Factors driving riverbed scouring and sedimentation in the Bayangaole to Toudaoguai reaches of the Upper Yellow River

The Inner Mongolia reaches of the Yellow River face problems of severe sedimentation caused by a variety of complex factors. The sedimentation process in those reaches has been characterized using the sediment balance method, and the key factors affecting the process have been analyzed using the correlation analysis method. The results show that during the period 1952–2012 the Bayangaole (Bayan Gol) to Toudaoguai reaches in Inner Mongolia have undergone successive processes of accumulative sedimentation, then relative balance, and then accumulative sedimentation once again. The total annual sedimentation is 12.0341×10⁸ m³, of which accumulations from July to October account for 95.1% and the reaches from Sanhuhekou to Toudaoguai account for 98.5%. The main factor affecting scouring and sedimentation of the Bayangaole to Sanhuhekou reaches is the combined water and sediment condition. The critical conditions for equilibrium are an incoming sediment coefficient < 0.007 kg·s·m^–6 and a flow discharge > 700 m³·s^–1. The main factor affecting scouring and sedimentation of the Sanhuhekou to Toudaoguai reaches is the incoming sediment from the tributaries on the south bank and the combined water and sediment condition of the main stream. The critical conditions of the main stream for maintaining equilibrium status are a flow discharge of the main stream exceeding 800 m³·s^–1 and a comprehensive incoming sediment coefficient < 0.005 kg·s·m^–6. The incoming sediment from the tributaries has little impact on the main stream when the annual sediment load is less than 0.1×10⁸ t. The incoming sediment coefficient of the main stream and the incoming sediment from the tributaries both play vital roles in the riverbed evolution of the Inner Mongolia reaches, but the latter contributes the most.     

Spatiotemporal distribution and historical evolution of polders in the Dongting Lake area,China

Dongting Lake is the largest lake in the middle reaches of the Yangtze River in China. For centuries, people inhabiting the Dongting Lake area have been reclaiming land and constructing dams for flood resistance, agricultural production, and rural settlement, forming geographical entities known as polders. In this study, the regional spatial distributions of polders in the Dongting Lake area in 1949, 1998, and 2013 were obtained using historical maps and modern remotely sensed data, revealing changes since the establishment of the People’s Republic of China. Nanxian County was then selected to demonstrate polder changes at the county level, because it has undergone the most dramatic changes in the area. Different polder change models for the Datonghu, Yule, and Renhe polders were analyzed for eight periods: 1644 (the early Qing Dynasty), 1911 (the late Qing Dynasty), 1930 (the Republic of China), 1949 (the People’s Republic of China), 1963, 1970, 1998, and 2013. Three resulting polder evolution models are: 1) reclaiming polders from lakes, 2) integrating polders by stream merging, and 3) abandoning polders for flood release. The polder evolution models demonstrate the wisdom of local people in using land resources according to the specific regional conditions. Throughout their long-term historical evolution, the spatial distribution of polders in the Dongting Lake area tended to be homogeneous, and the degree of human disturbance tended to be stable. However, a shift occurred, from pure polder area growth or removal to more comprehensive management and protection of the regional environment.     

Progress in China’s sustainable development research: Contribution of Chinese geographers

Sustainable development has always been a hotspot in Chinese geographical research. Herein, we conduct a systematic statistical analysis of the contribution of Chinese geographers to sustainable development research using bibliometric methods. Based on the review of a vast amount of literature, we identify the main research teams, research funding sources, journals, and key research fields. The findings are as follows: (1) the resources and environmental institutes of the Chinese Academy of Sciences have a significant influence on sustainable development research; (2) China’s central government foundations (the National Natural Science Foundation of China and National Social Sciences Fund) are the main research funding sources; (3) most of the highly cited articles are published in journals sponsored by the Geographical Society of China; and (4) sustainable development theory and its research areas are being constantly enriched and perfected. Based on the statistics of keywords, the theory, research methods, research regional scales, and key research areas are summarized and expounded.     

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.     

Uncertainty analysis of space–time prisms based on the moment-design method

Space–time prism (STP) is an important concept for the modeling of object movements in space and time. An STP can be conceptualized as the result of the potential path of a moving object revolving around in the three-dimensional space. Though the concept has found applications in time geography, research on the analysis and propagation of uncertainty in STPs, particularly under high degree of nonlinearity, is scanty. Based on the efficiency and effectiveness of the moment-design (M-D) method, this paper proposes an approach to deal with nonlinear error propagation problems in the potential path areas (PPAs) of STPs and their intersections. Propagation of errors to the PPA and its boundary, and to the intersection of two PPAs is investigated. Performance of the proposed method is evaluated via a series of experimental studies. In comparison with the Monte Carlo method and the implicit function method, simulation results show the advantages of the M-D method in the analysis of error propagation in STPs.     

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.