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The purpose of this study is to analyze the spatiotemporal variations in drought in Serbia over recent decades using the standardized precipitation evapotranspiration index (SPEI). The analysis is performed for time series of 1-, 3-, 6-, and 12-month SPEI indices (SPEI-01, SPEI-03, SPEI-06 and SPEI-12, respectively) calculated using the E -OBS gridded dataset with a horizontal resolution of 0.1° for the period of 1950–2022. In general, it was found that negative SPEI trends prevailed in Serbia for all months and all analyzed SPEI timescales, indicating a more frequent appearance of droughts in recent decades. Moreover, for a large majority of month-timescale combinations, the area with a negative trend was larger than the area with a positive trend. For several reasons, we focused on the SPEI for August. First, the SPEI for this month shows one of the most pronounced negative trends, which is rather consistent over different timescales and consistent in terms of the area over which the trend was found to be significant. Statistically significant negative trends of SPEI-03, −06 and − 12 for August were found over 11, 17 and 38% of the country area, respectively. To better understand the trends and changes in the SPEI for August, focusing on extreme and severe drought events, an innovative trend analysis (ITA) was applied. It was found that the occurrence of both extreme and severe SPEI drought categories has doubled over the last thirty years in comparison with the previous thirty-year period. Additionally, more frequent transitions from neutral and wet categories to dry categories were observed through an application of the Markov chain. Second, the special focus on SPEI-03, −06 and − 12 for August was motivated by the fact that we found a significant correlation between these index values and the annual maize production in Serbia, indicating that the SPEI for August can be useful for the assessment of drought impact on agriculture. To better understand interannual variability and long-term trends, this paper also explores the relations between the SPEI over Serbia and large-scale circulation patterns, together with general global and continental warming trends. Our findings indicate that drought changes over recent decades may be driven by the interplay of long-term warming and large-scale circulation patterns. In the context of climate change, especially climate projections, Serbia is situated in a region for which climate projections of mean precipitation change demonstrate weak signals and often with conflicting signs of change within multimodel ensembles. From this point of view, our results can be seen as a contribution to a better understanding of changes in extremes, in our case droughts, that can be hidden by inconclusive changes in the mean values. Finally, due to the similar situation in terms of precipitation and drought trends and changes, our results may be relevant for a wider region, and not only for Serbia. • For Serbia negative trends in SPEI index, for different months and timescales, are dominant. • Occurrence of extreme drought categories has doubled over the last decades. • Transitions from neutral and wet categories to dry categories are more frequent. • These changes are driven by long-term warming and large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Water option trading aims at solving the problem of uneven spatial and temporal distribution of water resources. With the change of global climate, rainfall has become an important factor leading to the uneven spatial and temporal distribution of water resources. In this paper, Markov chain is used to divide and forecast the rainfall scenarios. On the basis of rainfall forecast, the related costs of water option trading are determined. The forecast results of rainfall index are integrated into the determination of the ultimate overprice of water options. The improved Black- Scholes option pricing model is used to determine the rights of water options trading based on the forecast results of rainfall. Finally, the rationality of the calculation results is verified by using the extended linear expenditure model, which proves the rationality of the transaction price determined by the model. Taking Guangdong province as an example, this paper verifies the necessity of incorporating rainfall index into water option trading and the rationality of the related costs of water option trading determined by the model, and proves the superiority of fluctuating water price over stepped water price. Water option trading considering rainfall fully reflects the relationship between supply and demand in water market, and improves the scientificity of water option trading pricing in China. [ABSTRACT FROM AUTHOR]

