Armelle Decaulne
email: armelle.decaulne@cnrs.fr

Ionela-Georgiana Rȃchitȃ

Dariia Kholiavchuk
email: d.kholyavchuk@chnu.edu.ua

Olimpiu Pop b , Iulian Horia Holobâcǎ

Oles Ridush
email: o.ridush@chnu.edu.ua

Bogdan Ridush
email: b.ridush@chnu.eda.ua

Csaba Horváth
email: csaba.horvath@ubbcluj.ro

Patterns of snow avalanche activity during the last century in Chornohora Range (Eastern Carpathians, Ukraine): Tree-ring reconstruction coupled with synoptic conditions analysis

This contribution presents a first comprehensive study of snow-avalanche activity in three paths of the Chornohora range, located in southwestern Ukraine, based on historical chronicles and dendrochronology. The results are combined with a statistical analysis of meteorological drivers conducive to snow-avalanche release. While the written chronicles last from 1966 to 2015, the dendrological approach offers results back to the end of the 19th century; however, if the information covers a longer time-lapse, it loses accuracy as only the winter scale is documented through the analysis of tree-ring growing patterns. Weather data highlight the synoptic scenarios over some of the avalanche events that have been recognized as major, as the three paths were concerned : 1947-48, 1976-77, 1993-94, 1998-99, 2001-02. Three weather variables are highlighted: the formation of a consistent snow cover as early as November; positive mean daily temperature in April commands late winter avalanches if the snow cover is maintained with recurrent snow fall. Temperature warming and precipitation increase are also noted on the climatological trends in Chornohora range; however, the winter temperature remains stable, and the snow-avalanche regime might not be affected in the area in the near future.

Introduction

The ongoing climate change impacts the snow environment of mountain areas worldwide. Due to the combination of air temperature increase and precipitations shifting from solid to liquid, the seasonal snowline reaches higher elevations [START_REF] Micu | Snow pack in the Romanian Carpathians under changing climatic conditions[END_REF], and the snow season gets shorter; thus, the snow-avalanche regime is affected in all mountain ranges (e.g., [START_REF] Martin | Impact of a climate change on avalanche hazard[END_REF][START_REF] Gądek | Snow avalanche activity in Żleb Żandarmerii in a time of climate change (Tatra Mts., Poland)[END_REF][START_REF] Laute | Potential effects of climate change on future snow avalanche activity in western Norway deduced from meteorological data[END_REF][START_REF] Beato Bergua | Snow avalanches, land use changes, and atmospheric warming in landscape dynamics of the Atlantic mid-mountains (Cantabrian Range, NW Spain)[END_REF][START_REF] Strapazzon | Effects of Climate Change on Avalanche Accidents and Survival[END_REF][START_REF] Peitzsch | Tree-ring derived avalanche frequency and climate associations in a high-latitude, maritime climate[END_REF]. In areas where snow-avalanche hazard and risk must be documented, synoptic weather conditions of past snow-avalanche events should be highlighted. In fact, researches over the last decades documented either lower number of occurrences of snow-avalanche events at high altitudes, and a higher one at lower altitude, according to locations of the slope and land-use (e.g., [START_REF] Glazovskaya | Global distribution of snow avalanches and changing activity in the Northern Hemisphere due to climate change[END_REF][START_REF] Huggel | Recent Extreme Avalanches: Triggered by Climate Change?[END_REF][START_REF] Germain | Snow avalanche regime and climatic conditions in the Chic-Choc Range, eastern Canada[END_REF][START_REF] Mainieri | Impacts of land-cover changes on snow avalanche activity in the French Alps[END_REF][START_REF] Giacona | Upslope migration of snow avalanches in a warming climate[END_REF]. Over the recent years, an increasing number of warming and extreme precipitation events have contributed to the intensification of snow avalanches in subalpine areas as witnessed by the studies in the Himalayas, Alpine Region, Rocky Mountains (e.g., [START_REF] Ballesteros-Cánovas | Climate warming enhances snow avalanche risk in the Western Himalayas[END_REF]2018;[START_REF] Peitzsch | Climate drivers of large magnitude snow avalanche years in the U.S. northern Rocky Mountains[END_REF]. Warmer air temperature in winter and early spring has indeed favoured the wetting of snow [START_REF] Micu | Temperature changes and elevation-warming relationships in the Carpathian Mountains[END_REF], and the formation of wet snow avalanches is observed. These wet snow avalanches reach down to subalpine slopes, where are located the anthropogenic infrastructures, therefore, these avalanches are prone to cause damage (e.g., [START_REF] Jamieson | Snow avalanche hazard and management in Canada: challenges and progress[END_REF][START_REF] Stethem | Snow Avalanche Hazard in Canadaa Review[END_REF][START_REF] Lazar | Climate change in western ski areas: Potential changes in the timing of wet avalanches and snow quality for the Aspen ski area in the years 2030 and 2100[END_REF][START_REF] Gauthier | Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: northern Gaspésie, Québec, Canada[END_REF]. Over Ukrainian massifs, the snow covers got limited attention in the literature, and only a few studies were published on this topic over the last decade [START_REF] Rudyi | Analysis of mountain relief for the causes of snow avalanches[END_REF][START_REF] Hryshchenko | Reference book on snowcover in the mountains of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF][START_REF] Hryshchenko | Snow-avalanche cadastre of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF][START_REF] Tykhanovych | Snow avalanche slide conditions in Chornohora massif (Ukrainian Carpathians)[END_REF]. Therefore the preoccupation to snow avalanches is recent in Ukraine, specifically in the Carpathians, which have a high predisposition to snow avalanches. Dendrochronology applied to sowavalanche dynamics is often used in combination with historical sources (e.g., [START_REF] Carrara | The determination of snow avalanche frequency through tree-ring analysis and historical records at Ophir, Colorado[END_REF][START_REF] Butler | A history of high-magnitude snow avalanches, southern Glacier National Park, Montana[END_REF][START_REF] Reardon | Spatial reconstructions and comparisons of Historic Snow Avalanche Frequency and Extent Using Tree Rings in Glacier National Park, Montana[END_REF][START_REF] Corona | How much of the real avalanche activity can be captured with tree rings? An evaluation of classic dendrogeomorphic approaches and comparison with historical archives[END_REF][START_REF] Tumajer | Reconstruction ability of dendrochronology in dating avalanche events in the Giant Mountains, Czech Republic[END_REF][START_REF] Gądek | Snow avalanche activity in Żleb Żandarmerii in a time of climate change (Tatra Mts., Poland)[END_REF]. Numerous studies highlight the potential of tree-ring reconstruction of snow avalanches (e.g., [START_REF] Laxton | Dendrochronological reconstruction of snow avalanche activity in the Lahul Himalaya, Northern India[END_REF][START_REF] Corona | How much of the real avalanche activity can be captured with tree rings? An evaluation of classic dendrogeomorphic approaches and comparison with historical archives[END_REF][START_REF] Todea | Snow-avalanche history reconstructed with tree rings in Pârang Mountains (Southern Carpathians, Romania)[END_REF], and whatever the sample depth of the case studies [START_REF] Butler | Dendrogeomorphology and high-magnitude snow avalanches: a review and case study[END_REF][START_REF] Decaulne | A first dendrogeomorphologic approach of snow avalanche magnitude-frequency in Northern Iceland[END_REF], the knowledge of the snow-avalanche dynamic is significantly improved: dendrochronology documents the most active paths, the return period of avalanche winters, and the runouts. This last parameter is a key factor to deal with the potential risks for people present in the areas over winters [START_REF] Voiculescu | Snow avalanche assessment in the Sinaia ski area (Bucegi Mountains, Southern Carpathians) using the dendrogeomorphology method[END_REF].

The aim of this paper is double: we wish to contribute to highlight (i) the timing and spatial extent of long runout-distances past snowavalanches and (ii) the synoptic situations over the reconstructed main avalanche winters, proposing recognition of weather control, through meteorological archives, favourable to the triggering of avalanches. To do so, we combine data from written sources and tree rings, the compilation of each corpus enabling covering a longer period of time with rather precise information on some snow-avalanche events. In this paper, we also wish to assess the snow avalanche impact on the forest located in the snow-avalanche runout zone and to define the hazard occurrence, at the winter scale, in contiguous paths. Such knowledge would facilitate the estimation of the vulnerability of people travelling through areas prone to the occurrence of avalanches, providing new robust data to stakeholders.

