Practical Incorporation of Multivariate Parameter Uncertainty in Geostatistical Resource Modeling

There is a need to bridge theory and practice for incorporating parameter uncertainty in geostatistical simulation modeling workflows. Simulation workflows are a standard practice in natural resource and recovery modeling, but the incorporation of multivariate parameter uncertainty into those workflows is challenging. However, the objectives can be met without considerable extra effort and programming. The sampling distributions of statistics comprise the core theoretical notion with the addition of the spatial degrees of freedom to account for the redundancy in the spatially correlated data. Prior parameter uncertainty is estimated from multivariate spatial resampling. Simulation-based transfer of prior parameter uncertainty results in posterior distributions which are updated by data conditioning and the model domain extents and configuration. The results are theoretically tractable and practical to achieve, providing realistic assessments of uncertainty by accounting for large-scale parameter uncertainty, which is often the most important component impacting a project. A simulation-based multivariate workflow demonstrates joint modeling of intrinsic shale properties and uncertainty in estimated ultimate recovery in a shale gas project. The multivariate workflow accounts for joint prior parameter uncertainty given the current well locations and results in posterior estimates on global distributions of all modeled properties. This is achieved by transferring the joint prior parameter uncertainty through conditional simulations.     

从政策、资金和技术三者关系探讨破解海洋生态修复难题

为支撑海洋生态修复决策,推动海洋生态修复产业发展,文章分析当前我国海洋生态修复面临的问题,并提出对策建议。研究结果表明:当前我国海洋生态修复主要面临政策、资金和技术3个方面的问题,三者不是互相促进而是互相交织和抑制的关系;建议发挥我国体制优势,以政策为切入口,设立“细水长流”式海洋生态修复基金,以金融助力海洋生态修复,注重与银行贷款等政策的结合,与金融机构的要求衔接,发挥海洋生态修复基金的杠杆作用,用政策红利吸引社会资金,借鉴已有的“民办公助”“以奖代补”和“两项指标”等模式,设置修复效果的阶段性目标和最终目标,加强新技术的扶持和示范推广,充分发挥利益相关者的作用。     

Analysis of types and changes of village-level economy in rural Gongyi city,Henan Province since 1990

Based on the research on the rural living standard in China in terms of annual net income per capita, we define six types of village-level economy, i.e. ＂to be extremely poor＂, ＂to make a basic living＂, ＂to dress warmly and eat one＇s fill＂, ＂to try to enrich （to disengage poverty）＂, ＂to be well-off＂ and ＂to be affluent＂. The data of average annual net income of all the 292 villages between 1990 and 2004 in rural Gongyi City, Henan Province were collected, verified and classified. By using standard deviation, coefficient of variation and regression analysis, it is found that the Gongyi＇s rural economy has boosted up remarkably from the relative-poverty and absolute-poverty stages in 1990 to the well-off in 2004. However, the absolute differences between villages present a trend of enlargement, while the relative differences fluctuating. On the other hand, spatial analysis of village-level economy shows that most villages with relatively high economic development level were located along national expressway and most villages with absolute-poverty lay in remote mountainous areas in 1990. Since the 1990s, the rapid urbanization and industrialization have had strongly positive effects on rural economic growth. Initial economic foundation, natural resources and traditional techniques also contribute to village economy. From the perspective of geography, villages with location advantages, such as near urban center or industrial parks, have more chances for their economic development and the ＂core-periphery＂ economic structure has been presented in the process of rural development.     

Risk assessment of maize drought disaster in southwest China using the Environmental Policy Integrated Climate model

The East Asian monsoon has a tremendous impact on agricultural production in China. An assessment of the risk of drought disaster in maize-producing regions is therefore important in ensuring a reduction in such disasters and an increase in food security. A risk assessment model, EPIC(Environmental Policy Integrated Climate) model, for maize drought disasters based on the Erosion Productivity Impact Calculator crop model is proposed for areas with the topographic characteristics of the mountainous karst region in southwest China. This region has one of the highest levels of environmental degradation in China. The results showed that the hazard risk level for the maize zone of southwest China is generally high. Most hazard index values were between 0.4 and 0.5,accounting for 47.32% of total study area. However,the risk level for drought loss was low. Most of the loss rate was 0.1, accounting for 96.24% of the total study area. The three high-risk areas were mainlydistributed in the parallel ridge–valley areas in the east of Sichuan Province, the West Mountain area of Guizhou Province, and the south of Yunnan Province.These results provide a scientific basis and support for the reduction of agricultural drought disasters and an increase in food security in the southwest China maize zone.     

