Spatio-Temporal Synthesis of Continuous Precipitation Series Using Vine Copulas

Poduje, Ana Claudia Callau; Haberlandt, Uwe

doi:10.3390/w10070862

Cited by 9 publications

(3 citation statements)

References 65 publications

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“…In the last two decades, vine copulas have become an important tool for modeling multivariate dependence structures with applications in finance [1], biology [2], genetics [3], seismology [4], engineering [5], and hydrology [6] among others. Vine copulas provide a way to construct a d-dimensional copula, which is a special kind of multivariate distribution function.…”

Section: Motivation and Significancementioning

confidence: 99%

MATVines: A vine copula package for MATLAB

Coblenz¹

2021

SoftwareX

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Section: Motivation and Significancementioning

confidence: 99%

MATVines: A vine copula package for MATLAB

Coblenz¹

2021

SoftwareX

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“…Another field of application is within so-called weather generators. Since high-resolution time series of atmospheric variables are only available for a limited period (e.g., 30 years), it is necessary to extend these time series when assessing extreme events (Van de Velde et al, 2023) or designing hydrological applications (Callau Poduje & Haberlandt, 2018). Brunner et al (2019) evaluated several copula models to reproduce the spatial dependencies of gauging stations in a river catchment.…”

Section: Introductionmentioning

confidence: 99%

Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions

Schaumann,

Rempel,

Blahak

et al. 2024

Quart J Royal Meteoro Soc

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Many post‐processing methods improve forecasts at individual locations but remove their correlation structure. However, this information is essential for forecasting larger‐scale events, such as the total precipitation amount over areas like river catchments, which are relevant for weather warnings and flood predictions. We propose a method to reintroduce spatial correlation into a post‐processed forecast using an R‐vine copula fitted to historical observations. The method rearranges predictions at individual locations and ensures that they still exhibit the post‐processed marginal distributions. It works similarly to well‐known approaches, like the “Schaake shuffle” and “ensemble copula coupling.” However, compared to these methods, which rely on a ranking with no ties at each considered location in their source for spatial correlation, the copula serves as a measure of how well a given arrangement compares with the observed historical distribution. Therefore, no close relationship is required between the post‐processed marginal distributions and the spatial correlation source. This is advantageous for post‐processed seamless forecasts in two ways. First, meteorological parameters such as the precipitation amount, whose distribution has an atom at zero, have rankings with ties. Second, seamless forecasts represent an optimal combination of their input forecasts and may spatially shifted from them at scales larger than the areas considered herein, leading to non‐reasonable spatial correlation sources for the well‐known methods. Our results indicate that the calibration of the combination model carries over to the output of the proposed model, that is, the evaluation of area predictions shows a similar improvement in forecast quality as the predictions for individual locations. Additionally, the spatial correlation of the forecast is evaluated with the help of object‐based metrics, for which the proposed model also shows an improvement compared to both input forecasts.

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“…Huaxiong (2014) used the vine-copula pair-copula function to analyze the relationships among annual precipitation, annual evaporation capacity, and annual runoff [25]. Vernieuwe (2015) [26] and Callau Poduje (2018) [27] analyzed precipitation frequency through the vine-copula function. Shafaei (2017) analyzed flood frequency by vine-copula [28].…”

Section: Introductionmentioning

confidence: 99%

Probability Analysis and Control of River Runoff–sediment Characteristics based on Pair-Copula Functions: The Case of the Weihe River and Jinghe River

You

Jiang

Liu

et al. 2019

Water

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Analyzing the encounter frequency of high–low runoff and sediment yield is important for the appropriate dispatching of runoff–sediment resources, as well as river regulation. However, there have been no reports on the utilization of the pair-copula function in analyzing the runoff–sediment characteristics from a probabilistic perspective and conducting probability control on the runoff–sediment yields of different hydrologic stations. This paper builds marginal distribution functions on the basis of kernel distribution theory. In addition, this paper builds the joint distribution functions through pair-copula functions in order to analyze the encounter probability and the compensation characteristics of high–low runoff and sediment at different stations on the Weihe River in China, as well as the origins of runoff–sediment, to conduct probability control of river runoff–sediment resource allocation. The results show that, in different periods, the synchronous probability of high–low runoff of the Weihe River’s Xianyang and Huaxian Stations, and the Jinghe River’s Zhangjiashan Station differ, while that of high–low sediment at the three stations changes little—remaining at around 54%. Therefore, the sediment and runoff of the Weihe River apparently have different origins. In years of high and low runoff, if the runoffs of the Xianyang and Zhangjiashan Stations can be kept within a certain range, then the runoff of the Huaxian Station will be in a particular range, at a certain probability. Sediment at the Huaxian Station can be controlled, in a similar way. These results are of great significance for the water and sediment management department of the Weihe river, in order to reasonably allocate water and sediment resources.

show abstract

Spatio-Temporal Synthesis of Continuous Precipitation Series Using Vine Copulas

Cited by 9 publications

References 65 publications

MATVines: A vine copula package for MATLAB

MATVines: A vine copula package for MATLAB

Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions

Probability Analysis and Control of River Runoff–sediment Characteristics based on Pair-Copula Functions: The Case of the Weihe River and Jinghe River

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