scholarly journals Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2156
Author(s):  
George Pouliasis ◽  
Gina Alexandra Torres-Alves ◽  
Oswaldo Morales-Napoles
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Copula Models ◽  
Multiple Processes ◽  
Spatial Dependencies ◽  
Probabilistic Dependence ◽  
Temporal And Spatial ◽  
Vine Copula ◽  
Synthetic Time Series ◽  
Hydroclimatic Variables

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

scholarly journals Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

2021 ◽  
Vol 9 (4) ◽  
pp. 363
Author(s):  
Camilla Bertolini ◽  
Edouard Royer ◽  
Roberto Pastres
Keyword(s):  
Time Series ◽  
Primary Productivity ◽  
Clustering Analysis ◽  
Venice Lagoon ◽  
Spatial Effects ◽  
Spatial Gradients ◽  
Climate Forecasting ◽  
Oxygen Production Rate ◽  
Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

scholarly journals Precision of continuous GPS velocities from statistical analysis of synthetic time series

2018 ◽  
Author(s):  
Christine Masson ◽  
Stephane Mazzotti ◽  
Philippe Vernant
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Regression Tree ◽  
Simple Method ◽  
Position Time Series ◽  
Gps Velocities ◽  
Synthetic Time Series ◽  
Continuous Gps ◽  
Automatic Treatment ◽  
Gps Noise

Abstract. We use statistical analyses of synthetic position time series to estimate the potential precision of GPS velocities. The synthetic series represent the standard range of noise, seasonal, and position offset characteristics, leaving aside extreme values. This analysis is combined with a new simple method for automatic offset detection that allows an automatic treatment of the massive dataset. Colored noise and the presence of offsets are the primary contributor to velocity variability. However, regression tree analyses show that the main factors controlling the velocity precision are first the duration of the series, followed by the presence of offsets and the noise (dispersion and spectral index). Our analysis allows us to propose guidelines, which can be applied to actual GPS data, that constrain the velocity accuracies (expressed as 95 % confidence limits) based on simple parameters: (1) Series durations over 8.0 years result in high velocity accuracies in the horizontal (0.2 mm yr−1) and vertical (0.5 mm yr−1); (2) Series durations of less than 4.5 years cannot be used for high-precision studies since the horizontal accuracy is insufficient (over 1.0 mm yr−1); (3) Series of intermediate durations (4.5–8.0 years) are associated with an intermediate horizontal accuracy (0.6 mm yr-1) and a poor vertical one (1.3 mm yr−1), unless they comprise no offset. Our results suggest that very long series durations (over 15–20 years) do not ensure a better accuracy compare to series of 8–10 years, due to the noise amplitude following a power-law dependency on the frequency. Thus, better characterizations of long-period GPS noise and pluri-annual environmental loads are critical to further improve GPS velocity precisions.

Estimating standard errors in regular vine copula models

2013 ◽  
Vol 28 (6) ◽  
pp. 2679-2707 ◽  
Author(s):  
Jakob Stöber ◽  
Ulf Schepsmeier
Keyword(s):  
Standard Errors ◽  
Copula Models ◽  
Vine Copula

Stochastic Modeling of Experimental Chaotic Time Series

2007 ◽  
Vol 98 (4) ◽  
Author(s):  
Thomas Stemler ◽  
Johannes P. Werner ◽  
Hartmut Benner ◽  
Wolfram Just
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Chaotic Time Series

Benchmarking GPS stations: an improved way to identify the GPS sensitivity

2021 ◽  
Author(s):  
Anna Klos ◽  
Jürgen Kusche ◽  
Artur Lenczuk ◽  
Grzegorz Leszczuk ◽  
Janusz Bogusz
Keyword(s):  
Time Series ◽  
Global Positioning System ◽  
Spatial Density ◽  
Earth System ◽  
Global Networks ◽  
The Earth ◽  
Mass Redistribution ◽  
Wide Range ◽  
Global Positioning ◽  
Temporal And Spatial

