scholarly journals Links between karst hydrogeological properties and statistical characteristics of spring discharge time series: a theoretical study

2019 ◽  
Vol 78 (14) ◽  
Author(s):  
Adeline Dufoyer ◽  
Nicolas Massei ◽  
Nicolas Lecoq ◽  
Jean-Christophe Marechal ◽  
Dominique Thiery ◽  
...  
2004 ◽  
Vol 36 (4) ◽  
pp. 2012
Author(s):  
A. Μανάκος ◽  
Γ. Δημόπουλος

Several stochastic models, known as Box and Jenkins or SARIMA (Seasonal Autoregressive Integrated Moving Average) have been used in the past for forecasting hydrological time series in general and stream flow or spring discharge time series in particular. SARIMA models became very popular because of their simple mathematical structure, convenient representation of data in terms of a relatively small number of parameters and their applicability to stationary as well as nonstationary process.Application of the seasonal stochastic model SARIMA to the spring's monthly discharge time series for the period 1974-1993 in Krania Elassona karst system yielded the following results. Logarithms of the monthly spring discharge time series can be simulated on a SARIMA (4,1,1)(1,1,1)12 type model. This type of model is suitable for the Krania Elassona karst system simulation and can be utilised as a tool to predict monthly discharge values at Kafalovriso spring for at least a 2 year period. Seasonal stochastic models SARIMA seem to be capable of simulating both runoff and groundwater flow conditions on a karst system and also easily adapt to their natural conditions.Adapting the proper stochastic model to the karst groundwater flow conditions offers the possibility to obtain accurate short term predictions, thus contributing to rational groundwater resources exploitation and management planning


2021 ◽  
Author(s):  
Guillaume Cinkus ◽  
Naomi Mazzilli ◽  
Hervé Jourde

<p>10% of the world’s population is dependent on karst water resources for drinking water. Understanding the functioning of these complex and heterogeneous systems is therefore a major challenge for long term water resource management. Over the past century, different methods have been developed to analyse hydrological series, and subsequently characterize the functioning of karst systems. These methods can be considered as a preliminary step in the development and design of hydrological models of karst functioning for sustainable water resource management. Recent progress in analytical tools, as well as the emergence of data bases of discharge time series (e.g. the French SNO KARST database and the WoKaS database at global scale) allow reconsidering former typology of karst system hydrodynamic responses. Ten karst systems and associated spring discharge time series were considered for developing the typology. The systems are well-known with a high-quality monitoring and they cover a wide range of hydrological functioning, which ensure the relevance of the analyses. The methodology for the assessment and the development of the typology consisted in (i) the analysis of springs discharge time series according to four different methods, (ii) the selection or proposal of the most relevant indicators of karst systems hydrodynamics, and (iii) the interpretation of the information from these indicators based on principal component analysis and clustering techniques. A typology of karst systems accounting for 6 different classes is finally proposed, based on 3 aspects of functioning: the capacity of dynamic storage, the draining dynamic of the capacitive function and the variability of the hydrological functioning. The typology was applied to a wider dataset composed of spring discharge of 78 karst systems. The results show a relevant distribution of the systems among the different classes.</p>


2014 ◽  
pp. 93-101
Author(s):  
Marina Cokorilo-Ilic ◽  
Vesna Ristic-Vakanjac ◽  
Sibela Oudech ◽  
Boris Vakanjac ◽  
Dusan Polomcic ◽  
...  

A sufficiently long spring discharge regime monitoring data set allows for a large number of analyses, to better understand the process of transformation of precipitation into a discharge hydrograph. It is also possible to determine dynamic groundwater volumes in a karst spring catchment area, the water budget equation parameters and the like. It should be noted that a sufficiently long data set is deemed to be a continuous spring discharge time series of more than 30 years. Such time series are rare in Serbia. They are generally much shorter (less than 15 years), and the respective catchment areas therefore fall into the ?ungauged? category. In order to extend existing karst spring discharge time series, we developed a model whose outputs, apart from mean monthly spring discharges, include daily real evapotranspiration rates, catchment size and dynamic volume variation during the analytical period. So far the model has solely been used to assess the discharge regime and water budget of karst springs. The present paper aims to demonstrate that the model also yields good results in the case of springs that drain aquifers developed in marbles. Belo Vrelo (?White Spring?, source of the Tolisnica River), which drains marbles and marbleized limestones and dolomites of Cemerno Mountain, was selected for the present case study.


Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.


Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1853
Author(s):  
Alina Bărbulescu ◽  
Cristian Ștefan Dumitriu

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.


2016 ◽  
Vol 52 (7) ◽  
pp. 5555-5576 ◽  
Author(s):  
A. de Lavenne ◽  
J. O. Skøien ◽  
C. Cudennec ◽  
F. Curie ◽  
F. Moatar

Author(s):  
Adam Goliński ◽  
Peter Spencer

AbstractThe classic ‘logistic’ model has provided a realistic model of the behavior of Covid-19 in China and many East Asian countries. Once these countries passed the peak, the daily case count fell back, mirroring its initial climb in a symmetric way, just as the classic model predicts. However, in Italy and Spain, and now the UK and many other Western countries, the experience has been very different. The daily count has fallen back gradually from the peak but remained stubbornly high. The reason for the divergence from the classical model remain unclear. We take an empirical stance on this issue and develop a model that is based upon the statistical characteristics of the time series. With the possible exception of China, the workhorse logistic model is decisively rejected against more flexible alternatives.


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