Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators

Ndehedehe, Christopher E.; Ferreira, Vagner G.; Onojeghuo, Alex Okiemute; Agutu, Nathan O.; Emengini, Ebele Josephine; Getirana, Augusto

doi:10.1016/j.scitotenv.2020.139643

Cited by 30 publications

(9 citation statements)

References 102 publications

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“…Ndehedehe et al [136] Used Sentinel-2 data to calculate MNDWI to detect changes in the Lake Chad Basin from 2015 to 2019 and achieved an overall accuracy of 97.4%.…”

Section: Surface Water Body Index Reference Of Study Key Findings Limmentioning

confidence: 99%

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

et al. 2020

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Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.

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“…Ndehedehe et al [136] Used Sentinel-2 data to calculate MNDWI to detect changes in the Lake Chad Basin from 2015 to 2019 and achieved an overall accuracy of 97.4%.…”

Section: Surface Water Body Index Reference Of Study Key Findings Limmentioning

confidence: 99%

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

et al. 2020

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“…where, R Green and R SWIR1 refer to Landsat 8 OLI/TIRS surface reflectance values of consistent bands Green (band 3) and SWIR1 (band 6). MNWDI is a good indicator of liquid water and less sensitive to atmosphere that has been widely used in environmental research [38][39][40][41]. With MNWDI, those pixels with a threshold value of greater than 0 were represented as water bodies and masked from the image.…”

Section: Extraction Of Total Imperviousnessmentioning

confidence: 99%

Prediction of Total Imperviousness from Population Density and Land Use Data for Urban Areas (Case Study: South East Queensland, Australia)

Ramezani

Che

2021

Applied Sciences

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Total imperviousness (residential and non-residential) increases with population growth in many regions around the world. Population density has been used to predict the total imperviousness in large areas, although population size was only closely related to residential imperviousness. In this study, population density together with land use data for 154 suburbs in Southeast Queensland (SEQ) of Australia were used to develop a new model for total imperviousness estimation. Total imperviousness was extracted through linear spectral mixing analysis (LSMA) using Landsat 8 OLI/TIRS, and then separated into residential and non-residential areas based on land use data for each suburb. Regression models were developed between population density and total imperviousness, and population density and residential imperviousness. Results show that (1) LSMA approach could retrieve imperviousness accurately (RMSE < 10%), (2) linear regression models could be used to estimate both total imperviousness and residential imperviousness better than nonlinear regression models, and (3) correlation between population density and residential imperviousness was higher (R2 = 0.77) than that between population density and total imperviousness (R2 = 0.52); (4) the new model was used to predict the total imperiousness based on population density projections to 2057 for three potential urban development areas in SEQ. This research allows accurate prediction of the total impervious area from population density and service area per capital for other regions in the world.

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“…Especially in semi-arid and arid regions, surface water availability decreases due to increasing ET and decreasing precipitation rates on the one hand, and the overexploitation of available water resources on the other hand [9]. In the case of Lake Urmia in Iran, for example, human water use has reportedly led to a decrease of up to 45% of lake inflow and between 39% and 43% of lake water loss over the timeframe of 2003-2013 [10].…”

Section: Introduction 1surface Water In a Societal And Environmental ...mentioning

confidence: 99%

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

2022

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Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

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Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators

Cited by 30 publications

References 102 publications

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

Prediction of Total Imperviousness from Population Density and Land Use Data for Urban Areas (Case Study: South East Queensland, Australia)

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

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