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- ItemImpacts of meeting minimum access on critical earth systems amidst the Great Inequality(London : Springer Nature, 2022) Rammelt, Crelis F.; Gupta, Joyeeta; Liverman, Diana; Scholtens, Joeri; Ciobanu, Daniel; Abrams, Jesse F.; Bai, Xuemei; Gifford, Lauren; Gordon, Christopher; Hurlbert, Margot; Inoue, Cristina Y. A.; Jacobson, Lisa; Lade, Steven J.; Lenton, Timothy M.; McKay, David I. Armstrong; Nakicenovic, Nebojsa; Okereke, Chukwumerije; Otto, Ilona M.; Pereira, Laura M.; Prodani, Klaudia; Rockström, Johan; Stewart-Koster, Ben; Verburg, Peter H.; Zimm, CarolineThe Sustainable Development Goals aim to improve access to resources and services, reduce environmental degradation, eradicate poverty and reduce inequality. However, the magnitude of the environmental burden that would arise from meeting the needs of the poorest is under debate—especially when compared to much larger burdens from the rich. We show that the ‘Great Acceleration’ of human impacts was characterized by a ‘Great Inequality’ in using and damaging the environment. We then operationalize ‘just access’ to minimum energy, water, food and infrastructure. We show that achieving just access in 2018, with existing inequalities, technologies and behaviours, would have produced 2–26% additional impacts on the Earth’s natural systems of climate, water, land and nutrients—thus further crossing planetary boundaries. These hypothetical impacts, caused by about a third of humanity, equalled those caused by the wealthiest 1–4%. Technological and behavioural changes thus far, while important, did not deliver just access within a stable Earth system. Achieving these goals therefore calls for a radical redistribution of resources.
- ItemOverview of compressed sensing: Sensing model, reconstruction algorithm, and its applications(Basel : MDPI, 2020) Li, Lixiang; Fang, Yuan; Liu, Liwei; Peng, Haipeng; Kurths, Jürgen; Yang, YixianWith the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials. © 2020 by the authors.
- ItemPhenological model intercomparison for estimating grapevine budbreak date (Vitis vinifera L.) in Europe(Basel : MDPI, 2020) Leolini, Luisa; Costafreda-Aumedes, Sergi; Santos, João A.; Menz, Christoph; Fraga, Helder; Molitor, Daniel; Merante, Paolo; Junk, Jürgen; Kartschall, Thomas; Destrac-Irvine, Agnès; van Leeuwen, Cornelis; Malheiro, Aureliano C.; Eiras-Dias, José; Silvestre, José; Dibari, Camilla; Bindi, Marco; Moriondo, MarcoBudbreak date in grapevine is strictly dependent on temperature, and the correct simulation of its occurrence is of great interest since it may have major consequences on the final yield and quality. In this study, we evaluated the reliability for budbreak simulation of two modeling approaches, the chilling-forcing (CF), which describes the entire dormancy period (endo-and eco-dormancy) and the forcing approach (F), which only describes the eco-dormancy. For this, we selected six phenological models that apply CF and F in dierent ways, which were tested on budbreak simulation of eight grapevine varieties cultivated at dierent latitudes in Europe. Although none of the compared models showed a clear supremacy over the others, models based on CF showed a generally higher estimation accuracy than F where fixed starting dates were adopted. In the latter models, the accurate simulation of budbreak was dependent on the selection of the starting date for forcing accumulation that changes according to the latitude, whereas CF models were independent. Indeed, distinct thermal requirements were found for the grapevine varieties cultivated in Northern and Southern Europe. This implies the need to improve modeling of the dormancy period to avoid under-or over-estimations of budbreak date under dierent environmental conditions. © 2020 by the authors.
- ItemA review of the potential climate change impacts and adaptation options for European viticulture(Basel : MDPI, 2020) Santos, João A.; Fraga, Helder; Malheiro, Aureliano C.; Moutinho-Pereira, José; Dinis, Lia-Tânia; Correia, Carlos; Moriondo, Marco; Leolini, Luisa; Dibari, Camilla; Costafreda-Aumedes, Sergi; Kartschall, Thomas; Menz, Christoph; Molitor, Daniel; Junk, Jürgen; Beyer, Marco; Schultz, Hans R.Viticulture and winemaking are important socioeconomic sectors in many European regions. Climate plays a vital role in the terroir of a given wine region, as it strongly controls canopy microclimate, vine growth, vine physiology, yield, and berry composition, which together determine wine attributes and typicity. New challenges are, however, predicted to arise from climate change, as grapevine cultivation is deeply dependent on weather and climate conditions. Changes in viticultural suitability over the last decades, for viticulture in general or the use of specific varieties, have already been reported for many wine regions. Despite spatially heterogeneous impacts, climate change is anticipated to exacerbate these recent trends on suitability for wine production. These shifts may reshape the geographical distribution of wine regions, while wine typicity may also be threatened in most cases. Changing climates will thereby urge for the implementation of timely, suitable, and cost-effective adaptation strategies, which should also be thoroughly planned and tuned to local conditions for an effective risk reduction. Although the potential of the different adaptation options is not yet fully investigated, deserving further research activities, their adoption will be of utmost relevance to maintain the socioeconomic and environmental sustainability of the highly valued viticulture and winemaking sector in Europe. © 2020 by the authors.