An Analytical Model For Threshold Crossing Rates Of Concentration Fluctuations In Dispersing Plumes

A simple analytical model is developed for the meanupcrossing rate of plume concentration fluctuations assuming that thisprocess can be well approximated by a lognormal process. The resultingexpression requires only the specification of the in-plume fluctuationintensity and in-plume Taylor micro-time scale and, hence, does notexplicitly involve the joint probability density function of theconcentration and its derivative. The analytical model provides agood fit to some field measurements of the mean upcrossing rate ina dispersing plume.     

Damming of large river by debris flow: Dynamic process and particle composition

The frequency and extent of debris flows have increased tremendously due to the extreme weather and the Wenchuan earthquake on May 12, 2008. Previous studies focused on the debris flow from gullies damming the mountain streams. In this paper, an equation for the run-out distance of debris flow in the main river is proposed based on the dynamic equation of debris flow at different slopes given by Takahashi. By undertaking field investigations and flume experiments, a new calculation method of the volume of debris flow damming large river is obtained. Using the percolation theory and the renormalization group theory it was deduced that the large particles should comprise more than 50% for forming a stable debris flow dam. Hence, the criteria of damming large river by debris flow is presented in terms of run-out distance and grain composition which was then validated through the event of damming river by debris flow at Gaojia gully, the upper reaches of the Minjiang River, Sichuan, China, on July 3, 2011.     

Intercalibration of Solar Soft X-Ray Broad Band Measurements from SOLRAD 9 through GOES-12

The two-band soft X-ray observations of solar flares made by the Naval Research Laboratory’s (NRL) SOLar RADiation (SOLRAD) satellites and by the Geostationary Orbiting Environmental Satellites (GOES) operated by the National Oceanic and Atmospheric Administration’s (NOAA) Space Weather Prediction Center have produced a nearly continuous record of solar flare observations over a period of more than forty years (1969 – 2011). However, early GOES observations (i.e., GOES-2) and later (GOES-8 and subsequent missions) are not directly comparable due to changes in the conversion of measured currents to integrated fluxes in the two spectral bands that were adopted: 0.05 – 0.3 (or 0.4) nm, which we refer to as XS and 0.1 – 0.8 nm (XL). Furthermore, additional flux adjustments, using overlapping data sets, were imposed to provide consistency of flare-flux levels from mission to mission. This article evaluates the results of these changes and compares experimental GOES-8/GOES-2 results with changes predicted from modeled flare spectra. The factors by which recent GOES observations can be matched to GOES-2 are then optimized by adapting a technique first used to extrapolate GOES X-ray fluxes above saturation using ionospheric VLF radio phase enhancements. A nearly 20% increase in published GOES-8 XL data would be required to match to GOES-2 XL fluxes, which were based on observed flare spectra. On the other hand, a factor of 1.07 would match GOES-8 and later flat-spectrum 0.1 – 0.8 nm fluxes to GOES-2 XL if the latter data were converted to a flat-spectrum basis. Finally, GOES-8 observations are compared to solar soft X-ray estimates made concurrently with other techniques. Published GOES-8 0.1 – 0.8 nm fluxes are found to be 0.59 of the mean of these other determinations. Rescaling GOES to a realistic flare spectrum and removing a 30% downward adjustment applied to the GOES-8 measurements during initial data processing would place GOES-8 and later GOES XL fluxes at 0.94 of this XL mean. GOES-2 on the same scale would lie at about 0.70 of this mean. Significant uncertainties in the absolute levels of broad band soft X-ray fluxes still remain, however.     

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity. Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns. Five southern islands in the Miaodao Archipelago, North China were studied herein. The spatial distribution of herbaceous plant diversity on these islands was analyzed, and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA. The results reveal 114 herbaceous plant species, belonging to 94 genera from 34 families in the 50 plots sampled. The total species numbers on different islands were significantly positively correlated with island area, and the average α diversity was correlated with human activities, while the β diversity among islands was more affected by island area than mutual distances. Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude, slope, total nitrogen, total carbon, and canopy density, and lower moisture content, pH, total phosphorus, total potassium, and aspect. Among the environmental factors, pH, canopy density, total K, total P, moisture content, altitude, and slope had significant gross effects, but only canopy density exhibited a significant net effect. Terrain affected diversity by restricting plantation, plantation in turn influenced soil properties and the two together affected diversity. Therefore, plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.     

