Top PDF Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Situated in inland Eurasia and far away from the oceans, the Tianshan Mountains (TS) are called the water tower of Central Asia. The main rivers that are recharged by glacier/snow melt water (e.g., the Ili River, Syr Darya River, Amu Darya River, Tarim River, and Chu River) originate from the TS, forming one of the largest irrigated zones in the world [ 41 – 43 ]. Previous studies indicated that the TS have experienced a significant warming trend over the past few decades [ 44 – 46 ]. The snowfall/precipitation ratio showed a decreasing trend accompanied by increasing temperatures in the TS [ 47 ], which intensified glacier and snow melt [ 2 ], leading to increasing runoff and earlier peak runoff from glacier/snow melt in these recharging river basins [ 45 , 48 – 50 ]. Several studies have investigated the impact of glacier change on water resources in the TS [ 42 , 51 – 53 ], but only a few studies have addressed the snow cover changes with optical remote sensing [ 54 , 55 ]. In addition, different moisture sources cause diverse climate zones in the TS [ 56 , 57 ], but most studies have focused on only a part of the TS, especially on the areas within China [ 43 , 58 ]. Research on the snow phenology concerning the entire TS and its response to the different climate types in the sub-regions is lacking. Therefore, it is necessary to survey the spatiotemporal variability of snow phenology and its potential influencing factors throughout the entire TS.
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Changes in snow phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Changes in snow phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Situated in inland Eurasia and far away from the oceans, the Tianshan Mountains (TS) are called the water tower of Central Asia. The main rivers that are recharged by glacier/snow melt water (e.g., the Ili River, Syr Darya River, Amu Darya River, Tarim River, and Chu River) originate from the TS, forming one of the largest irrigated zones in the world [ 41 – 43 ]. Previous studies indicated that the TS have experienced a significant warming trend over the past few decades [ 44 – 46 ]. The snowfall/precipitation ratio showed a decreasing trend accompanied by increasing temperatures in the TS [ 47 ], which intensified glacier and snow melt [ 2 ], leading to increasing runoff and earlier peak runoff from glacier/snow melt in these recharging river basins [ 45 , 48 – 50 ]. Several studies have investigated the impact of glacier change on water resources in the TS [ 42 , 51 – 53 ], but only a few studies have addressed the snow cover changes with optical remote sensing [ 54 , 55 ]. In addition, different moisture sources cause diverse climate zones in the TS [ 56 , 57 ], but most studies have focused on only a part of the TS, especially on the areas within China [ 43 , 58 ]. Research on the snow phenology concerning the entire TS and its response to the different climate types in the sub-regions is lacking. Therefore, it is necessary to survey the spatiotemporal variability of snow phenology and its potential influencing factors throughout the entire TS.
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Modelling hydrological responses to climate change in a data-scarce semiarid basin in the Tianshan Mountains

Modelling hydrological responses to climate change in a data-scarce semiarid basin in the Tianshan Mountains

Mountain regions supply a large amount of fresh water for lowland society and ecosystems (Viviroli & Weingartner, 2004), yet hydrological systems in mountain regions are susceptible to climate change (Barnett et al., 2005; Viviroli et al., 2011; Grafton et al., 2013; Piontek et al., 2014). Water resources from the Tianshan Mountains (known as the “Water Tower of Central Asia”) not only play a key role in sustaining downstream rivers, socio-economic development and ecosystems in semiarid Xinjiang, northwestern China but also important indicators of climate change which dramatic affects the variability of meltwater-depended streamflow (Shen & Chen, 2010; Sorg et al., 2012; Chen, 2014; Chen et al., 2016a). Water availability pressure will be aggravated by the ongoing climate change together with the increasing water demands (Shen et al., 2013b; Guo & Shen, 2016). There is a broad consensus about the variability and availability of water resources under the changing climate. However, the impacts of climate change and their hydrological significance are still not well understood, especially in glacierized basins (Chen et al., 2016b). Currently, climate change is evidenced from rising temperature and precipitation trends in the Tianshan Mountains (Chen et al., 2006; Shi et al., 2007); which may result in an accelerated and unstable regional hydrological cycle (Shen & Chen, 2010). Examples can be seen from increased streamflow (Tao et al., 2011; Chen et al., 2016b), earlier snowmelt runoff timing (Liu et al., 2011) and remarkable glacier shrinkage (Yao et al., 2004; Ye et al., 2005; Yong et al., 2007). It was demonstrated that the changes of runoff are more significant in highly glacierized areas (Chen et al., 2016a). A comprehensive understanding of hydrological processes, especially in scarce monitored mountain basins, is one of the most important challenging works which needs to be solved in scientific community.
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Recent changes in snow cover and evolution of supratimberline vegetation in the Puerto de los Neveros, Guadarrama Mountains (Spain)

