Forecasting the water inflow into the Krasnoyarsk and Sayano-Shushenskoe reservoirs in the second quarter of the year

We consider the various methods of constructing models intended to forecast the average water inflow, in the second quarter of the year, into two reservoirs on the Yenisei river. To solve modeling problems used a new computer technology implemented in the specialized “Stochastic Modeling” software package. Independent data were employed to verify the variants of the models for the formation of variability in quarterly inflow as generated based on different algorithms. A more sophisticated and robust model for forecasting the inflow was constructed as an ensemble of partial models. Based on aggregate results of modeling, we suggest the method of constructing a forecast of the average (for the second quarter) lateral inflow into the Krasnoyarsk reservoir and the inflow into the Sayano-Shushenskoe reservoir by use of observational data accumulated by Srednesibirskoe UGMS (Weather Control and Environmental Monitoring Service), based on an ensemble of partial models. It is established that such an operation reduces the probability of forecasting errors implying an arbitrary selection of models. We constructed forecasts of the aforementioned characteristics using real-time data for 2015. It is stated that the solution of the forecasting problem can be facilitated by using additional information.     

Influence of melioration in natural ecological processes of a small river valley (Poland)

On the basis of the topographic maps from 1933, 1951 and 1962 (sc 1:10 000, 1:25 000 and 1:100 000, respectively) and the satellite image from 2014 (sc 1:10 000), we determined changes in the channel of the small Brynica river (Southern Poland) as well as in the land-use pattern along this river valley after its melioration. Terrain regulation and drainage brought about an intensification of the overland runoff, and a decrease in irrigation of the territory. Due to soil overdrying, some tracts of the valley are experiencing a moisture deficiency. Peat drying can lead to negative consequences, because shallow peats mostly occur on the study territory. Disappearance of peaty soils acts to decrease the degree of suitability of the riverside territories for economic purposes as well as their capacity for recovery. The schematic map of vegetation distribution along the river valley prior to and after melioration as compiled from results of field observations displays differences between mosaic quasi-natural vegetation and areas of typical agrocenoses. The preservation of the natural values along the Brynica valley is possible through balanced economic management, while the differentiation of topography contributes to creating favorable conditions for the formation of biodiversity.     

Analysis of the contribution of multiple factors to the recent decrease in discharge and sediment yield in the Yellow River Basin,China

The Yellow River basin is well known for its high sediment yield. However, this sediment yield has clearly decreased since the 1980s, especially after the year 2000. The annual average sediment yield was 1.2 billion tons before 2000, but has significantly decreased to 0.3 billion tons over the last 10 years. Changes in discharge and sediment yield for the Yellow River have attracted the attention of both the Central Government and local communities. This study aimed to identify the individual contributions of changes in precipitation and human activities (e.g. water conservancy projects, terracing, silt dams, socio-economic and needs, and soil and water conservation measures) to the decrease in discharge and sediment yield of the Yellow River. The study used both improved the hydrological method and the soil and water conservation method. The study focused on discharge analysis for the upper reaches and the investigation of sediments for the middle reaches of the river. The results showed that discharge and sediment yield have both presented significant decreasing trends over the past 50 years. Precipitation showed an insignificant decreasing trend over the same period. The annual average discharge decreased by 5.68 billion m³ above Lanzhou reach of the Yellow River from 2000 to 2012; human activities (e.g. socio-economic water use) contributed 43.4% of the total reduction, whereas natural factors (e.g. evaporation from lakes, wetlands and reservoirs) accounted for 56.6%. The decrease in annual discharge and sediment yield of the section between Hekouzhen station and Tongguan station were 12.4 billion m³ and 1.24 billion tons, respectively. Human activities contributed 76.5% and 72.2% of the total reduction in discharge and sediment yield, respectively, and were therefore the dominant factors in the changes in discharge and sediment yield of the Yellow River.     

A Comparative Analysis of Weights of Evidence,Evidential Belief Functions,and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.     

Metallic Mineral Resources in the Twenty-First Century. I. Historical Extraction Trends and Expected Demand

Industrial, technological, and economic developments depend on the availability of metallic raw materials. As a greater fraction of the Earth’s population has become part of developed economies and as developed societies have become more affluent, the demand on metallic mineral resources has increased. Yet metallic minerals are non-renewable natural resources, the supply of which, even if unknown and potentially large, is finite. An analysis of historical extraction trends for eighteen metals, going back to the year 1900, demonstrates that demand of metallic raw materials has increased as a result of both increase in world population and increase in per-capita consumption. These eighteen metals can be arranged into four distinct groups, for each of which it is possible to identify a consistent pattern of per-capita demand as a function of time. These patterns can, in turn, be explained in terms of the industrial and technological applications, and in some cases conventional uses as well, of the metals in each group. Under the assumption that these patterns will continue into the future, and that world population will grow by no more than about 50% by the year 2100, one can estimate the amount of metallic raw materials that will be required to sustain the world’s economy throughout the twenty-first century. From the present until the year 2100, the world can be expected to require about one order of magnitude more metal than the total amount of metal that fueled technological and economic growth between the age of steam and the present day. For most of the metals considered here, this corresponds to 5–10 times the amount of metal contained in proven ore reserves. The two chief driving factors of this expected demand are growth in per-capita consumption and present-day absolute population numbers. World population is already so large that additional population growth makes only a small contribution to the expected future demand of metallic raw materials. It is not known whether or not the amount of metal required to sustain the world’s economy throughout this century exists in exploitable mineral resources. In the accompanying paper, I show that it is nevertheless possible to make statistical inferences about the size distribution of the mineral deposits that will need to be discovered and developed in order to satisfy the expected demand. Those results neither prove nor disprove that the needed resources exist but can be used to improve our understanding of the challenges facing future supply of metallic raw materials.     

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.     

Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.     

Managing Depleting Gold Revenues in Mali: An Assessment of Policy Options

This paper analyzes the impact of medium-term policy options in the context of gold resources depletion in Mali. Using a recursive-dynamic computable general equilibrium model calibrated to a 2006 Malian Social Accounting Matrix, we assess the impact of gold resources depletion in Mali and two policy options: the adoption of the permanent income hypothesis (PIH) and a “borrow and invest” scenario consisting at boosting public investment by 5% points of GDP. The depletion of gold resources in Mali would cause a substantial fall in GDP growth, and lead to unsustainable fiscal path if the government were to keep its current pattern of spending. Adopting either the “borrow and invest” fiscal approach or the PIH is likely to generate higher growth and a more sustainable fiscal framework compared to the status quo.     

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.