Urban heat island intensity (UHU) is calculated as the spatially averaged temperature difference between an urban and its surrounding rural area. This concept, however, provides an umbrella for a range of diversified ideas that include the temperature difference between the densely developed urban area and least developed area or between two different built-up areas. There are also averages for the season, for the year, for multiple years, etc., and UHll quoted for the day and another for the night. The objective of this work is to examine the urban heat island effect for cities around the world, using readily available data. The innovation is in using data from the Landsat satellites for different cities previously not studied. Thermal images of the Earth were obtained and analyzed to produce suiface-temperature maps. These maps showed that the temperature in the urban environments were significantly higher than the temperature in the surrounding countryside, a defining characteristic of urban heat island. Eurthermore, the urban and rural areas in the images were separated and analyzed individually to quantitatively measure the temperature difference. It was found that the UHII could be 0.3-5.1 °C for the eleven cities investigated. Miami and Shenzen are two cities which seem to have been missed in previous studies because they were limited in their scope and responsibilities, and their methods required much more resources for the longer term studies. It is not the claim here that a UHl is definitively established by the analysis presented of the Landsat satellite data. The present work demonstrates the use of a possible planning tool in terms of understanding where urban areas may be subjected to additional heat. Our use of the method shows that a UHI is probably taking place at the time of observation, and precautionary notices should be sent out to the community to take preventative measures to ensure their health and wellbeing. The minimal resources required is the demonstration shown by our work of the usefulness of this method.
There are regions in the world experiencing the energy-food-water nexus problems. These regions tend to have high population density, economy that depends on agriculture and climates with lower annual rainfall that may have been adversely affected by climate change. A case in point is the river basin of the Indus. The Indus River is a large and important river running through four countries in East Asia and South Asia: China, India, Afghanistan, and Pakistan. The region is highly dependent on water for both food and energy. The interlinkage of these three components is the cause for the energy-water-food nexus. The difficulty in effectively managing the use of these resources is their very interdependence. For instance, water availability and policies may influence food production, which is governed by agricultural policies, which will further affect energy production from both water and biofuel sources, which will in turn require the usage of water. The situation is further complicated when climate change is taken into account. On the surface, an increase in temperatures would be devastating during the dry season for a region that uses up to 70% of the total land for agriculture. There are predictions that crop production in the region would decrease; the Threedegreeswarmer organization estimated that crop production in the region could decrease by up to 30% come 2050. Unfortunately, the suspected effects of climate change are more than just changes in temperature, precipitation, monsoon patterns, and drought frequencies. A huge concern is the accelerating melting of glaciers in the Himalayas. Some models predict that a global increase in temperature of just 1°C can decrease glacial volume by 50%. The loss of meltwaters from the Himalayan glaciers during the dry season will be crippling for the Indus River and Valley. In a region where up to 90% of accessible water is used for agriculture, there will be an increased strain on food supply. This will further deteriorate the current situation in the region, where almost half of the world’s hungry and undernourished people reside. While the use of hydropower to generate electricity is already many times lower than the potential use, future scarcity of water will limit the potential ability of hydropower to supply energy to people who already experience less than 50% access to electricity. In the current work, suggestions have been put forward to save the increased glacier melt for current and future use where necessary, improve electricity generation efficiency, use sea water for Rankine power cycle cooling and combined cycle cooling, and increase use desalination for drinking water. Energy conservation practices should also be practiced. All of these suggestions must be considered to address the rising issues in the energy-water-food nexus.
Urban Heat Island Intensity (UHII) is calculated as the spatially-averaged temperature difference between an urban and its surrounding rural area. This concept, however, provides an umbrella for a range of diversified ideas that include the temperature difference between the densely developed urban area and least developed area or between two different built-up areas. There are also averages for the season, for the year, for multiple years, etc. and UHII quoted for the day and another for the night. The objective of this work is to examine the urban heat island effect for cities around the world, using readily available data. The innovation is in using data from the Landsat satellites for different cities previously not studied. Thermal images of the Earth were obtained and analyzed to produce surface temperature maps. These maps showed that the temperature in the urban environments were significantly higher than the temperature in the surrounding countryside, a defining characteristic of urban heat island. Furthermore, the urban and rural areas in the images were separated and analyzed individually to quantitatively measure the temperature difference. It was found that the UHII could be 0.3–5.1°C for the eleven cities investigated. Miami and Shenzen are two cities which seem to have been missed in previous studies because they were limited in their scope and responsibilities, and their methods required much more resources for the longer term studies. It is not the claim here that a UHI is definitively established by the analysis presented of the Landsat satellite data. The present work demonstrates the use of a possible planning tool in terms of understanding where urban areas may be subjected to additional heat. Our use of the method shows that a UHI is probably taking place at the time of observation, and precautionary notices should be sent out to the community to take preventative measures to ensure their health and wellbeing. The minimal resources required is the demonstration shown by our work of the usefulness of this method.
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