Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate

The Huai River Basin (HRB) is an important grain and industrial production area in eastern China with frequent droughts. Under the background of current climate change, the hydrological and meteorological characteristics of the basin may be changed, which may lead to the changes of regional drought characteristics. It should be paid more attention on the drought research under climate change and the difference between different drought indices. Coupled Model Intercomparison Project phase 5 (CMIP5) as an important tool for climate change research has been used in the study and the study chosen three global circulation models (GCMs)—such as CNRM-CM5 (CNR), HadGEM2-ES (Had), and MIROC5 (MIR)—to gather an ensemble model (EnM) for providing the future climate information. The Standardized Precipitation Index (SPI) and Humidity Index (HI) were used to evaluate and compare the drought situations in the past and the future periods with two representative concentration path scenarios (RCP4.5 & RCP8.5). Some sequence statistics methods, such as Mann–Kendall test and run theory, were carried out to analyze the trend and the changes of extreme drought frequency and characteristics values. The research showed that the simulation accuracy of the EnM would better. SPI and HI take different factors into count and thus lead to differences in describing drought trend, extreme drought frequency, and characteristic values, such as drought severity, drought duration, mean drought severity, and max drought intensity. The research showed that both SPI and HI showed the same wetting or drying trend in the same timescales (except winter) as in the historical data. However, the future annual and seasonal drought trend reflected by SPI shows a wetting trend while HI shows a drying trend. Both in the past and the future, extreme drought frequency and characteristic values reflected by HI are higher than SPI. The drought trend is greater, and the extreme drought frequency and characteristics tend to be strengthened under RCP8.5. Low precipitation and high potential evapotranspiration (PET), especially the PET caused by temperature rise, are the main influencing factors of drought in the future. Therefore, the influence of the PET should not be ignored in drought analysis and we should strengthen the comparative study of different drought indices in future drought analysis under climate change.

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“…In this study, the original M-K test is employed to identify the trend of SPI and HI series. Fang et al [38] given the detailed descriptions.…”

Section: Mann-kendall Test For Drought Trendmentioning

confidence: 99%

Drought Trends and the Extreme Drought Frequency and Characteristics under Climate Change Based on SPI and HI in the Upper and Middle Reaches of the Huai River Basin, China

Hou

Wang

Fan

et al. 2020

Water

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“…This phenomenon, which is attributed to greenhouse gas emissions (Trenberth et al , ; Handmer et al , ; Shen et al , ; Chen et al , ), is expected to significantly impact natural systems and human health. Hence, many efforts have been exerted to investigate the increase in temperature, as well as its impact and adaptation at the global, regional, and country scales (Held and Soden, ; Fang et al , ; Bulygina et al , ; IPCC, ; Yu et al , ).…”

Section: Introductionmentioning

confidence: 99%

Characterization of temperature difference between the neighbouring days in China and its potential driving factors

Tian

Xue

2019

Intl Journal of Climatology

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Understanding the temperature difference between neighbouring days (TDN) is beneficial for reducing the harmful impacts of sudden temperature changes on human health and establishing adaptation policies considering global TDN characteristics. This study analyses the spatiotemporal characteristics of TDN in Mainland China on the basis of the daily observation data from 839 stations in 1961–2017. Results show that the TDN increases with the latitude from south to north. Meanwhile, TDN in winter is higher but lower in summer. These characteristics potentially arise from the variations in solar elevation angle, that is, a decreased TDN corresponds to an increased solar elevation angle in most parts of China. Areas with high solar elevation angle are usually characterized by low TDN, which implies that temperature is stable for neighbouring days in areas with high solar elevation angle. Moreover, the variation rate of the annual mean TDN is negative in most parts of China, especially in the north. These findings are expected to provide an overview of the spatiotemporal characteristics of TDN and support future studies on the impacts of TDN variations on regional environmental changes.

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“…Due to the influences of weather, environment, and power generation equipment on the output of wind power generation, its power curve has strong volatility and intermittency. When a certain threshold is exceeded, it has a great impact on the power quality and grid operation reliability after grid connection [2,3]. Therefore, it is of great practical significance to carry out more accurate forecasting in advance, so as to extend the forecasting step of wind power production, adjust the promising results.…”

Section: Introductionmentioning

confidence: 99%

Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

et al. 2018

Self Cite

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A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting (WPF) accuracy. According to theory of direct and recursive multi-step prediction, this study firstly proposes the models of R (recursive)-VMD (variational model decomposition)-LSTM (long short-term memory) and D (direct)-VMD-LSTM for the hourly forecast of day-ahead wind power by using a combination of a novel and in-depth neural network forecasting model called LSTM and the variational model decomposition (VMD) technique. The data from these model tests were obtained from two real-world wind power series from a wind farm located in Henan, China. The experimental results show that LSTM can achieve more precise predictions than traditional neural networks, and that VMD has a good self-adaptive ability to remove the stochastic volatility and retain more adequate data information than empirical mode decomposition (EMD). Secondly, the R-VMD-LSTM and D-VMD-LSTM are comparatively studied to analyze the accuracy of each step. The results verify the effectiveness of the combination of the two models: The R-VMD-LSTM model provides a more accurate prediction at the beginning of a day, while the D-VMD-LSTM model provides a more accurate prediction at the end of a day.Energies 2018, 11, 3227 2 of 20 dispatching plan in time, reduce the operating cost of the power system, and determine the appropriate wind power price [4].Wind power forecasts (WPFs) can be divided into four different types: very short-term (a few seconds to 30 min), short-term (30 min to 6 h), medium-term (6 h to 24 h), and long-term (one day and more) [5]. The China National Energy Bureau (NEB) enacted a regulation in 2011 that requires the hourly prediction of day-ahead WPFs for dispatching preparation. In addition, the maximum error of the daily forecast curve should not exceed 25%, and the root mean square error (RMSE) of the all-day forecast results should be less than 20% [6]. Due to the randomness of wind power output forecasts, wind power has brought new demands to the safe operation of the power system. Hence, day-ahead WPFs, especially for WPFs up to 1-24 h, have become a hot button issue in wind power systems and new energy domains with the implementation of large-scale wind power projects [7].The mainstream WPF methods are generally divided into physical methods and machine learning methods [8]. Physical methods aim to describe the physical process of the transformation from wind power to electric energy, and physical models rely on numerical weather prediction (NWP) [9]. Since multiple parameters are involved in NWP, and wind farms are located in sparsely populated regions, complete data can be hardly guaranteed [10]. The statistical methods applied in the WPF field are mostly time-series-based appr...

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Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate

Cited by 89 publications

References 91 publications

Drought Trends and the Extreme Drought Frequency and Characteristics under Climate Change Based on SPI and HI in the Upper and Middle Reaches of the Huai River Basin, China

Drought Trends and the Extreme Drought Frequency and Characteristics under Climate Change Based on SPI and HI in the Upper and Middle Reaches of the Huai River Basin, China

Characterization of temperature difference between the neighbouring days in China and its potential driving factors

Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

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