The assessment of climate change impacts on frequency of floods is important for management of flood disasters. It is recognized that methods for the assessment are subject to various sources of uncertainty (choice of climate model and emission scenario, course spatial and temporal scales, etc.). This study investigates the climate change related uncertainty in the frequency of flood flows for the Upper Thames River basin (Ontario, Canada) using a wide range of climate models. Climate model outputs are downscaled using the change factor approach for 30-year time slices centered on years 2020, 2050 and 2080. To estimate natural variability, a stochastic weather generator is used to produce synthetic time series for each horizon and for each climate scenario. A number of realizations out of historical range are also produced for the 1979-2005 baselines using the weather generator. A continuous daily hydrologic model was then used to generate daily ï¬‚ow series for the baseline and for the future time horizons. A peak-over-threshold (POT) with Generalized Pareto Distribution is used to produce ï¬‚ood frequency curves for the four time horizons. The uncertainty involved with the POT modelling is also considered. The results indicate that use of unbounded GPD model should be employed for flood frequency analysis. A large uncertainty exists in all the projected future design floods. Probabilistic assessment of the uncertainty is carried out and it provides the estimation of flood magnitude-return period relationship with high level of confidence.
Aims: This paper examines the interactions between climate parameters and cardamom capsule yield and its sustainability in Indian Cardamom Hills. Methodology: Temporal trends were evaluated at annual, seasonal and monthly time scale using Mann-Kendall method. Significant trends were identified at annual, seasonal and monthly scale using two tailed Z-Test. The temporal trends were evaluated using the non-parametric Mann-Kendall test. To quantify the slope we used Sen’s non-parametric estimator of slope. The significance of the test was evaluated using two tailed Z-Test. A p value of <0.05 was used to indicate statistical significance, using two tailed Z test. Results: Climate warming was significant in the recent decades in the Indian Cardamom Hills, which is recognized as one of the ecologically sensitive and biologically diverse areas. Considerable and significant spatial and temporal variations have occurred in the main climatic elements like air temperature, rainfall and relative humidity in the hill region. Significant positive trend in day-night time temperature has been observed and the trend differed from one station to another. Significant increasing trend was also observed for minimum temperature than maximum temperature and this had caused decline in diurnal temperature. Both winter and summer monsoon rainfall as well as high relative humidity had a positive influence on the yield of cardamom. However, the variability in these two types of rainfall was high for the entire region and the trend is negative. The variability of monthly mean precipitation is high for May, December and January under AR4 climate scenario. Conclusion: The sustainable yield of cardamom may be possible only when the winter and summer rainfall variabilities were minimal. Increasing trend of soil temperature from 0-10 cm depth was recorded, which was significant at 5 cm depth and can cause considerable negative implications for sustainable cardamom production both in terms of reduced soil moisture availability and altered pest population dynamics.
Climate change will deeply affect the precipitation and evapotranspiration around the world. The sustainability of groundwater resources is crucial for regional and local communities, which is intimately tied to the changing recharge rate. To accurately assess the recharge rate, different methods were used to estimate hydraulic conductivity of an unconfined aquifer in this study. Particle size method with four empirical formulae, together with in-situ aquifer tests and the inverse modelling techniques were integrated to evaluate their potential for the determination of hydraulic conductivity of unconsolidated aquifer materials in order to improve groundwater recharge estimation. Results showed a wide disparity between the granulometric estimates of the hydraulic conductivity and the in-situ and modelling techniques. Slug test values range from 5.13 x 10-6 m/s to 4.96 x 10-5 m/s whereas the infiltration test (Porchet method) results vary from 1.91 x 10-7 m/s to 1.16 x 10-6 m/s. The simulated hydraulic conductivity values range from 2.54 x 10-7 m/s to 6.36 x 10-7 m/s, with a decreasing trend in the northeast-southwest (NE-SW) direction. The infiltration method appeared to be better than the granulometric one in the estimation of the vertical hydraulic conductivity within the unsaturated zone of porous formations. This study also pointed out that within an anisotropic formation, the hydraulic conductivity ratio (Kv/Kh) should not always be taken as equal to 10. Specific tests should be implemented to access this value in a given aquifer.The inverse modelling results showed the net recharge values varying from 68.5 mm/yr to 180 mm/yr. The modelling technique appears to be consistent with the in-situ estimates. Therefore, the application of groundwater modelling tool in this study has shown excellent promise for characterizing the spatial distribution of hydraulic conductivity and net recharge values within the targeted aquifer system.
