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In this paper, we study the empirical likelihood and estimation of parameters of interest in single-index varying coefficient models with right censored data. A bias-corrected empirical log-likelihood ratio statistic for the regression parameter is proposed. It is shown the the statistic is asymptotically standard chi-squared, and thus the confidence region of the regression parameter is constructed. The estimators for both the regression parameter and the coefficient functions are constructed, their asymptotic distributions are obtained, and the consistent estimators for the asymptotic variances are given. The obtained results can be directly used to construct the confidence regions of the regression parameter and the pointwise confidence intervals of the coefficient functions. Our approach is to directly calibrate the empirical log-likelihood ratio, so that the resulting ratio is asymptotically chi-squared, undersmoothing of the coefficient functions is avoided, and the existing data-driven methods can effectively select the optimal bandwidth. The finite-sample behavior of the new methods is evaluated through simulation studies, and the application to a real data is illustrated. [ABSTRACT FROM AUTHOR]

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Double gamma quantile mapping (DGQM) can outperform single gamma quantile mapping (SGQM) for bias correction of global circulation models (GCMs) using two gamma functions for two segments based on a specific quantile. However, there are two ambiguous points, the use of specific quantile and only Gamma probability distribution function. Therefore, this study introduced a flexible dividing point, δ (%), which can be adjusted to the regionally observed values at the station and consider the combination of various probability distributions for the two separate segments (e.g., Weibull, lognormal, and Gamma). The newly proposed method, flexible double distribution quantile mapping (F-DDQM), was employed to correct the bias of 8 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) at 22 stations in South Korea. The results clearly show a higher performance of F-DDQM than DGQM and Flexible-DGQM (F-DGQM) by 27% and 19%, respectively, in root mean square error. The F-DGQM also performed better in replicating probability distribution, spatial variability and extremes of observed precipitation than other methods. This study contributes to improving the bias correction method for better projection of extreme values. [ABSTRACT FROM AUTHOR]

The double gamma quantile mapping (DGQM) can outperform single gamma quantile mapping (SGQM) for bias correction of global circulation models (GCMs) using two gamma functions for two segments based on 90th quantile. However, there are two ambiguous points: the 90th quantile and considering only the Gamma probability function. Therefore, this study introduced a flexible dividing point, δ (%), which can be adjusted to the regionally observed values at the station and considered the combination of various probability distributions, Weibull, lognormal, and Gamma, for two separate segments. The newly proposed method, flexible double distribution quantile mapping (F-DDQM), was employed to correct the bias of 8 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) to correct bias at 22 stations in South Korea. The results clearly showed higher performance of F-DDQM than DGQM and Flexible-DGQM (F-DGQM) by 25 % and 5 %, respectively, in root mean square error. The F-DGQM also showed better performance in replicating probability distribution, spatial variability and extremes of observed precipitation than other methods. This study contributes to improving the bias correction method for the better projection of extreme values. [ABSTRACT FROM AUTHOR]

In this study, the wind and solar energy resources over China are projected during 2031–2060 under the Representative Concentration Pathway (RCP) 8.5 scenario with the Weather Research and Forecasting (WRF) model and Regional Climate Model version 4 (RegCM4). The bias correction method based on probability distribution is applied to correct the simulated 100-m wind speed and surface down-welling shortwave radiation. The results show that the bias correction method can remarkably improve the skill of the individual simulations and the ensemble mean in capturing the features of wind speed and shortwave radiation from the aspects of spatial distribution, annual cycle, quantile distribution, probability distribution and cumulative probability distribution. The future projections from the bias-corrected ensemble show that both the wind and solar energy resources are abundant over northwestern China, with higher wind power density reaching up to 130 W m ^−2 in winter and richer surface down-welling shortwave radiation above 240 W m ^−2 in summer. Generally, the wind and solar energy resources will decrease in the future, by around 6% over the western regions and 4% over southeastern China in winter, respectively. The interannual variability of the two resources is projected to increase over most areas of northern China, indicating a decrease in the stability of resources, while tends to decrease over Southeastern China.

