Prediction of depth‐integrated fluxes of suspended sediment in the Amazon River: particle aggregation as a complicating factor

Large rivers have been previously shown to be vertically heterogeneous in terms of suspended particulate matter (SPM) concentration, as a result of sorting of suspended solids. Therefore, the spatial distribution of suspended sediments within the river section has to be known to assess the riverine sedimentary flux. Numerous studies have focused on the vertical distribution of SPM in a river channel from a theoretical or experimental perspective, but only a few were conducted so far on very large rivers. Moreover, a technique for the prediction of depth‐integrated suspended sediment fluxes in very large rivers based on sediment transport dynamics has not yet been proposed. We sampled river water along depth following several vertical profiles, at four locations on the Amazon River and its main tributaries and at two distinct water stages. Depending on the vertical profile, a one‐ to fivefold increase in SPM concentration is observed from river channel surface to bottom, which has a significant impact on the ‘depth‐averaged’ SPM concentration. For each cross section, a so‐called Rouse profile quantitatively accounts for the trend of SPM concentration increase with depth, and a representative Rouse number can be measured for each cross section. However, the prediction of this Rouse number would require the knowledge of the settling velocity of particles, which is dependent on the state of aggregation affecting particles within the river. We demonstrate that in the Amazon River, particle aggregation significantly influences the Rouse number and renders its determination impossible from grain‐size distribution data obtained in the lab. However, in each cross section, the Rouse profile obtained from the fit of the data can serve as a basis to model, at first order, the SPM concentration at any position in the river cross section. This approach, combined with acoustic Doppler current profiler (ADCP) water velocity transects, allows us to accurately estimate the depth‐integrated instantaneous sediment flux. Copyright © 2010 John Wiley & Sons, Ltd.     

Implementing quality control on a random number stream to improve a stochastic weather generator

For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non‐uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30‐year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness‐of‐fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process‐based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.     

Time-Expanded Sampling Approach for Ensemble Kalman Filter:Experiment Assimilation of Simulated Soundings

In the Ensemble Kalman Filter (EnKF) data assimilation-prediction system, most of the computation time is spent on the prediction runs of ensemble members. A limited or small ensemble size does reduce the computational cost, but an excessively small ensemble size usually leads to filter divergence, especially when there are model errors. In order to improve the efficiency of the EnKF data assimilation-prediction system and prevent it against filter divergence, a time-expanded sampling approach for EnKF based on the WRF (Weather Research and Forecasting) model is used to assimilate simulated sounding data. The approach samples a series of perturbed state vectors from Nb member prediction runs not only at the analysis time (as the conventional approach does) but also at equally separated time levels (time interval is △t) before and after the analysis time with M times. All the above sampled state vectors are used to construct the ensemble and compute the background covariance for the analysis, so the ensemble size is increased from Nb to Nb+2M￡Nb=(1+2M)×Nb) without increasing the number of prediction runs (it is still Nb). This reduces the computational cost. A series of experiments are conducted to investigate the impact of △t (the time interval of time-expanded sampling) and M (the maximum sampling times) on the analysis. The results show that if △t and M are properly selected, the time-expanded sampling approach achieves the similar effect to that from the conventional approach with an ensemble size of (1+2M)×Nb, but the number of prediction runs is greatly reduced.     

Simulation of probability distributions commonly used in hydrological frequency analysis

Random variable simulation has been applied to many applications in hydrological modelling, flood risk analysis, environmental impact assessment, etc. However, computer codes for simulation of distributions commonly used in hydrological frequency analysis are not available in most software libraries. This paper presents a frequency‐factor‐based method for random number generation of five distributions (normal, log–normal, extreme‐value type I, Pearson type III and log‐Pearson type III) commonly used in hydrological frequency analysis. The proposed method is shown to produce random numbers of desired distributions through three means of validation: (1) graphical comparison of cumulative distribution functions (CDFs) and empirical CDFs derived from generated data; (2) properties of estimated parameters; (3) type I error of goodness‐of‐fit test. An advantage of the method is that it does not require CDF inversion, and frequency factors of the five commonly used distributions involves only the standard normal deviate. Copyright © 2006 John Wiley & Sons, Ltd.     

Using a H∞ filter assimilation procedure to estimate root zone soil water content

Root zone soil water content impacts plant water availability, land energy and water balances. Because of unknown hydrological model error, observation errors and the statistical characteristics of the errors, the widely used Kalman filter (KF) and its extensions are challenged to retrieve the root zone soil water content using the surface soil water content. If the soil hydraulic parameters are poorly estimated, the KF and its extensions fail to accurately estimate the root zone soil water. The H‐infinity filter (HF) represents a robust version of the KF. The HF is widely used in data assimilation and is superior to the KF, especially when the performance of the model is not well understood. The objective of this study is to study the impact of uncertain soil hydraulic parameters, initial soil moisture content and observation period on the ability of HF assimilation to predict in situ soil water content. In this article, we study seven cases. The results show that the soil hydraulic parameters hold a critical role in the course of assimilation. When the soil hydraulic parameters are poorly estimated, an accurate estimation of root soil water content cannot be retrieved by the HF assimilation approach. When the estimated soil hydraulic parameters are similar to actual values, the soil water content at various depths can be accurately retrieved by the HF assimilation. The HF assimilation is not very sensitive to the initial soil water content, and the impact of the initial soil water content on the assimilation scheme can be eliminated after about 5–7 days. The observation interval is important for soil water profile distribution retrieval with the HF, and the shorter the observation interval, the shorter the time required to achieve actual soil water content. However, the retrieval results are not very accurate at a depth of 100 cm. Also it is complex to determine the weighting coefficient and the error attenuation parameter in the HF assimilation. In this article, the trial‐and‐error method was used to determine the weighting coefficient and the error attenuation parameter. After the first establishment of limited range of the parameters, ‘the best parameter set’ was selected from the range of values. For the soil conditions investigated, the HF assimilation results are better than the open‐loop results. Copyright © 2010 John Wiley & Sons, Ltd.     

Multi-agent Blind Model and Its Application to Regional Eco-environmental Quality Assessment

1 Introduction Eco-environmental quality assessment is an activity that is affected by man-made factors. As other environmental systems, regional eco-environment is also a complicated and uncertain system (Wu et al., 2005). Owing to these reasons, people may produce a style of subjective un- certainty in mind in evaluating such system (Liu et al. 1999a; Wu et al., 2004). According to unascertained mathematics (Liu et al., 1997; Liu et al., 1999b), we know that this kind of subjective uncerta…     

STUDY ON THE MIGRANT LABOUR FORCE OF CHINA IN RECENT YEARS──A Case Study of Nanhai City of Guangdong Province

I.INT~IOWFOralOngthe,thesyStemofPermanentresidencempistrationhasdividedChineseampleintOthO8ewith"tirbanresidence"andthOSehaving"rUralresidence".TherationingsyStem,underwhichedibleoilandgrainwasdistributedonlytOthOSewith"urbanresidence"Calds,hasbeendondnant.Bothsystems,beingcentraltorestrictingthegrowthofurbanpopulation,havestagnatedrUral-tirbaningration.Duringthe30yearsbefore1978,thestatedidsPOnsorpopulationmovementsfromrUraltourbanareasbymeansofenrollingnewworkers,butingeneral,thiss…     

快速数论变换的一种算法

本文构造了一种快速数论变换算法,该算法是一种以数论为基础,有效高速的数论变换算法,并且这种算法易于在计算机上实现。