Study area

The selected study area is located in the Eastern Carpathians, within the eastern part of Chornohora range. Chornohora is the highest massif of the Ukrainian Carpathians, and the studied paths are located on the eastern flank of the highest peak, Hoverla (2061 m a.s.l.). The summit ridge, about 30 m wide, has a convex-concave shape, and then reaches a series of glacial cirques with 30-40 • rock slopes mainly composed of shales and gneisses, that reach the valley bottom at 1400 m a.s.l. after passing rocky outcrops located at 1700 m a.s.l. [START_REF] Kłapyta | Glaciation in the highest parts of the Ukrainian Carpathians (Chornohora and Svydovets massifs) during the local last glacial maximum[END_REF]. These are the starting zones of the snow avalanches for the three paths studied, which extend downward in the valley to about 1350-1400 m a. s.l., with a vertical drop of 300 to 350 m. The Breskul path is located on the north-eastern slope below the Breskul Peak (1911 m a.s.l.), close to Pogegevskaya weather station. A second avalanche path is located on the northern slope below Breskul peak, on the right side of the Prut River, starting below the upper Hoverla cirque, by the Prut River waterfall. A third path is located on the north-eastern slope of Hoverla Peak (2061 m a.s.l.). Several hiking trails and forest roads cross the avalanche paths in their track and runout zones. This area entirely included in the Carpathian National Park became an attractive recreational area in the last decades, being frequented year-round by hikers, walkers, and skiers (Fig. 1). The forest develops on the margins of the peat bogs present at the valley bottoms between 1360 and 1320 m a.s.l., as the central part of the rising bogs impede the tree growth due to the water excess close to the surface as well as the supposedly acid pH of the peat substrate. a.s.l.) mainly composed of Norway spruce (Picea abies (L.) Karst.) tree species; (iii) subalpine belt (1500-1800 m a.s.l.) with shrubs of green alder (Alnus viridis) and mountain pine (Pinus mugo); (iv) alpine belt (above 1800 m a.s.l.) with alpine meadows [START_REF] Lóczy | Recent landform evolution: The carpatho-balkan-dinaric region[END_REF]. Meadow vegetation, largely present in the starting zone of snow avalanches, is favourable to the release of snow avalanches; in early winter, shrubs still has a role to stabilize snow on the slope, however from early January to mid-March shrubs might be completely covered with snow over more or less long periods of time.

The climatic conditions of the massif, retrived from Pogegevskaya meteo station, are influenced by the Azores and Siberian anticyclones, the southern and south-western lows coming from the Atlantic and the Mediterranean. South-western advections are observed the most frequently, with high speeds sometimes exceeding 40 m/s. The average annual wind speed reaches 5 m/s, the cold period average wind speed is 7.4 m/s [START_REF] Hryshchenko | Snow-avalanche cadastre of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF]. The windiest is January, with an average monthly speed of over 8 m/s. January gathers to the highest number of days with a wind speed exceeding 15 m/s. The steady decrease of air temperature below 0 • C is observed from early November. Average annual sum of precipitation is about 1480 mm with 40% during the cold period falling as snow.

On average, snow cover lasts about six months in Chornohora range. Usually, the persistent snow cover is formed in the third decade of November. The earliest period of its formation was October 23rd, 1974, and the long-term average is November 27th [START_REF] Hryshchenko | Reference book on snowcover in the mountains of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF]. The melting of the snow cover occurs at the end of April (the average long-term date is 16.04), although in some years it persists almost until the middle of May (May 12th, 1995), and in some areas until July [START_REF] Hryshchenko | Reference book on snowcover in the mountains of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF]. However, snow cover is spatially unevenly distributed, with maximum depth reported in terrain depressions (25.03.1974, 470 cm of snow was measured in the Prut peatbog depressions) and on the top of ridges, up to 800 cm thick snow cornices, whom collapse trigger snow avalanches [START_REF] Hryshchenko | Snow-avalanche cadastre of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF]. Dry snow avalanches after fresh snowfall and snowstorms have been reported commonly, as well as wet snow avalanches following sudden advection and progressive intrusion of warm spells. The avalanche volume often exceeds 100,000 m 3 , as reported by the snow-avalanche service of the Pogegevskaya meteorological station, the closest from the study area (Fig. 1C).

Topographic parameters are prone to snow-avalanche formation, the large ridge gathering snow accumulation drifted to the north-eastern facing slopes exposing gradients of 20-40 • (Fig. 1C).

Data and methods

In this study, we associated historical data to tree-ring ones, in order to analysis the weather data and synoptic situations at the time of snow avalanches. Then workflow is described in Fig. 2.

Historical snow-avalanche records

Historical data of snow avalanches are provided over the period of 1967-2012 in the Ukrainian Carpathians, and in the Prut River upper catchment. They are based on data collected by the Snow avalanche chronicle of Ukraine [START_REF] Hryshchenko | Reference book on snowcover in the mountains of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF]. The chronicle covers (i) a compilation of the snow-avalanche survey performed by the employees of the Carpathian avalanche group (later Ukrainian) of the Ukrainian Scientific and Research Hydrometeorological Institute (USRHI) over the period 1961-1991, (ii) stationary snow-avalanche observations at the meteorological stations of Play (48 • 42′N, 23 • 12′E, 1330 m a.s.l.) and Pogegevskaya from 1961 to 2012; (iii) information provided by the forest and tourism services, and narratives from local residents. Based on the generalised data, the classification of snow avalanche polygons in the Ukrainian Carpathians is provided. The area covering the three snow-avalanche paths investigated is labelled "polygon Prut 15.1" (Upper Prut catchment). Accordingly, the historical record for the polygon 15.1 is used to highlight past snow-avalanche events in the area that encompasses the three paths investigated. The record includes several characteristics on the snow-avalanche events, such as the date, the runout altitude, the type of snow (dry/wet) involved, the general weather conditions at the time of the occurrence (especially wind conditions), temperature and precipitation data, and in some cases the impact on the ecosystems in the valleys.

Within the area of polygon 15.1, which includes Pogegevskaya weather station, the snow depth was periodically measured on permanent sites, from in situ measurements and from a network of 50 snow gauge poles installed in the altitude zone of 1400-1800 m a.s.l. since 1967. Accordingly, for some events a precise spatial indication is provided, but in several cases path names are lacking in the chronicle.

The historical snow-avalanche chronicle provides a minimum record of the snow avalanches that have occurred in upper Prut catchment; the three investigated paths were sometimes precisely named in the record. However, details on the event descriptions are uneven over the years.

Tree-ring methods

Field observations enabled to identify typical signs of disturbances (e.g., tilted and/or wounded stems, uprooted and/or topped trees, topped crown) on the spruce trees bordering the peat bog, due to the mechanical impact caused by snow avalanches. After a field inspection of the valley bottoms, the three sites were considered as appropriate and selected for tree-ring investigations. In the field, the tree sampling strategy was oriented to sample preferentially those trees located inside the avalanche path presenting clear signs of snow-avalanche disturbances. Using Pressler borers and a hand saw, and following classical dendrogeomorphic sampling procedures [START_REF] Shroder | Dendrogeomorphological analysis of mass-movement, table Cliff Plateau[END_REF][START_REF] Butler | A history of high-magnitude snow avalanches, southern Glacier National Park, Montana[END_REF], a total number of 242 trees have been sampled, with 126 trees (202 cores from 100 trees and stem discs from 26 trees) sampled in Breskul path, 64 trees (104 cores from 52 trees and stem discs from 12 trees) sampled in Prut path, and 52 trees (90 cores from 45 trees and stem discs from 7 trees) sampled in Hoverla path (Table 2, Fig. 3).

For each tree sampled, information regarding their spatial location (GPS records), type of disturbance, sample position and heights on the stem, social position and photos had been collected. Additionally, increment cores have been extracted from 16 undisturbed trees (two cores per tree, 32 cores in total) located outside the avalanche path in an adjacent area to the western side, to obtain the local reference chronology.

In the laboratory, core samples have been air dried for several days, and discs for several weeks, then sanded using a sanding machine with sanding belts of increasing grits (40,80,120,220,400,600), allowing to clearly identify the ring structure and ring boundaries. Tree-age determination was firstly estimated by ring counting; then checked by ringwidth measurements using a LINTAB 5 system (measurement table and the stereomicroscope Leica DMS 1000) and TSAP Win TM Scientific software (Rinntech, 2021).

A reference chronology was built using the core samples extracted from 16 undisturbed trees. It served to compare with the individual growth series from disturbed trees, to correct for missing or false rings as well as to discriminate between growth anomalies caused by snow avalanches in disturbed trees and normal growth under climatic conditions of the reference trees. In each disturbed tree, growth anomalies e.g., scars, tangential rows of traumatic resin ducts, compression wood and growth suppression sequences of strong and moderate intensities [START_REF] Meseșan | Reconstructing snow-avalanche extent using remote sensing and dendrogeomorphology in Parâng Mountains[END_REF][START_REF] Stoffel | Dendroecological dating of geomorphic disturbance in trees[END_REF] identified in the ring structure and attributed to the mechanical impact of snow avalanches served to reconstruct avalanche winters for the period covered by the age of the sampled trees.

An Avalanche Activity Index (AAI, %), as proposed by [START_REF] Shroder | Dendrogeomorphological analysis of mass-movement, table Cliff Plateau[END_REF] was calculated for each year t, based on the ratio between the number of disturbed trees and the number of trees alive in that specific year t:

AAI = ( ∑ i n 1Rt/ ∑ i n 1At ) × 100(%) (1)
where R is the number of trees disturbed during a given year t, and A is the number of trees alive in a given year t. A year is considered as revealing an avalanche winter when the following conditions were fulfilled simultaneously:

(i) at least 10 living trees counting a minimum of 10 tree-rings at sample height; (ii) a minimum of three trees show growth disturbances (iii) the AAI ≥ 10%.

