), but exact quantification requires calculation of the SWE by measuring the snow depth and density. Apart from ground-based investigations (e.g. by manual probing or Ground Penetrating Radar (GPR) measurements), which are time-consuming and only valid for points or transects (Anderton et al. 2004, Binder et al. 2009), different remote sensing techniques enable area-wide determination of the snow depth. It can be inferred from the backscatter of active and passive microwave systems, but their application in mountainous areas is limited due to radar shadows and coarse spatial resolution (Dozier & Painter 2004, Seidel & Martinec 2004). In contrast, LiDAR (Light Detection And Ranging) altimetry using airborne or terrestrial laser scanning has proved to be a very promising technique to calculate precise snow depth data, particularly in high mountain catchments (e.g. Grünewald et al. 2010, Prokop 2008, Wirz et al. 2009). Tab. 2.3 sums up the different parameters that affect the shortwave albedo and longwave emissivity of snow. Terrestrial photography, as an alternative method to space- and airborne monitoring of the snow cover, is discussed in the next section.
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Nowcasting snow for airports at heterogeneous terrain

Nowcasting snow for airports at heterogeneous terrain

In the two-year project PNOWWA (Probabilistic Nowcasting of Winter Weather for Airports) various professionals at airports were approached to discuss their needs and interest in using probabilistic forecasts. Three major groups of users were identified. The runway maintenance needed the accumulation of snow in millimetres during each 15-minute step. Thresholds were expressed separately for dry snow, wet snow and slush. In addition, they were interested in the probability of freezing rain, which cannot be determined by an algorithm solely based on weather radar measurements. The avia-
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Cover graphs and order dimension

Cover graphs and order dimension

Nevertheless, let me give at least some basic ideas for the proof of this theorem. Given a poset whose cover graph has tree-width 2, we start with some standard tools in dimension theory. First, we apply the Min-Max Reduction (Lemma 2.1.1 ), which allows us to focus on incomparable pairs consisting of a minimal element and a maximal element. Second, we use the dual version of the Global Min Support Reduction (Lemma 2.2.2 , which is an application of the Unfolding Lemma), to obtain the additional assumption that there exists a minimal element that is below all the maximal elements in the poset. From here it is then a long way of deriving properties of incomparable pairs with respect to a fixed tree-decomposition of the poset’s cover graph. Many cases have to be considered until the final partition of incomparable pairs into reversible sets is done.
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Bayesian Inference in Snow Avalanche Simulation with r.avaflow

Bayesian Inference in Snow Avalanche Simulation with r.avaflow

The Kerngraben avalanche event (Salzburg, AT, Vrel = 65,000 m 3 ) is utilized to perform the back calculation, yielding optimized parameter distributions for the process model parameters[r]

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