Recent changes in snow cover and evolution of supratimberline vegetation in the Puerto de los Neveros, Guadarrama Mountains (Spain)

Se diferencian claramente tanto en la fotografía aérea como sobre el terreno por ser áreas colonizadas por formaciones de gramíneas siempre verdes fuertemente enraizadas en formaciones edáficas de textura y naturaleza turbosas ricas en humedad e incluso local- mente saturadas. Estos pastizales, de muy alta densidad, están compuestos por gramíneas de montaña particularmente higrófilas, entre las que destacan por su abundancia Nardus stricta (“cervuno”) y Festuca iberica. Estas dos especies dominantes se encuentran acom- pañadas por Luzula campestris, Hieracium pilosella, Ranunculus bulbosus, Polytrichium juniperinum, Galium rivulare y Trifolium repens, en los sectores no saturados de agua, y por Spagnum sp., Carex nigra, Carex echinata, Viola palustris, Erica tetralix y Drosera rotundifolia, en enclaves pantanosos o próximos a cursos de arroyos (Sanz, 1979; Rivas- Martínez et al., 1999). La extensión de estos “cervunales”, que con mucha frecuencia apa- recen salpicados por grandes matas rastreras de Juniperus alpina (“jabino”), es relativamente modesta –entre el 7 y el 10% de la superficie del área– y se ha mantenido con escasas variaciones a lo largo del período analizado (Fig. 3 y 4b; Foto 3).
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Baseflow simulation using SWAT model in an inland river basin in Tianshan Mountains, Northwest China

Baseflow simulation using SWAT model in an inland river basin in Tianshan Mountains, Northwest China

Abstract. Baseflow is an important component in hydrolog- ical modeling. The complex streamflow recession process complicates the baseflow simulation. In order to simulate the snow and/or glacier melt dominated streamflow reced- ing quickly during the high-flow period but very slowly dur- ing the low-flow period in rivers in arid and cold northwest China, the current one-reservoir baseflow approach in SWAT (Soil Water Assessment Tool) model was extended by adding a slow- reacting reservoir and applying it to the Manas River basin in the Tianshan Mountains. Meanwhile, a digital filter program was employed to separate baseflow from stream- flow records for comparisons. Results indicated that the two- reservoir method yielded much better results than the one- reservoir one in reproducing streamflow processes, and the low-flow estimation was improved markedly. Nash-Sutcliff efficiency values at the calibration and validation stages are 0.68 and 0.62 for the one-reservoir case, and 0.76 and 0.69 for the two-reservoir case. The filter-based method estimated the baseflow index as 0.60, while the model-based as 0.45. The filter-based baseflow responded almost immediately to surface runoff occurrence at onset of rising limb, while the model-based responded with a delay. In consideration of wa- tershed surface storage retention and soil freezing/thawing effects on infiltration and recharge during initial snowmelt season, a delay response is considered to be more reasonable. However, a more detailed description of freezing/thawing processes should be included in soil modules so as to deter- mine recharge to aquifer during these processes, and thus an accurate onset point of rising limb of the simulated baseflow.
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Central Asia/ South Asia

Central Asia/ South Asia

Twice a year, billions of birds migrate vast distances across the globe. Typically, these journeys follow a predominantly north-south axis, linking breeding grounds in arctic and temperate regions with non-breeding sites in temperate and tropical areas. Many species migrate along broadly similar, well-established routes known as flyways. Recent research has identified eight such pathways: the East Atlantic, the Mediterranean/Black Sea, the East Asia/East Africa, the Central Asia, the East Asia/Australasia, and three flyways in the Americas and the Neotropics.
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Long-term ice phenology records from eastern–central Europe

