Stochastic generation of multi-site daily precipitation for applications in risk management

Breinl, Korbinian; Turkington, Thea; Stowasser, M.

doi:10.1016/j.jhydrol.2013.06.015

Cited by 34 publications

(55 citation statements)

References 70 publications

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“…In TripleM, the reshuffling is conducted over all generated years separately for all months or seasons depending on the chosen model setup. The choice of parametric models for the synthetic precipitation amounts is another influencing factor in general, which has been discussed in the past 36, 73, 74 and is not specific to TripleM. The MATLAB code offers the Gamma distribution, the Weibull distribution or a compound distribution of the Weibull distribution for lower and a Generalized Pareto distribution for higher and extreme precipitation amounts with a user-defined threshold between both curves.…”

Section: Methodsmentioning

confidence: 99%

Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

Breinl

Baldassarre

Lopez

et al. 2017

Sci Rep

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Stochastic weather generators can generate very long time series of weather patterns, which are indispensable in earth sciences, ecology and climate research. Yet, both their potential and limitations remain largely unclear because past research has typically focused on eclectic case studies at small spatial scales in temperate climates. In addition, stochastic multi-site algorithms are usually not publicly available, making the reproducibility of results difficult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three different climatic regions in the United States. By resampling observations, we investigated for the first time the performance of a multi-site precipitation generator as a function of the extent of the gauge network and the network density. The definition of the role of the network density provides new insights into the applicability in data-poor contexts. The performance was assessed using nine different statistical metrics with main focus on the inter-annual variability of precipitation and the lengths of dry and wet spells. Among our study regions, our results indicate a more accurate performance in wet temperate climates compared to drier climates. Performance deficits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.

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Section: Methodsmentioning

confidence: 99%

Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

Breinl

Baldassarre

Lopez

et al. 2017

Sci Rep

Self Cite

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“…Breinl et al (2013) applied a model of daily precipitation based on the Markov chain, using as its validation, among other statistical parameters, the transition probabilities of states. The authors found that the results of P00, in general, were satisfactory and can be used in the simulation of events, corroborating the results of this work.…”

Section: Resultsmentioning

confidence: 99%

Stochastic modeling using Markov chain on the forecast standardized precipitation index

Teixeira-Gandra¹,

Damé²,

Silva³

2017

Científica

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Grain production and livestock are the basis of the state of Rio Grande do Sul/Brazil economy and favorable rainfall conditions accounts for about 20% of Brazilian production. However, the state has faced periods of drought, especially in the campaign area, decisively affecting agricultural production and hence the economy. The objective of this paper is to predict the Standardized Precipitation Index (SPI) on a monthly scale, from a series of rainfall from 1991 to 2012, for the city of Bagé/RS/Brazil, in order to extend it by over 22 years, using a Markov chain to simulate the occurrence of rainfall and the Gamma distribution for predicting the precipitated amount. The results showed that the model simulated two-state Markov sequences of dry and wet days, keeping the statistical characteristics of the series. There were underestimations of the number on events classified as extreme and severe drought, obtained by serial SPI simulated compared to observed ones.Additional keywords: climate anomaly; drought index; SPI. ResumoA produção de grãos e a pecuária constituem a base da economia do Estado do Rio Grande do Sul/Brasil, e em condições pluviais favoráveis produz cerca de 20% da produção. No entanto, o estado tem enfrentado períodos de estiagem, especialmente na região da campanha, afetando de forma decisiva a produção agrícola e, consequentemente, a economia. Assim, objetivou-se prever o comportamento do Índice Padronizado de Precipitação (SPI) em escala mensal, a partir de uma série de precipitações pluviais de 1991 a 2012, para o município de Bagé/RS/Brasil, de forma a estendê-la por mais 22 anos, utilizando a cadeia de Markov para a simulação da ocorrência da precipitação e a distribuição Gama para a predição da quantidade precipitada. Os resultados obtidos permitiram concluir que o modelo de Markov de dois estados simulou as sequências de dias secos e chuvosos, mantendo as características estatísticas das séries. Houve subestimativa no número de eventos classificados como seca extrema e severa, obtidos mediante a série de SPI simulada, comparativamente à observada.Palavras-chave adicionais: anomalia climática; índice de seca; SPI.

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“…These rainfall runoff models may also be driven by weather generators (e.g. Breinl et al 2013). However, often, discharge measurements are indirectly quantified from water level information using rating curves (Figure 3.9).…”

Section: Inflow Dischargementioning

confidence: 99%

Flood Hazard Mapping: Uncertainty and its Value in the Decision-making Process

Mukolwe

2017

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SUMMARYFloods are natural events that can disrupt vulnerable societies and cause significant damages. Floodplain mapping, i.e. the assessment of the areas that can potentially be flooded, can help reduce the negative impact of flood events by supporting the process of landuse planning in areas exposed to flood risk. Flood inundation modelling is one of the most common approaches to develop floodplain maps.The recent literature has shown that hydraulic modelling of floods is affected by numerous sources of uncertainty that can be reduced (but not eliminated) via calibration and validation. For instance, many studies have shown that models may fail to simulate flood events of magnitude different from that of calibration and validation events. This can be caused by the fact that river flow mechanisms are nonlinear and are characterised by thresholds that demarcate flow regimes.One of the challenges in using uncertain outcomes is that decision makers (e.g. spatial planners) often have to take decisive binary actions, for instance, either to change the landuse (e.g. urbanize) or not. From the perspective of a modeller, one can provide precise (but potentially wrong) results based on both expert knowledge and the results of calibrated-and-validated models. However, this is neither prudent nor pragmatic, given that expert knowledge is variable and unavoidably subjective. As a matter of fact, different modellers using the same input data and models often attain different results. Thus, it is more scientifically sound to provide the results of flood inundation models in probabilistic terms.The objective of this thesis is to contribute to the scientific work on assessing Probabilistic flood hazard maps are generated using a Monte Carlo approach to capture the impact of these sources of uncertainty. Lastly, a new methodology for assessing the benefits of flood hazard mitigation measures (i.e. the KULTURisk framework as a result of an EU FP7 project) was used.The utility of probabilistic model output is then assessed using two approaches: (i)Value of Information, and (ii) Prospect theory. Implementation of these two approaches is based on the premise of a welfare trajectory, whereby the value of (and vii generated from) assets and investments in the floodplain accrue over time. Thus, the occurrence of a flood event results in damages that lower the welfare trajectory.Landuse in the floodplain can be altered based on the needs of the community as well as on potential flood risk. In this case, a higher investment yields higher returns,

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Stochastic generation of multi-site daily precipitation for applications in risk management

Cited by 34 publications

References 70 publications

Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

Stochastic modeling using Markov chain on the forecast standardized precipitation index

Flood Hazard Mapping: Uncertainty and its Value in the Decision-making Process

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