Mid-21st century climate and weather extremes in Cyprus as projected by six regional climate models

We present climate change projections and apply indices of weather extremes for the Mediterranean island Cyprus using data from regional climate model (RCM) simulations driven by the IPCC A1B scenario within the ENSEMBLES project. Daily time-series of temperature and precipitation were used from six RCMs for a reference period 1976–2000 and for 2026–2050 (‘future‘) for representative locations, applying a performance selection among neighboring model grid-boxes. The annual average temperatures of the model ensemble have a ±1.5°C bias from the observations (negative for maximum and positive for minimum temperature), and the models underestimate annual precipitation totals by 4–17%. The climatological annual cycles for the observations fall within the 1σ range of the 6-model average, highlighting the strength of using multi-model output. We obtain reasonable agreement between models and observations for the temperature-related indices of extremes for the recent past, while the comparison is less good for the precipitation-related extremes. For the future, the RCM ensemble shows significant warming of 1°C in winter to 2°C in the summer for both maximum and minimum temperatures. Rainfall is projected to decrease by 2–8%, although this is not statistically significant. Our results indicate the shift of the mean climate to a warmer state, with a relatively strong increase in the warm extremes. The precipitation frequency is projected to decrease at the inland Nicosia and at the coastal Limassol, while the mountainous Saittas could experience more frequent 5–15 mm/day rainfall. In future, very hot days are expected to increase by more than 2 weeks/year and tropical nights by 1 month/year. The annual number of consecutive dry days shows a statistically significant increase (of 9 days) in Limassol. These projected changes of the Cyprus climate may adversely affect ecosystems and the economy of the island and emphasize the need for adaptation strategies.     

Drivers that help adopting stabilised earth construction to address urban low-cost housing crisis: an understanding by construction professionals

Addressing urban housing crisis is an enormous challenge for most of the countries due to the increasing cost of the building material. Therefore, affordable alternative building material can make a breakthrough to the urban housing crisis. In the light of current success of stabilised earth construction in urban low-cost housing, it is important to find out the potential drivers that can help to adopt this building material. This paper aims to identify and highlight these drivers from the method of literature review and validates through a Delphi technique.     

Manganese in the urban atmosphere: identifying anomalous concentrations and sources

Purpose  

Industrial emissions can raise urban background levels of inhalable Mn particles in an order of magnitude above normal, eclipsing the contribution made by natural sources and traffic.     

Consistent personality differences in house-hunting behavior but not decision speed in swarms of honey bees (<Emphasis Type="BoldItalic">Apis mellifera</Emphasis>)

Speed-accuracy tradeoffs are a common feature of decision-making processes, both in individual animals and in groups of animals working together to reach a single collective decision. Individual organisms display consistent differences in their “impulsivity,” and vary in their tendency to make rapid, impulsive choices as opposed to slower, more accurate decisions. However, we do not yet know whether groups of animals consistently differ in their tendency to prioritize decision speed over accuracy. We challenged 17 swarms of honey bees (Apis mellifera) to simultaneously choose a new nest site in each of three locations, and measured their decision speeds in each trial. We found that swarms displayed consistent personality differences in the number of waggle dances and shaking signals they performed and in how actively they scouted for new nest sites. However, swarms did not consistently differ in how long they took to choose a nest site. We suggest that house-hunting A. mellifera swarms may place an especially high emphasis on decision accuracy when choosing a nest site, and that chance events—such as the time when each swarm discovers a sufficiently high-quality nest site—may consequently play a greater role in determining a swarm’s decision speed than intrinsic characteristics such as a swarm’s “impulsivity.”     

The Dual Model for Tourism Resource Productivity Improvement

For natural resource science, resource productivity studying is an important subject. But researches on tourism resource productivity are limited. The most significant influencing factor on tourism resource productivity is the potential market scale of tourist locations, and second most important influencing factor is the resource endowment. Regional urban population is significantly correlated with region tourist numbers, being the decisive factor of region potential tour market scale. In tourism development, the dual model should be adopted: on one hand to enhance tour spot attractiveness, on the other hand to cultivate the potential market by improving urbanization level and other means. In the situation of tourism development fever spreading, the dual model for improving tourism productivity helps to avoid the "Great Leap Forward" which means that too rapid tourism construction divorces from actual market demand.     

Prediction of the Fate and Transport Processes of Atrazine in a Reservoir

The fate and transport processes of a toxic chemical such as atrazine, an herbicide, in a reservoir are significantly influenced by hydrodynamic regimes of the reservoir. The two-dimensional (2D) laterally-integrated hydrodynamics and mass transport model, CE-QUAL-W2, was enhanced by incorporating a submodel for toxic contaminants and applied to Saylorville Reservoir, Iowa. The submodel describes the physical, chemical, and biological processes and predicts unsteady vertical and longitudinal distributions of a toxic chemical. The simulation results from the enhanced 2D reservoir model were validated by measured temperatures and atrazine concentrations in the reservoir. Although a strong thermal stratification was not identified from both observed and predicted water temperatures, the spatial variation of atrazine concentrations was largely affected by seasonal flow circulation patterns in the reservoir. In particular, the results showed the effect of flow circulation on spatial distribution of atrazine during summer months as the river flow formed an underflow within the reservoir and resulted in greater concentrations near the surface of the reservoir. Atrazine concentrations in the reservoir peaked around the end of May and early June. A good agreement between predicted and observed times and magnitudes of peak concentrations was obtained. The use of time-variable decay rates of atrazine led to more accurate prediction of atrazine concentrations, while the use of a constant half-life (60 days) over the entire period resulted in a 40% overestimation of peak concentrations. The results provide a better understanding of the fate and transport of atrazine in the reservoir and information useful in the development of reservoir operation strategies with respect to timing, amount, and depth of withdrawal.     

麦草化学制浆造纸的清洁生产工艺

提出了有机麦草加无机化肥的麦草化肥制浆造纸清洁生产工艺,以亚硫酸无机复盐替代钠碱蒸煮麦草制浆造纸,提取黑液中木质类、腐殖酸、无机氮磷等有效成分,将其制备成可固沙保土的新型环保肥料.解决了麦草化学制浆造纸黑液的污染问题,技术经济效益和环保效益俱佳.     

Spatial Validation of Land Use Change Models Using Multiple Assessment Techniques: A Case Study of Transition Potential Models

Using land use and cover change (LUCC) models for the urban growth planning, environmental assessment, and decision-making needs the establishment of an appropriate level of confidence in their performance. The objective of this research is to explore the importance of using multiple assessment techniques in order to fairly evaluate the performance of land use models. An application is conducted by using the Land Change Modeler for Ecological Sustainability (LCM) which is an empirical and transition potential model. LCM is applied to model the agricultural to developed areas transition in Rennes metropolitan area (France). The land demand is estimated using the Markov Chain model; whereas, the transition potential map is implemented using the Multi-Layer Perceptron Neural Network (MLP) method based on historical changes and driving variables. The model performance is assessed based on a variety of the most commonly used validation techniques. At the study area level, the correctness and disagreement analysis shows that LCM performs better at predicting the amount than the allocation of developed areas. Additionally, landscape metrics reveal that LCM tends to predict a fragmented urban form, which seems evident because of the large number of the individual urban patches. At the municipality level, the error budget analysis shows that the model performance, which varies highly between different subareas, needs to be improved. Moreover, the cross-tabulation between the transition potential map and both the observed and the predicted agricultural to developed areas transitions reveals that the order of the transition potential values does not perfectly fit the observed change; whereas, the predicted change is not solely limited to areas with high potential.