Study on uncertainty of inversion and resolution limit in the presence of noise

This study analyzed the uncertainty of inversion and the resolution limit in the presence of noise by means of statistical experiments. The exhaustive method is adopted to obtain the global optimal solution in each experiment. We found that even with small level of noise, solutions fluctuate in a large range for the thin bed. The distribution of solutions in the presence of noise is closely related to the spread of the cost function in the absence of noise. As a result, the area of a certain neighborhood around the true solution on the spread of the cost function in the absence of noise is used to evaluate the uncertainty of inversion and the resolution limit in the presence of noise. In the case that the SNR (signal-to-noise ratio) is 5 in this study, solutions focus around the true solution with a very small uncertainty only when the bed thickness is greater than the reciprocal of the double predominant frequency of the convoluting wavelet.     

An inter-comparison of tidal solutions computed with a range of unstructured grid models of the Irish and Celtic Sea Regions

Three finite element codes, namely TELEMAC, ADCIRC and QUODDY, are used to compute the spatial distributions of the M₂, M₄ and M₆ components of the tide in the sea region off the west coast of Britain. This region is chosen because there is an accurate topographic dataset in the area and detailed open boundary M₂ tidal forcing for driving the model. In addition, accurate solutions (based upon comparisons with extensive observations) using uniform grid finite difference models forced with these open boundary data exist for comparison purposes. By using boundary forcing, bottom topography and bottom drag coefficients identical to those used in an earlier finite difference model, there is no danger of comparing finite element solutions for “untuned unoptimised solutions” with those from a “tuned optimised solution”. In addition, by placing the open boundary in all finite element calculations at the same location as that used in a previous finite difference model and using the same M₂ tidal boundary forcing and water depths, a like with like comparison of solutions derived with the various finite element models was possible. In addition, this open boundary was well removed from the shallow water region, namely the eastern Irish Sea where the higher harmonics were generated. Since these are not included in the open boundary, forcing their generation was determined by physical processes within the models. Consequently, an inter-comparison of these higher harmonics generated by the various finite element codes gives some indication of the degree of variability in the solution particularly in coastal regions from one finite element model to another. Initial calculations using high-resolution near-shore topography in the eastern Irish Sea and including “wetting and drying” showed that M₂ tidal amplitudes and phases in the region computed with TELEMAC were in good agreement with observations. The ADCIRC code gave amplitudes about 30 cm lower and phases about 8° higher. For the M₄ tide, in the eastern Irish Sea amplitudes computed with TELEMAC were about 4 cm higher than ADCIRC on average, with phase differences of order 5°. For the M₆ component, amplitudes and phases showed significant small-scale variability in the eastern Irish Sea, and no clear bias between the models could be found. Although setting a minimum water depth of 5 m in the near-shore region, hence removing wetting and drying, reduced the small-scale variability in the models, the differences in M₂ and M₄ tide between models remained. For M₆, a significant reduction in variability occurred in the eastern Irish Sea when a minimum 5-m water depth was specified. In this case, TELEMAC gave amplitudes that were 1 cm higher and phases 30° lower than ADCIRC on average. For QUODDY in the eastern Irish Sea, average M₂ tidal amplitudes were about 10 cm higher and phase 8° higher than those computed with TELEMAC. For M₄, amplitudes were approximately 2 cm higher with phases of order 15° higher in the northern part of the region and 15° lower in the southern part. For M₆ in the north of the region, amplitudes were 2 cm higher and about 2 cm lower in the south. Very rapid M₆ tidal-phase changes occurred in the near-shore regions. The lessons learned from this model inter-comparison study are summarised in the final section of the paper. In addition, the problems of performing a detailed model–model inter-comparison are discussed, as are the enormous difficulties of conducting a true model skill assessment that would require detailed measurements of tidal boundary forcing, near-shore topography and precise knowledge of bed types and bed forms. Such data are at present not available.