The Greening and Wetting of the Sahel Have Leveled off since about 1999 in Relation to SST

Chen, Tiexi; Zhou, Shengjie; Liang, Chuanzhuang; Hagan, Daniel Fiifi Tawia; Zeng, Ning; Wang, Jun; Shi, Tingting; Chen, Xin; Dolman, A. J.

doi:10.3390/rs12172723

Cited by 11 publications

(3 citation statements)

References 55 publications

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“…SST information helps identify optimal fishing grounds by indicating areas with suitable temperature conditions for target species [44,45]. Furthermore, monitoring changes in SST can provide insights into the health of marine ecosystems and aid in the assessment and management of protected areas and biodiversity hot spots [46,47,48].…”

Section: Sea Surface Temperature (Sst)mentioning

confidence: 99%

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

et al. 2023

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The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt to the planet’s dynamic environment. Here, we present a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) for discerning the response of watersheds to water-cycle fluxes and their extremes. We examine four United States drainage basins of varying acreage from smaller to very large (Bear, Colorado, Connecticut, and Mississippi). F2F combines model and ground observations of water-cycle fluxes in the form of surface runoff, subsurface baseflow, and gauged streamflow. We use these time series datasets to simulate, visualize, and analyze the watershed basin response to the varying climates and magnitudes of hydroclimatic fluxes in each river basin. Experiments modulating the time lag between remotely sensed and ground-truth measurements are performed to assess the metrological limits of forecasting with this platform. The resultant mean Nash–Sutcliffe and Kling–Gupta efficiency values are both greater than 90%. Our results show that a hydrological machine learning platform such as F2F can become a powerful resource to simulate and forecast hydroclimatic extremes and the resulting watershed responses and natural hazards in a changing global climate.

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Section: Sea Surface Temperature (Sst)mentioning

confidence: 99%

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

et al. 2023

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“…Moreover, the reforestation activities carried out would have improved the humidity rate and local rainfall amounts on the different sites studied (2% to 8% of the annual total and 4% to 8% of the JAS rainy season). Thus, the increase in vegetation indices (NDVI and VHI) leads to an increase in evapotranspiration and favors the transport of humidity into the middle layers of the atmosphere [54] [55] [56].…”

Section: Comparative Analysis Of the Impact Of Reforestation Overmentioning

confidence: 99%

Recent Vegetation Cover Dynamics and Climatic Parameters Evolution Study in the Great Green Wall of Senegal

Gore¹,

Aman²,

Kouadio³

et al. 2023

JEP

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The drought recorded in 1970s and 1980s, particularly in the Sahara and Sahel region has greatly affected the population as well as the economies and the eco-systems of this area. In 2007, the African Union launched a Pan-African program, the Great Green Wall for the Sahara, the Sahel Initiative (GGWSSI) to reverse land degradation and desertification by planting a wall of trees stretching from Dakar to Djibouti. The objective is to improve food security, and support local people to adapt to climate change. This paper aims to evaluate the impacts of the reforestation program in Senegal, fifteen years after it was launched. This study uses a time series of satellite-derived vegetation cover and climatic parameters data to analyze the sustainability of these interventions. Change detection approaches were applied to identify and characterize the drives of the eventual changes. A comparative analysis of reforestation on climatic parameters was explored through the temporal analysis of the vegetation index over the periods 2000-2008 and 2009-2020. An increase in vegetation activity was noted through the NDVI at the interannual (+2% to +8%) and seasonal (+1.5% to 7% for the wet season and 1% to 4% for the dry season) scale and a positive and significant evolution is noted on the trace of the GGW. Also, the period 2009-2020 recorded an increase in rainfall of 2% to 8% of the average value 2000-2020 and 4% to 8% of the rainy season. Soil moisture is the climatic parameter that has increased the most, with an increase of 25% to 54% of the 2000-2020 average, i.e. between 20 mm and 70 mm more.This study shows a significant improvement in the relationship between NDVI and climate parameters after the different reforestation actions of the GGW.

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“…Therefore, a common practice in previous studies has been to make a general distinction between natural and human drivers. As a result, it is rather difficult to study specific management processes in depth (d'Amour et al, 2017;Erb et al, 2017;Meyfroidt et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Land Management Contributes significantly to observed Vegetation Browning in Syria during 2001–2018

Chen

Guo

et al. 2022

Biogeosciences

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Abstract. Climate change and human activities have significant impacts on terrestrial vegetation. Syria is a typical arid region with a water-limited ecosystem and has experienced severe social unrest over the last decades. In this study, changes in vegetation and potential drivers in Syria are investigated. By using an enhanced vegetation index (EVI), a general browning trend is found in Syria during 2001–2018, with the EVI decreasing at a rate of −0.8 × 10−3 yr−1 (p<0.1). The decrease of the EVI is mainly found in the north region, whereas the west region still maintains an increasing trend. The residual analysis indicates that besides precipitation, human activities also contribute significantly to the EVI decrease, which is confirmed by the decrease in rainfall use efficiency. Moreover, a paired land-use experiment (PLUE) analysis is carried out in the Khabur River basin where croplands are widely distributed in adjacent regions of Syria and Turkey. The time series of the EVIs over these two regions are highly correlated (r=0.8027, p<0.001), indicating that both regions are affected by similar climate forcing. However, vegetation in Syria and Turkey illustrates contrary browning (−3 × 10−3 yr−1, p<0.01) and greening trends (4.5 × 10−3 yr−1, p<0.01), respectively. Relevant reports have noted that social unrest induced insufficient irrigation and lack of seeds, fertilizers, pesticides and field management. Therefore, we concluded that the decline in vegetation in the north Syria is driven by the change of land management.

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The Greening and Wetting of the Sahel Have Leveled off since about 1999 in Relation to SST

Cited by 11 publications

References 55 publications

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

Recent Vegetation Cover Dynamics and Climatic Parameters Evolution Study in the Great Green Wall of Senegal

Land Management Contributes significantly to observed Vegetation Browning in Syria during 2001–2018

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