Biomass burning, land-cover change, and the hydrological cycle in Northern sub-Saharan Africa

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Video in TIB AV-Portal: Biomass burning, land-cover change, and the hydrological cycle in Northern sub-Saharan Africa

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Biomass burning, land-cover change, and the hydrological cycle in Northern sub-Saharan Africa
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The Northern Sub-Saharan African (NSSA) region, which accounts for 20%–25% of the global carbon emissions from biomass burning, also suffers from frequent drought episodes and other disruptions to the hydrological cycle whose adverse societal impacts have been widely reported during the last several decades. This paper presents a conceptual framework of the NSSA regional climate system components that may be linked to biomass burning, as well as detailed analyses of a variety of satellite data for 2001–2014 in conjunction with relevant model-assimilated variables. Satellite fire detections in NSSA show that the vast majority (>75%) occurs in the savanna and woody savanna land-cover types. Starting in the 2006–2007 burning season through the end of the analyzed data in 2014, peak burning activity showed a net decrease of 2–7%/yr in different parts of NSSA, especially in the savanna regions. However, fire distribution shows appreciable coincidence with land-cover change. Although there is variable mutual exchange of different land cover types, during 2003–2013, cropland increased at an estimated rate of 0.28%/yr of the total NSSA land area, with most of it (0.18%/yr) coming from savanna. During the last decade, conversion to croplands increased in some areas classified as forests and wetlands, posing a threat to these vital and vulnerable ecosystems. Seasonal peak burning is anti-correlated with annual water-cycle indicators such as precipitation, soil moisture, vegetation greenness, and evapotranspiration, except in humid West Africa (5°–10° latitude), where this anti-correlation occurs exclusively in the dry season and burning virtually stops when monthly mean precipitation reaches 4 mm d−1. These results provide observational evidence of changes in land-cover and hydrological variables that are consistent with feedbacks from biomass burning in NSSA, and encourage more synergistic modeling and observational studies that can elaborate this feedback mechanism.

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hello my name is Charles each who I and II a need to talk about our new paper published in the environment to recent letters especially issue on the African environment a process somewhat a cycle dynamics we're looking at
the middle section of Africa from coast to coast a running
from about lot it is 0 2 letters 20 which we call the northern Sub-Saharan Africa
and they researchers are trying to understand what's responsible for the downward trend of rainfall and an increase in dry out
over the last 50 or more
years several the scientists are attributing the drought situation to various phenomena for instance overgrazing or and so Union lining or industrial sulfur emissions or even a volcanic eruption so we observe a lot of fires in that region year after
year during the dry season from satellite and so we've been wondering what might this fires because in with regard to the water cycle so it's a very good person to us but they're not a very easy 1 to answer because the fires happen in the
dry season with peak around December January whereas the precipitation B is around
June July so we sat down and kicked
out what they possible pathways might be that these files will go through various phenomenon eventually effect precipitation patterns we're
looking at the the plot of land
cover so we use data a wider a lot of data over the last decade to examine the relationship between the fires and the land cover
change 1 of our findings is
that there has been an increase over the last decade of conversion of Savannah forest and weapons to croplands
also we found as regards so the water cycle indicators that there is a mainly a negative correlation between air fires and somewhat a cycle indicators
precipitation so I was to evapotranspiration in this hell region which is in northern part of our region this negative correlation happens mainly when the data analysis is done on an annual basis whereas south of this of this negative correlation happens manly during the dry season
these have provided us some hands which have
enabled us to begin looking even farther using a powerful computer models to try to understand the
phenomenon but the hints we have given in this paper are worth looking at thank you