Comparison of daily flows simulated for the year 2060 on the Kaczawa River for various scenarios of climate change by simple time series analysis

Kuchar, Leszek; Broszkiewicz-Suwaj, Ewa; Iwański, Sławomir; Jelonek, Leszek

doi:10.1051/e3sconf/201910000041

Cited by 3 publications

(6 citation statements)

References 25 publications

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“…In connection with the calculations contained in the paper, there are subsequent stages of work that should be performed. These tasks should concern further analysis of the application of daily precipitation simulation, including introduction of scaling parameters for variation (WGENK model; Kuchar, 2004), spatial precipitation generation taking into account various locations in the Lower Silesian region (Kuchar et al 2019, Szturc et al 2018, as well as a daily model based on higher-order Markov chains and / or precipitation set by a distribution other than the gamma distribution. We can also consider using the call option to protect against too heavy rain.…”

Section: Resultsmentioning

confidence: 99%

“…The valuation of weather derivatives was the subject of numerous research papers (Alaton et al,2002;Cao and Wei 1999). In the present work, two models based on Monte Carlo simulations are used to valuate these instruments: a model based on simulation of the daily rainfall value (Kuchar et al, 2019) and -in the next step -determining the underlying index value, as well as a model based on direct estimation of the index distribution (Odening et al, 2007). Calculations is made for data from the Lower Silesia region.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Derivatives as a Risk Management Tool for Grain Producers: Daily Model vs. Index Model

Broszkiewicz-Suwaj¹,

Kuchar²

2020

Acta Sci. Pol., Formatio Circumiectus

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Aim of the study The first weather derivatives appeared in 1996. Soon later such instruments began to be traded on the CME (Chicago Mercantile Exchange). The group of underlyings included indices related to temperature as well as the amount of precipitation. But the specificity of weather derivatives led to the commodity exchanges stopping trade in some of them. However, climate change is increasing the risk associated with adverse weather conditions. The grain producers' sector is highly exposed to this risk, which is why the subject of this work is to build a strategy to protect against the risk of low rainfall during the growing season of plants. Material and methods The valuation of rainfall derivatives is made using Monte Carlo simulation for two types of models: a model based on daily rainfall value simulation and a model based on direct estimation of the index distribution. Then these instruments are used to build a hedging strategy against the risk of low yields in the Lower Silesian District. In the last step, the effectiveness of such a strategy is examined using percentage reduction in volatility of a secured portfolio and average squared loss. Results and conclusions Based on the calculations, we can conclude that the amount of precipitation is an important factor affecting the level of cereal yield. Therefore, it is reasonable for grain producers to apply hedging strategies against low rainfall. Additionally we derived that daily precipitation model used in the work underestimates the derivative instrument price, whereas the model based on direct simulation of the index produces acceptable results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rainfall Derivatives as a Risk Management Tool for Grain Producers: Daily Model vs. Index Model

Broszkiewicz-Suwaj¹,

Kuchar²

2020

Acta Sci. Pol., Formatio Circumiectus

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“…As a set of four time series was analyzed and the correlation coefficients between all data are significant, an attempt was made to fit the 4-dimensional VAR(1) model, with astable innovations, described in the previous section, to the data under consideration. The VAR(1) model was used as the optimal according to multiple literature positions analysing this type of data [2,8,9]. Due to the fact that in the case of the daily sum of precipitation, the VAR model based on maximum, minimum, and average temperatures cannot reflect the exact values and the methodology of forecasting precipitation has a different nature [2,8,9] (which does not change the fact that precipitation has a significant impact on daily temperatures), only the estimation of the model and forecasts for maximum and minimum temperature is considered.…”

Section: Model Fittingmentioning

confidence: 99%

“…According to the analyzes, greenhouse gases (mainly carbon dioxide) are responsible for the temperature increase observed in many places around the world. In this case, depending on the RCP (Representative Concentration Pathways) scenario [1,2], the content of these gases in the atmosphere is forecast using climatic indicators up to the year 2100. The ability to model climate variables can be used for forecasting as well as for generating synthetic data in any time horizon and in any resolution using weather generators.…”

Section: Introductionmentioning

confidence: 99%

“…There are numerous models based on various types of statistical analysis [3][4][5], and from the point of view of new technologies, models based on artificial intelligence are becoming more and more popular [6]. From the point of view of generating meteorological data [7], the most popular models that are in use are models that in the case of precipitation, the model total precipitation by means of the first-order Markov chain to determine the occurrence of wet/dry days, and then for the amount of precipitation, the multidimensional two-parameter gamma distribution is used [2,8,9]. For other variables such as daily minimum temperature, maximum temperature, solar radiation, and wind speed, multivariate autoregression models are usually used [10].…”

Section: Introductionmentioning

confidence: 99%

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Application of non-Gaussian multidimensional autoregressive model for climate data prediction

Broszkiewicz-Suwaj

Wyłomańska

2021

Int J Adv Eng Sci Appl Math

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From the point of view of agriculture, ecology, or environmental engineering, the capability of forecasting meteorological variables in the long and short term is crucial. Short-term forecasts enabling the planning of field work in agriculture, management of mass events, or tourism are important, while long-term forecasts related to advancing climate change are also very interesting. In the literature, there are known many approaches that can be used to forecast climate time series. The most common is based on the statistical modelling of the corresponding data, and the prediction is made on the fitted model. There are known one-dimensional approaches, where single variables are modeled separately; however, in the last decade, there appears a new trend which assumes the importance of the relationship between different time series. This is the approach considered in this paper. We propose to examine the climate data (temperature and precipitation) using the multidimensional vector autoregressive model (VAR). However, because in the time series we observe non-Gaussian behaviour, the classical VAR model can not be applied and the multidimensional Gaussian noise is replaced by the $$\alpha -$$ α - stable one. This model was previously analyzed by the authors in the context of financial data description where also non-Gaussian characteristics are observed. The main goal of this paper is to answer the question whether there are reasons to go from the Gaussian model to the generalized models, like $$\alpha -$$ α - stable based. The second purpose is to link total precipitation data with temperature time series. In the classical approach, precipitation was treated as a variable not correlated with temperature, which, as we will show in the paper, is inconsistent with reality. We hope the presented in this paper results open new areas of interest related to climate data modelling and prediction.

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Four decades of lake surface temperature in the Northwest Territories, Canada, using a lake-specific satellite-derived dataset

Attiah,

Kheyrollah Pour,

Scott

2023

Journal of Hydrology: Regional Studies

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Comparison of daily flows simulated for the year 2060 on the Kaczawa River for various scenarios of climate change by simple time series analysis

Cited by 3 publications

References 25 publications

Rainfall Derivatives as a Risk Management Tool for Grain Producers: Daily Model vs. Index Model

Rainfall Derivatives as a Risk Management Tool for Grain Producers: Daily Model vs. Index Model

Application of non-Gaussian multidimensional autoregressive model for climate data prediction

Four decades of lake surface temperature in the Northwest Territories, Canada, using a lake-specific satellite-derived dataset

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