Long-term ice phenology records from eastern–central Europe

Abstract. A dataset of annual freshwater ice phenology was compiled for the largest river (Danube) and the largest lake (Lake Balaton) in eastern–central Europe, extending regular river and lake ice monitoring data through the use of historical observations and documentary records dating back to AD 1774 and AD 1885, re- spectively. What becomes clear is that the dates of the first appearance of ice and freeze-up have shifted, arriving 12–30 and 4–13 days later, respectively, per 100 years. Break-up and ice-off have shifted to earlier dates by 7–13 and 9–27 days/100 years, except on Lake Balaton, where the date of break-up has not changed significantly. The datasets represent a resource for (paleo)climatological research thanks to the strong, physically determined link between water and air temperature and the occurrence of freshwater ice phenomena. The derived centennial records of freshwater cryophenology for the Danube and Balaton are readily available for detailed analysis of the temporal trends, large-scale spatial comparison, or other climatological purposes. The derived dataset is publicly available via PANGAEA at https://doi.org/10.1594/PANGAEA.881056.
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Attribution of streamflow trends in snow and glacier melt-dominated catchments of the Tarim River, Central Asia

Attribution of streamflow trends in snow and glacier melt-dominated catchments of the Tarim River, Central Asia

Despite these advantages, there are only few studies using simulation-based approaches for attributing streamflow trends in snow/glacier melt-dominated catchments to their possible causes. Examples are Hamlet et al. [2005, 2007], who applied a hydrological model to investigate the influence of precipitation and temperature changes on changes in snow water equivalent and the timing of runoff and soil mois- ture recharge over the western United States, or Engelhardt et al. [2014], who used a glaciohydrological model to explain discharge changes between different time periods. Using global climate models runs with and without anthropogenic forcing, Hidalgo et al. [2009] were able to attribute shifts toward earlier streamflow timing in the western United States to anthropogenic climate change. Zhao et al. [2013] inves- tigated the runoff increase in the Aksu catchment by applying the Variable Infiltration Capacity model, and they attributed the runoff increase mostly to an increase in precipitation. However, their study did not consider parameter uncertainties, and observations on mass balances were not taken into account for model calibration. In such a region with large uncertainties in the precipitation data, this can lead to wrong conclusions, as an underestimation of precipitation can be compensated in the model by an over- estimation of glacier melt and vice versa [Stahl et al., 2008; Schaefli and Huss, 2011]. It is therefore neces- sary to include glacier mass balance estimates in the model calibration procedure [e.g., Schaefli et al., 2005; Stahl et al., 2008; Konz and Seibert, 2010; Mayr et al., 2013]. As mass balance data derived by the gla- ciological method are only available for few individual glaciers, geodetic glacier mass balances may be applied as an alternative [Jost et al., 2012].
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Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack

Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack

Keywords: lake-effect snow, Catskills, Lake Erie, Lake Ontario, IMS, MODIS INTRODUCTION Snowmelt is an important source of water for approximately nine million people in New York City (NYC) as well as others in New York State who rely on the NYC Water Supply System (NYCWSS) for their water needs. The NYCWSS is the largest unfiltered water supply system in the United States (Matonse et al., 2011). On average, runoff emanating from the six basins of the Catskill/Delaware watershed in the Catskill Mountains has supplied 90 percent of NYC’s water demands. The westernmost basin, the Cannonsville, contains the second largest reservoir used for NYC drinking water. The contribution of snowfall to total annual precipitation in the Catskill/Delaware Basin has been 20%–30% between ~1950 through ~2010 (Frei et al., 2002; Pradhanang et al., 2011; Anandhi et al., 2011). Some portion of the snowfall in the Catskills emanates from lake-effect (LE) snow from Lake Erie and Lake Ontario.
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Changes in the snow water equivalent in mountainous basins in Slovakia over recent decades

Changes in the snow water equivalent in mountainous basins in Slovakia over recent decades