Finally, the temporal reconstruction and spatial extent of the avalanche winters have been obtained in the three avalanche paths investigated. The return intervall (or return period) considered as being the average interval of time within which the runout distance is reached by snow avalanches or exceeded at a given location [START_REF] Mcclung | The Avalanche Handbook, 3rd edn[END_REF][START_REF] Meses ¸an | Calculating snow-avalanche return period from tree-ring data[END_REF] was calculated by dividing the time interval (in years) of the tree-ring reconstructed snow-avalanche chronology by the number of snow avalanches events recorded during this period [START_REF] Boucher | Reconstitution dendrochronologique et fréquence des grosses avalanches de neige dans un couloir subalpin du mont Hog's Back, en Gaspésie centrale (Québec)[END_REF]. For each of three paths investigated, the average return intervalls at the scale of the entire snow-avalanche path were obtained.

Weather data

Daily weather data (raw data, average and extremes of temperature, precipitation, wind and snow cover 30 • gradient slopes at 1400 m a.s.l.) over the period 1961-2015 are available from the Pogegevskaya weather station (1429 m a.s.l.), adjacent to Breskul path. Data are examined during the presence of snow cover, i.e., from November to April. The P. Hess and H. Brezowski classification of circulation forms is used to link the weather data with historical snow avalanche records. The 29 weather patterns (the Hess-Brezowsky Groβwetterlagen (HBGWL),) presented with abbreviations in the Table 1, Fig. 10, and hereafter in the text, are related to zonal, mixed, and meridional macrocirculations [START_REF] Werner | Katalog der Großwetterlagen Europas (1881-2009[END_REF]. The last letter in the abbreviations indicates cyclonic (Z) or anticyclonic (A) type. Zonal circulation forms are the west-east moving air mass flows between the subtropical high-pressure zone over the North Atlantic and the lowpressure zone over the subpolar regions. Meridional circulation is generated by stationary and blocking high pressure processes forming north-south directed ridges. The mixed forms relate to both zonal and meridional air mass flows when the exchange of air masses at different latitudes does not take the shortest (meridional) route, but with a clear zonal flow component [START_REF] Werner | Katalog der Großwetterlagen Europas (1881-2009[END_REF].

Multiple regression

The tree-ring reconstructed avalanche winters extracted from the AAI were correlated with 50 monthly climate variables , including air temperature, wind, precipitation and snow cover parameters (Table 3), using multiple regressions. These climate variables were chosen empirically based on the weather parameters recorded at the Pogegevskaya weather station. Multiple regressions also enable determining the overall fit (explained variance) of the model and the relative contribution of each of the predictors to the total explained variance. For the calculation of the regression parameters by the stepwise method, we used SPSS software. In order to keep climate variables with a significant contribution to the total variance, a 95% confidence level was considered.

Results

Historical records

Over the period of 1966-2012, 689 snow avalanches were recorded within the upper Prut catchment, according to the historical written sources (Fig. 4A), from December to April. The distribution over years is uneven, and two main activity periods seem to be highlighted: one in the second half of the 1980 s, and another one in the 2000 s. The recorded snow avalanches occurred from December to April, the main activity being from February to April with a very clear peak of the activity in March (Fig. 4B).

The snow-avalanche chronicle also mentions synoptic conditions or snow conditions at the time of the event, highlighting three categories of meteorological control (Fig. 5A):

-Wet snow avalanches, linked to water infiltration within the snow cover, either during advective thaw periods (sudden temperature rise with rainfall, wet snowfall or no precipitation) or radiation thaw periods (no precipitation, but direct solar radiation and positive temperature), or advective radiation (sudden temperature rise during spring warming). March and April are the months gathering most of this type of snow avalanches; the later in the season the more this type of snow avalanches. -Fresh snow avalanches, with no or weak wind conditions, with an increasing number of snow-avalanche days over winter; the maximum avalanche activity is reached in March. -Storms and snowstorms inducing important snow drifted accumulations at the top of lee slopes are known for snow-avalanche departures over the whole winter, most often as slab, with a maximum of activity in February, gradually decreasing until April.

The details provided within the snow-avalanche chronicles enable picturing the evolution of synoptic situations over years (Fig. 5B). Prior to the late 1970 s, very few snow-avalanche days were recorded. Then, the distribution of the three synoptic situations is highly variable from one year to another. Wet snow weather situations are recorded more and more often over the entire chronology; fresh snow looks also more present with time, although this situation is clearly not as common as wet snow. Snowstorms are much less common from 2007 to 2012 period.

Dendrochronological results

The analysis of tree rings in the 242 samples document several winters with snow avalanches impacting the trees over a century (Fig. 6); avalanche winters pointed out by less than three trees disturbed and/or with AAI < 10% are not represented. In the Breskul path, a number of 24 avalanche winters have been evidenced, reconstructed over the period 1902-2018. In Prut path, only 19 avalanche winters have been reconstructed covering the period 1921-2018. In Hoverla path, 22 avalanche winters have been reconstructed spanning over the period 1921-2018. In the three paths, a higher occurrence of avalanche winters is recorded since 1950 s, and a denser activity is observed since the 1980 s, although with differences from one path to another. In this respect, Breskul and Prut paths, with contiguous starting areas, are significantly more active than Hoverla path.

For the reconstructed time span, the average recurrence interval of avalanche winters have been calculated, with values ranging between 4.4 years in the Hoverla path, 4.8 years in the Breskul path, and 5.1 years in the Prut path.

Tree-ring evidence of past snow avalanches enables to recognize five winters when an activity has been recorded in all the three paths investigated (Fig. 7). Those winter seasons are 1947-48, 1976-77, 1993-94, 1998-99, 2001-02. Other winters saw the concomitant activity in Breskul and Prut paths, which are partially sharing the same ridgeline below Breskul peak (1967-68; 1986-87; 2009-10; 2012-13). Finally, Breskul and Hoverla paths, which have the same aspect and similar characteristics of their snow-avalanche departure zones, also had recorded activity over the same winters: 1957-58, 1961-62, 1973-74, 1978-79, 1981-82, 2005-06, 2014-15. However, in this last case, the AAI is noticeably lower, in general. The dendrochronological approach does not report snow-avalanche activity happening concomitantly in Prut and Hoverla paths.

The clear snow-avalanche signals in the tree-ring records enables mapping their maximum runouts over the five main winters common to the three paths. Fig. 8 provides examples of event maps with common avalanche winters in the three pats, distinguishing, from the set of living trees at these times, those that were disturbed by snow avalanches. In all cases, the more distal trees show disrupted tree-ring patterns, indicating that snow avalanches had reached long runout distances during these five winters.

Synoptic scenarios

During the period of meteorological observations 1961-2015 at the study site, the main avalanche winters reconstructed based on tree-ring analyses are addressed for synoptic analyses. The Fig. 9 describes three of the five avalanche winters (1976-77, 1993-94, 2001-02) recorded simultaneously in all path investigated. All synoptic events are associated with the fluctuations of air temperature and changes of snow cover depth. Over winters 1976-77 and 1993-94, the rapid snow Fig. 7. Avalanche winter distribution over time within the three investigated paths, highlighting common activity in the three paths during five winters (1947-48, 1976-77, 1993-94, 1998-99, 2001-02).

Fig. 8. Example of event maps

showing the tree-ring reconstructed extent of snow avalanches during four of the five main winters of snow-avalanche occurrence recorded in the three paths investigated (1947-48, 1976-77, 1993-94, 2001-02).

accumulation is obviously the reason for snow-avalanche release in December-February, sometimes associated with wind conditions, and mostly over negative daily average air temperature periods. Winter 2001-2002 is different, with a significantly thinner snow cover and a larger number of snow avalanches over the spring months of March and April during and following snowfall episodes, or over snowmelt phases. For these three winters, the variety of weather types is reported (Fig. 10). According to the Hess & Brezowsky classification (Fig. 9), the combination of zonal, meridional, and mixed macro-circulation forms contribute to such a variety.

Four apparent synoptic patterns are highlighted during the 14 snowavalanche days recorded over these three winters, with strong influence on the increase (i, ii) or decrease (iii, iv) of air temperature (Figs. 9, 10 and 11). Tree-ring evidence of past snow avalanches were recorded also in only two common paths, not in the three paths investigated, for instance Bretskul and Hoverla paths. One of the more devastating events detected was a snow avalanche occurring between March the 11-23, 1968 on the north-eastern slope of Breskul. As a result of a series of snow avalanches, the employees of the USRHI observed over 100 trees of Picea abies fall down within Breskul path. The uprooted and/or broken trees at that time had diameters ranging between 20 and 100 cm, some individuals having approximately 120 years old. The synoptic background during this period is characteristic for advection with temperature increase, recording 137 h of precipitation totalling 97.2 mm of snow, associated with snowstorm, and ending with snowmelt. In this specific case, the avalanche series were triggered by the long-lasting combination of all the meteorological conditions mentioned before: temperature increase, rapid snow accumulation due to heavy snowfall and strong snow drift. Zonal and mixed circulation forms are characteristic of this avalanche period. The circulation forms are represented by the north and west cyclonic weather patterns (NZ, WZ) bringing excessive snowfall, finally ending with the penetration of southwestern anticyclonic weather (SWA) and warm air masses.

Favourability of the climatic parameters to snow-avalanche occurrence

The tree-ring reconstructed winters within the three paths investigated has been correlated with 50 monthly climate variables to verify the set of climate factors responsible for major events. The regression analysis provided seven models. Two models obtained from multiple regression analyses that explain more than 90% of the total variation have been selected (Table 4).