Climate change has caused great pressure on the air transport sector. European airlines are subject to a pioneering CO 2 emissions regime. This paper sets out, first, to ascertain whether the European aviation sector has adapted since the launch of the European Union (EU) Emissions Trading Scheme (ETS). Second, based on the answer to the above, we predict the situation of the sector in the medium and long term. The study sample was formed by the top 10 European airlines. The years covered are those since the inclusion of the airline sector in the emissions regime (2012–2019). Clusters were defined by eco-efficiency level using K -means cluster analysis and the Markov model was applied to predict future behaviour. The results have shown, on the one hand, that the larger airlines were the most eco-efficient throughout the study period. On the other hand, companies focused on a low-cost strategy have behaved worse since, although they have also experienced significant improvements in their use of air traffic capacity, even above the large airlines, they have not evolved to a higher level of CO 2 emission efficiency and even getting worse in some cases. However, in the medium and long term, our estimate for the sector is a trend towards minimal eco-efficiency in CO 2 emissions. As governments continue to increase emission limitation requirements, in order not to jeopardise the sustainability of the aviation sector, it is advisable to encourage investments in the development of biofuels and their supply chain, international agreements to optimise flight routes or projects to improve the energy efficiency of new engines. • Eco-efficiency for European airlines in the framework of the EU ETS was analysed. • Larger airlines were found to be more eco-efficient than low-cost airlines. • Medium and long term, a trend towards lower eco-efficiency has been estimated. • More government support is recommended to clearly drive the zero emission strategy. • Greater coherence and coordination between international legal regimes is needed. [ABSTRACT FROM AUTHOR]

Climate change and rapid urbanization are important factors restricting future land use. Situational analysis, as an important foundation for the optimization of land use, needs to focus on the impact of climate factors and socio-economic factors. In this paper, the Markov model and the DLS (Simulation of Land System Dynamics) model are combined for the first time, and the land use pattern in 2020 is simulated based on the data of land use in 2000 and 2010 as well as the climate, soil, topography and socio-economic factors of Yunnan Province. In his paper, we took Yunnan Province as the case study area, and selected 12 driving factors by logistic regression method, then the land use demands and layout of Yunnan Province in 2020 has been forecasted and simulated under business as usual (BAU) scenario and farmland protection (FP) scenario and the changes in ecosystem service value has been calculated. The result shows that: (1) after the regression analysis and ROC (Relative Operating Characteristics) test, the 12 factors selected in this paper have a strong ability to explain the land use change in Yunnan Province. (2) Under the two scenarios, the significant reduction of arable land area is a common feature of land use change in Yunnan Province in the future, and its main land use type will be construction land. However, under FP scenario, the current situation where construction land encroach on arable land will be improved. Compared with the change from 2000 to 2010, the trend of arable land, forest land, water area, construction land and unused land will be the same under the two scenarios, whereas the change trend of grassland was opposite. (3) From 2000 to 2020, the value of ecosystem services in Yunnan Province is on the rise, but the ecosystem service value under FP scenario is higher than that of the ecosystem services under BAU scenario. In general, land use in 2020 in Yunnan Province continues the pattern of 2010, but there are also significant spatial differences. Under the BAU scenario, the construction land is mainly in the south of Lijiang City and the northeastern part of Kunming. Under the FP scenario, the new construction land is concentrated near the Lashi dam in northern Yunnan Province, and the high-quality arable land in the valley will be better protected. The research results can provide reference for the optimization of land use pattern in Yunnan Province, and provide scientific basis for land use management and planning. Based on the value of ecosystem services, we should implement the policy of strict protection of arable land, both to ensure food supply and promote the healthy development of ecological environment. [ABSTRACT FROM AUTHOR]

Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems. This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event. The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process. At each decision epoch, the system topology is used to construct a Markov state. Transition probabilities are evaluated according to failure rates caused by extreme events. For each state, a recursive value function, including a current cost and a future cost, is established with operation constraints and intertemporal constraints. An optimal strategy is established by optimizing the recursive model, which is transformed into a mixed integer linear programming by using the linear scalarization method, with the probability of each state as the weight of each objective. The IEEE 30-bus system, the IEEE 118-bus system, and a realistic provincial power grid are used to validate the proposed method. The results demonstrate that the proposed proactive operation strategies can reduce the loss of load due to the development of extreme events. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels. [ABSTRACT FROM AUTHOR]