Abstract. Changes in snowpack and duration of snow cover can cause changes in the regime of snow and rain- snow induced floods. The recent IPCC report suggests that, in snow-dominated regions such as the Alps, the Carpathian Mountains and the northern parts of Europe, spring snowmelt floods may occur earlier in a future climate because of warmer winters, and flood hazards may increase during wetter and warmer winters, with more frequent rain and less frequent snowfall. The monitoring and modelling of snow accumulation and snow melting in mountainous catchments is rather complicated, especially due to the high spatial variability of snow characteristics and the limited availability of terrestrial hydrological data. An evaluation of changes in the snow water equivalent (SWE) during the period of 1961–2010 in the Upper Hron river basin, which is representative of the mountainous regions in Central Slovakia, is provided in this paper. An analysis of the snow cover was performed using simulated values of the snow water equivalent by a conceptual semi-distributed hydrological rainfall-runoff model. Due to the poor availability of the measured snow water equivalent data, the analysis was performed using its simulated values. Modelling of the SWE was performed in different altitude zones by a conceptual semi-distributed hydrological rainfall-runoff model. The evaluation of the results over the past five decades indicates a decrease in the simulated snow water equivalent and the snow duration in each altitude zone and in all months of the winter season. Significant decreasing trends were found for December, January and February, especially in the highest altitude zone.
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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

4 Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari e Politecnico di Bari, 70126 Bari, Italy; gfioreing@gmail.com * Correspondence: sarah.asam@dlr.de; Tel.: +49-8153281230 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 farming: conserving snow over the summer season

Snow farming: conserving snow over the summer season

In general, we rate the accuracy of the snow volumes cal- culated from TLS data as very high. As described before (Sect. 2.2.1), short measurement distances, convenient scan angles, high point densities, and overlapping multiple-scan positions provide favourable conditions for highly accurate measurements. Based on operating experiences with similar settings we estimate the vertical accuracy of the TLS mea- surements to about 1 cm. Nevertheless, scan shadows still caused some data gaps, especially at the crown of the heaps for the two surveys in autumn (Figs. 5b, 6b). These data gaps were caused by a rougher surface such as local depressions. They therefore had to be closed by linear interpolation, intro- ducing some uncertainty. The extent of the gaps is, however, limited to a few square metres, meaning that the effect on the total mass balance can be rated as marginal (below 0.1 % if we assume an area of 10 m 2 and a mean deviation of 10 cm). Another source of potential error is introduced by the lack of (accurate) bare-ground elevation models. For Flüela no such model could be monitored but the flat and only slightly sloped ground area allowed for a good approximation with a sloped plane defined by the margins of the snow heap. For Martell, a bare-ground elevation model was measured after most of the snow had been distributed in autumn. The re- maining snow in the depot, however, could only be estimated based on visual impression and the rating of the local expert. As described in Sect. 2.2.1 we assumed snow volume to be in the range of 600 to 1000 m 3 and used 800 m 3 for the correc- tions. Applying the maximum or minimum estimates would reduce/alter relative snow volume loss by 1 to 2 %. Nonethe- less, this correction only affects snow volumes. Snow volume changes or dHS calculations do not require a bare-ground el- evation model and are therefore not affected. Finally, thick- ness of sawdust and fresh snow were obtained from a limited number of probe measurements (see Sect. 2.2.1). Neverthe- less, a bias of a few centimetres would be small in relation to the large volume and HS of the snow heaps.
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Altitude-dependent influence of snow cover on alpine land surface phenology

Altitude-dependent influence of snow cover on alpine land surface phenology

Our results show that the correlations between ΔSCD and ΔSOS and between ΔSCD and ΔLOS differ consid- erably between the four subregions (Figure 3 and Table 1), being generally stronger in the northern than in the southern Alpine subregions. These findings are in agreement with the fact that the correlation strength between SCD and onset of spring is dependent on the climate of a geographical region [Jönsson et al., 2010]. We conclude that the correlation between SCD and alpine phenology is stronger in geographical regions with longer SCD than in regions with shorter SCD. Our study goes one step further in showing that the cor- relation between ΔSCD and ΔSOS and between ΔSCD and ΔLOS vary between vegetation types (Table 1). We found that ΔSCD has a strong positive correlation with ΔSOS and a negative correlation with ΔLOS for NG, MH, SV, and midaltitude TWS (Table 1 and Figures S6 and S7). These results indicate that a later start of grow- ing season and a shorter length of growing season are always in parallel with a longer snow cover duration in high and middle altitudinal vegetation types, and vice versa. These findings support the suggestion by Julitta et al. [2014] that snow cover plays an important role in determining the start of phenological development in alpine grasslands and agree with the finding by Galvagno et al. [2013] that snow cover limits the length of the growing season in high-altitude grasslands. Yu et al. [2013] report that the winter snow depth has stronger effect on grasslands and shrubs than on broadleaf deciduous forests and needleleaf forests in temperate China, which is what we also find for the Alps. Our results provide evidence that SCD is correlated with forest phenology in middle and low altitudes across the Alps. Indeed, the phenology of forests (i.e., 10–22% of the total area of BF, CF and MF) and low-altitude TWS significantly correlates (|R| < 0.5) with snow cover duration at middle and low altitudes (Figures S6 and S7). Although temperature strongly regulates the start of the growing season of both temperate deciduous broadleaf and coniferous forest [Yu et al., 2013], our results consequently support the suggestion that phenological events of most temperate tree species are not solely driven by air temperature [Yu et al., 2013] but also by snow cover duration. Furthermore, in European Alps, the dynamic of forest ecosystem could be affected by the variation of interannual snow cover duration. Our choice of land cover data set entails that changes in land cover type within the study period are not taken into account in our analysis. However, excluding pixels with land cover change between 2000 and 2012 had no significant influence on our results.
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Inter-comparison of high-resolution satellite precipitation products over Central Asia

