Spatio-Temporal Variation Characteristics of North Africa’s Climate Potential Productivity

Abstract

Africa is becoming one of the most sensitive and vulnerable regions of the global ecosystem due to its variable climate, complex topography, and diversity of natural ecosystems. In the context of global warming, climate change not only alters the spatial distribution of temperature and precipitation in North Africa, but also affects the spatial distribution of vegetation as well as the structure and function of ecosystems, causing changes in the North African ecosystem and inducing a series of food security problems. In this regard, this paper analyzed the spatio-temporal distribution of climate change, climate production potential (CPP), and influencing factors in Africa based on meteorological data for 1901–2019, using the Thornthwaite Memorial model, Mann–Kendall mutation test, and Pearson correlation model. The results indicated that from 1901 to 2019, the CPP in North Africa decreased by 4.9%, while the region’s precipitation experienced a decline of 5.2%. The phased trend in CPP was consistent with the change in precipitation. Temperature and precipitation were the main limiting factors for CPP in North Africa, with precipitation being more limiting. In general, North Africa’s CPP was more sensitive to precipitation, and a continued ‘warm and dry’ climate in the future could lead to an increasing downward trend.

A bayesian spatio-temporal dynamic analysis of food security in Africa

“…This gap is highlighted in a review conducted by Nicholson et al 11 examining over 90 papers on household food security models and 26 on regional models from the past decade. Additional relevant works that have evaluated food security and nutrition (FSN) in Africa include studies by Waha, Van Wijk 12 , Yuan, NourEldeen 13 , Li and Zhang 14 , Wegenast and Beck 15 , Bi, Wan 16 and Egbebiyi, Lennard 17 .…”

“…Dynamic spatio-temporal modeling of the interrelated factors influencing FSN over time in Africa is crucial, given the multifaceted nature of the FSN challenge, with its factors evolving both spatial and temporal. Dynamic spatio-temporal models address the limitations of traditional static modeling techniques 11 , 16 – 19 by accounting for the spatio temporal interaction dynamic evolution of components in modeling an outcome. The research scope encompasses various dimensions, including countries 16 , 20 , ecosystems 21 , climate 20 , Coarse-grained reconfigurable architectures 22 , modeling infectious diseases 23 , transportation 24 and more.…”

“…Dynamic spatio-temporal models address the limitations of traditional static modeling techniques 11 , 16 – 19 by accounting for the spatio temporal interaction dynamic evolution of components in modeling an outcome. The research scope encompasses various dimensions, including countries 16 , 20 , ecosystems 21 , climate 20 , Coarse-grained reconfigurable architectures 22 , modeling infectious diseases 23 , transportation 24 and more. However, despite numerous evaluation studies, scant attention has been given to food security, particularly in the context of Africa.…”

An Intelligent Water Monitoring IoT System for Ecological Environment and Smart Cities

“…However, contaminated water sources pose significant health and environmental risks, emphasizing the importance of protecting and conserving water resources. This is the reason why, in recent papers, many researchers use machine learning to train past statistics into models suitable for future predictions [16][17][18].…”