Abstract: The paper describes a methodology for creating synthetic time series of the fifteen standard sky types considered by the Commission International d'Eclairage (CIE). Since the measurements of sky luminance and accordingly CIE sky types are limited to a small number of locations, it is important to develop models to generate the time series of these parameters. The method is applied to two different locations [Pamplona (Spain) and Arcavacata di Rende (Italy)] where experimental luminance data are available. Firstly, the standard sky types corresponding to the observed luminance values are determined every 10 min and from these, Markov's Transition Matrices (MTM) are obtained corresponding to the four seasons. Secondly, it is statistically verified that the process follows a Markov's chain of first order and that it is stationary. Thirdly, obtained MTMs are used as a basis for generating the synthetic series of types of sky. Finally, experimental and synthetic time series are compared for the two locations, exhibiting good fitting results. As a conclusion, it is verified that first order MTM method can be used to generate time series of occurrence of CIE standard sky types, for the two locations. To clarify the general applicability, it should be applied to different locations having different climates and in addition with longer data sets. [Copyright &y& Elsevier]

The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents new methods for an automated analysis of the double InterTropical Convergence Zone (dITCZ) phenomena on a daily time scale over the east Pacific. Long-term Geostationary Operational Environmental Satellite (GOES) visible and infrared data are used to spatially identify and segment the convection zones over the east Pacific basin on both sides of the equator and to track the temporal variability of the ITCZ, specifically to identify cases of dITCZs, northern or southern ITCZ, or non-presence events. For the segmentation approach, image processing techniques are developed to extract information about the spatial features of the ITCZ in both hemispheres for each satellite image. These features serve as input to a temporal classification algorithm that is based on a combination of hidden semi Markov model (HsMM) and support vector machine (SVM) methods. The performance of the proposed method is competitive with human experts and the methodology can thus be used to conduct an in-depth analysis of the dITCZ. Such an analysis could provide precise information for refining existing weather and climate models over the sparsely observed east Pacific where the dITCZ is greatly over-represented in most models. [Copyright &y& Elsevier]

Abstract: Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings’ indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models. [Copyright &y& Elsevier]

In terms of the effects of future climate change upon society, some of the most important parameters to estimate are associated with changing risks of extreme rainfall events, both floods and droughts. However, such aspects of the climate system are hard to estimate well using general circulation models (GCMs)—in particular, for a small mountainous landmass such as New Zealand. This paper describes a downscaling technique using broad-scale changes simulated by GCMs to select past analogs of future climate. The analog samples are assumed to represent an unbiased sample of future rainfall and are used to develop detailed descriptions of rainfall statistics using hidden semi-Markov models of rainfall breakpoint information. Such models are used to simulate long synthetic rainfall time series for comparison with the historical record. Results for three New Zealand sites show overall increases in rainfall with climate change, brought about largely by an increased frequency of rainfall events rather than an increase in rainfall intensity. There was little evidence for significant increases in high-intensity short-duration rainfalls at any site. Such results suggest that, although regional increases of rainfall are consistent with expected future climate changes, it may be that circulation changes, rather than temperature (and vapor pressure) changes, will be the more important determinant of future rainfall distributions, at least for the coming few decades. [ABSTRACT FROM AUTHOR]

• Residential peninsular microgrid participation in bid-based power injection contract through an aggregator. • Enhanced weather forecast method based on recursive least-squares has been adopted. • Uncertain behavior of solar irradiation using Markov chains is modeled. In the context of smart cities, the growing share of solar power induces uncertainty in power generation due to inherent climatic variations. Accurate forecasting will be a key point for future residential microgrids since inability to do so could dramatically impact power balance and grid stability. This paper proposes an enhanced energy management framework which aims to efficiently address uncertainty issues due to local climatic variations in a peninsula context. The proposed framework uses a ten-state Markov chain to generate stochastic solar irradiation as well as a forecast correction method based on recursive least-squares updated every hours in order to efficiently take part in hour-ahead power bidding process. Numerical results highlight benefits obtained by combining proposed forecast correction method and storage in a practical example of Saint-Nazaire, a city located in peninsula of Guérande, France. Besides, a sensitivity analysis regarding impact of storage size and aggregator penalty on operation cost and commitment indices is investigated. Obtained results demonstrates better accuracy in delivering power to the grid and will lead residential microgrids dealing with strong climatic variations to decrease their operation cost and increase power balance for all grid stakeholders. [ABSTRACT FROM AUTHOR]