Inter-comparison of high-resolution satellite precipitation products over Central Asia

The GSMaP is developed by Japan Science and Technology Agency (JST) and Japan Aerospace Exploration Agency (JAXA) [22, 23]. In order to get high temporal and spatial resolution of global precipitation estimates, GSMaP integrates passive microwave (PMW) retrievals and infrared (IR) retrievals with a Microwave-IR Combined Algorithm, a backward and forward morphing technique from IR images [17] and a Kalman filter [57]. The rain types from the TRMM Precipitation Radar (PR), the melting layer model, and the scattering algorithm are used in the Radiative Transfer Model (RTM) calculation to improve the rain/no-rain classification (RNC) methods [79] over land [80]. The highest resolution of GSMaP is 0.1°-daily. Three versions of GSMaP, which include the near-real-time version of GSMaP (GSMaP_NRT), GSMaP_MVK and GSMaP_Gauge, are available. GSMaP_MVK and GSMaP_Gauge are both selected for this study. GSMaP_STD is a satellite-only product without bias-correction procedure, while GSMaP_Gauge dataset is merged with NOAA Climate Prediction Center (CPC) global rain gauge data set [27]. Tian et al. [56] evaluated the GSMaP over the Contiguous United States, and the results showed that GSMaP gives comparable performance to other satellite-based precipitation products (i.e., CMORPH, PERSIANN, NRL, TMPA 3B42) with slightly better probability of detection during summer.
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The deglaciation of the mountains of Mexico and Central America

The deglaciation of the mountains of Mexico and Central America

montañas tropicales representan una oportunidad excepcional para evaluar la temporalidad de los cambios y la sensibilidad de los climas tropicales ante los fenómenos atmosféricos globales y regionales. En este artículo presentamos el estado del conocimiento sobre la cronología glacial de las altas montañas del centro de México (19.5°N) y América Central (Altos Cuchumatanes en Guatemala, 15.5°N; Cerro Chrirripó en Costa Rica, 9.5°N), con énfasis en la transición entre el último máximo glacial local (UMGL) y el Holoceno temprano y con especial atención en cronologías basadas en dataciones cosmogénicas de geoformas. El UMGL en México (20-14 ka) y América Central (~21-18 ka) coincide con el final del Último Máximo Glacial planetario (26.5-19 ka). La altitud de la línea de equilibrio (ALE) de los glaciares se encontraba deprimida 1500-1000 m con respecto a la actual e indica temperaturas 9-6°C por debajo de las actuales. La deglaciación en Costa Rica se inició en 18 ka, mientras que en México los glaciares permanecieron en su posición máxima o cerca de ella hasta ~15 ka, probablemente en respuesta al evento Heinrich-1. El retroceso de los glaciares en el centro de México comenzó en 15-14 ka y se aceleró entre 14 y 13 ka, en coincidencia con la fase cálida del Bølling-Allerød. Una pausa en el retroceso o un avance ocurrió entre 13 y 10.5 ka en México. En Costa Rica, morrenas no fechadas se formaron entre 18 y 10 ka. Hacia ~10 ka se registra una deglaciación total en toda la región estudiada, con excepción de las montañas de >4200 m, y puede atribuirse a un ascenso de la ALE de 300 a 450 m (calentamiento de ~2-3°C) con respecto a valores del UMGL. En general las cronologías de la deglaciación del centro de México y América Central parecen estar controladas por cambios en la temperatura. Sin embargo, el patrón temporal de deglaciación de Costa Rica es similar al de los Andes tropicales del norte, mientras que el de México se asemeja más al de las montañas del occidente de los EUA.
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Correction of two Upper Paleozoic stratigraphic units in the Tianshan Mountains region, Xinjiang Uygur Autonomous Region and implications on the Late Paleozoic evolution of Tianshan tectonic complex, Northwest China