The variables retained by the regression (Table 5) enabled the identification of multiple scenarii for the occurrence of major snow avalanches. Because the measurement scales of variables entering a multiple regression are often different, the interpretation of the regression coefficients (b) in this sense can distort the image of the importance of independent variables in the model. For this reason, the standardized regression coefficients are introduced, defined as the estimated regression coefficients of the model. Modification with a standard deviation of the value of the variable × produces a change with βi standard deviations of the value of the dependent variable. In this way, the size of the standardized coefficients reflects the importance of independent variables in the prediction of the dependent variable. The standardized β coefficients helped to prioritize the influence of each variable in the total variation.

According to the parameters of the two models, favourable and unfavourable factors are highlighted:

The favourable factors to the occurrence of major avalanches are:

the presence of very heavy precipitation (mostly snow, although the recorded precipitation is not discriminated) at the beginning of the snow-avalanche season. The early building up of the snow cover prepares future snow avalanches over winter: [START_REF] Hryshchenko | Snow-avalanche cadastre of Ukraine (Carpathians, Crimea). Ukrainskyi hidrometeorolohichnyi instytut[END_REF] insist that in the snow-avalanche records 30 cm of accumulated snow is a prerequisite for snow avalanche release. That is translated in the analysis by the number of days with precipitation above average precipitation plus 2x Standard Deviations in November.

destabilisation of the snow layer in February by passing the air temperature above the threshold of 0 The unfavourable factors are determined as:

maintaining a medium-thick layer of snow over a long period of time in January (Number of days with snow depth between 40 and 50 cm). the presence of very warm consecutive days at the beginning of the snow-avalanche season, which lead to the melting of the snow layer (Number of days with Tmed above Tmed plus 2x Standard Deviations in April). -high average air temperature in March leading to the melting of snow (Average temperature April).

Discussion

Historical records

Historical records of snow avalanches are of prime interest to document past events. The combination of the data recorded by the Carpathian avalanche group and those collected at the Pogegevskaya weather station provide valuable information, mainly descriptive, and of varying quality. Quantitative data are also recorded, although some information is evasive, such as 'temperature increase', or 'snowfall with snowstorm'. This valuable information enables further insights into weather conditions over the period of occurrence [START_REF] Sawyer | A chronology of high magnitude snow-avalanches reconstructed from archived newspapers[END_REF][START_REF] Gądek | Snow avalanche activity in Żleb Żandarmerii in a time of climate change (Tatra Mts., Poland)[END_REF], the avalanche period being highlighted by all kinds of useful sources of data [START_REF] Giacona | A 240-year history of avalanche risk in the Vosges Mountains based on non-conventional (re)sources[END_REF].

Dendrochronological records

The use of tree rings to highlight snow-avalanche winters surely extends the time span of observations, up to the late 19th century in Breskul path. Following the literature (e.g., [START_REF] Christophe | Spatio-temporal reconstruction of snow avalanche activity using tree rings: Pierres Jean Jeanne avalanche talus[END_REF][START_REF] Corona | How much of the real avalanche activity can be captured with tree rings? An evaluation of classic dendrogeomorphic approaches and comparison with historical archives[END_REF][START_REF] Dubé | Tree-ring reconstruction of high-magnitude snow avalanches in the Northern Gaspé Peninsula, Québec[END_REF][START_REF] Reardon | Spatial reconstructions and comparisons of Historic Snow Avalanche Frequency and Extent Using Tree Rings in Glacier National Park, Montana[END_REF][START_REF] Boucher | Reconstitution dendrochronologique et fréquence des grosses avalanches de neige dans un couloir subalpin du mont Hog's Back, en Gaspésie centrale (Québec)[END_REF], we selected an AAI greater than 10% to consider a winter as avalanchy, and added further criteria to counter low sample depths in the earlier years of the reconstructed chronology. Some other researches, based on a weaker sample depth, did consider higher AAI values [START_REF] Butler | A history of high-magnitude snow avalanches, southern Glacier National Park, Montana[END_REF][START_REF] Butler | Tree-ring analysis and natural hazard chronologies: minimum sample sizes and index values[END_REF][START_REF] Bryant | A statistical analysis of tree-ring dating in conjunction with snow avalanches: comparison of on-path versus off-path responses[END_REF][START_REF] Decaulne | A multi-scale resolution of snowavalanche activity based on geomorphological investigations at Fnjóskadalur, northern Iceland[END_REF][START_REF] Decaulne | A 100-year extreme snowavalanche record based on tree-ring research in upper Bødalen, inner Nordfjord, western Norway[END_REF]. In Breskul path, the runout distance extends over 500 m on flat land, at the valley bottom, which is a terrain configuration seldom described in the literature, most studies being conducted along steeper slopes; this is comparable extreme runout distance to what was studied earlier in Iceland by [START_REF] Decaulne | A multi-scale resolution of snowavalanche activity based on geomorphological investigations at Fnjóskadalur, northern Iceland[END_REF], in Norway by [START_REF] Decaulne | A multi-scale resolution of snowavalanche activity based on geomorphological investigations at Fnjóskadalur, northern Iceland[END_REF][START_REF] Decaulne | A 100-year extreme snowavalanche record based on tree-ring research in upper Bødalen, inner Nordfjord, western Norway[END_REF], in Gaspé Peninsula (Canada) by [START_REF] Germain | Snow avalanche activity after fire and logging disturbances, northern Gaspé Peninsula, Québec, Canada[END_REF], and in Argentinian Andes by [START_REF] Casteller | Validating numerical simulations of snow avalanches using dendrochronology: the Cerro Ventana event in Northern Patagonia, Argentina[END_REF]. Here the dendrochronological approach records only the most extreme events: avalanches with short return periods are not impacting the trees located at the furthest recognized runout, and those might be recorded in the written sources, not in tree-rings. However, the 1968 extreme runout snow avalanche, well reported in the avalanche chronicles, is not visible with such a strong signal in the dendrological record; if the sampled trees were located in the far runout zone of extreme avalanches, the magnitude of the avalanche is of importance. In fact, the 1968 event destroyed most of the forest, leaving only few of the tree proxies on the less violent trajectories of the snow fluxes. The dendrochronological record is therefore done in this path on trees that survived the event or survived it, i.e., those located on the margins of the extreme 1968 snow-avalanche deposits.

Climatic signals

Several studies have attempted to interpolate the future snowavalanche activity from winter climatic trends, highlighting that over the last decades are recorded warmer temperature, more rain events and a shorter duration of the presence of shallower snow cover due to earlier snow melting (e.g., [START_REF] Teich | Snow and weather conditions associated with avalanche releases in forests: Rare situations with decreasing trends during the last 41 years[END_REF], with variations due to altitudes (e.g., [START_REF] Laterneser | Long-term snow climate trends of the Swiss Alps (1931-99)[END_REF]. In the US Rocky Montains, a shift is observed on large magnitude snow-avalanche release from stormy winters drivers with abundant snow packs to warmer temperature synoptic situations associated to shallower snow covers (e.g., [START_REF] Peitzsch | Climate drivers of large magnitude snow avalanche years in the U.S. northern Rocky Mountains[END_REF]. In the Helmos Mts. (Northern Peloponeese, Greece), the rising temperatures during the transition period of late winter to early spring seasons may accounts for a higher probability of rain-on-snow events, which may trigger debris-flow activity instead of snow-avalanche activity [START_REF] Tichavský | Occasional but severe: past debris flows and snow avalanches in the Helmos Mts. (Greece) reconstructed from tree-ring records[END_REF]. The observations of warmer winter temperature and /or winter snow depth are the main changes worldwide (e.g., [START_REF] Sinickas | Snow avalanches in western Canada: investigating change in occurrence rates and implications for risk management and mitigation[END_REF][START_REF] Eckert | A spatio-temporal modelling framework for assessing the fluctuations of avalanche occurrence resulting from climate change: application to 60 years of data in the northern French Alps[END_REF][START_REF] Jamieson | Climate change and planning for snow avalanches in transportation corridors in western Canada[END_REF]. Although winter precipitation has significantly increased, for instance in the Swiss Alps or in western Canada, long-term changes in snow-avalanche activity are not flagrant, due to the lack of long term precise knowledge of snow avalanches (e.g., [START_REF] Bellaire | Analysis of longterm weather, snow and avalanche data in Glacier National Park, B.C., Canada[END_REF]Castebrunet et al., 2012;[START_REF] Laterneser | Temporal trend and spatial distribution of avalanche activity during the last 50 uears in Switzerland[END_REF]; however, a decrease is predicted for dry avalanche activity in the French Alps and an increase for wet avalanche activity [START_REF] Martin | Impact of a climate change on avalanche hazard[END_REF][START_REF] Castebrunet | Projected changes of snow conditions and avalanche activity in a warming climate: the French Alps over the 2020-2050 and 2070-2100 periods[END_REF]; an earlier start of wet avalanches is predicted in the Colorado Mountains [START_REF] Lazar | Climate change in western ski areas: Potential changes in the timing of wet avalanches and snow quality for the Aspen ski area in the years 2030 and 2100[END_REF]. Contrastingly, Ballesteros-Cánovas et al. ( 2018) demonstrate a clear link between warming and increased snow-avalanche frequency in the Western Indian Himalyas.

