Understanding the patterns of human concentrations within megacities is of fundamental importance to our understanding of megacity dynamics, and for megacity management and policy making. This study presents an updated investigation of the historical expansion of densely inhabited districts (DIDs) in the world's largest megacity, Tokyo. Long-term DID data (1960-2010) at 5- year intervals were analyzed in a geographic information systems framework. Results show that Tokyo completed rapid growth phase and is now in a maturity phase with minimal growth. Extension was the main form of expansion, although fragmented growth in the form of patches was also noted. The rate of DID expansion was strongly related to economic trends. However the direction and shape of expansion was influenced much by geographic and policy related factors. West and southern directions had earlier and greater expansion, likely related to the historical Tōkaidō corridor. Over 95% of all DIDs are located within 4km distance from a railway line. The coastline and distance from the CBD had some modifying influence. During the course of expansion, there was substantial decrease of population density in the inner wards. Future trends in Tokyo's DIDs will be greatly influenced by aging demographic trends. This study therefore shows that megacity spatial expansion is a dynamic process influenced by various processes whose roles vary over time.

Precipitation variability poses significant risks to people's livelihoods, especially in semi-arid countries whose economies rely on rain-fed agriculture. Agricultural decision making and land use planning during the growing season can be enhanced by agro-meteorological applications such as response farming. Using Makonde District in Zimbabwe as a case study, this study investigates how locally captured precipitation data are used for local-level agricultural advisory services. We find that though abundant climatic information is being captured locally, this information is not effectively accessible to local farmers. Local agricultural extension personnel are also limited in their ability to derive the benefits of the available data and they rely mainly on sensory perceptions. Precipitation analysis results showing inter-annual variability and correlations with the Southern Oscillation Index (SOI) highlight the potential of response farming in this region. A participatory approach is recommended involving university scientists practicing agro-meteorology, farmers, and agricultural extension. Training extension personnel in agro-meteorology is recommended.

ABSTRACTThis study investigates the temporal evolution of extreme rainfall seasons over Botswana, and their relationships to the growing season cycle of natural vegetation. Ground‐based precipitation data and remotely sensed Advanced Very High Resolution Radiometer (AVHRR)‐normalized difference vegetation index (NDVI) data are analysed for the July 1981–June 2006 period. Results confirm that Botswana's annual cycle of precipitation is characterized by substantial intra‐seasonal variation, which is resonated in natural vegetation cover. During extreme wet (dry) years, the most extreme surpluses (shortfalls) of monthly rainfall were observed in the middle of the rainfall season (January–February). While rainfall receipts during season onset and cessation may not be the highest, they were found to have strong influence on NDVI coefficient of variation. Extreme wet seasons could be distinguished from moderate wet seasons by examining their monthly peak patterns. Furthermore, the November–December period was identified through the NDVI as the critical period when extreme conditions may begin to emerge. These findings could have important implications for supporting seasonal forecasts and optimizing rainfed agricultural adaptation and natural resources management in southern Africa.

The dominant modes of vegetation variability over Zimbabwe are investigated using principal component analysis PCA on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer NOAA-AVHRR normalized difference vegetation index NDVI monthly imagery from 1982 to 2006. Spectral analysis is also used to determine the periodicities of the component loadings. NDVI PCA-1 corresponds to the major vegetation types of Zimbabwe, and we demonstrated that grasslands and dry savannah have the strongest relationship with mean annual precipitation. Furthermore, the March–April loadings showed the highest correlation r = 0.73 with mean annual precipitation. NDVI PCA-1 sheds some light on the land reform challenge in Zimbabwe. NDVI PCA-2 is highly correlated r = 0.87 with the mean annual relative variability of the rainfall map indicating a southeast/north mode of anomalies associated with the convectional rainfall-bearing systems over Zimbabwe. NDVI PCA-2 is also highly correlated r = 0.86 with precipitation PCA-2. NDVI PCA-3 shows a southeast/west mode and is highly correlated r = 0.87 with precipitation PCA-3. A high correlation r = 0.66 is also noted between NDVI PCA-4 and the elevation map. Spectral analysis of the PCA loadings revealed several periodicities corresponding to those found in tropical sea surface temperatures SSTs.