The state of land subsidence and prediction approaches due to groundwater withdrawal in China

This article gives a general introduction to land subsidence with the prediction approaches due to withdrawal of groundwater in three subsided/subsiding regions in China: the deltaic plain of Yangtse River (YRDP), North China Plain (NCP), and Fenwei Plain (FP). On YRDP, Shanghai is the typical subsided/subsiding city; on NCP Tianjin is the typical subsided/subsiding city, and on FP Taiyuan is the typical subsided/subsiding city. The subsided area with subsidence over 200 mm on YRDP is about 10,000 km² and the maximum subsided value reached 2.9 m at Shanghai; on NCP the subsided area reached 60,000 km² with the maximum subsidence of 3.9 m at Tianjing; on FP the subsided area is relatively smaller than that on the other two plains and is about 1,135 km² with maximum subsidence of 3.7 m at Taiyuan city. In order to protect the civil and industrial facilities, it is necessary to predict the future development of land subsidence based on present state. Many researchers proposed several approaches to predict the land subsidence due to groundwater withdrawal according to different geological conditions and groundwater withdrawal practice. This article classifies these approaches into five categories: (i) statistical methods; (ii) 1D numerical method; (iii) quasi-3D seepage model; (iv) 3D seepage model; (v) fully coupled 3D model. In China, the former four categories are presently employed in the prediction practice and their merits and demerits are discussed. According to the prediction practice, 3D seepage model is the best method presently.     

Climate change mitigation scenarios and policies and measures: the case of Kazakhstan

This article illustrates the main difficulties encountered in the preparation of GHG emission projections and climate change mitigation policies and measures (P&M) for Kazakhstan. Difficulties in representing the system with an economic model have been overcome by representing the energy system with a technical-economic growth model (MARKAL-TIMES) based on the stock of existing plants, transformation processes, and end-use devices. GHG emission scenarios depend mainly on the pace of transition in Kazakhstan from a planned economy to a market economy. Three scenarios are portrayed: an incomplete transition, a fast and successful one, and even more advanced participation in global climate change mitigation, including participation in some emission trading schemes. If the transition to a market economy is completed by 2020, P&M already adopted may reduce emissions of CO₂ from combustion by about 85 MtCO₂ by 2030 – 17% of the emissions in the baseline (WOM) scenario. One-third of these reductions are likely to be obtained from the demand sectors, and two-thirds from the supply sectors. If every tonne of CO₂ not emitted is valued up to US$10 in 2020 and $20 in 2030, additional P&M may further reduce emissions by 110 MtCO₂ by 2030.     

Conditional Probability Approach of the Assessment of Tsunami Potential: Application in Three Tsunamigenic Regions of the Pacific Ocean

We develop stochastic approaches to determine the potential for tsunami generation from earthquakes by combining two interrelated time series, one for the earthquake events, and another for the tsunami events. Conditional probabilities for the occurrence of tsunamis as a function of time are calculated by assuming that the inter-arrival times of the past events are lognormally distributed and by taking into account the time of occurrence of the last event in the time series. An alternative approach is based on the total probabilitiy theorem. Then, the probability for the tsunami occurrence equals the product of the ratio, r (= tsunami generating earthquakes/total number of earthquakes) by the conditional probability for the occurrence of the next earthquake in the zone. The probabilities obtained by the total probability theorem are bounded upwards by the ratio r and, therefore, they are not comparable with the conditional probabilities. The two methods were successfully tested in three characteristic seismic zones of the Pacific Ocean: South America, Kuril-Kamchatka and Japan. For time intervals of about 20 years and over the probabilities exceed 0.50 in the three zones. It has been found that the results depend on the approach applied. In fact, the conditional probabilities of tsunami occurrence in Japan are slightly higher than in the South America region and in Kuril-Kamchatka they are clearly lower than in South America. Probabilities calculated by the total probability theorem are systematically higher in South America than in Japan while in Kuril-Kamchatka they are significantly lower than in Japan. The stochastic techniques tested in this paper are promising for the tsunami potential assessment in other tsunamigenic regions of the world.     

