Description: Publication date: Available online 19 January 2018 Source: Journal of Hydrology: Regional Studies Author(s): Alfonso Rivera, Lucila Candela Study region Global scale. Study focus This paper highlights the main outputs and outcomes of the Internationally Shared Aquifer Resources Management Initiative (ISARM, 2000–2015) of UNESCO on the global scale. We discuss the lessons learned, what is still relevant in ISARM, and what we consider irrelevant and why. We follow with discussion on the looming scenarios and the next steps following the awareness on transboundary aquifers (TBAs) as identified by ISARM. New insights for the region This analysis emphasizes the need for more scientific data, widespread education and training, and a more clearly defined role for governments to manage groundwater at the international level. It describes the links, approach and relevance of studies on TBAs to the UN Law of Transboundary Aquifers and on how they might fit regional strategies to assess and manage TBAs. The study discusses an important lesson learned on whether groundwater science can solve transboundary issues alone. It has become clear that science should interact with policy makers and social entities to have meaningful impacts on TBAs. Bringing together science, society, law, policy making, and harmonising information, would be important drivers and the best guidance for further assessments. ISARM can still make contributions, but it could be redesigned to support resolving TBAs issues which, in addition to science (hydrogeology), require considering social, political, economic and environmental factors. ISARM can increase its international dimension in the continents that still lag behind the assessment and shared management of TBAs, such as Asia and Africa.

Description:    Soil carbon stocks and sequestration have been given a lot of attention recently in the study of terrestrial ecosystems and global climate change. This review focuses on the progress made on the estimation of the soil carbon stocks of China, and the characterization of carbon dynamics of croplands with regard to climate change, and addresses issues on the mineralization of soil organic carbon in relation to greenhouse gas emissions. By integrating existing research data, China’s total soil organic carbon (SOC) stock is estimated to be 90 Pg and its inorganic carbon (SIC) stock as 60 Pg, with SOC sequestration rates in the range of 20–25 Tg/a for the last two decades. An estimation of the biophysical potential of SOC sequestration has been generally agreed as being 2 Pg over the long term, of which only 1/3 could be attainable using contemporary agricultural technologies in all of China’s croplands. Thus, it is critical to enhance SOC sequestration and mitigate climate change to improve agricultural and land use management in China. There have been many instances where SOC accumulation may not induce an increased amount of decomposition under a warming scenario but instead favor improved cropland productivity and ecosystem functioning. Furthermore, unchanged or even decreased net global warming potential (GWP) from croplands with enhanced SOC has been reported by a number of case studies using life cycle analysis. Future studies on soil carbon stocks and the sequestration potential of China are expected to focus on: (1) Carbon stocks and the sequestration capacity of the earths’ surface systems at scales ranging from the plot to the watershed and (2) multiple interface processes and the synergies between carbon sequestration and ecosystem productivity and ecosystem functioning at scales from the molecular level to agro-ecosystems. Soil carbon science in China faces new challenges and opportunities to undertake integrated research applicable to many areas. Content Type Journal Article Category Review Pages 1-11 DOI 10.1007/s11434-011-4693-7 Authors JuFeng Zheng, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China Kun Cheng, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China GenXing Pan, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China Pete Smith, Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, 23 St Machar Drive, Aberdeen, AB24 3UU UK LianQing Li, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China XuHui Zhang, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China JinWei Zheng, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China XiaoJun Han, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China YanLing Du, Institute for Resource, Ecosystem and Environment of Agriculture, and Research Center of Agriculture and Climate Change, Nanjing Agricultural University, Nanjing, 210095 China Journal Chinese Science Bulletin Online ISSN 1861-9541 Print ISSN 1001-6538

Description: Global climate change is exerting profound effects on organisms and ecosystems. As resource managers and policymakers must contend with the ongoing and future effects of global climate change, they challenge scientists to predict where, when, and with what magnitude these effects are most likely to occur. By understanding the processes by which human-managed and natural ecosystems respond to a changing climate, and by quantifying levels of confidence in our ability to predict these effects, we may be able to prepare for some of these impacts, a form of adaptation to climate change. Here, we describe how knowledge of physiology can help to inform management decisions. Because physiological tolerance to environmental factors varies between species, there will likely be “winners” and “losers” in the face of climate change. We explore how a failure to consider the details of an organism’s physiology and ecology can hamper efforts to respond proactively to climate change and, conversely, how an understanding of how nonhuman organisms interact with their environment can help to provide a framework for anticipating and preparing for future changes in natural and managed ecosystems. We examine some of the physiological responses of marine organisms to climate change in three examples: thermal stress in marine invertebrates, ramifications of water temperature changes on fish bioenergetics and thus on fish reproduction and growth, and effects of changes in wave forces on damage to corals and kelp. Because factors such as temperature interact with other stressors like overexploitation and pollution to drive patterns of mortality, it may be possible to prevent some damage by reducing the impact of stressors not related to climate change. Methods such as ecological forecasting and the utilization of bioenergetic budgets can be used to help guide future adaptation to climate change by providing forecasts within a probabilistic framework. Author:  Brian Helmuth Lauren Yamane Katharine J. Mach Shilpi Chhotray Phil Levin Sarah Woodin Issue:  Climate change Download:  61_Helmuth Final.pdf

