ﻻ يوجد ملخص باللغة العربية
This study investigates the trend in Rainfall Onset Dates (ROD), Rainfall Cessation Dates (RCD), Length of Growing Seasons (LGS) and Rainfall Amount at Onset of Rainfall (RAO) using linear regression, Mann-Kendall, Sen Slope and Hurst Exponent for four locations in tropical Nigeria and the development of a Fourier based model for ROD and RCD. Daily data was obtained from the Nigerian Meteorological Agency for thirty-four (34) years (1979 - 2013). ROD and RCD were computed using the method of cumulative percentage mean rainfall values. Maiduguri, Gusau and Ikom showed positive trends in ROD and RCD while Ibadan exhibited negative trends in the two parameters. Anti-persistence was observed in ROD, RCD and LGS for three locations (Maiduguri, Gusau and Ibadan). A Fourier based model with seven (7) coefficients was developed to model ROD and RCD for all the locations. The model developed performed very well in all locations with the best performance obtained in Gusau and Ibadan for ROD and RCD respectively. The effects of climate change on agricultural output for the four (4) locations under consideration were highlighted and adaption techniques suggested for mitigating the impact on agricultural output and livelihood of citizens in the areas.
Currently, agriculture in Africa contributes only a tenth to global Green House Gas (GHG) emissions from agriculture. Despite its relatively low contribution to GHG, a conundrum of climate justice, adverse impacts of climate change disproportionately
The Centre for Climate Change Research (CCCR;http://cccr.tropmet.res.in) at the Indian Institute of Tropical Meteorology (IITM; http://www.tropmet.res.in), Pune, launched in 2009 with the support of the Ministry of Earth Sciences (MoES), Government o
Quantifying the impact of climate change on future air quality is a challenging subject in air quality studies. An ANN model is employed to simulate hourly O3 concentrations. The model is developed based on hourly monitored values of temperature, sol
This paper examines how subsistence farmers respond to extreme heat. Using micro-data from Peruvian households, we find that high temperatures reduce agricultural productivity, increase area planted, and change crop mix. These findings are consistent
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-network models do not intrinsically conserve energy and mass, which is a