Calibration and validation of a general infiltration model

A general infiltration model proposed by Singh and Yu (1990) was calibrated and validated using a split sampling approach for 191 sets of infiltration data observed in the states of Minnesota and Georgia in the USA. Of the five model parameters, f_c (the final infiltration rate), S_o (the available storage space) and exponent ‘n’ were found to be more predictable than the other two parameters: m (exponent) and a (proportionality factor). A critical examination of the general model revealed that it is related to the Soil Conservation Service (1956) curve number (SCS‐CN) method and its parameter S_o is equivalent to the potential maximum retention of the SCS‐CN method and is, in turn, found to be a function of soil sorptivity and hydraulic conductivity. The general model was found to describe infiltration rate with time varying curve number. Copyright © 1999 John Wiley & Sons, Ltd.     

Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

志愿者民航客机夜光遥感方法与数据验证

夜光遥感是遥感技术一个活跃的分支,能够反映人类社会活动的痕迹。在城市监测、经济参数评估、重大事件评估等方面有着独特的优势。近年来夜光数据的应用不断拓展,这对传统的卫星夜光遥感数据提出了更高的要求。本文基于大众遥感、志愿者遥感理念,提出了一种夜光遥感数据获取的新方法——民航客机遥感（PARS）。以长沙市为研究区域获取PARS夜光遥感影像,并将所得数据与传统卫星遥感数据作对比。结果表明,PARS捕获的夜光遥感数据在分辨率、波段及时效性上比传统方式表现得更为优秀,是一种低成本、灵活及多样化的遥感数据获取方式,具有很大的发展潜力。     

PRINCIPAL COMPONENTS REGRESSION FOR ROUTINE MULTICOMPONENT UV DETERMINATIONS: A VALIDATION PROTOCOL

A validation protocol for multicomponent spectroscopic assays based on principal componentsregression is described. Factorial design and hypothesis tests are used to establish the linearity andabsence of interaction between components in the regression model. Testing considers multiple responsevariables simultaneously so that correlation between residuals is properly treated. Assay reproducibilityand sensitivity to related substances are evaluated.     

Identifying linear and non-linear behaviour in reduced complexity modelling output using surrogate data methods

Validation of model output is an important issue in the environmental sciences. This is particularly the case for reduced complexity modelling approaches where verification of the underlying equations is often problematic. Hence, methods that go beyond validation based on average values or the histogram of model output, are clearly advantageous. In this paper we show that a validation method based on the serial properties of time–space data, and developed in geomorphology, is a useful alternative but is nevertheless still insensitive to certain asymmetric characteristics of the signal. Hence, use of this method for validation may be enhanced by the adoption of an accompanying test for asymmetry in the model output.     

Comparing future patterns of energy system change in 2 °C scenarios with historically observed rates of change

This paper systematically compares modeled rates of change provided by global integrated assessment models aiming for the 2 °C objective to historically observed rates of change. Such a comparison can provide insights into the difficulty of achieving such stringent climate stabilization scenarios. The analysis focuses specifically on the rates of change for technology expansion and diffusion, emissions and energy supply investments. The associated indicators vary in terms of system focus (technology-specific or energy system wide), temporal scale (timescale or lifetime), spatial scale (regional or global) and normalization (accounting for entire system growth or not). Although none of the indicators provide conclusive insights as to the achievability of scenarios, this study finds that indicators that look into absolute change remain within the range of historical growth frontiers for the next decade, but increase to unprecedented levels before mid-century. Indicators that take into account or normalize for overall system growth find future change to be broadly within historical ranges. This is particularly the case for monetary-based normalization metrics like GDP compared to energy-based normalization metrics like primary energy. By applying a diverse set of indicators alternative, complementary insights into how scenarios compare with historical observations are acquired but they do not provide further insights on the possibility of achieving rates of change that are beyond current day practice.     

Validation of Land Surface Temperature Derived from 37-GHz AMSR-E over Northern China

A validation study of land surface temperature (LST) obtained from the Ka band (37 GHz) vertically polarized brightness temperature over northern China is presented.The remotely sensed LST derived jointly by the Vrije Universiteit Amsterdam and the NASA Goddard Space Flight Center (VUA-NASA) from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) were compared to the daily in-situ top soil temperature/infrared surface temperature observations from eleven/three Enhanced Coordinated Observation stations in arid and semi-arid regions of northern China.The VUA-NASA LST from the descending path exhibited a stronger correspondence to the in-situ infrared surface temperature than soil temperature observations,whereas correlations (R 2) of the latter ranged from 0.41 to 0.86.Meanwhile,the ascending overpass LST was generally warmer than the in-situ soil temperature observations at all stations,and the correlation (R 2) was between 0.07 and 0.72.Furthermore,the correlation of the descending path was generally greater than that of the ascending path at the same station.The descending path VUA-NASA LST was sensitive to precipitation and presented good agreement with ground temperature dynamics.The analyses demonstrated that the descending overpass LST was reliable to reflect reasonable patterns of temperature dynamics for land surface temperature in the region.     

Provisional Elemental Values for IAEA‐Sewage Sludge by Instrumental Neutron Activation Analysis

The International Atomic Energy Agency (IAEA) organised a proficiency test (PT), IAEA‐CU‐2010‐02, for the determination of elements in sewage sludge. The PT sample was analysed by semi‐absolute standardless k₀‐instrumental neutron activation analysis (k₀‐INAA). Results for seven elements (As, Co, Cr, Fe, Hg, Se, Zn) were submitted to the IAEA by our laboratory. All of our results were scored ‘acceptable’ by the ‘result evaluation criteria’ adopted by the IAEA. The same analytical methodology produced quantitative results for twenty‐six additional elements. In total, thirty‐six elements were determined with uncertainty varying from 4 to 11%. This paper presents the provisional mass fractions of twenty‐six additional elements (Ag, Al, Br, Ca, Ce, Cl, Dy, Eu, Ga, Hf, I, K, La, Mg, Mn, Na, Rb, Sb, Sc, Sm, Ta, Tb, Th, V, U, W) not reported by the IAEA. The analytical methodology was discussed with important sources of spectral, nuclear and fission‐product interferences. It was shown that the important components of uncertainties were the k₀ factor, Q₀ factor, detector efficiency, mass and counting statistics. The methodology was validated by analysing the IAEA‐S7 reference material.     

Effects of misclassification costs on mapping mineral prospectivity

Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area.