Description:    Since Westman (1977) and Ehrlich (1982) put forward the concepts of “the service of nature” and “ecosystem service functions”, respectively, methods for conducting value accounting for them, and their practical application have become the subjects of intense study. Based on an overview of available research findings, we discuss three scientific hypotheses. First, the terrestrial ecosystem offers both positive and negative service functions. Second, changes in terrestrial ecosystem service functions lie not only in the number of ecosystem types and the coverage area of each type, but also in their quality. Third, the value of terrestrial ecosystem service functions should be assessed both in terms of the value stocked and the value added. We collected land use data from China during the period 1999–2008, and Normalized Difference Vegetation Index data based on remote sensing images from the Global Inventory Modeling and Mapping Studies for the same period. We then calculated and analyzed spatial and temporal changes in China’s terrestrial ecosystem service values over the 10-year period. Considering temporal change, the total value (stocked) of China’s terrestrial ecosystem service functions decreased from 6.82 trillion Yuan RMB in 1999 to 6.57 trillion Yuan RMB in 2008. During that period, the positive value decreased by 240.17 billion Yuan RMB and the negative value increased by 8.85 billion Yuan RMB. The decrease in total value lies mainly in the humidity control, soil formation, and waste recycling functions. The total value (added) of China’s terrestrial ecosystem service functions increased by 4.31 billion Yuan RMB in 2000, but decreased by 0.13 billion Yuan RMB in 2008 (based on the constant price of China in 1999). The value (added) was a negative figure. From the perspective of spatial change, we can see that the supply of China’s terrestrial ecosystem service functions fell slightly over the past 10 years, mainly in Northeast and Southern China. As a result of human activities on ecosystems, the loss of ecosystem service functions’ value was relatively prominent in Shanxi and Gansu provinces, compared with an increase in value in Shaanxi Province. Terrestrial ecosystem service functions’ value per unit area was relatively high in mid- and East China, showing a prominent spatial change over the 10-year period, but low in Western China. Some conclusions are drawn after an in-depth analysis of the factors causing the spatial and temporal changes in China’s terrestrial ecosystem service functions, in the hope that our suggestions will be helpful for the management of China’s terrestrial ecosystems. Content Type Journal Article Category Article Pages 1-12 DOI 10.1007/s11434-012-4978-5 Authors Yao Shi, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China RuSong Wang, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China JinLou Huang, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China WenRui Yang, Beijing Municipal Institute of City Planning and Design, Beijing, 100045 China Journal Chinese Science Bulletin Online ISSN 1861-9541 Print ISSN 1001-6538

Description:    Understanding the characteristics of historical droughts will benefit water resource managers because it will reveal the possible impacts that future changes in climate may have on drought, and subsequently, the availability of water resources. The goal of this study was to reconstruct historical drought occurrences and assess future drought risk for the drought-prone Blue River Basin in Oklahoma, under a likely changing climate using three types of drought indices, i.e., Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI) and Standardized Runoff Index (SRI). No similar research has been conducted in this region previously. Monthly precipitation and temperature data from the observational period 1950–1999 and over the projection period 2010–2099 from 16 statistically downscaled Global Climate Models (GCM) were used to compute the duration, severity, and extent of meteorological droughts. Additionally, soil moisture, evapotranspiration (ET), and runoff data from the well-calibrated Thornthwaite Monthly Water Balance Model were used to examine drought from a hydrological perspective. The results show that the three indices captured the historical droughts for the past 50 years and suggest that more severe droughts of wider extent are very likely to occur over the next 90 years in the Blue River Basin, especially in the later part of the 21st century. In fact, all three indices display lower minimum values than those ever recorded in the past 50 years. This study also found that SRI and SPI (PDSI) had a correlation coefficient of 0.81 (0.78) with a 2-month (no appreciable) lag time over the 1950–2099 time period across the basin. There was relatively lower correlation between SPI and PDSI over the same period. Although this study recommends that PDSI and SRI are the most suitable indices for assessing future drought risks under an increasingly warmer climate, more drought indices from ecological and socioeconomic perspectives should be investigated and compared to provide a complete picture of drought and its potential impacts on the dynamically coupled nature-human system. Content Type Journal Article Pages 1-19 DOI 10.1007/s11269-012-0044-y Authors Lu Liu, School of Civil Engineering and Environmental Science, University of Oklahoma, 202 W. Boyd St., Room 334, Norman, OK 73019-1024, USA Yang Hong, School of Civil Engineering and Environmental Science, University of Oklahoma, 202 W. Boyd St., Room 334, Norman, OK 73019-1024, USA Christopher N. Bednarczyk, Research Experiences for Undergraduates Program, National Weather Center, University of Oklahoma, Norman, OK, USA Bin Yong, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098 China Mark A. Shafer, Southern Climate Impacts Planning Program, Oklahoma Climatological Survey, University of Oklahoma, 120 David L. Boren Blvd., Suite 2900, Norman, OK 73072, USA Rachel Riley, Southern Climate Impacts Planning Program, Oklahoma Climatological Survey, University of Oklahoma, 120 David L. Boren Blvd., Suite 2900, Norman, OK 73072, USA James E. Hocker, Southern Climate Impacts Planning Program, Oklahoma Climatological Survey, University of Oklahoma, 120 David L. Boren Blvd., Suite 2900, Norman, OK 73072, USA Journal Water Resources Management Online ISSN 1573-1650 Print ISSN 0920-4741

