Copula-based downscaling of spatial rainfall: a proof of concept

Berg, M. J. van den; Vandenberghe, S.; Baets, Bernard De; Verhoest, Niko

doi:10.5194/hess-15-1445-2011

Cited by 23 publications

(9 citation statements)

References 41 publications

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“…Copulas have been extensively used in various fields of hydrology and meteorology, such as for improving the spatial resolution of modeled precipitation [29], and for bias-correcting the output from RCMs (e.g., References [30,31]). They have also been applied in the field of data fusion of different hydrometeorological datasets (e.g., References [32,33]).…”

Section: Empirical P and T Copulamentioning

confidence: 99%

Physics Parameterization Selection in RCM and ESM Simulations Revisited: New Supporting Approach Based on Empirical Copulas

2019

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This study aims at a new supplementary approach to identify optimal configurationsof physics parameterizations in regional climate models (RCMs) and earth system models (ESMs).Traditional approaches separately evaluate variable performance, which may lead to an inappropriateselection of physics parameterization combinations. Besides traditional approaches, we suggest anadditional selection approach by considering the joint dependence structure (covariance structure)between key meteorological variables, i.e., precipitation P and temperature T. This is accomplishedby empirical P and T copula functions and the χ2-test, and is demonstrated in two locationsin Kenya with different major precipitation processes. It is shown that the selection based ontraditional approaches alone may lead to nonoptimal decisions in terms of joint dependence structurebetween P and T. It was found that the copula-based approach may reduce the need for complexmultivariate bias correction, as demonstrated using local intensity scaling for P and linear scaling forT. The new approach may contribute to improving RCM and ESM simulations and climate-impactstudies worldwide.

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Section: Empirical P and T Copulamentioning

confidence: 99%

Physics Parameterization Selection in RCM and ESM Simulations Revisited: New Supporting Approach Based on Empirical Copulas

2019

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“…Not all the reasons that explain this increasing variability are brand new. Because of that, there is a consequently strong need to have powerful and reliable analytical methods to build accurate models that reproduce and forecast the future hydrological behavior of a river system [19][20][21][22][23][24][25]. Also, there is a growing necessity to design analytical strategies that allow: (a) an increase of knowledge on temporal Indeed, the annual average rainfall in Spain presents a latitudinal decrease pattern, from wet north-west (around 2000 mm), to dry south-east with less than 200 mm [28,38].…”

Section: Introductionmentioning

confidence: 99%

Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers

Molina¹,

Zazo

Martín³

2019

Water

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Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing uncertainty over water system behavior, especially river runoff. Understanding these modifications is a crucial and not trivial challenge that requires new analytical strategies like Causality, addressed by Causal Reasoning. Through Causality over runoff series, the hydrological memory and its logical time-dependency structure have been dynamically/stochastically discovered and characterized. This is done in terms of the runoff dependence strength over time. This has allowed determining and quantifying two opposite temporal-fractions within runoff: Temporally Conditioned/Non-conditioned Runoff (TCR/TNCR). Finally, a successful predictive model is proposed and applied to an unregulated stretch, Mijares river catchment (Jucar river basin, Spain), with a very high time-dependency behavior. This research may have important implications over the knowledge of historical rivers´ behavior and their adaptation. Furthermore, it lays the foundations for reaching an optimum reservoir dimensioning through the building of predictive models of runoff behavior. Regarding reservoir capacity, this research would imply substantial economic/environmental savings. Also, a more sustainable management of river basins through more reliable control reservoirs’ operation is expected to be achieved.

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“…Based on the properties of the Kendall correlation and TDC, a copula-based mixed model for modelling the dependence structure and marginals is suggested. Recently, van den Berg et al (2011) developed a copula-based approach for statistical downscaling of precipitation fields obtained from radar observations. Copula-based models for estimating error fields of radar information are described e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, they are often also called dependence functions. Sklar (1959) proved that every multivariate distribution F (x 1 , ...x n ) can be expressed in terms of a copula C and its marginals F X i (x i ):…”

Section: General Introduction To Copula Theorymentioning

confidence: 99%

Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields

Vogl

Laux

Qiu

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. This study addresses the problem of combining radar information and gauge measurements. Gauge measurements are the best available source of absolute rainfall intensity albeit their spatial availability is limited. Precipitation information obtained by radar mimics well the spatial patterns but is biased for their absolute values. In this study copula models are used to describe the dependence structure between gauge observations and rainfall derived from radar reflectivity at the corresponding grid cells. After appropriate time series transformation to generate "iid" variates, only the positive pairs (radar >0, gauge >0) of the residuals are considered. As not each grid cell can be assigned to one gauge, the integration of point information, i.e. gauge rainfall intensities, is achieved by considering the structure and the strength of dependence between the radar pixels and all the gauges within the radar image. Two different approaches, namely Maximum Theta and Multiple Theta, are presented. They finally allow for generating precipitation fields that mimic the spatial patterns of the radar fields and correct them for biases in their absolute rainfall intensities. The performance of the approach, which can be seen as a bias-correction for radar fields, is demonstrated for the Bavarian Alps. The bias-corrected rainfall fields are compared to a field of interpolated gauge values (ordinary kriging) and are validated with available gauge measurements. The simulated precipitation fields are compared to an operationally corrected radar precipitation field (RADOLAN). The copula-based approach performs similarly well as indicated by different validation measures and successfully corrects for errors in the radar precipitation.

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Copula-based downscaling of spatial rainfall: a proof of concept

Cited by 23 publications

References 41 publications

Physics Parameterization Selection in RCM and ESM Simulations Revisited: New Supporting Approach Based on Empirical Copulas

Physics Parameterization Selection in RCM and ESM Simulations Revisited: New Supporting Approach Based on Empirical Copulas

Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers

Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields

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