- ItemData-Driven Discovery of Stochastic Differential Equations(Beijing : Engineering Sciences Press, 2022) Wang, Yasen; Fang, Huazhen; Jin, Junyang; Ma, Guijun; He, Xin; Dai, Xing; Yue, Zuogong; Cheng, Cheng; Zhang, Hai-Tao; Pu, Donglin; Wu, Dongrui; Yuan, Ye; Gonçalves, Jorge; Kurths, Jürgen; Ding, HanStochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system's dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.
- ItemWildlife-vehicle collisions in hurungwe safari area, northern zimbabwe(Amsterdam [u.a.] : Elsevier, 2020) Gandiwa, Edson; Mashapa, Clayton; Muboko, Never; Chemura, Abel; Kuvaoga, Phillip; Mabika, Cheryl T.This study is the first to assess wildlife-vehicle collisions (WVC) in Zimbabwe. The study analysed the impact and factors that influence vehicle collisions with large wild mammals along the Harare-Chirundu road section in the protected Hurungwe Safari Area, northern Zimbabwe. Data were retrieved from the Hurungwe Safari Area records and covered the period between 2006 and 2013. Descriptive statistics were used to analyse the recorded variables across the sampled area and to show trends of the prevalence of large wild mammals roadkill over time. Using STATISTICA version 10 for Windows, a two-tailed Mann-Whitney U test was used to determine differences between the number of wild mammal animal roadkills and seasons. A total of 47 large wild mammal animals were killed between 2006 and 2013. The large wild mammal animals that died as a result of vehicle collisions constituted a total of 11 species, with the African buffalo and spotted hyena being the most hit and killed animal species. Most WVC involved heavy haulage trucks and passenger buses. There was no significance difference (P = 0.936) between number of large wild mammal animals killed from WVC between dry and wet seasons. The large wild mammal animals were mostly killed in areas near water sources. We recommend for the inclusion of wildlife protection safeguards in road infrastructure network design and development, particularly on roads that traverse across protected areas in Zimbabwe and beyond. © 2020 The Author(s)
- ItemClimate change and specialty coffee potential in Ethiopia([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Chemura, Abel; Mudereri, Bester Tawona; Yalew, Amsalu Woldie; Gornott, ChristophCurrent climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience.
- ItemRobustly forecasting maize yields in Tanzania based on climatic predictors([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Laudien, Rahel; Schauberger, Bernhard; Makowski, David; Gornott, ChristophSeasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.
- ItemThe Long-Term Evolution of the Atmosphere of Venus: Processes and Feedback Mechanisms: Interior-Exterior Exchanges(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2022) Gillmann, Cedric; Way, M. J.; Avice, Guillaume; Breuer, Doris; Golabek, Gregor J.; Höning, Dennis; Krissansen-Totton, Joshua; Lammer, Helmut; O’Rourke, Joseph G.; Persson, Moa; Plesa, Ana-Catalina; Salvador, Arnaud; Scherf, Manuel; Zolotov, Mikhail Y.This work reviews the long-term evolution of the atmosphere of Venus, and modulation of its composition by interior/exterior cycling. The formation and evolution of Venus’s atmosphere, leading to contemporary surface conditions, remain hotly debated topics, and involve questions that tie into many disciplines. We explore these various inter-related mechanisms which shaped the evolution of the atmosphere, starting with the volatile sources and sinks. Going from the deep interior to the top of the atmosphere, we describe volcanic outgassing, surface-atmosphere interactions, and atmosphere escape. Furthermore, we address more complex aspects of the history of Venus, including the role of Late Accretion impacts, how magnetic field generation is tied into long-term evolution, and the implications of geochemical and geodynamical feedback cycles for atmospheric evolution. We highlight plausible end-member evolutionary pathways that Venus could have followed, from accretion to its present-day state, based on modeling and observations. In a first scenario, the planet was desiccated by atmospheric escape during the magma ocean phase. In a second scenario, Venus could have harbored surface liquid water for long periods of time, until its temperate climate was destabilized and it entered a runaway greenhouse phase. In a third scenario, Venus’s inefficient outgassing could have kept water inside the planet, where hydrogen was trapped in the core and the mantle was oxidized. We discuss existing evidence and future observations/missions required to refine our understanding of the planet’s history and of the complex feedback cycles between the interior, surface, and atmosphere that have been operating in the past, present or future of Venus.
- ItemA network-based microfoundation of Granovetter’s threshold model for social tipping([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Wiedermann, Marc; Smith, E. Keith; Heitzig, Jobst; Donges, Jonathan F.Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges. Here, we refine Granovetter’s widely acknowledged theoretical threshold model of collective behavior as a numerical modelling tool for understanding social tipping processes and resolve issues that so far have hindered such applications. Based on real-world observations and social movement theory, we group the population into certain or potential actors, such that – in contrast to its original formulation – the model predicts non-trivial final shares of acting individuals. Then, we use a network cascade model to explain and analytically derive that previously hypothesized broad threshold distributions emerge if individuals become active via social interaction. Thus, through intuitive parameters and low dimensionality our refined model is adaptable to explain the likelihood of engaging in collective behavior where social-tipping-like processes emerge as saddle-node bifurcations and hysteresis. © 2020, The Author(s).
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