Description: Publication year: 2011 Source: Journal of Hydrology, Available online 25 November 2011 Shahbaz Khan The sample papers collected in this special volume represent the interdisciplinary studies presented at a major international conference that took place in San Diego, USA, October 11 – 13, 2010 in collaboration with UNESCO’s International Hydrological Program (IHP) Hydrology for the Environment Life and Policy (HELP) network and the Elsevier Journal of Hydrology. This conference targeted the emerging interdisciplinary science themes at the interface between hydrology and other scientific disciplines, including climate change, biology, chemistry and social sciences. These subjects are of particular relevance to current global water crisis, since population increases and a changing climate is bringing new pressures on hydrological systems around the world. The papers presented at the conference focused on the following five interdisciplinary themes:•Hydrology and climate change.•Hydrology, bio-geochemistry and environmental management.•Hydrology, health and improved socio-economic conditions.•Hydrology, history and conflicts.•Hydrology: past, present and future developments.This effort has highlighted the need to further focus hydrological research at the interdisciplinary interfaces between biophysical, social and economic sciences to assist with evidence based legislation and policy making in real catchments while empowering stakeholders in pursuit of real answers.

Description:    Four wetland maps for all China have been produced, based on Landsat and CBERS-02B remote sensing data between 1978 and 2008 (1978, 1990, 2000 and 2008). These maps were mainly developed by manual interpretation and validated by substantial field investigation in 2009. Based on these maps, we analyzed the 2008 wetland distribution in China and discussed wetland changes and their drivers over the past 30 years. (i) There were about 324097 km 2 of wetlands in 2008, for which inland marshes or swamps were the most common wetland type (35%), with lakes (26%) second. Most of the wetlands were in Heilongjiang, Inner Mongolia, Qinghai and Tibet, occupying about 55% of the national wetland area. (ii) From 1978 to 2008, China’s wetland area continually and significantly decreased, by about 33% based on changes in the wetland map. This was in sharp contrast to the increase in artificial wetlands, which increased by about 122%. Inland marshes accounted for the main loss of total wetlands from 1978 to 2000. From 2000 through 2008, riverine and lacustrine wetlands constituted the main wetland loss. Fortunately however, the rate of wetland loss decreased from 5523 to 831 km 2 /a. (iii) The change ratio of lost natural wetlands (including inland and coastal wetlands) to non-wetlands has decreased slightly over the past 30 years. From 1978 to 1990, nearly all natural wetlands (98%) lost were transformed into non-wetlands. However, the ratio declined to 86% from 1990 to 2000, and to 77% from 2000 to 2008. (iv) All Chinese provinces were divided into three groups according to patterns of wetland changes, which could relate to the driving forces of such changes. Tibet was completely different from other provinces, as it was one representative example in which there was a net wetland increase, because of global warming and decreased human activity since 1990. Increased economic development caused considerable wetland loss in most eastern provinces, and artificial wetlands increased. Content Type Journal Article Category Article Pages 1-11 DOI 10.1007/s11434-012-5093-3 Authors ZhenGuo Niu, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China HaiYing Zhang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China XianWei Wang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China WenBo Yao, Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing, 100084 China DeMin Zhou, Resource Environment and Tourism, Capital Normal University, Beijing, 100037 China KuiYi Zhao, Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun, 130012 China Hui Zhao, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China NaNa Li, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China HuaBing Huang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China CongCong Li, Department of Geography and Remote Sensing, Beijing Normal University, Beijing, 100875 China Jun Yang, College of Forestry, Beijing Forestry University, Beijing, 100083 China CaiXia Liu, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Shuang Liu, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Lin Wang, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Zhan Li, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China ZhenZhong Yang, Department of Geography and Remote Sensing, Beijing Normal University, Beijing, 100875 China Fei Qiao, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China YaoMin Zheng, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China YanLei Chen, Department of Environmental Science, Policy and Management, University of California, Berkeley, 94720 USA YongWei Sheng, Department of Geography, University of California, Los Angeles, 90095 USA XiaoHong Gao, Department of Life and Geographic Sciences, Qinghai Normal University, Xining, 810008 China WeiHong Zhu, Department of Geography, Yanbian University, Yanbian, 133002 China WenQing Wang, School of Environment and Ecology, Xiamen University, Xiamen, 361005 China Hong Wang, School of Geographical Information Science, Hohai University, Nanjing, 211100 China YongLing Weng, School of Surveying and Mapping Transportation Engineering, Southeast University, Nanjing, 210096 China DaFang Zhuang, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China JiYuan Liu, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China ZhiCai Luo, School of Surveying and Mapping, Wuhan University, Wuhan, 430079 China Xiao Cheng, Department of Geography and Remote Sensing, Beijing Normal University, Beijing, 100875 China ZiQi Guo, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Peng Gong, State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101 China Journal Chinese Science Bulletin Online ISSN 1861-9541 Print ISSN 1001-6538