The prerequisite seasonal climate and atmospheric circulation variables for avalanche release in Chornohora massif are heavy and intense snowfall to form a thick snowpack in the departure area, plus a sudden reduction of the snowpack cohesion during periods of positive temperature. The weather data collected in the Chornohora study area show a statistically significant increase in air temperature and precipitation over the period 1961-2015. This trend is the most significant for precipitation time series, with a positive level of significance for the whole avalanche season (December-April) as well as on a monthly basis (Table 6). However, while monthly and seasonal air temperature has also increased over the period, no significant trend is detected over the months of February, March and April. Therefore, over these three months, the precipitation increase is, so far, associated to rather stable temperature trends, so the snow-avalanche activity over these months is drove only by the snowpack characteristics.

Limitations of the used methods

The accuracy of the locations is sometimes questionable within historical records, as well as the completeness of the evidence. In fact, we can't expect exhaustive observations of snow-avalanche release or deposits given the fact that the process often occurs during bad weather conditions preventing direct observations and forbidding outdoor work for understandable security seasons. The experience of the observer, his conscientiousness or his training might affect the quality of the data. Those are shortcomings often put forward to invalidate the use of historical sources. However, the historical data used here are compiling direct record [START_REF] Ibsen | The nature, use and problems of historical archives for the temporal occurrence of landslides, with specific reference to the south coast of Britain, Ventnor, Isle of Wight[END_REF] of snow avalanche occurrence, especially the most extreme ones, providing date and further characteristics.

One of the main limitations of the tree-ring reconstructions is that tree rings are able to record a single avalanche signal per winter season. In case of multiple avalanche-events occurrence in a given avalanche path over a given winter season, tree rings record together evidence of all events that occurred during the snow season, without distinguishing between multiple events. Therefore, tree-ring records will never provide the complete avalanche-event chronology, only a minimum yearly avalanche occurrence may be reconstructed within the investigated avalanche path. The temporal precision of tree rings is lost, as avalanches are then observed at the season scale, not at the event one. Also, in the case of low-magnitude snow-avalanche occurrence restricted to the deforested avalanche path, the tree-ring approach is inefficient to reconstruct any event.

The main limitation of the statistical approach we used is that it does not deal with individual events. Even though it provides a logical and well-founded scenario of the connection between snowpack dynamics over the avalanche season and the occurrence of major avalanches, it cannot predict the very moment of occurrence of these phenomena. Another limitation is that the results of the study depend on the representativeness, quality, and availability of meteorological data. For mountainous areas, all these aspects are source of issues. To explore better the weather conditions during the avalanche event, a further analysis would be needed to recognize specific weather types according to Hess & Brezowsky classification for each individual snow avalanche day within the records. That would represent a massive work, that would surely better document the local situation in eastern Chornohora massif. However, the synoptic analysis is based on the classical Hess-Brezowsky classification, which is considered subjective and representative for the large Central Europe domain [START_REF] Werner | Katalog der Großwetterlagen Europas (1881-2009[END_REF]. Accordingly, possible smaller-scale orographic-induced barometric patterns over the Carpathians may influence the precipitation trajectory [START_REF] Barbu | Selecting spatial domain size for air circulation types over Romania in connection to climatological parameters[END_REF]. Still, no clear preference to any other classification of circulation types is given in the recent studies; the one used in this study is found appropriate for the Carpathian region [START_REF] Cahynová | Atmospheric circulation influence on climatic trends in Europe: an analysis of circulation type classifications from the COST733 catalogue[END_REF][START_REF] Łupikasza | The climatology of air-mass and frontal extreme precipitation: study of meteorological data in Europe[END_REF].

Conclusion

In this paper we examined the snow-avalanche activity in three contiguous paths on the Eastern side of Chornohora range, in SW Ukraine. Our findings are based on a historical approach (period 1966-2015) associated to a dendrochronological one (period 1896-2017). We were able to reconstruct the spatial and temporal distribution of avalanche winters since the end of the 19th century. A great variability of avalanche activity is shown over paths, Breskul being the one that encountered the most numerous events, and a very destructive one in 1968. The runout of snow avalanches is also the longest there, approaching 500 m.

Thanks to the analysis of synoptic situations over specific periods of winters when snow avalanches concerned the three paths, and with a statistical analysis of the weather parameters recorded over time, the main meteorological drivers for snow-avalanche release are identified as air temperature increase, especially conducive to snow-avalanche events at the end of the season (March, April), and involves wet snow; snowstorms are responsible for avalanche release in February and March mainly; and abundant snowfall are also responsible for avalanche release in February and March. Three main prerequisites for snow avalanche occurrence are defined according to the analysis of weather data: (i) early winter thick snow cover; (ii) snow cover thinning and rapid metamorphism over February with positive daily mean temperature, (iii) late winter snow cover sensitive to quick snowmelt. If the temperature shows a general warming trend, as well as more abundant

Table 6

Mann-Kendall trend statistics for precipitation at temperature time series at the Pogegevskaya station . precipitation over the analyzed period with meteorological records, the winter regime of temperature is not affected: no significant change is therefore expected on the snow-avalanche regime in Chornohora range in the near future. However, the accuracy of the snow-avalanche portrait in Chornohora range suffers from a limited amount of recorded events in the historical archives, and partial description of recorded events; this shortcoming is only partially documented by the combination with the dendrochronological approach that provides information at the winter scale rather than at the event scale. Finally, a more in-depth analysis of the weather data could be useful on specific short periods of times, or specific season, to better understand the triggering factors for avalanches in an area that was, over the last decade, growing in popularity with larger numbers of winter recreation practitioners. Therefore, risk and vulnerability were emerging issues before 2022, and will be in the future.

CRediT authorship contribution statement

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: All co-authors reports financial support was provided by Agence Universitaire de la Francophonie (AUF) and Institutul de Fizica Atomica (IFA) Romania.

  Fig. 1. Location of the study area: in Ukraine (A), Chornohora range (B) is investigated on the north-eastern facing slope of the main eastern Chornohora massif range, in between Hoverla and Breskul Peaks (C), where three snow-avalanche paths are investigated (image GoogleEarth 3d, October 1, 2017).

Fig. 2 .

 2 Fig. 2. Workflow for the study of snow-avalanche occurrence on three paths of eastern Chornohora range.

Fig. 3 .

 3 Fig. 3. Location of sampled trees in Breskul, Prut and Hoverla snow-avalanche paths.

Fig. 4 .

 4 Fig. 4. Historical chronicles of snow-avalanche events observed within the Prut upper catchment (A), and monthly snow-avalanche distribution (B) over the period 1966-2011.

Fig. 6 .

 6 Fig. 6. Dendrochronological reconstruction of avalanche winters in the three avalanche paths investigated.

Fig. 9 .

 9 Fig. 9. Meteorological parameters describing weather conditions previous and during snow-avalanche days for the reconstructed winters of1976-1977, 1993-1994, 2001-2002, which are recorded in the three paths investigated.

Fig. 10 .

 10 Fig. 10. Weather types according to Hess & Brezowsky classification for the reconstructed winters of 1976-77, 1993-94, 2001-2002 with the indication of snow avalanche days (bold squares).

Fig. 11 .

 11 Fig. 11. Four synoptic patterns dominating over snow-avalanche days: (1) mixed and zonal circulation forms with air temperature increase and precipitation, exampled by April 13, 2002 synoptic situation; (2) zonal circulation causes air temperature increase, illustrated by February 11, 1977 situation; (3) mixed circulation forms with air temperature decrease together with heavy snow accumulation, as on January 18, 1994; (4) meridional circulation forms with air temperature decrease, on December 13, 1976. The study area is marked in red.

Armelle Decaulne :

 Decaulne Investigation, Conceptualization, Methodology. Ionela-Georgiana Rȃchitȃ: Investigation. Dariia Kholiavchuk: Investigation, Resources, Methodology. Olimpiu Pop: Investigation, Resources, Methodology. Iulian Horia Holobâcǎ ǎ: Investigation, Resources, Methodology. Oles Ridush: Investigation, Resources, Methodology. Bogdan Ridush: Investigation. Csaba Horváth: Data curation, Methodology.

Table 1

 1 Circulation forms and weather types according to the Hess and Brezowski classification[START_REF] Werner | Katalog der Großwetterlagen Europas (1881-2009[END_REF].

	Circulation	Weather types	Weather patterns
	forms
	zonal	West	WA, WZ, WS, WW
	mixed	Southwest	SWA, SWZ
		Northwest	NWA, NWZ
		High Central	HM, BM
		Europe
		Low Central	TM
		Europe
	meridional	North	NA, NZ, HNA, HNZ, HB, TRM
		East	NEA, NEZ, HFA, HFZ, HNFA, HNFZ,
			SEA, SEZ
		South	SA, SZ, TB, TRW

Table 2

 2 Sample characteristics in the three sites investigated.

	Breskul	126	202 (100)	26 (26)
	Prut	64	104 (52)	12 (12)
	Hoverla	52	90 (45)	7 (7)
	Total	242	296 (197)	45 (45)

Path

# tree sampled # cores (# trees) # discs (# trees)

  • C (Number of consecutive days with Tmed above 0 • C and Snow depth above 5 cm in February).maintaining a consistent layer of snow in April (Number of days with snow depth growth above 1 × Standard Deviation in April).

