Toward improved hydrologic prediction with reduced uncertainty using sequential multi-model combination

The contemporary usage of hydrologic models has been to rely on a single model to perform the simulation and predictions. Despite the tremendous progress, efforts and investment put into developing more hydrologic models, there is no convincing claim that any particular model in existence is superior to other models for various applications and under all circumstances. This results to reducing the size of the plausible model space and often leads to predictions that may well-represent some phenomena or events at the expenses of others. Assessment of predictive uncertainty based on a single model is subject to statistical bias and most likely underestimation of uncertainty. This endorses the implementation of multi-model methods for more accurate estimation of uncertainty in hydrologic prediction. In this study, we present two methods for the combination of multiple model predictors using Bayesian Model Averaging (BMA) and Sequential Bayesian Model Combination (SBMC). Both methods are statistical schemes to infer a combined probabilistic prediction that possess more reliability and skill than the original model members produced by several competing models. This paper discusses the features of both methods and explains how the limitation of BMA can be overcome by SBMC. Three hydrologic models are considered and it is shown that multi-model combination can result in higher prediction accuracy than individual models. © 2008 ASCE

Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting

In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union

Effect of salicylic acid treatment on cadmium toxicity and leaf lipid composition in sunflower

The ameliorative effect of salicylic acid (SA) on cadmium (Cd) toxicity in sunflower plants was studied by investigating plant growth and fatty acid composition. Sunflower plants in two leaves stage were exposed to CdCl2 treatment (0, 50, 100, 150 and 200 µM) and then were treated with salicylic acid (0, 250 and 500 µM) as foliage spraying. One week after the last salicylic acid treatment,plants were harvested and growth parameters were measured . Oil of leaf was extracted in a Soxhlet system and fatty acid composition were measured by gas chromatography (GC). Statistical analyses showed excess Cd reduced growth parameters (fresh weight and length of stems and roots, fresh weight and number of leaves)and SA increased them compared with the control. Maximum reduction in these parameters was at 200 µmol Cd and 0µmol of SA. Cd caused a shift in fatty acids composition, resulting in a lower degree of their unsaturation and an increase in saturated fatty acids in sunflower leaves,whereas SA improved them. SA, particularly increased the percentage of linolenic acid and lowered that of palmitic acid by the same proportion. These results sugg membrane integrity due to lipids est that SA could be used as a potential growth regulator and a stabilizer ofprotection of cadmium-induced oxidative stress to improve plant resistance to Cd stres

Mutations in the paralogous human α-globin genes yielding identical hemoglobin variants

The human α-globin genes are paralogues, sharing a high degree of DNA sequence similarity and producing an identical α-globin chain. Over half of the α-globin structural variants reported to date are only characterized at the amino acid level. It is likely that a fraction of these variants, with phenotypes differing from one observation to another, may be due to the same mutation but on a different α-globin gene. There have been very few previous examples of hemoglobin variants that can be found at both HBA1 and HBA2 genes. Here, we report the results of a systematic multicenter study in a large multiethnic population to identify such variants and to analyze their differences from a functional and evolutionary perspective. We identified 14 different Hb variants resulting from identical mutations on either one of the two human α-globin paralogue genes. We also showed that the average percentage of hemoglobin variants due to a HBA2 gene mutation (α2) is higher than the percentage of hemoglobin variants due to the same HBA1 gene mutation (α1) and that the α2/α1 ratio varied between variants. These α-globin chain variants have most likely occurred via recurrent mutations, gene conversion events, or both. Based on these data, we propose a nomenclature for hemoglobin variants that fall into this category

Molecular characterization of glucose-6-phosphate dehydrogenase deficiency in Jeddah, Kingdom of Saudi Arabia

International audienceABSTRACT: BACKGROUND: The development of polymerase chain reaction (PCR)-based methods for the detection of known mutations has facilitated detecting specific red blood cell (RBC) enzyme deficiencies. We carried out a study on glucose-6-phosphate dehydrogenase (G6PD) deficient subjects in Jeddah to evaluate the molecular characteristics of this enzyme deficiency and the frequency of nucleotide1311 and IVS-XI-93 polymorphisms in the glucose-6-phosphate dehydrogenase gene. RESULTS: A total of 1584 unrelated Saudis (984 neonates and 600 adults) were screened for glucose-6-phosphate dehydrogenase deficiency. The prevalence of glucose-6-phosphate dehydrogenase deficiency was 6.9% (n=110). G6PD Mediterranean mutation was observed in 98 (89.1%) cases, G6PD Aures in 11 (10.0%) cases, and G6PD Chatham in 1 (0.9%) case. None of the samples showed G6PD A mutation. Samples from 29 deficient subjects (25 males and 4 females) were examined for polymorphism. The association of two polymorphisms of exon/intron 11 (c.1311T/IVS XI 93C) was observed in 14 (42.4%) of 33 chromosomes studied. This association was found in 9 (31.0%) carriers of G6PD Mediterranean and in 4 (13.8%) carriers of G6PD Aures. CONCLUSIONS: The majority of mutations were G6PD Mediterranean, followed by G6PD Aures and <1% G6PD Chatham. We conclude that 1311T is a frequent polymorphism in subjects with G6PD Mediterranean and Aures variants in Jeddah

Systematic documentation and analysis of human genetic variation in hemoglobinopathies using the microattribution approach

We developed a series of interrelated locus-specific databases to store all published and unpublished genetic variation related to hemoglobinopathies and thalassemia and implemented microattribution to encourage submission of unpublished observations of genetic variation to these public repositories. A total of 1,941 unique genetic variants in 37 genes, encoding globins and other erythroid proteins, are currently documented in these databases, with reciprocal attribution of microcitations to data contributors. Our project provides the first example of implementing microattribution to incentivise submission of all known genetic variation in a defined system. It has demonstrably increased the reporting of human variants, leading to a comprehensive online resource for systematically describing human genetic variation in the globin genes and other genes contributing to hemoglobinopathies and thalassemias. The principles established here will serve as a model for other systems and for the analysis of other common and/or complex human genetic diseases

A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis

We present a general Bayesian hierarchical framework for conducting nonstationary frequency analysis of multiple hydrologic variables. In this, annual maxima from each variable are assumed to follow a generalized extreme value (GEV) distribution in which the location parameter is allowed to vary in time. A Gaussian elliptical copula is used to model the joint distribution of all variables. We demonstrate the utility of this framework with a joint frequency analysis model of annual peak snow water equivalent (SWE), annual peak flow, and annual peak reservoir elevation at Taylor Park dam in Colorado, USA. Indices of largescale climate drivers—El Ni~no Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) are used as covariates to model temporal nonstationarity. The Bayesian framework provides the posterior distribution of the model parameters and consequently the return levels. Results show that performing a multivariate joint frequency analysis reduces the uncertainty in return level estimates and better captures multivariate dependence compared to an independent model