Correction of two Upper Paleozoic stratigraphic units in the Tianshan Mountains region, Xinjiang Uygur Autonomous Region and implications on the Late Paleozoic evolution of Tianshan tectonic complex, Northwest China

The unconformity beneath the Karamay Formation is not only pronounced in AWEG (i.e., at the site near the No. 106 km milestone marker of UA highway; see Xiao et al., 2008; op.cit. fig. 4), but also is well exposed in entire southern margin areas of the Junggar Basin (CGRSCX, 1981; Zhang and Wu, 1991). At the coal mining site within the western AWEG, this uncon- formity is defined by the Karamay Formation overlying the Lower Permian volcanics and clastics of the Taoxigou Group or the carbonates of the Carboniferous Qijiagou Formation (Fig. 7). This unconformity is characterized by the Late Triassic Huangshanjie Formation overlying the Carboniferous volca- nics eastwards to Keerjian coal mines and western Hon- gshanzui area. Therein the Karamay Formation is absent. In the Turpan-Hami Basin, east of the AWEG, the conglomerates of the Karamay Formation unconformably overlie the Lower Triassic Changfanggou Formation in the Taodonggou, Kekeya and Zhaobishan areas (CGRSCX, 1981; Liao et al., 1987, 1998; Thomas et al., 2011; Yang et al., 2007, 2010; Zhang and Wu, 1991; Zhou et al., 2000). Similarly, the Karamay Formation overlies unconformably the Lower Triassic terrestrial strata or even older non-marine (or marine) strata in many areas of the southern margins of the Junggar Basin (CGRSCX, 1981; Hu et al., 1991; Liao et al., 1987, 1998; Ouyang et al., 2003; Wu et al., 1997a; Zhang and Wu, 1991; Zhou et al., 2000). Clearly, the unconformity between the Permian black rocks and Triassic reddish strata seen in AWEG (Xiao et al., 2008; op.cit. fig. 4) represents a tectonic uplift event which took place in terrestrial basins of the accreted Junggar blocks during the late Early Triassic to early Middle Triassic times. Prior to the for- mation of the unconformity, a thick succession of molasse to fluvial to lacustrine facies was deposited during the Early Permian to Early Triassic. This uplift event is therefore much younger than the orogenic uplift events of the Tianshan
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Monitoring changes in forestry and seasonal snow using surface albedo during 1982–2016 as an indicator

Monitoring changes in forestry and seasonal snow using surface albedo during 1982–2016 as an indicator

and some of the scatter can be attributed to using a different variable for the data (albedo) and the model (snow depth). For the model calculations, the snow depth was used as the indicator of the snowmelt onset rather than the albedo, be- cause the snow depth is more related to the snow accumula- tion throughout the seasonal cold period, whereas the albedo is sensitive to the prevailing weather conditions just before the melt onset. The snow depth describes the whole snow- pack, whereas the albedo describes only the topmost layer of it. The surface albedo product showed a negative trend (about half a day per year) in two areas: Northern Karelia–Kainuu and Southwestern Lapland (Table 3, Fig. 5). For the other areas, the coefficient of determination for a linear trend of 5-year average values was smaller than 0.5. The annual vari- ation of the melt onset date is so large (the standard deviation being on average 12 days for the time range 1982–2015) that it easily masks a long-term trend. This was even more evident in the land ecosystem model calculation results, for which a low frequency variation dominated the time window so that no region showed a marked trend even in the 5-year mov- ing average curve. In particular, variable melt onset timing is in the coastal regions (Southwestern Finland and South- ern Ostrobothnia) and in the Lake district. For those regions, the standard deviation values of the melt onset date are 14.3, 14.7 and 14.6, respectively. For the Northern Fjeld Lapland, the vicinity of the Barents Sea also causes a higher standard deviation (13.4) of the melt onset date. A large part of Fin- land is coastal, and consequently the sea ice extent of the Baltic Sea has a strong effect on the weather and climate, not only changes in air temperature or precipitation preceding the melt onset, which are the dominating drivers in some regions of the Northern Hemisphere (Anttila et al., 2018). When us- ing 10-year moving averages for the trend analysis based on the albedo data, a negative trend (R 2 >0.5) of the melt onset date was detected in Northern Karelia–Kainuu, Southwest- ern Lapland, Ostrobothnia, Kuusamo district and Lake dis- trict. Although the time series of 34 years is not really long enough for using 10-year averages, the results, however, con- firm the intuitive impression that the snowmelt starts earlier than it used to do in the past, as the two distinct areal trends showed.
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Regional Cooperation in Central Asia: Viewpoint from Uzbekistan