Table 3

 3 Monthly weather variables selected to perform the multiple regressions (revised version). Tmin below Tmin minus 1x Standard Deviations N_Tmin < 1STD; Tmin below Tmin minus 2x Standard Deviation N_Tmin < 2STD; Tmed above Tmed plus 1X Standard Deviation N_Tmed > 1STD; Tmed above Tmed plus 2x Standard Deviation N_Tmed > 2STD; Tmax above Tmax plus 1x Standard Deviation N_Tmax > 1STD; Tmax above Tmax plus 2x Standard Deviation N_Tmax > 2STD; Tmed above 0 • C N_Tmed > 0 • C; Tmed above 0 • C and snow depth above 5 cm

	-AIR TEMPERATURE -
	Average temperature Tmed
	Minimum temperature Tmin
	Maximum temperature Tmax
	Number of days with: N_Tmed > 0 • C_SC > 5
	-WIND -
	Average wind speed Vmed
	Maximum wind speed Vmax
	Number of days with average speed above Vmed plus 1x Standard Deviation N_Vmed
	> 1STD
	-PRECIPITATIONS -
	Precipitation amount RR
	Precipitation amount for the days with snow cover RRmed SC
	Maximum precipitation amount in: one day RRmax24h; in 3 consecutive days
	RRmax72h
	Number of days with: RR above RR plus 1x Standard Deviation N_RR > 1STD; RR
	above RR plus 2X Standard Deviation N_RR > 2STD
	-SNOW COVER -
	Snow depth SD
	Maximum snow depth: in one day SDMax24h;
	Number of days with snow depth between: 1 and 10 cm N_SD < 10 cm; 10 and 20
	cm N_SD_10_20 cm; 20 and 30 cm N_SD_20_30 cm; 30 and 40 cm N_SD_30_40 cm; 40
	and 50 cm N_SD_40_500 cm; 50 and 60 cm N_SD_50_60 cm; above 60 cm N_SD > 60
	cm
	Number

of days with snow depth growth: days

  with snow depth growth N_UP_SD; above 1x Standard Deviation N_UP_SD > 1STD; above the average depth N_UP_SD > UP_med; above 10 cm one day SDUP_1_10cm_24h; above 10 cm 3 days SDUP_10 cm_7; from 15 to 20 cm in one day SDUP_15_20cm_24h; from 20 to 25 cm in one day SDUP_20_25cm_24h; above 25 cm in one day SDUP > 25cm_24h; from 1 to 25 cm in 3 days SDUP_25cm_72h; from 25 to 50 cm in 3 days SDUP_25_50_72h; above 50 cm in 3 days SDUP > 50cm_72h

Table 4

 4 Regression analysis summary (selected models are in bold).

	Models	R	R	Adjusted R	Std error of	Change statistics
			square	square	the estimate	R square	F
						change	change
	1	0.637	0.405	0.381	0.1038097	0.405	16.356
	2	0.751	0.564	0.526	0.0907820	0.159	8.383
	3	0.834	0.696	0.655	0.774809	0.132	9.575
	4	0.893	0.798	0.759	0.0647464	0.101	10.505
	5	0.931	0.866	0.832	0.539833	0.068	10.209
	6	0.964	0.929	0.906	0.0404445	0.63	16.631
	7	0.981	0.963	0.949	0.297939	0.035	17.012

Table 5

 5 Multiple regression coefficients.

	Models	Unstandardized	Standardized
			coefficients		coefficients (β)
			β	standard
				error
	6	Intercept	-0.14	0.034	-
		N_RR > 2STD⋅NOV	0.179	0.015	0.775
		N_Tmed > 0 • C_SD > 5	0.024	0.004	0.437
		cm⋅FEB
		RR⋅APR	0.021	0.005	0.293
		N_UP_SD_>1STD⋅APR	0.051	0.013	0.272
		N_SD50_60cm⋅JAN	-0.015	0.002	-0.645
		N_Tmax > 1STD⋅NOV	-0.090	0.016	-0.350
	7	Intercept	-0.064	0.028	-
		N_RR > 2STD⋅NOV	0.151	0.013	0.652
		N_Tmed > 0 • C_SD > 5	0.030	0.003	0.551
		cm⋅FEB
		RR⋅APR	0.023	0.004	0.324
		N_UP_SD_>1STD⋅APR	0.024	0.011	0.130
		N_SD50_60cm⋅JAN	-0.014	0.001	-0.595
		N_Tmax > 1STD⋅NOV	-0.072	0.004	-0.270
		Tmed⋅MAR	-0.018	0.004	-0.270

Acknowledgements

This work is a contribution to the project ACTIVNEIGE «Activité des avalanches de neige dans les Carpates Orientales Roumaines et Ukrainiennes» (Snow avalanche activity in Romanian and Ukrainian Eastern Carpathians), funded by the Agence Universitaire de la Francophonie (AUF) and Institutul de Fizica Atomica (IFA) Romania.

Data availability

Data will be made available on request.


Katia Stankovic Stojanovic

Sophie Georgin-Lavialle

Christine Poitou

David Buob

Serge Amselem

Gilles Grateau

Valery Salle

Alexis Mathian

Rateb Al Khayat

Laurent Duflot

AA amyloidosis is an emerging cause of nephropathy in obese patients

Keywords:

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Dear Sir, AA amyloidosis (AAA) is a rare disease secondary to chronic inflammatory states. The purpose of our study was to suggest that obesity was a potential cause for AAA since low-grade inflammation has been found in morbid-obesity [START_REF] Visser | Elevated C-reactive protein levels in overweight and obese adults[END_REF]. Thus we conducted a retrospective study between 2006 and 2015. Inclusion criteria for this study were: i) patients sent in the Center since 2006; ii) histologic demonstration of amyloid showing apple-green bi-refringence in polarized light after Congo red and immunohistochemically-classified as AA; iii) with extensive explorations showing no common etiology for AAA including infectious diseases, autoantibodies especially anti-CCP, chest and abdominal scan and/or positron emission tomography (PET) scan, and genetic testing for monogenic auto-inflammatory diseases; iiii) with obesity defined by body mass index (BMI) ≥ 30 Kg/m 2 , classified as moderate (BMI = 30-34.9 Kg/m 2 ), severe (BMI = 35-39.9 Kg/m 2 ), and morbid (≥40 Kg/m 2 ). The study complies with the French regulations regarding clinical research and respects the Declaration of Helsinki.

Our study included 13 patients which represented 4% with all type amyloidosis and 12% of AAA amyloidosis referred in the French national center for AAA. The clinical and biological characteristics of the patients at time of AAA diagnosis are reported in Table 1. Most patients had mild to moderate proteinuria, 4 patients had nephrotic syndrome. Seven patients had moderate chronic kidney disease (CKD), 5 had severe CKD, and one had end-stage renal failure. Four patients had proteinuria under 0.5 g/day associated with moderate (3 patients) to severe (1 patient) CKD. All patients had a chronically elevation of acute phase reactants proteins (C reactive protein (CRP) and serum amyloid A (SAA)). AAA was diagnosed on renal biopsy for all patients, and was also documented by minor salivary glands biopsy for 4 patients, intestinal tract biopsy for 2 patients and thyroid biopsy in one patient who had a goiter. Renal biopsy revealed predominant AA amyloid deposits in glomerular capillaries in the 9 patients with higher proteinuria, and predominant vascular amyloid deposits in the 4 other patients with CKD and mild proteinuria (≤1 g/day). Obesity was moderate in 6 patients, severe in 4 patients, and morbid in 3 patients. Only one patient had a family history of end-stage chronic renal failure. Five patients had diabetes. No case was referred to our center before 2010. All patients underwent large explorations in a reference center for inflammatory diseases which allowed excluding another cause of chronic inflammation including immunologic disorders, infectious diseases, malignancy, and autoinflammatory diseases. We concluded than no other cause but their obesity explained chronic inflammation, and enhanced AAA.

The putative role of obesity-associated chronic inflammation and the occurrence of AAA in humans were first described in 2005 in two sisters with extreme obesity due to a genetic leptin receptor deficiency [START_REF] Poitou | Serum amyloid A: production by human white adipocyte and regulation by obesity and nutrition[END_REF]. Another case was described in Spain in 2009 [START_REF] Alsina | Renal AA amyloidosis secondary to morbid obesity?[END_REF]. In our series, inflammatory markers were mildly but chronically increased as it was previously described [3; 4]. Unlike cases published so far which included extremely obese (BMI 50 to 63 Kg/m 2 ) patients [2; 3], the obesity in our series was milder (median BMI = 35 Kg/m 2 ). The severity of renal disease at the time of AAA diagnosis in our series and in the literature is striking. In fact, renal biopsy in obese patients is not systematically done in case of mild renal impairment or proteinuria because it is often considered as a renal dysfunction linked to obesity itself [START_REF] Adelman | Obesity and renal disease[END_REF] or due to diabetic nephropathy in case of associated diabetes. In our series, biopsies were performed in case of progressive deterioration of chronic renal function and/or nephrotic-range proteinuria. Contrasting with the classical description in the literature of 90% glomerular form and 10% of vascular form of amyloid pattern [START_REF] Naeser | Amyloidosis in ageing obese-hyperglycemic mice and their lean litter-mates. A morphological study[END_REF], our series showed a larger proportion of vascular form. This underlines that the diagnosis of AAA should be raised not only in case of proteinuria but also in case of renal failure with mild or no proteinuria, and biopsy should thus be performed. Since renal biopsy cannot easily be done in obese patients, a less invasive procedure such as minor salivary gland biopsy should be considered at first line.

