Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology

Wani, Omar; Scheidegger, Andreas; Cecinati, Francesca; Espadas, Gabriel; Rieckermann, Jörg

doi:10.1016/j.jhydrol.2019.06.006

Cited by 10 publications

(1 citation statement)

References 31 publications

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“…Mathematical models of earth and environmental systems can be considered the sum of a deterministic term and a random variable (Higdon et al, 2004;Wani et al, 2017b, a;Kennedy and O'Hagan, 2001;Wani et al, 2019). If the predicted variable is time or space continuous, we need to borrow more advanced concepts from statistics and use random functions or fields as additive terms.…”

Section: Nonsmooth Stochastic Processesmentioning

confidence: 99%

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Noh,

Wani,

Dunne

et al. 2023

Preprint

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Abstract. Lateral migration of meandering rivers poses erosional risks to human settlements, roads, and infrastructure in alluvial floodplains. While there is a large body of scientific literature on the dominant mechanisms driving river migration, it is still not possible to accurately predict river meander evolution over multiple years. This is in part because we don't fully understand the relative contribution of each mechanism and because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model-structure deficits and unknown parameter values remains. For a more reliable assessment of risks, we, therefore, need probabilistic forecasts. Here, we present a workflow to generate geomorphic risk maps for river migration using probabilistic modeling. We start with a simple geometric model for river migration, where nominal migration rates increase with local and upstream curvature. We then account for model structure deficits using smooth random functions. Probabilistic forecasts for river channel position over time are generated by monte carlo runs using a distribution of model parameter values inferred from satellite data. We provide a recipe for parameter inference within the Bayesian framework. We demonstrate that such risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. Our results show that with longer prediction time horizons, the spatial uncertainty of erosional hazard within the entire channel belt increases – with more geographical area falling within 25 % < probability < 75 %. However, forecasts also become more confident about erosion for regions immediately in the vicinity of the river, especially on its cut-bank side. Probabilistic modeling thus allows us to quantify our degree of confidence – which is spatially and temporally variable – in river migration forecasts. We also note that to increase the reliability of these risk maps, we need to describe the first-order dynamics in our model to a reasonable degree of accuracy, and simple geometric models do not always possess such accuracy.

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Section: Nonsmooth Stochastic Processesmentioning

confidence: 99%

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Noh,

Wani,

Dunne

et al. 2023

Preprint

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Coincidence probability analysis of hydrologic low-flow under the changing environment in the Wei River Basin

Yang

Wang

Yao³

et al. 2020

Nat Hazards

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Accounting for erroneous model structures in biokinetic process models

Villez

Giudice

Neumann

et al. 2020

Reliability Engineering & System Safety

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sn engineering prtieD modelEsed design requires not only good proessE sed modelD ut lso good desription of stohsti disturnes nd mesurement errors to lern redile prmeter vlues from oservtionsF roweverD typil methods use qussin error modelsD whih often nnot deE srie the omplex temporl ptterns of residulsF gonsequentlyD this results in overon(dene in the identi(ed prmeters ndD in turnD optimisti retor designsF sn this workD we ssess the strengths nd weknesses of method to sttistilly desrie these ptterns with utoorrelted error modelsF his method produes inresed widths of the redile predition intervls folE lowing the inlusion of the is termD in turn leding to more onservtive design hoiesF roweverD we lso show tht the ugmented error model is not universl toolD s its pplition nnot gurntee the desired reliility of the resulting wstewter retor designF

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Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology

Abstract: Exploring a copula-based alternative to additive error models-for non-negative and autocorrelated time series in hydrology.

Cited by 10 publications

References 31 publications

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Coincidence probability analysis of hydrologic low-flow under the changing environment in the Wei River Basin

Accounting for erroneous model structures in biokinetic process models

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