Assessment of Shared Socioeconomic Pathway (SSP) climate scenarios and its impacts on the Greater Accra region

Siabi, Ebenezer K.; Awafo, Edward A.; Kabo-bah, Amos T.; Derkyi, Nana Sarfo Agyemang; Akpoti, Komlavi; Mortey, Eric Mensah; Yazdanie, Mashael

doi:10.1016/j.uclim.2023.101432

Cited by 25 publications

(4 citation statements)

References 76 publications

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“…SSP245 represents a medium radiative forcing pathway (4.5Wm −2 ) and 3°C‐limited warming. SSP585 represents a high unsustainable radiative forcing pathway (8.5Wm −2 ) and 5°C‐limited warming (Cook et al., 2020; IPCC, 2023; Li, Li, et al., 2021; Siabi et al., 2023; Zhang, He, et al., 2022). Precipitation, temperature and solar radiation across three CMIP6 scenarios will generate different potential evapotranspiration and WY.…”

Section: Methodsmentioning

confidence: 99%

A Refined Supply‐Demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals

Li,

Xin,

et al. 2024

Earth's Future

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As water‐related ecosystem services (WRES) encounter unprecedented threats with climate change and human activities, it is important to understand the impact of variations in the supply and demand for WRES on Sustainable Development Goals (SDGs). Previous studies commonly overlooked water quality and water‐use sectors when using comprehensive supply‐demand index (SDIcom), defined as the difference between water supply and demand, leading to challenges in applying WRES estimation to SDGs assessment. We constructed a refined supply‐demand framework for WRES, categorizing the water supply by water quality and the water demand by sectors. It allows the calculation of refined supply‐demand index (SDIref), addressing the limitations of SDIcom. Our findings demonstrate that SDIref could be effectively employed to assess SDGs and rectify the overestimation of SDIcom in the case study in Xinjiang. The supply‐demand relationship of water resources indicates surpluses in domestic and industrial water use and deficits in agricultural and ecological water use, with the latter worsening. SDIref is projected to be higher in the scenario with SSP2‐4.5 and high growth rate of irrigation efficiency. Our study suggested informed decisions based on SDG‐specific targets to either maintain or enhance SDG progress scores. The proposed refined supply‐demand framework of WRES established a quantitative link between WRES and human well‐being, providing applicable insights for other ecosystem service assessments in other regions.

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Section: Methodsmentioning

confidence: 99%

A Refined Supply‐Demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals

Li,

Xin,

et al. 2024

Earth's Future

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“…The SSP 126 is one of the good future scenarios. According to the SSP126 scenario, a sustainable future society will develop with signi cant economic growth, investments in health and education, and strong governance (Siabi et al 2023). This scenario anticipates a 1.8 C increase in global average temperature by 2100, which is in line with the Paris Agreement's goals of limiting warming to below 2.0 C (Rogelj et al 2016).…”

Section: Ssp 126mentioning

confidence: 99%

“…This scenario is consistent with the climate initiatives taken by different nations to uphold their current obligations to cut emissions. The total radiative forcing is anticipated to increase in SSP 245 to around 4.5 Wm − 2 before declining after 2070 (Siabi et al 2023). This leads to increased heat extremes and catastrophic ooding from precipitation extremes also predicted to cause a drop in food production throughout the planet.…”

Section: Ssp 245mentioning

confidence: 99%

Forecasting of Groundwater Level Variation Under Changing Climate in Chhattisgarh State Using Deep Learning Technique

Dey,

Singh

2024

Preprint

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Groundwater is the most precious natural resource in modern days. India is the largest consumer of groundwater globally, with over 25% of the world's groundwater extraction. Climate change affects the groundwater level both in direct and indirect ways. Recently developed machine learning approaches have led to the consideration of selected climate variables that can govern the groundwater dynamic. The inclusion of indirect key drivers such as anthropogenic activities and lithology to forecast groundwater levels using machine learning techniques is not well understood. This paper aims to consider both the direct and indirect key drivers for forecasting seasonal groundwater levels. In this context, a modified approach based on a deep learning model has been formulated that considers land cover dynamics, lithological properties, and climatic variables such as temperature and precipitation. The model was calibrated and validated to forecast seasonal groundwater levels for four Shared Socioeconomic Pathways (SSPs) scenarios. The results show that the accuracy level, R2 is 0.86 which is acceptable. Overall, the results obtained broadly correspond to an acceptable degree of accuracy. The proposed methodology is applicable for seasonal groundwater level forecasting and can be useful to farmers and key stakeholders.

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“…The superiority of CMIP6 scenarios lies in that the data are updated from previous climate models and deeply coupled with SSP data [65][66][67]. The simulation of land use in this research also involves regional social and economic development and policy formulation.…”

Section: Implications Of Scenario Analysis For Plesmentioning

confidence: 99%

Spatiotemporal Variations of Production–Living–Ecological Space under Various, Changing Climate and Land Use Scenarios in the Upper Reaches of Hanjiang River Basin, China

Wang,

Li,

Zhang

et al. 2023

Land

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Land is an important resource that supports the production, life, and ecological development of human society. The current research on production–living–ecological space (PLES) is mainly focusing on the identification of single and dominant functions of land space, and the comprehensive spatial function measurement index of PLES (PLESI) is less known in the effective quantitative evaluation of multifunctionality of different land use categories. Integrating the CMIP6 (Coupled Model Intercomparison Project phase 6) scenario data and the future land use simulation model (FLUS), this research took the upper reaches of the Hanjiang River (URHR) as an example to explore the temporal and spatial variations in land use, PLES, and PLESIs during 2000–2020, and in the SSP2-4.5 and SSP5-8.5 scenarios from 2021 to 2100. The findings were as follows: (1) Forest land is the most widely distributed type of land; correspondingly, ecological space has the widest distribution area in PLES, followed by production space. (2) The area of dry land and building land increased between 2000 and 2010, accompanied by the increase in living space. From 2010 to 2020, the growth rate of building land tended to slow down while forest land increased, and the conflict of PLES eased. (3) The transfer between forest land and dry land is projected to intensify under the SSP2-4.5 scenario, while it is projected to occur between forest land and grassland under the SSP5-8.5 scenario. As for the changes in PLES, the SSP2-4.5 scenario has a greater impact than the SSP5-8.5 scenario. Spatially, several sub-basins in the northern URHR are the main areas of land use and PLES change. (4) PLESI presents a significant downward trend from 2000 to 2020 while trending upward under the SSP5-8.5 scenario and trending downward slightly under the SSP2-4.5 scenario between 2020 and 2100. Combining climate scenarios and the future land use simulation, this research would support the effective utilization of regional land resources and ecosystem management decision-making.

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Assessment of Shared Socioeconomic Pathway (SSP) climate scenarios and its impacts on the Greater Accra region

Cited by 25 publications

References 76 publications

A Refined Supply‐Demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals

A Refined Supply‐Demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals

Forecasting of Groundwater Level Variation Under Changing Climate in Chhattisgarh State Using Deep Learning Technique

Spatiotemporal Variations of Production–Living–Ecological Space under Various, Changing Climate and Land Use Scenarios in the Upper Reaches of Hanjiang River Basin, China

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