• Hydrologic regimes over western Himalayan basin were investigated. • The peak flow is projected to shift 47 days earlier in the future. • Uncertainty in future hydrologic projections varies seasonally. • Model parameters are influential in hydrological variations of glaciated regions. Melt water from snow and glaciers is a major contributor to the streamflow over the Himalayan mountainous region. However, rising temperatures and uncertain precipitation trends may change the timing of snow melt and snow accumulation, altering the hydrological cycle. This study investigated future hydrological changes and the associated uncertainties over a snow-fed catchment of the western Himalayan River, Astore. A process-based hydrological model (WRF-Hydro/Glacier) was used with meteorological forcing from global climate models (GCMs), bias correction methods (BCMs), and multiple optimized hydrological parameters (MPs). The simulated results of streamflow, the snow-covered area (SCA), and evapotranspiration (ET) were examined over three periods: baseline (BL; 2000–2019), mid-future (MF; 2040–2059), and far-future (FF; 2080–2099). With early snow melt and late accumulation of snow, the peak streamflow is projected to shift from July during the BL, to mid-June during MF, and to mid-May during FF. Moreover, a decrease in the SCA and an increase in ET was projected during both MF and FF periods, leading to reduced solid water reserves in snow and glaciers. Furthermore, by quantifying the uncertainty contribution of GCMs, BCMs, and MPs in projecting future hydrological components, it was determined that more emphasis should be placed on the selection of an appropriate GCM and MP than that of a BCM in projecting streamflow and SCA. When projecting SCA, the GCM and MP are major contributors to uncertainty in fall–winter and spring–summer, respectively. Conversely, when projecting ET, GCM and BCM have more influence than MP. Overall, this study highlights the importance of uncertainty assessments to develop robust adaptation plans in ensemble-based climate change projections over the western Himalayan region. [ABSTRACT FROM AUTHOR]

In regression analysis, a sample selection scheme often applies to the response variable, which results in missing not at random observations on the variable. In this case, a regression analysis using only the selected cases would lead to biased results. This paper proposes a Bayesian methodology to correct this bias based on a semiparametric Bernstein polynomial regression model that incorporates the sample selection scheme into a stochastic monotone trend constraint, variable selection, and robustness against departures from the normality assumption. We present the basic theoretical properties of the proposed model that include its stochastic representation, sample selection bias quantification, and hierarchical model specification to deal with the stochastic monotone trend constraint in the nonparametric component, simple bias corrected estimation, and variable selection for the linear components. We then develop computationally feasible Markov chain Monte Carlo methods for semiparametric Bernstein polynomial functions with stochastically constrained parameter estimation and variable selection procedures. We demonstrate the finite-sample performance of the proposed model compared to existing methods using simulation studies and illustrate its use based on two real data applications. [ABSTRACT FROM AUTHOR]

Abstract: Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale. [ABSTRACT FROM AUTHOR]

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041-2065) over the reference (1971-1995), simulated using bias-corrected and nonbias-corrected simulations, is compared using mean flow, spring high flow, and summer-autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias-corrected or nonbias-corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias-corrected results should be provided to end users along with the bias-corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions. [ABSTRACT FROM AUTHOR]

Estimation of a source term, i.e. release rate, of atmospheric radionuclide emissions is of key interest for nuclear emergency response and further accident analysis. The source term estimate is, however, often very inaccurate due to biases in atmospheric transport and used meteorological analysis. We propose a method for atmospheric plume bias correction which uses not only concentrations modeled at a measuring site but also the information on concentration gradient from the neighborhood of each measuring site, i.e. information already available from the atmospheric transport model. To properly regularize the model, we propose an elastic model of the plume bias correction based on regularization with the use of known topology of the measurement network. The proposed plume bias correction method can be coupled with an arbitrary source term estimation algorithm and can be instantly applied to any other atmospheric release of hazardous material. We demonstrate the method in two real cases. First, we use data from the European Tracer Experiment to validate the methodology. Second, we use data from the 106Ru occurrence over Europe in 2017 to demonstrate the methodology in a more demanding case where agreement with state-of-the-art estimates is shown with much better reconstruction of measurements. [Display omitted] • Fully interpretable bias correction method based on measuring network topology. • Usage of gradient of the modelled concentration in the neighborhood of each sensor. • Significant improvement of measurement-reconstruction mismatch. • Applicable as plug-in to any selected inverse method, demonstration on two of them. • Validation using controlled ETEX experiment and ruthenium-106 release in 2017. [ABSTRACT FROM AUTHOR]