The occurrence of amyloidosis in obese mice had already been observed in 1977. Amyloid deposits were found in the adrenal glands, the liver, the spleen and the kidneys which suggested a similarity with secondary amyloidosis, known nowadays as AAA [START_REF] Naeser | Amyloidosis in ageing obese-hyperglycemic mice and their lean litter-mates. A morphological study[END_REF]. Recent experimental studies confirmed that a high fat diet in mice was associated with the occurrence of renal AAA; these high fat diet challenged mice had significant elevated systemic SAA compared to low fat diet mice; other pro-inflammatory cytokines were increased especially tumor necrosis factor (TNF)-α and interleukine-6 [START_REF] Van Der Heijden | Obesity-induced chronic inflammation in high fat diet challenged C57BL/6J mice is associated with acceleration of age-dependent renal amyloidosis[END_REF]. Part of the systemic inflammation associated with obesity originates from adipose tissue in which many inflammatory cells accumulate, especially macrophages [START_REF] Cancello | Increased infiltration of macrophages in omental adipose tissue is associated with marked hepatic lesions in morbid human obesity[END_REF]. The production of SAA by adipocytes in obese patients has thus been shown and may contribute to the increased circulating SAA levels along with SAA liver product [START_REF] Poitou | Serum amyloid A: production by human white adipocyte and regulation by obesity and nutrition[END_REF].

It has been shown that caloric restriction with significant weightloss was associated with reduced SAA plasma levels [2; 8]. Since low SAA is associated with better prognosis in patients with AAA [START_REF] Lachmann | Natural history and outcome in systemic AA amyloidosis[END_REF], we suggest that intensive weight-loss program, inducing significant reduction of SAA levels, should slow down the progression of AAA.

In conclusion, AAA should be considered as a potential cause of nephropathy in obese patients, even in moderate obesity. Proteinuria can be mild in case of predominant vascular AA amyloid deposition. Thus, histological exploration should be performed in case of severe or rapidly progressive renal failure and/or proteinuria. In case of chronic elevation of circulating acute phase reactants, minor salivary gland biopsy with Congo-red staining may be considered the first-line diagnostic test since renal biopsy cannot easily be performed in these patients. Considering the link between obesity, inflammation, and AAA, we recommend to improve the low-grade inflammation by intensive weight-loss program that should lower the progression of amyloid deposits.

Table 1

 1 Patient features (N = 13) at the time of AA amyloidosis diagnosis. Serum amyloid A; AAA: AA amyloidosis; BMI: body mass index. Results are given in medians; range was indicated into brackets.a SAA was available for 7 patients.

	Age	56 years [43-74]
	Sex	12 women (92%)/1 man (8%)
	Origin	France (7)/Italy (2)/North Africa (3)/Pakistan
		(1)
	Protéinuria	1 g/24 h [0.06-7]
	Serum creatinine	176 μmol/L [120-1524]
	CRP	21 mg/L [12-67]
	SAA a	19 mg/L [8-41]
	Sites of biopsies confirming AAA	Kidney (N = 13)
		Minor salivary glands (N = 4, one negative)
		Intestinal tract (N = 2)
		Thyroid (N = 1)
	BMI	35 Kg/m 2 [30-50]
	Type 2 diabetes (number of	5
	patients)
	CRP: C reactive protein; SAA:

Conflict of interest

All authors received no financial support for this work and declare no conflicts of interest.

Firas Easy


Maike Paetzel-Prüsmann
email: paetzel-pruesmann@uni-potsdam.de

Julie Hunter
email: jhunter@linagora.com

Kranti Chalamalasetti
email: kranti.chalamalasetti@uni-potsdam.de

Kate Thompson
email: cthompson@linagora.com

Alexandros Nicolaou
email: alexandros.nicolaou@uni-potsdam.de

Ozan Güngör

David Schlangen
email: david.schlangen@uni-potsdam.de

Nicholas Asher
email: nicholas.asher@irit.fr

Conversational Programming for Collaborative Robots

Keywords: Cognitive Human-Robot Interaction, Industrial Robots, Robust/Adaptive Control of Robotic Systems

In this position paper, we describe a novel approach of programming industrial robots via conversational dialogue. We believe that conversational programming, unlike other interfaces for humans to reprogram industrial robots, will enable novices to teach a robot complex new procedures without any knowledge of programming required. Using a sample conversation between a human User and an industrial robotic arm, we discuss how our approach differs from other (spoken) human-robot interfaces and why it has the potential to solve difficulties of such interfaces when it comes to learning to abstract from specific examples. We also describe the unique challenges conversational programming involves and how, once these are solved, it could be integrated into industrial settings of the future.

I. Introduction

In the Industry 4.0 vision, collaborative robots (cobots) will assist workers in factories of the future, operating on assembly lines or assisting with maintenance, fetching tools for workers or helping them by sorting or preparing tools and assembly parts [START_REF] Lasi | Industry 4.0[END_REF], [START_REF] Olsen | Industry 4.0: Opportunities and challenges for operations management[END_REF].

Today's cobots are not yet flexible enough to implement the Industry 4.0 vision and rely on extensive manual programming for complex tasks, which is very time consuming and generally requires robotics experts [START_REF] Matheson | Human-robot collaboration in manufacturing applications: a review[END_REF]. These This work is supported by the German Bundesministerium für Bildung und Forschung (BMBF) and the French Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovationn (MESRI).

experts, however, have often limited knowledge of the domain in which the robot is situated, which prolongs the programming process and leaves it highly prone to errors. Especially in small lot production, robotic systems need to be reprogrammed on a regular basis as the type of work they are required to do frequently changes. We believe the difficult and time-consuming process of teaching the robot new skills is one of the major bottlenecks that limits the usage of robots in small lot production environments.

Recently, more industrial robotics systems have incorporated specific human-robot interaction interfaces, that in theory allow novices to demonstrate a task to the robot by guiding its joints (e.g., [START_REF] Perzylo | Intuitive instruction of industrial robots: Semantic process descriptions for small lot production[END_REF]- [START_REF] Tykal | Incrementally assisted kinesthetic teaching for programming by demonstration[END_REF]; see [START_REF] Berg | Review of interfaces for industrial humanrobot interaction[END_REF] for a recent survey). The robot then learns from the human guidance and after seeing several examples it is able to abstract the knowledge to changing environments. However, as soon as the constraints become more complex, these systems, again, reach their limits [START_REF] Berg | Review of interfaces for industrial humanrobot interaction[END_REF].

To address these issues, we aim to develop a spoken dialogue interface for industrial human-robot collaboration that can be used by a typical human user without sophisticated programming skills or access to special training data [START_REF] Mavridis | A review of verbal and non-verbal human-robot interactive communication[END_REF]. This interface will allow a robot to take in instructions and descriptions expressed through conversation and convert them into code. In particular, given that our motivation is to teach robots new skills, our goal is to provide them with a means to exploit conversation to construct general, repeatable programs.

This objective introduces challenges on two levels. First, a program must be specified in such a way as to prompt the robot to execute the program in any situation that provides the requisite equipment and assembly parts within the robot's reach. That is, the robot must be able to exploit multimodal (visual and linguistic) representations at the type level to recognize particular and appropriate instances of those types at execution time. Second, effective programming through conversation will require much more than simply being able to translate natural language instructions directly into an executable program. Crucially, it will require the robot to be able to produce and exploit conversational moves as tools that allow it to collaborate with a human on the task of constructing the program itself [START_REF] Appelgren | Interactive task learning via embodied corrective feedback[END_REF]. As an illustration, consider the following dialogue.

human: Begin by picking up one flat head screw.

Next, place the screw in a washer. robot: Any kind of washer? human: No, actually you should only use square washers for this task. While we might expect translations of the two first lines of instruction expressed in the sample dialogue to figure in the final code for the program that the human and robot are building, the following question-answer exchange is not meant to add new steps to the program under construction. It rather brings to light, and then settles, an ambiguity in the program up to that point -an objective that it achieves very efficiently. It is such a general capacity to engage in interactive conversation, as humans do, that we believe will allow a robot to quickly get the point of an instruction without being shown hundreds or thousands of examples.

In this position paper, we introduce our efforts to bring together research on discourse structure, conversational grounding [START_REF] Appelgren | Interactive task learning via embodied corrective feedback[END_REF], multimodal grounding [START_REF] Silberer | Learning grounded meaning representations with autoencoders[END_REF], [START_REF] Tan | Vokenization: Improving language understanding with contextualized, visual-grounded supervision[END_REF] and robotics [START_REF] Tellex | Robots that use language[END_REF] to develop a conversational programming toolkit that would allow a robot to learn complex programs from simpler ones through multimodal conversation. While we use both statistical and symbolic data representations of natural language found in many robotics applications, our approach extends the current body of work by using discourse information to construct an intermediate representation of human instructions which then informs robot code creation. We elaborate on this aspect further in Section III after we describe our robotics use case in Section II. The paper concludes in Section IV by discussing the potential of our work for industrial settings of the future.

