Qualitative and quantitative uncertainties in regional rainfall frequency analysis

Zhu, Qiankun; Xu, Xiao; Gao, Chao; Xu, Yue‐Ping

doi:10.1631/jzus.a1400123

Cited by 7 publications

(4 citation statements)

References 24 publications

(28 reference statements)

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“…However, most of the discussed sampling methods require more than 10,000 simulations to cover the full parametric space and increase exponentially with an increase in the number of uncertain parameters, thus, requiring more computational resources. Therefore, researchers use an alternative cost-effective emulator to estimate the model outputs using a small number of model simulations from a specific sampling technique at the selected points, to minimize the high computing resource demand 19,[27][28][29][30][31][32][33][34][35][36] . In this study, we perform the qualitative SA analysis using the Morris method 37,38 due to its computational efficiency.…”

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confidence: 99%

Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model

Pathak

Sahany

Mishra

2020

Sci Rep

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Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds ($$rhminl)$$ r h m i n l ) and the auto-conversion-size-threshold-for-ice-to-snow $$\left( {dcs} \right).$$ dcs . The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on $$dcs$$ dcs and $$rhminl$$ rhminl . The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in $$dcs$$ dcs . Overall, we find that $$rhminl$$ rhminl , $$dcs$$ dcs , $$ai,$$ a i , and $$as$$ as are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate.

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confidence: 99%

Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model

Pathak

Sahany

Mishra

2020

Sci Rep

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“…For subjective method, such as the Pedigree Matrix (Barnett, 2006;Zhu et al, 2015), Carlo, Bayesian model averaging, and 30 generalized likelihood methods (Vrugt et al, 2009;Tian et al, 2015), uncertainty estimation is often used for the approximation of the confidence intervals of quantiles of probability distributions (Coles and Pericchi, 2003;Parent andHydrol. Earth Syst.…”

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confidence: 99%

Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example

Sun

Jiang

Cheng

et al. 2017

Preprint

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Abstract. Return period estimation plays an important role in the engineering practices of water resources and disaster 10 management, but uncertainties accompany the calculation process. Based on the daily discharge records at two gauging stations (Cuntan and Pingshan) on the upper Yangtze River, three sampling methods (SMs; (annual maximum, peak over threshold, and decadal peak over threshold), five distribution functions (DFs; gamma, Gumbel, lognormal, Pearson III, and general extreme value), and three parameterization methods (PMs; maximum likelihood, L-Moment, and method of moment) were applied to analyze the uncertainties in return period estimation. The estimated return levels based on the different 15 approaches were found to differ considerably at each station. The range of discharge for a 20-year return period was 63,800.8-74,024.1 m3 s-1 for Cuntan and 23,097.8-25,595.3 m3 s-1 for Pingshan, when using the 45 combinations of SMs, DFs, and PMs. For a 1000-year event, the estimated discharge ranges increased to 74,492.5-125,658.0 and 27,339.2-41,718.1 m3 s-1 for Cuntan and Pingshan, respectively. Application of the analysis of variance method showed that the total sum of the squares of the estimated return levels increased with the widening of the return periods, suggestive of increased 20 uncertainties. However, the contributions of the different sources to the uncertainties were different. For Cuntan, where the discharge changed significantly, the SM appeared to be the largest source of uncertainty. For Pingshan, where the discharge series remained almost stable, the DF contributed most to the uncertainty. Therefore, multiple uncertainty sources in estimating return periods should be considered to meet the demands of different planning purposes. The research results also suggest that uncertainties of return level estimation could be reduced if an optimized DF were used, or if the decadal peak 25 over threshold SM were used, which is capable of representing temporal changes of hydrological series.

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“…The IDF curves in each region were estimated according to the selected distributions and the associated parameters. To assess the uncertainty associated with the fitting of distribution curves and the parameter estimation, we compared the skewness and kurtosis calculated from NARR data with those calculated from the fitted distributions (Zhu et al , ).…”

Section: Methodsmentioning

confidence: 99%

Assessment of NARCCAP model in simulating rainfall extremes using a spatially constrained regionalization method

Gao

Carbone

Guo

2015

Intl Journal of Climatology

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Capturing the intensity and return period of extreme rainfall events in the historic record and projecting them into the future are essential to managing, planning, and designing infrastructure. In this study, we assess the performance of the combination of four General Circulation Models (GCMs) and six Regional Climate Models (RCMs) that comprise the North American Regional Climate Change Assessment Program (NARCCAP) and evaluate their performance in simulating rainfall extremes in the continental United States. We adopt a regionalization method to objectively delineate 12 regions in the continental United States with relatively homogenous annual maximum 24‐h rainfall patterns from the North American Regional Reanalysis (NARR) data set. We then compare the Intensity–Duration–Frequency (IDF) curves generated from control simulations of NARCCAP models with those from NARR in each of these regions. We find significant spatial variability of model performance. The models perform reasonably well in many parts of the country, but poorly in the southeastern United States. The GCM providing boundary conditions strongly influences results – output from those RCMs driven by the Community Climate System Model (CCSM) and Canadian Global Climate Model version 3 (CGCM3) matched the NARR data best. Performance of individual RCMs also varied, often in response to nudging, wherein the regional model is constrained by the GCM fields. We also measure changes in bias‐corrected IDF curves generated from NARCCAP projections of the future. In most regions, most models project intensified 24‐h rainfall events in the future (exceptions include some model‐projected decreases in southern California, the extreme north‐central US, Florida, and the Texas Plains). This study provides a valuable means of assessing NARCCAP models' performance in simulating rainfall extremes at the regional scale and understanding how the GCMs, RCMs, and spatial variability affect model performance.

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Qualitative and quantitative uncertainties in regional rainfall frequency analysis

Cited by 7 publications

References 24 publications

Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model

Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model

Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example

Assessment of NARCCAP model in simulating rainfall extremes using a spatially constrained regionalization method

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