Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

Effect of rare disaster risks on crude oil: evidence from El Niño from over 145 years of data

Theoretical and Applied Climatology - We extend the literature on the effect of rare disaster risks on commodities by examining the effect of the El Niño-Southern Oscillation (ENSO) on crude...     

Acacia exchanges: Wattles, thorn trees, and the study of plant movements

Plants are frequently moved around the world, creating new regional landscapes and environmental imaginaries. Building on previous work in environmental history and geography, we develop a three-part approach to analyzing plant movements and apply it to trees from the Acacia genus (sens. lat.) exchanged between Australia and the rest of the world. First, we investigate the agents, circuits, and frequencies of acacia movements, including transoceanic transfers, regional diffusion, and ecological dispersal. Second, we trace bundles of knowledge or technology that accompany the acacias, highlighting how they help shape regional biogeographies. Finally, we analyze how different societies, with distinct economies, politics, and environmental sensibilities, receive introduced plants. This approach allows us to see transferred plants as active agents in region-forming processes, and to avoid normative tropes like ‘miracle plants’ or ‘alien invasives’. The highlighted species include Acacia colei, Acacia melanoxylon, Acacia mearnsii, Acacia farnesiana, Acacia nilotica, Acacia mangium, and their close relatives.     

Carbon Budget of the Indian Forest Ecosystem

The paper quantifies the role of Indian forests as source or sink of carbon. The model used in the study takes into account the growing stock, additional tree organs, dead biomass, litter layer and soil organic matter, harvesting and harvesting losses, effects of pests, fire etc., allocation of timber to wood products, life span of products including recycling and allocation to landfills. The net carbon balance calculated as the net source or sink of the forest sector was assessed for the year 1993–94. The study isimportant in view of the obligation placed by the United Nations Framework Convention on Climate Change (UNFCCC) on the signatory nations to provide a periodic update of carbon budget in the atmosphere. For the available data and the underlying assumptions, the results of the carbon budget model indicated that the Indian forest sector acted as a source of 12.8 TgC (including accumulation of carbon in the dead biomass) for the year 1994. The results obtained reinforced the notion that an integrated approach is required in order to evaluate the forest sector's influence on the global atmospheric carbon levels. The model used in this study has the advantage that all the factors determining the carbon budget can be integrated and altered to determine their influence. The study also throws light on the issues that stand in the way of preparing through carbon budget for developing countries like India.