II. The Domain: Pre-Assembly of a Pegboard

We designed a sample domain that is representative of real scenarios from the industry while allowing for adapting to different levels of difficulty depending on the number and diversity of assembly parts as well as the complexity of the combined procedures to be learned.

The aim for the robot, in our case a Universal Robotics1 robot arm with six degrees of freedom, is to assemble a pegboard using individual parts that are randomly distributed on a source board. The parts consist of different types of 3D-printable screws, washers, nuts and plates that differ both in their type and color to add more variety and complexity for the program to be learned. The generic program of the robot allows it to find and pick up the individual parts from the source board, and place them on the target board at a desired pose. What it needs to learn is how exactly the target board needs to be assembled, which requires it to learn how to combine parts into complex structures and repeat this procedure until certain conditions are met. A setup of our use-case in the Webots2 simulator is shown in Figure 1. We aim for our use case to not only work in simulation, but to translate to the real world, requiring as little adaptation as possible. Consequently, the generic program the robot generates needs to be agnostic to the type of environment it was learned in. A secondary program responsible for the grounding to the real environment then ensures that the learned processes can be executed in various environments.

III. Our Vision for Conversational Programming

Let's say that a human wants the robot to fill the target pegboard with bridges, which are made of nuts, screws and a plate. A dialogue to get the robot to assemble the board could look as follows:

user user: Great, this is how you build a bridge. Now can you please do that until there are no red plates available anymore? Moving from the conversation above to an ability to execute the task of building a bridge raises challenges related both to moving from conversation to a general program and to executing that program, as mentioned in the introduction and visualized in Figure 2. A program is an abstract object that can be executed or instantiated on multiple occasions and in multiple environments, and program execution requires a particular set of skills. When executing the program the robot must ensure that the requisite objects are actually available in the scene it is operating in; the execution fails if this is not the case. To do this, the robot will need to exploit multimodal (vision and linguistic) representations at the type level to recognize tokens of the corresponding concepts that are appropriate for the task. We believe this will require building a flexible coupling between visual and linguistic representations both at the type level and token level.

The execution of the program need not, of course, always require a grounding of a definite description to a particular object. In the dialogue above, the robot may potentially pick up any object of the type that is required and position it on the pegboard according to the instructions. To resolve the pick_and_place calls, a separate program with access to the visual scene queries the position of the next potential object with the required properties and sends this as well as the position of the next free space on the board to the robot arm. Separating these two parts of the program allows the programming to work independently of the visual scene as much as possible. More generally, the distinction between programs and executions, and between type grounding and token grounding, reveals an important distinction between instructing a robot with natural language commands on a particular occasion of use to some sequence of actions (as frequently used in the related work on human-robot collaborative task-solving and natural language programming), and programming by conversation, in which the robot learns a program that it can then execute in multiple environments.

Language is a helpful tool for drawing attention to important properties and specifying conditions: when the User says "Now we need to add a cross-slot screw", they make it clear in one shot that the type of screw matters. If they were to only use images or, say, demonstrations without language, it would likely require considerably more examples before the robot (justifiably) felt confident that the type of screw matters. Nevertheless, language use is often vague and underspecified and human teachers can forget to include important details. In this case conversation becomes key-a tool that allows two (or more) agents to collaboratively spell out and even correct a program under construction.

Let us see how this plays out in our sample dialogue, which illustrates multiple examples of underspecified instructions involving the type of washers needed as well as the color of the plates and screws. In some cases, the robot explicitly asks for clarification (e.g., "Can I use any washer?'') and the User answers by either specifying the missing property or making it explicit that no further specification is needed. The robot's questions in these exchanges are motivated by knowledge that multiple types of washers may be available and by reasoning about these possibilities. Another strategy adopted by the robot is to simply try something and see if it works, as it does with the plate. In this case, it is upon seeing the result that the human realizes they have left out an important detail. This exchange requires an interaction between visual and linguistic information as well as a strategy how to balance potential errors in the task execution and unnecessarily extending the teaching dialogue.

A second type of ambiguity illustrated by the dialogue concerns interpreting an underspecified command like fix what you have done. By itself this command doesn't make clear what the robot needs to do. To resolve that ambiguity, the robot needs to understand that it has made a particular error (using a plate of the wrong color), which requires it to understand the corrective move made by the User and its relation to what the robot has done so far.

These examples make clear that using conversation to collaboratively construct a program requires being able to infer how conversational moves are linked to each other, or even to nonlinguistic actions such as placing a plate on top of two washers. When the robot asks "Can I use any washer?'' the human must understand that it is asking specifically about whether it can use any washer when it carries out the command of placing a washer in each of two consecutive holes. When the User replies affirmatively, the robot needs to relate the answer to the preceding question. But what's more, the robot needs to know what it should do as a response to a particular type of conversational move. It needs to know that an answer, for example, means that it should disregard certain alternatives that it previously thought possible when it asked its question; an acknowledgment like "Excellent'' means that the robot has done well and can move on in its program construction; a corrective move tells it to delete certain content from its program and replace it with other content, and so on.

To build a program from the conversation, the robot requires an understanding of the structure of the conversation as well as its content. Conversational moves are anaphorically related to other conversational moves in semantically significant ways and these different relations affect how a conversational move will influence the content of the final program including whether or not it shows up at all [START_REF] Asher | Logics of Conversation[END_REF]. A correction move, for instance, will both remove and add content to some part of the final program, but crucially, the program will not have a representation of the corrective move itself.

Turning back to the sample dialogue, we see that many of the elaborations and sequential moves should be reflected in the final program. In fact, if we focus only on these moves, we see that they form a coherent substructure on their own and in particular, we start to get something that looks much more like the final desired abstract program as detailed in Algorithm 1. program_state ← f ail This algorithm consequently needs to be interpreted by a different program that grounds the abstract program given the available types of assembly parts as well as the specific robot and its distinct abilities to execute certain moves.The algorithm responsible for grounding the program also needs to be able to handle underspecified abstract commands like pick_and_place of w 1 and w 2 , which can be any type of washer on the board.

Algorithm 1 Assemble Bridge

It is important to note that the attempt to ground a program in a given setting could lead to errors. For example, it could be possible that the robot finds itself in an environment certain parts are unavailable. It is important that the robot is then able to communicate the problem and differentiate between responses that require a change in its abstract program and the current environment. The robot could, for example, say: "I don't find any blue screws on the board, but I was told to use blue screws. What do you want me to do?" The human could then either correct a misunderstanding in the abstract program (i.e., the screws can be of any color), or make the missing type of screws available to the robot.

IV. Discussion and Future Work

In this position paper, we described our vision to design a novel way of programming industrial robots using spoken conversations. We presented the use-case of preassembling a pegboard and how a human User could teach a robot a new routine in the given scenario. The aim is to scale the scenario to become increasingly complex and to show that we can adapt the grounding of the program to different environments. With our German industrial partner Synergeticon 3 we plan to develop a proof-ofconcept in a realistic industrial setting and consequently highlight the potential of our project for the Industry 4.0 vision. We will evaluate our conversational programming technique against standard kinesthetic and visual teaching interfaces common in the industry. We expect our system to not only decrease the time it takes to program the robot to learn a new task and lead to less errors in the execution of new programs, we believe that this will also decrease the cognitive load of the human User as well as increase their perception of the robot and comfort in the task.

We foresee that our project will also bring to light future challenges for roboticists and speech processing researchers to tackle. For example, learning how to pick up objects of unknown size, shape and material is still challenging for robots. In our work, we hence rely on assembly parts that are known to the robot. However, we are confident that our work can be extended to unknown objects once grasping algorithms become more reliable. Similarly, automatic Speech Recognition (ASR) in an industrial setting may produce very unreliable results given the noise level common in such environment. While more robust ASR systems are an active area of research, we believe that using simulated environments could present an intermediate solution to the problem: Instead of performing the entire teaching dialogue in the factory, the robot could be preprogrammed in simulation and the program then merely executed in the physical environment. With our work, we aim to enable such sim2real transfer of conversational programming.

Figure 1 .

 1 Figure 1. The sample industrial use-case shown in the Webots simualated environment. On the right side of the table is the source board with the randomly placed pieces. To the left is the currently empty target board the robot needs to fill.

Figure 2 .

 2 Figure 2. A schematic overview of the proposed system. The conversational dialogue leads to the construction of the general program, which can then be executed by a secondary program in varying environments.

0: w 1

 1 , w 2 ← object(type : washer) 0: s 1 , s 2 ← object(type : screw, color : blue) 0: p ← object(type : plate, size : 2, color : red)0: required_objects ← [w 1 , w 2 , s 1 , s 2 , p] 0: if scene has required_objects then 0: (x, y) ← f ind_space_on_board(2) 0:board.pick_and_place((x, y), w 1 ) 0:board.pick_and_place((x, y + 1), w 2 ) 0:board.pick_and_place((x, y), (x, y + 1), p)0:board.pick_and_place((x, y), s 1 ) 0:board.pick_and_place((x, y + 1), s 2 ) 0: else 0:

https://www.universal-robots.com/

https://cyberbotics.com/