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- ItemDeclining glaciers endanger sustainable development of the oases along the Aksu-Tarim River (Central Asia)(London : Taylor & Francis, 2021) Bolch, Tobias; Duethmann, Doris; Wortmann, Michel; Liu, Shiyin; Disse, MarkusTarim River basin is the largest endorheic river basin in China. Due to the extremely arid climate the water supply solely depends on water originating from the glacierised mountains with about 75% stemming from the transboundary Aksu River. The water demand is linked to anthropogenic (specifically agriculture) and natural ecosystems, both competing for water. Ongoing climate change significantly impacts the cryosphere. The mass balance of the glaciers in Aksu River basin was clearly negative since 1975. The discharge of the Aksu headwaters has been increasing over the last decades mainly due to the glacier contribution. The average glacier melt contribution to total runoff is 30–37% with an estimated glacier imbalance contribution of 8–16%. Modelling using future climate scenarios indicate a glacier area loss of at least 50% until 2100. River discharge will first increase concomitant with glacier shrinkage until about 2050, but likely decline thereafter. The irrigated area doubled in the Aksu region between the early 1990s and 2020, causing at least a doubling of water demand. The current water surplus is comparable to the glacial runoff. Hence, even if the water demand will not grow further in the future a significant water shortage can be expected with declining glacial runoff. However, with the further expansion of irrigated agriculture and related industries, the water demand is expected to even further increase. Both improved discharge projections and planning of efficient and sustainable water use are necessary for further socioeconomic development in the region along with the preservation of natural ecosystems.
- ItemCombining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA(London : Taylor & Francis, 2021) Lezhnina, Olga; Kismihók, GáborIn our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined.
- ItemCase studies of the wind field around Ny-Ålesund, Svalbard, using unmanned aircraft(London : Taylor & Francis, 2022) Schön, Martin; Suomi, Irene; Altstädter, Barbara; van Kesteren, Bram; zum Berge, Kjell; Platis, Andreas; Wehner, Birgit; Lampert, Astrid; Bange, JensThe wind field in Arctic fjords is strongly influenced by glaciers, local orography and the interaction between sea and land. Ny-Ålesund, an important location for atmospheric research in the Arctic, is located in Kongsfjorden, a fjord with a complex local wind field that influences measurements in Ny-Ålesund. Using wind measurements from UAS (unmanned aircraft systems), ground measurements, radiosonde and reanalysis data, characteristic processes that determine the wind field around Ny-Ålesund are identified and analysed. UAS measurements and ground measurements show, as did previous studies, a south-east flow along Kongsfjorden, dominating the wind conditions in Ny-Ålesund. The wind measured by the UAS in a valley 1 km west of Ny-Ålesund differs from the wind measured at the ground in Ny-Ålesund. In this valley, we identify a small-scale catabatic flow from the south to south-west as the cause for this difference. Case studies show a backing (counterclockwise rotation with increasing altitude) of the wind direction close to the ground. A katabatic flow is measured near the ground, with a horizontal wind speed up to 5 m s-1. Both the larger-scale south-east flow along the fjord and the local katabatic flows lead to a highly variable wind field, so ground measurements and weather models alone give an incomplete picture. The comparison of UAS measurements, ground measurements and weather conditions analysis using a synoptic model is used to show that the effects measured in the case studies play a role in the Ny-Ålesund wind field in spring.
- ItemShort-dated smile under rough volatility: asymptotics and numerics(London : Taylor & Francis, 2021) Friz, Peter K.; Gassiat, Paul; Pigato, PaoloIn Friz et al. [Precise asymptotics for robust stochastic volatility models. Ann. Appl. Probab, 2021, 31(2), 896–940], we introduce a new methodology to analyze large classes of (classical and rough) stochastic volatility models, with special regard to short-time and small-noise formulae for option prices, using the framework [Bayer et al., A regularity structure for rough volatility. Math. Finance, 2020, 30(3), 782–832]. We investigate here the fine structure of this expansion in large deviations and moderate deviations regimes, together with consequences for implied volatility. We discuss computational aspects relevant for the practical application of these formulas. We specialize such expansions to prototypical rough volatility examples and discuss numerical evidence.