This work compares the performance of six bias correction methods for hydrological modeling over 10 North American river basins. Four regional climate model (RCM) simulations driven by reanalysis data taken from the North American Regional Climate Change Assessment Program intercomparison project are used to evaluate the sensitivity of bias correction methods to climate models. The hydrological impacts of bias correction methods are assessed through the comparison of streamflows simulated by a lumped empirical hydrology model (HSAMI) using raw RCM-simulated and bias-corrected precipitation time series. The results show that RCMs are biased in the simulation of precipitation, which results in biased simulated streamflows. All six bias correction methods are capable of improving the RCM-simulated precipitation in the representation of watershed streamflows to a certain degree. However, the performance of hydrological modeling depends on the choice of a bias correction method and the location of a watershed. Moreover, distribution-based methods are consistently better than mean-based methods. A low coherence between the temporal sequences of observed and RCM-simulated (driven by reanalysis data) precipitation was observed over 5 of the 10 watersheds studied. All bias corrections methods fail over these basins due to their inability to specifically correct the temporal structure of daily precipitation occurrence, which is critical for hydrology modeling. In this study, this failure occurred on basins that were distant from the RCM model boundaries and where topography exerted little control over precipitation. These results indicate that bias correction performance is location dependent and that a careful validation should always be performed, especially on studies over new regions. [ABSTRACT FROM AUTHOR]

• A new BC method, normalized quantile mapping (NQM), is proposed. • Selecting a suitable BC method for each model works better than using one BC method. • Temporal disaggregation by ANN is applied to obtain future sub-daily extremes. • Rainfall intensity at higher return periods is likely to increase more in the future. To date, no previous studies based on the Coordinated Regional Climate Downscaling Experiment Southeast Asia (CORDEX-SEA), which represents the most comprehensive set of regional climate model simulations in Southeast Asia, have reported the climate change impacts on Intensity-duration-frequency (IDF) curves for designing urban drainage systems. In this study, a framework to project future IDF curves based on a temporal disaggregation of bias-corrected CORDEX-SEA simulations was developed and applied to two cities in Southern Vietnam, i.e. Ho Chi Minh City (HCMC) and Can Tho City (CTC). First, a new bias correction (BC) method, which we call normalized quantile mapping (NQM), was proposed and compared with two other BC methods, quantile mapping (QM) and quantile delta mapping (QDM), to select a suitable BC method for downscaling each dataset of CORDEX-SEA multi-model simulations. A temporal disaggregation model in an artificial neural network (ANN) framework was introduced to obtain future sub-daily rainfall extremes, and the results were compared with the widely used stochastic model, Hyetosminute. Selecting a suitable BC method for each dataset performs better than using one BC method for all datasets in decreasing the ensemble spread by 47.1% in HCMC and 61.4% in CTC. The temporal disaggregation by ANN performs much better than Hyetosminute in durations shorter than or equal to six hours. IDF curves (based on ensemble mean of seven climate models) projected for the far future period (2066–2085) reveal that the rainfall intensity for 25-yr return period is expected to increase by 27.4–32.6% in HCMC and 55.4–72.8% in CTC (depending on the rainfall duration) from those observed for the historical period (1986–2005), while expected to change by −1.1–11.3% for 2-yr return period. Generally, more increase of rainfall intensity for rarer rainfall events (especially for longer durations) urges us to update the existing drainage systems for higher return periods. The framework developed in this study can serve as an important reference for studying climate impacts on the urban drainage systems in other cities located in the same region. [ABSTRACT FROM AUTHOR]