Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models

Zhu, Jianting; Forsee, W. J.; Schumer, R.; Gautam, Mahesh R.

doi:10.1007/s10584-012-0639-6

Cited by 42 publications

(68 citation statements)

References 26 publications

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“…Besides, some scientists (Greene et al, 2006;Zhu et al, 2013) recommend a model-weighting scheme as a feasible approach to reducing uncertainties in climate impact studies. This method gives large weights to the skillful models and minor weights to the models that do not match the observed dynamics.…”

Section: Uncertainties From Different Sources: What Are the Ways To Rmentioning

confidence: 99%

Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents

et al. 2015

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Abstract. Climate change impacts on hydrological processes should be simulated for river basins using validated models and multiple climate scenarios in order to provide reliable results for stakeholders. In the last 10-15 years, climate impact assessment has been performed for many river basins worldwide using different climate scenarios and models. However, their results are hardly comparable, and do not allow one to create a full picture of impacts and uncertainties. Therefore, a systematic intercomparison of impacts is suggested, which should be done for representative regions using state-of-the-art models. Only a few such studies have been available until now with the global-scale hydrological models, and our study is intended as a step in this direction by applying the regional-scale models. The impact assessment presented here was performed for three river basins on three continents: the Rhine in Europe, the Upper Niger in Africa and the Upper Yellow in Asia. For that, climate scenarios from five general circulation models (GCMs) and three hydrological models, HBV, SWIM and VIC, were used. Four representative concentration pathways (RCPs) covering a range of emissions and land-use change projections were included. The objectives were to analyze and compare climate impacts on future river discharge and to evaluate uncertainties from different sources. The results allow one to draw some robust conclusions, but uncertainties are large and shared differently between sources in the studied basins. Robust results in terms of trend direction and slope and changes in seasonal dynamics could be found for the Rhine basin regardless of which hydrological model or forcing GCM is used. For the Niger River, scenarios from climate models are the largest uncertainty source, providing large discrepancies in precipitation, and therefore clear projections are difficult to do. For the Upper Yellow basin, both the hydrological models and climate models contribute to uncertainty in the impacts, though an increase in high flows in the future is a robust outcome ensured by all three hydrological models.

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Section: Uncertainties From Different Sources: What Are the Ways To Rmentioning

confidence: 99%

Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents

et al. 2015

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“…These L-moments and L-ME have been used widely in many research fields including meteorology, civil engineering, and hydrology (for example, Busababodhin et al, 2016;Meshgi and Khalili, 2009;Murshed et al, 2014;Zhu et al, 2013). We used R package 'lmom' developed by Hosking (2015) to calculate the sample L-moments and the L-ME of GEVD.…”

Section: Methods Of L-moments Estimationmentioning

confidence: 99%

“…Generalized extreme value distribution (GEVD) has been used widely as a significant modelling tool to make an inference of extreme events such as heavy rainfall, wind speed, snowfall, earthquake and other related disciplines (Castillo et al, 2005;Coles 2001;Katz et al, 2002;Zhu et al, 2013). Several estimation methods for GEVD parameters were developed in previous studies such as maximum likelihood estimation (MLE) or the method of L-moments estimation (L-ME).…”

Section: Introductionmentioning

confidence: 99%

A data-adaptive maximum penalized likelihood estimation for the generalized extreme value distribution

Lee

Shin

Park

2017

CSAM

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Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

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“…Similar results were observed by a study in the southwest of United States by Gershunov et al (2013), in which extreme rainfalls are predicted to become more frequent and more severe in the wintertime. Likewise, results in a study at multiple locations in the United States by Zhu et al (2013) concluded that the intensity of extreme rainfall is predicted to be higher at all locations although the rate of increase varies among locations. The same phenomenon is occurring in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Climate Projections of Future Extreme Events in Malaysia

Syafrina¹,

Zalina²,

Norzaida³

2017

American Journal of Applied Sciences

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In Malaysia, extreme rainfall events are often linked to a number of environmental disasters such as landslides, monsoonal and flash floods. In response to the negative impacts of such disaster, studies assessing the changes and projections of extreme rainfall are vital in order to gather climate change information for better management of hydrological processes. This study investigates the changes and projections of extreme rainfall over Peninsular Malaysia for the period 2081-2100 based on the RCP 6.0 scenario. In particular, this study adopted the statistical downscaling method which enables high resolution, such as hourly data, to be used for the input. Short duration and high intensity convective rainfall is a normal feature of tropical rainfall especially in the western part of the peninsular. The proposed method, the Advanced Weather Generator model is constructed based on thirty years of hourly rainfall data from forty stations. To account for uncertainties, an ensemble multi-model of five General Circulation Model realizations is chosen to generate projections of extreme rainfall for the period 2081-2100. Results of the study indicate a possible increase in future extreme events for both the hourly and 24 h extreme rainfall with the latter showing a wider spatial distribution of increase.

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Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models

Cited by 42 publications

References 26 publications

Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents

Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents

A data-adaptive maximum penalized likelihood estimation for the generalized extreme value distribution

Climate Projections of Future Extreme Events in Malaysia

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