Description:    Qingjiang River, the second largest tributary of the Yangtze River in Hubei Province, has taken on the important tasks for power generation and flood control in Hubei Province. The Qingjiang River watershed has a subtropical monsoon climate and, as a result, has dramatic diversity in its water resources. Recently, global warming and climate change have seriously affected the Qingjiang watershed’s integrated water resources management. In this article, general circulation model (GCM) and watershed hydrological models were applied to analyze the impacts of climate change on future runoff of Qingjiang Watershed. To couple the scale difference between GCM and watershed hydrological models, a statistical downscaling method based on the smooth support vector machine was used to downscale the GCM’s large-scale output. With the downscaled precipitation and evaporation, the Xin-anjiang hydrological model and HBV model were applied to predict the future runoff of Qingjiang Watershed under A2 and B2 scenarios. The preformance of the one-way coupling approach in simulating the hydrological impact of climate change in the Qingjiang watershed is evaluated, and the change trend of the future runoff of Qingjiang Watershed under the impacts of climate change is presented and discussed. Content Type Journal Article Category Original paper Pages 1-12 DOI 10.1007/s00477-011-0524-2 Authors Hua Chen, State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072 China Tiantian Xiang, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, 430072 China Xing Zhou, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, 430072 China Chong-Yu Xu, State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072 China Journal Stochastic Environmental Research and Risk Assessment Online ISSN 1436-3259 Print ISSN 1436-3240

Description: Publication date: Available online 1 September 2017 Source: The Egyptian Journal of Remote Sensing and Space Science Author(s): H.A. Bharath, M.C. Chandan, S. Vinay, T.V. Ramachandra Metropolitan cities in India are emerging as major economic hubs with an unprecedented land use changes and decline of environmental resources. Globalisation and consequent relaxations of Indian markets to global players has given impetus to rapid urbanisation process. Urbanisation being irreversible and rapid coupled with fast growth of population during the last century, contributed to serious ecological and environmental consequences. This necessitates monitoring and advance visualisation of spatial patterns of landscape dynamics for evolving appropriate management strategies towards sustainable development approaches. This study visualises the growth of Indian mega cities Delhi, Mumbai, Pune, Chennai and Coimbatore, through Cellular Automata Markov model considering the influence of agent(s) of urban growth through soft computing techniques. CA Markov model is considered to be one of most effective algorithm to visualise the growth of urban spatial structures. Prediction of growth using agent based modelling considering the spatial patterns of urbanisation during the past four decades has provided insights to the urban dynamics. The industrial, infrastructural, socio-economic factors significantly influence the urban growth compared to the biophysical factors. Visualisation of urban growth suggest agents driven growth in the cities and its surroundings with large land use transformations in urban corridors and upcoming Industrial and ear marked developmental zones. Integrating local agents of urban growth help in identifying specific regions of intense growth, likely challenges and provide opportunities for evolving appropriate management strategies towards sustainable cities during the 21st century.