参与式农村发展理论与实践—来自滇川黔地区的经验

参与式发展是80年代末初形成的一种发展学理论和方法，完成从学术研究到发展实践的变成，已经在大范围的政府资助的项目中应用。通过总结从事参与式农村发展引进、试验及应用的10年经验，认为参与式是发展的一种思想和一套简单和可视的方法，能够有效地协助受教育少的村民分析自己面临的问题，寻找可行和适合于他们自身条件的解决办法，提高项目受益者自己的能力。参与式的应用和推广需要一批有参与式理念和经验的发展工作者，更重要的是培养基层工作者，他们在日常与村民接触的工作应用参与式的方法。实践者除了行为、态度的转变和熟练应用参与式方法的技巧外，还需要有培训的能力、协调和协助的技巧、机构管理与项目管理的知识、以及咨询服务的技能。     

中国蓄滞洪区洪水管理与可持续发展途径

蓄滞洪区的行蓄洪启用频率和蓄水方式决定着洪水管理与可持续发展途径,影响流域经济社会的可持续发展.根据各大流域蓄滞洪区的设计启用频率,设置了未来35年内蓄滞洪区发展的3种情景:维持现状、部分水库化和部分湿地化,构建了涵盖防洪、社会经济和生态发展等因子的途径选择指标体系,运用数据包络分析法(DEA),识别了不同设计启用频率的蓄滞洪区洪水管理与可持续发展的最优途径.结果表明:① 不同设计启用频率的蓄滞洪区在建成35年内,部分湿地化情景是中国70%蓄滞洪区的最优发展途径.② 设计启用频率50年一遇是蓄滞洪区发展途径选择的边界值;当等于或低于50年一遇时,部分湿地化是其最优途径;当高于50年一遇时,维持其现状是最优途径.     

清末耕地空间分布格局重建方法比较

揭示历史时期土地利用/覆盖变化是认识人类活动对气候和环境影响的基础。本文在耕地面积、人口数量、土地利用及森林分布等多源数据基础上,分别以近代耕地空间分布格局和历史时期耕地潜在分布区为边界条件,通过构建耕地垦殖倾向指数模型分配耕地面积,在1 km×1 km象元尺度上重建了清末(1908年)松嫩平原耕地空间格局,并对重建结果进行分析比较。结果表明:1两种方法重建的耕地空间分布范围格局基本一致,耕地空间定位吻合率约为68%。清末(1908年)耕地集中分布在松嫩平原东部和南部地区;2以历史时期耕地潜在分布为边界条件的重建结果,较以近代耕地空间分布格局为限制范围的重建结果更准确,更符合历史事实。     

Trend and Interannual Variability of Chinese Air Pollution since 2000 in Association with Socioeconomic Development: A brief Overview

air pollution, trend and variability, socioeconomic development, satellite measurements, bottom-up emission inventories, top-down constraints     

Top-down and bottom-up modelling to support low-carbon scenarios: climate policy implications

Bottom-up and top-down models are used to support climate policies, to identify the options required to meet GHG abatement targets and to evaluate their economic impact. Some studies have shown that the GHG mitigation options provided by economic top-down and technological bottom-up models tend to vary. One reason for this is that these models tend to use different baseline scenarios. The bottom-up TIMES_PT and the top-down computable general equilibrium GEM-E3_PT models are examined using a common baseline scenario to calibrate them, and the extend of their different mitigation options and its relevant to domestic policy making are assessed. Three low-carbon scenarios for Portugal until 2050 are generated, each with different GHG reduction targets. Both models suggest close mitigation options and locate the largest mitigation potential to energy supply. However, the models suggest different mitigation options for the end-use sectors: GEM-E3_PT focuses more on energy efficiency, while TIMES_PT relies on decrease carbon intensity due to a shift to electricity. Although a common baseline scenario cannot be ignored, the models’ inherent characteristics are the main factor for the different outcomes, thereby highlighting different mitigation options.

Policy relevance

The relevance of modelling tools used to support the design of domestic climate policies is assessed by evaluating the mitigation options suggested by a bottom-up and a top-down model. The different outcomes of each model are significant for climate policy design since each suggest different mitigation options like end-use energy efficiency and the promotion of low-carbon technologies. Policy makers should carefully select the modelling tool used to support their policies. The specific modelling structures of each model make them more appropriate to address certain policy questions than others. Using both modelling approaches for policy support can therefore bring added value and result in more robust climate policy design. Although the results are specific for Portugal, the insights provided by the analysis of both models can be extended to, and used in the climate policy decisions of, other countries.