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Accurate spatial estimation of extremes is an increasingly important topic in environmental research and risk assessment. Conditional bias (CB)-penalized kriging (CBPK) improves such estimation by minimizing linearly weighted sum of error variance and variance of Type-II error. However, CBPK requires skillful prescription of the weight for the CB penalty which is a significant challenge in practice. In this paper, we describe an extension of CBPK, referred to herein as adaptive conditional bias-penalized kriging (ACBPK), which objectively prescribes the weight for improved estimation of extremes without deteriorating performance in the unconditional mean squared error sense. For comparative evaluation in the real world, cross validation experiments were carried out for precipitation estimation using hourly rain gauge data in the Arkansas-Red River Basin (AB), central Texas (TX) and southeastern US (SE) areas. The results show that CB is detected for about 26, 24 and 25% of all data points in the AB, TX and SE cases, respectively, and that, given detection of CB, ACBPK reduces root mean square error of hourly precipitation exceeding 12.7 mm by 15, 21 and 9% and hourly precipitation exceeding 25.4 mm by 14, 26 and 10% relative to ordinary kriging (OK) for the AB, TX and SE cases, respectively. The overall findings indicate that, if accurate spatial estimation in the tails of the distribution is important or accurate modeling of spatiotemporally-varying correlation structure is a challenge, ACBPK should be favored over OK. [ABSTRACT FROM AUTHOR]

In this study, a sulfonated poly(ether sulfone) having cardo-type fluorenyl groups (FL-SPES) was investigated as a cathodic binder to improve fuel cell performance via increased the oxygen diffusion in the cathode. The maximum power density achieved by using the membrane electrode assembly (MEA) prepared with FL-SPES with a low ion exchange capacity (IEC) of 1.31 meq g–1 was 520 mW cm–2, which is more than twice as high as that of BP-SPES (210 mW cm–2) having typical biphenyl groups with a similar IEC. At high IEC of 1.55 meq g–1, the power density obtained by using BP-SPES was improved to 454 mW cm–2 but remained lower than that of FL-SPES. In addition, although the IEC, swelling degree, and specific resistance were similar to each other, the gas permeability of FL-SPES was improved by approximately three times compared to that of BP-SPES. The steric structure of cardo-type FL-SPES increased the free volume between the polymer backbones, leading to an increase in gas transfer. Consequently, oxygen diffusion was promoted at the cathode, resulting in improved fuel cell performance. [ABSTRACT FROM AUTHOR]

Objective merging of multiple forecasts to improve forecast accuracy is of large interest in many disciplines. Multiple linear regression (MLR) is an extremely attractive technique for this purpose because of its simplicity and interpretability. For modeling and prediction of extremes such as floods using MLR, however, attenuation bias is a very serious issue as it results in systematic under- and over-prediction in the upper and lower tails of the predictand, respectively. In this work, we introduce conditional bias-penalized multiple linear regression (CBP-MLR) which reduces attenuation bias by jointly minimizing mean squared error (MSE) and Type-II error squared. Whereas CBP-MLR improves prediction over tails, it degrades the performance near median. To retain MLR-like performance near median while exploiting the ability of CBP-MLR to improve prediction over tails, we employ composite MLR (CompMLR) which linearly weight-averages the MLR and CBP-MLR estimates. For comparative evaluation, we apply the proposed technique to multi-model streamflow prediction using several operationally produced streamflow forecasts as predictors. The results for multiple forecast groups in the US National Weather Service Middle Atlantic River Forecast Center's service area show that the relative performance among different input forecasts varies most significantly with the range of the verifying observed streamflow, and that CompMLR is generally superior to the best performing forecasts in the mean squared error sense under widely varying conditions of predictability and predictive skill. [ABSTRACT FROM AUTHOR]

Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions. [ABSTRACT FROM AUTHOR]

