Selection of scale can reverse the importance of stochastic controls on community assembly

Reconciling the long-standing debate on the importance of niche-related and neutral factors on community assembly, recent research suggests that both deterministic and stochastic processes operate simultaneously along a continuum in many biogeographical systems. In particular, stochastic processes seem to lose importance under stressful conditions, which results in the increasing influence of deterministic counterparts. In this paper, I show that, at local scales, this stochasticity–stress relationship can be reversed along the environmental stress gradients within a salt marsh: stochasticity manifested under harshness. Here, low-lying, more stressful sites contained all of the plant species of the local pool (i.e. a greater chance of ecological drift), whereas high-elevation, less stressful sites exhibited reduced species richness due to competitive exclusion (i.e. a lower chance of ecological drift). I conclude that determining whether the importance of stochasticity in shaping community structure is greater under benign or harsh conditions depends on the scale of interest.     

Enhanced aerosol-cloud relationships in more stable and adiabatic clouds

Modification of cloud microphysics and cloud albedo by cloud-active aerosol is generally identified and accepted, but the nature and magnitude of aerosol-cloud interactions are vaguely understood and thought to include a myriad of processes that vary regionally and confound the application of simple physical models of cloud-aerosol sensitivity. This paper presents observations demonstrating that cloud top stability through its regulation of mixing and vertical development is one of the critical mechanisms that regulate cloud response to cloud-active aerosol in some cloud systems. Strong above-cloud inversions are shown to buffer marine stratocumulus from the effects of mixing with drier, warmer inversion air. This buffering reduces the variability of the cloud liquid water path (LWP) and enables the clouds to remain nearly adiabatic. While weaker above-cloud inversions in continental stratocumulus promote variability in the LWP and sub-adiabatic LWPs, stronger inversions in marine stratocumulus enables a relatively adiabatic existence that increases the relationship of cloud microphysical alteration to cloud-active aerosol. This study has important implications for Geoengineering in that it demonstrates that cloud systems overlain by strong thermal inversions are more likely to respond predictably to intentional manipulation of the in-cloud concentration of cloud-active aerosol.     

Assessment model for earthquake-triggered landslides based on quantification theory I: case study of Jushui River basin in Sichuan, China

Earthquakes that occur in a mountainous region can trigger a mass of landslides (ETL), and these hazards can cause relatively heavy casualties. Compared with other evaluation models and methods, quantification theory I is a multivariate statistical approach that can simultaneously process qualitative and quantitative variables. On the basis of the ETL features and the influencing factors, in this paper, we select lithology, slope gradient, slope height, slope structure, distance to seismogenic fault, hanging wall/footwall of the seismogenic fault, and rock integrity, a total of seven impact factors, as the assessment index. By randomly selecting 128 single slopes in an intense seismic area as the samples, we develop an assessment model and apply it to investigate the regional stability of the Jushui River basin to prove its applicability. Moreover, this method calculates the factor weight; this can also prove the method through a comparison with the existing research results obtained using other methods.     

应对地面气象观测业务改革的一些措施

根据《综合气象观测系统发展规划（2010—2015）》和《地面气象观测业务改革》的要求,分析了崇左市气象业务现状及存在问题,提出了加快建立与现代气象业务相适应的地面气象观测业务改革的一些措施。     

A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia

Land Data Assimilation Systems have been developed to generate the surface initial conditions such as soil moisture and temperature for better prediction of weather and climate. We have constructed Korea Land Data Assimilation System (KLDAS) based on an uncoupled land surface modeling framework that integrates high-resolution in-situ observation, satellite data, land surface information from the WRF Preprocessing System (WPS) and the MODIS land products over the East Asia. To present better surface conditions, the KLDAS is driven by atmospheric forcing data from the in-situ rainfall gauges and satellite. In this study, we 1) briefly introduce the KLDAS, 2) evaluate the meteorological states near the surface and the surface fluxes reproduced by the KLDAS against the in-situ observation, and then 3) examine the performance of the mesoscale model initialized by the KLDAS. We have generated a 5-year, 10 km, hourly atmospheric forcing dataset for use in KLDAS operating across East Asia. The KLDAS has effectively reproduced the observed patterns of soil moisture, soil temperature, and surface fluxes. Further scrutiny reveals that the numerical simulations incorporating the KLDAS outputs show better agreement in both the simulated near-surface conditions and rainfall distribution over the Korean Peninsula, compared to those without the KLDAS.     

Influence of trace element fluxes from submarine groundwater discharge (SGD) on their inventories in coastal waters off volcanic island,Jeju, Korea

The concentrations of trace elements in groundwater and seawater were measured in a coastal embayment (Bangdu Bay) of a volcanic island (Jeju) off the southernmost coast of Korea in August and December of 2009. The concentrations of trace elements (Al, Mn, Fe, Co, Ni, and Cu) in the groundwater in summer were approximately 20-fold higher than those in winter, with good correlation to each other. Overall, the concentrations of most of the trace elements in the groundwater were 3- to 70-fold higher than those in the seawater of this Bay. Simple budget calculations showed that large fluxes of submarine groundwater discharge (SGD)-driven trace elements were responsible for the unusually enhanced concentrations of trace elements (particularly for Al, Fe and Co) in the summer seawater. The results imply that the temporal changes in SGD-driven trace element fluxes are large and may contribute considerably to the budget of trace elements in the coastal ocean, particularly off a highly permeable volcanic island.     

Variations of δ<Superscript>18</Superscript>O in Rivers of Crimea in Winter

The results of studies of variations of δ¹⁸O values in river water in Crimea Peninsula in January–February 2015–2017 are given. The variation range of δ¹⁸О in river water over the three years of studies never exceeded 3‰. A tendency toward an increase in δ¹⁸О in the water of the Salgir, Kacha, Al’ma, Bel’bek, Biyuk-Karasu from their sources to mouths was identified and explained by a decrease in evaporation in the mouth areas of the rivers relative to their sources and upper reaches, and the inflow of isotopically light precipitation (rain and snow) into the rivers in their upper reaches. The values of δ¹⁸О in waters of the rivers with regulated stream were found to increase under the effect of the Simferopol Reservoir on the Salgir River and the Izobil’nenskoe Reservoir on the Ulu-Zen’ River. The values of δ¹⁸О in the upper reaches of the large rivers of Kacha and Bel’bek (the northwestern slope of the Crimean Mountains) vary from ?8.7 to ?9.7‰, except for the rivers of Al’ma (?7.7‰) and Kokozka (?10.2‰) because of the different shares of groundwater in the recharge of these rivers.     

China and the changing economic geography of coffee value chains

For the past three centuries, the economic geography of the global coffee sector has been characterized by the supply of beans from tropical countries for consumption in North America and Europe, with various modes of value chain coordination enacted by lead firms to ensure reliable and affordable supply. This pattern is now fundamentally changing, with growth in coffee consumption in emerging markets, including China, exceeding that in established markets. But China is not only a growing consumer market, it is less well known that rapidly increasing agricultural production in Yunnan province of southwest China has also inserted the country as an important source region for coffee, and this has been pivotal in facilitating the emergence of Chinese lead firms in the sector. This article presents the emergence of China, and Chinese firms, at a critical juncture for the structure and governance of the global value chain for coffee. The processes through which this is occurring are outlined, and the implications for regional development prospects across Southeast Asia are discussed. We argue that the changing economic geography of coffee value chains, and their increasing driven‐ness by Chinese actors, is starting to reshape the regional coffee industry in profoundly new ways.     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.