Regional Cooperation in Central Asia: Viewpoint from Uzbekistan

longstanding Soviet water and energy exchange arrangement among the republics. Kyrgyzstan and Tajikistan, the upstream countries along the two main rivers of the region — the Amu Darya and the Syr Darya—prefer to maximize the use of the water for generating electricity for export and to meet domestic energy demand, especially in the winter. The downstream countries, Kazakhstan, Turkmenistan and Uzbekistan, prefer to have maximum access to water for irrigation during the summer months, while also avoiding the floods caused by winter water releases. To cope with these interrelationships in regional trade, the Central Asian governments have resorted to bilateral and multilateral agreements that determine the quantities of water and energy (coal, electricity, and gas) that are exchanged between the countries and the values at which they are exchanged. The ADB report (2002) notes that pricing is the key to providing incentives for power trade. Regional approaches to the water-energy nexus in Central Asia would bring large benefits in terms of more efficient management of these scarce resources, a greater potential for exports of electricity, more reliable availability for communities and a reduction in the potential for conflict. However, such regional solutions would require compromises involving each country’s interests and principles, and a fundamental trust that agreements once entered would actually be implemented.
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Relative influence of timing and accumulation of snow on alpine land surface phenology

Relative influence of timing and accumulation of snow on alpine land surface phenology

Our findings indicate that the SWE m impacts vegetation growth in an alpine ecosystem (Figures 4c and 4f). This is in line with experimental studies of Dunne (2003), which reported on shallower snowpacks leading to an earlier SOS for most of the subalpine species in Gunnison County, Colorado (USA). Our findings are also in agreement with Trujillo et al. (2012), who report that maximum SWE explained 50% of the significant varia- bility in maximum NDVI between 1,900 and 2,600 m asl in the Sierra Nevada region during the period 1982– 2006. Furthermore, the negative relationship between LOS and SWE m (Figures 3b and S7d–S7f) may support the fact that increased snow thickness often results in a short-term ecosystem process (Hejcman et al., 2006; Morgner et al., 2010). More specifically, the relationships between SWE m and phenology (i.e., SOS and LOS) metrics above 1,500 m asl may be due to the fact that a deep snowpack always delays and reduces plant development, thus shortening the growing season (Borner et al., 2008; Inouye, 2008). In addition, snow metrics may influence LOS through their effect on SOS (White et al., 2009) and greenness (Trujillo et al., 2012). The latter can be attributed to the effect that winter snow has an effect on soil water reserves, such as keeping soils moist through the growing season (Hiller et al., 2005; Richardson et al., 2013; Trujillo et al., 2012). However, our results indicate that both SOS and LOS showed no significant responses to LSD across elevation (Figure 3) and subregions (Figures 4b and 4e). This is neither in line with Trujillo et al. (2012), where the LSD explained significant change in vegetation greenness in the Sierra Nevada region, nor with Paudel and Andersen (2013), where the LSD was highly correlated with the start of the growing season in high- elevational drier regions of Nepal Trans Himalaya. These differences may be due to the fact that the climate and other environmental factors in these two regions are different from the Swiss Alps. Specifically, our find- ings at high elevations corroborate the different relationships of vegetation with snow accumulation and snow cover found between alpine vegetation zones, topography, and climate conditions in the Tibetan Plateau (Wang, Wang, et al., 2017; Wang, Xiao, et al., 2017).
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