Kalman filter (KF) and its variants are widely used for real-time state updating and prediction in environmental science and engineering. Whereas in many applications the most important performance criterion may be the fraction of the times when the filter performs satisfactorily under different conditions, in many other applications estimation and prediction specifically of extremes, such as floods, droughts, algal blooms, etc., may be of primary importance. Because KF is essentially a least squares solution, it is subject to conditional biases (CB) which arise from the error-in-variable, or attenuation, effects when the model dynamics are highly uncertain, the observations have large errors and/or the system being modeled is not very predictable. In this work, we describe conditional bias-penalized KF, or CBPKF, based on CB-penalized linear estimation which minimizes a weighted sum of error variance and expectation of Type-II CB squared and comparatively evaluate with KF through a set of synthetic experiments for one-dimensional state estimation under the idealized conditions of normality and linearity. The results show that CBPKF reduces root mean square error (RMSE) over KF by 10-20% or more over the tails of the distribution of the true state. In the unconditional sense CBPKF performs comparably to KF for nonstationary cases in that CBPKF increases RMSE over all ranges of the true state only up to 3%. With the ability to reduce CB explicitly, CBPKF provides a significant new addition to the existing suite of filtering techniques for improved analysis and prediction of extreme states of uncertain environmental systems. [ABSTRACT FROM AUTHOR]

Simple and ordinary kriging, or SK and OK, respectively, represent the best linear unbiased estimator in the unconditional sense in that they minimize the unconditional (on the unknown truth) error variance and are unbiased in the unconditional mean. However, because the above properties hold only in the unconditional sense, kriging estimates are generally subject to conditional biases that, depending on the application, may be unacceptably large. For example, when used for precipitation estimation using rain gauge data, kriging tends to significantly underestimate large precipitation and, albeit less consequentially, overestimate small precipitation. In this work, we describe an extremely simple extension to SK or OK, referred to herein as conditional bias-penalized kriging (CBPK), which minimizes conditional bias in addition to unconditional error variance. For comparative evaluation of CBPK, we carried out numerical experiments in which normal and lognormal random fields of varying spatial correlation scale and rain gauge network density are synthetically generated, and the kriging estimates are cross-validated. For generalization and potential application in other optimal estimation techniques, we also derive CBPK in the framework of classical optimal linear estimation theory. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the expression patterns of pathogenesis-related proteins (chitinase, β-1,3-glucanase and peroxidase) using activity staining of native-polyacrylamide gel electrophoresis (PAGE) and sodium dodecyl sulfate (SDS)-PAGE during germination of rape seed ( Brassica napus L. cv. Saturnin). The crude enzymes were extracted by distilled water (DW, pH 6.0) and 100 mM K-PO buffer (pH 7.0). The expression patterns of chitinase isozymes changed clearly on 10% native-PAGE gel with DW and K-PO buffer extract and on 12% SDS-PAGE gel with K-PO buffer extract, except for 12% SDS-PAGE conducted using DW during germination. The active bands of the chitinase isozymes were observed as four major bands (ch1, ch2, 86, and 78 kDa) and three minor bands (71, 60, and 54 kDa) on 10% native-PAGE gel conducted using DW and K-PO buffer extract. The two active bands on the 12% (w/v) SDS-PAGE gel presented as 34 and 29 kDa with DW extract, whereas one active band of 34 kDa was observed when the K-PO buffer extract was used. Active bands of β-1,3-glucanase isozymes changed slightly on 10% native-PAGE gel with DW and K-PO buffer extract during germination. The active band of β-1,3-glucanase isozymes were shown to have a high molecular weight (G1 and G2) on native-PAGE gel with DW extract at 0, 1, 2, and 3 days after germination, but not at 4 and 5 days. One active band of β-1,3-glucanase presented as G1 in the K-PO buffer extract. Active staining of peroxidase was stronger earlier in the DW extract than K-PO buffer extract at 2 days. The active bands showed as P1 and P2 in both DW and K-PO buffer extract at 5 days after germination. [ABSTRACT FROM AUTHOR]

In the original publication, Eqs. (3) and (4) should read. [ABSTRACT FROM AUTHOR]