A green roof test bed, established at the Nanyang Technological University in Singapore, was used to investigate its benefit for storm water management and urban heat island effect mitigation. The system comprised 3 units, 2 in the form of vegetated roofs and the other a bare roof. The system was equipped with automatic monitoring devices for measuring the hydrological data. Continuous data monitoring on the roofs was conducted to evaluate the thermal and hydrological effects. The study shows that the green roof test bed can significantly reduce the roof surface temperature (by an average of 7.3ºC) and lower the ambient air temperature (by an average of 0.5ºC) when compared with a bare roof during day time hours (from 10:00 am to 4:00 pm). The ability to reduce and delay the peak runoff was demonstrated by using a typical rainfall event with 18 mm depth. The designed system is useful in evaluating both thermal and hydrological benefits of a green roof system in tropical areas and can offer guidance to local managers in mitigating the urban heat island effect and designing storm water management strategies.
Growing interest in development of innovative solutions for enhancement of sustainability in the built environments has been observed in recent years. According to the main constituents of buildings particularly in building envelopes, facades are expected to play a significant role towards the promotion of sustainable design in low energy buildings. This study presents a holistic review towards the analysis of ‘intelligent facades’ according to their types, current implementations, challenges, and ultimate impacts. Intelligent facades need to be responsive and conscious to the local climate, outdoor environment, and indoor spaces with view to parameters such as energy performance, thermal comfort, indoor air quality, visual comfort, etc. The findings demonstrate that energy modeling and simulations should be performed during the early stage of design process of buildings to ensure the practicality and effectiveness of any green implementations in buildings. In conclusion, the study recommends the intelligent facades to become an inherent constituent of green buildings for future development of low energy buildings.
It is noticed that intelligent buildings are aimed to consider social, environmental and economic values beside a substantial focus to the automated technological attributes. Due to many promising green building initiatives, the accelerated level of interests towards the applications of information technology and advanced control techniques in architecture design has been observed. With a viewpoint to the sustainable development of future cities, attributing the eventual impacts of climate change, various interrelated green building design approaches have been implemented. This study aims to elucidate the significant advancements of intelligent building design as a key constituent of eco-city development for creating greener and effective built environments. Current effort in this study is also geared toward considerable and practical implementations that were carried out in order to create buildings with zero energy consumption. Emphasis is placed upon reviewing the recent theories, attempts, implementations, and challenges towards the development of zero energy intelligent buildings (ZEIB). The findings inferred from the theoretical analysis confirm that the significant contribution of ZEIB concept will end up for the sustainable development of future eco-cities.
Aims: Global change studies need to manipulate large volume of observation and prediction data, most likely from multiple sources. From the researchers’ perspective, the whole research process consists of the follow stages: data discovery, data access, data processing, data analysis and result dissemination. The aim of paper is to review the state-of-the-art of geospatial data systems to reveal the way towards a better support of global change studies. Methodology: This paper reviews the capabilities of exemplar geospatial data systems. It further analyzes the needs of manipulating large volume of diverse data when performing global change studies. By comparing the available capabilities with the real needs, this study shows the strengths and limitations of existing data systems when supporting global change studies. Results: The analysis shows that data systems are helpful for researchers to fulfill data discovery and access, while most of them do not provide further functionalities to cover other stages in the whole research process. This suggests that a new generation of data systems is highly needed to provide efficient and enough support for scientists to perform global change studies. Instead of simply moving data from sources to researchers’ local archives, it will enable more on-line data manipulation functionality and the interoperability of data and systems. Conclusion: Traditional geospatial data systems are designed to operate locally without built-in interoperability and sharing capabilities. Such systems are operated under the paradigm of “everything-locally-owned-and-operated”. Conducting global change studies using such a system requires moving a large volume of data from providers’ sites to researchers’ site. Such a system does not provide strong support for the entire research process. Since climate research requires manipulating a huge volume of complex and diverse multi-source data, a new paradigm of “everything-shared-over-the-Web” is promising when designing a new generation of geospatial data systems, which are standard-based, interoperable, and sharable, for global change studies.