<p>Global Positioning System (GPS) stations are affected by a plethora of real and system-related signals and errors that occur at various temporal and spatial resolutions. Geophysical changes related to mass redistribution within the Earth system, common mode components, instability of GPS monuments or thermal expansion of ground, all contribute to the GPS-derived displacement time series. Different spatial resolutions that real and system-related errors occur within are covered thanks to the global networks of GPS stations, characterized presently by an unprecedented spatial density. Various temporal resolutions are covered by displacement time series which span even 25 years now, as estimated for the very first stations established. However, since the GPS sensitivity remains unrecognized, retrieving one signal from this wide range of processes may be very uncertain. Up to now, a comparison between GPS-observed displacement time series and displacements predicted by a set of models, as e.g. environmental loading models, was used to demonstrate the accuracy of the model to predict the observed phenomena. Such a comparison is, however, dependent on the accuracy of models and also on the sensitivity of individual GPS stations. We present a new way to identify the GPS sensitivity, which is based on benchmarking of individual GPS stations using statistical clustering approaches. We focus on regional sets of GPS stations located in Europe, where technique-related signals cover real geophysical changes for many GPS permanent stations and those located in South America and Asia, where hydrological and atmospheric loadings dominate other effects. We prove that combining GPS stations into smaller sets improves our understanding of real and system-related signals and errors.</p>

Temporal and spatial considerations in data-model comparisons involving transient paleoclimatic simulations

2021 ◽  
Author(s):  
Patrick Bartlein ◽  
Sandy Harrison
Keyword(s):  
Time Series ◽  
Best Practices ◽  
Data Model ◽  
Environmental Data ◽  
Climatic Effects ◽  
Data Archiving ◽  
Model Comparisons ◽  
Climate Reconstructions ◽  
Temporal And Spatial ◽  
Further Development

<p>The increasing availability of time-evolving or transient palaeoclimatic simulations makes it imperative to develop “best-practices” for comparing simulations with palaeoclimatic observations including both climate reconstructions and environmental data.  There are two sets of considerations, temporal and spatial, that should guide those comparisons.  The chronology of simulations can in some ways be viewed as exact, as determined by the insolation forcing, but data archiving and reporting conventions, such as reporting summaries that use the modern calendar (that leads to the long-recognized palaeo-calendar effect) can, if ignored, lead to “built-in” temporal offsets of thousands of years in such features as temperature or precipitation maxima or minima.  Likewise, there are age uncertainties in time series of palaeoclimatic data that are often ignored, despite the fact that these are large during “climatically interesting times” such as the Younger Dryas chronozone.  Similarly, although model resolution is increasing, there is still a mismatch in topography (and its climatic effects) between a model and the “real world” sensed by the palaeoclimatic data sources. </p><p>There are existing approaches for dealing with some of these issues, such as calendar-adjustment programs, Monte-Carlo approaches for describing age uncertainties in palaeoclimate time series, or clustering approaches for objectively defining appropriate regions for the calculation of area averages, but there is certainly room for further development.  This abstract is intended to serve as platform for discussion of some of best practices for data-model comparisons in transient mode.</p>

scholarly journals An ARFIMA-based model for daily precipitation amounts with direct access to fluctuations

2020 ◽  
Vol 34 (10) ◽  
pp. 1487-1505
Author(s):  
Katja Polotzek ◽  
Holger Kantz
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Confidence Intervals ◽  
Daily Precipitation ◽  
Rainfall Simulation ◽  
Direct Access ◽  
Effective Sample Size ◽  
Sample Mean ◽  
Daily Data ◽  
Synthetic Time Series

Abstract Correlations in models for daily precipitation are often generated by elaborate numerics that employ a high number of hidden parameters. We propose a parsimonious and parametric stochastic model for European mid-latitude daily precipitation amounts with focus on the influence of correlations on the statistics. Our method is meta-Gaussian by applying a truncated-Gaussian-power (tGp) transformation to a Gaussian ARFIMA model. The speciality of this approach is that ARFIMA(1, d, 0) processes provide synthetic time series with long- (LRC), meaning the sum of all autocorrelations is infinite, and short-range (SRC) correlations by only one parameter each. Our model requires the fit of only five parameters overall that have a clear interpretation. For model time series of finite length we deduce an effective sample size for the sample mean, whose variance is increased due to correlations. For example the statistical uncertainty of the mean daily amount of 103 years of daily records at the Fichtelberg mountain in Germany equals the one of about 14 years of independent daily data. Our effective sample size approach also yields theoretical confidence intervals for annual total amounts and allows for proper model validation in terms of the empirical mean and fluctuations of annual totals. We evaluate probability plots for the daily amounts, confidence intervals based on the effective sample size for the daily mean and annual totals, and the Mahalanobis distance for the annual maxima distribution. For reproducing annual maxima the way of fitting the marginal distribution is more crucial than the presence of correlations, which is the other way round for annual totals. Our alternative to rainfall simulation proves capable of modeling daily precipitation amounts as the statistics of a random selection of 20 data sets is well reproduced.

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