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Surface water is the world’s largest store of consumable water. It plays an important role in sustaining ecosystems and enabling human adaptation to various climate changes. Although surface freshwater is necessary for life, there is a startling lack of understanding in the current literature of the spatial and temporal dynamics of surface freshwater outflow and storage variations across a large country: India. Current studies have several limitations, such as the use of relatively short data series and inappropriate machine learning methods. This study develops an innovative machine learning approach. It uses a long data series to generate a correct understanding of the spatial and temporal dynamics of surface freshwater outflow and storage variations in a large geographical area. Using the JRC dataset, the authors mapped the change in intensity of surface water, performed an analysis of the thematic maps (Indian subcontinent) generated using ArcMap 10.8 from a period of June 2000 to June 2020. The research presented in this paper devised Google Earth Engine in Python API and remote sensing techniques to detect changes in the trends of surface water levels and quantify changing map trends. ArcMap is used for the viewing and editing of Raster images and to perform raster calculations. Values of the attributes are averaged over 13 rivers over the period of 20 years to find the correlation between the decrease in Surface water levels with the rainfall intensity and land surface temperature changes. The authors use the correlation between the parameters to obtain an average R-squared adjusted value of 0.402 with the dependence of surface water change to the independent variables of change in land surface temperature and rainfall intensity. The results of the study provide an improved understanding of the effects of the change in the land surface temperature and rainfall levels or the causes of the decline and changes in the surface waters of India. The innovative machine learning and data analysis approach developed in this study can be applied effectively to other regions of the world.
Keywords
Google Earth Engine, Remote Sensing, ArcMap 10.8, MODIS, climate change, surface water, LST, rainfall, PERSIANN
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CURRENT STATUS: Under Review
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Posted 08 Nov, 2021
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Received 02 Nov, 2021
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This preprint is under consideration at Water Resources Management. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 08 Nov, 2021
No community comments so far
Reviews received
Received 02 Nov, 2021
Reviewers invited
Invitations sent on 02 Nov, 2021
Editor assigned
On 14 Sep, 2021
First submitted
On 03 Sep, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Surface water is the world’s largest store of consumable water. It plays an important role in sustaining ecosystems and enabling human adaptation to various climate changes. Although surface freshwater is necessary for life, there is a startling lack of understanding in the current literature of the spatial and temporal dynamics of surface freshwater outflow and storage variations across a large country: India. Current studies have several limitations, such as the use of relatively short data series and inappropriate machine learning methods. This study develops an innovative machine learning approach. It uses a long data series to generate a correct understanding of the spatial and temporal dynamics of surface freshwater outflow and storage variations in a large geographical area. Using the JRC dataset, the authors mapped the change in intensity of surface water, performed an analysis of the thematic maps (Indian subcontinent) generated using ArcMap 10.8 from a period of June 2000 to June 2020. The research presented in this paper devised Google Earth Engine in Python API and remote sensing techniques to detect changes in the trends of surface water levels and quantify changing map trends. ArcMap is used for the viewing and editing of Raster images and to perform raster calculations. Values of the attributes are averaged over 13 rivers over the period of 20 years to find the correlation between the decrease in Surface water levels with the rainfall intensity and land surface temperature changes. The authors use the correlation between the parameters to obtain an average R-squared adjusted value of 0.402 with the dependence of surface water change to the independent variables of change in land surface temperature and rainfall intensity. The results of the study provide an improved understanding of the effects of the change in the land surface temperature and rainfall levels or the causes of the decline and changes in the surface waters of India. The innovative machine learning and data analysis approach developed in this study can be applied effectively to other regions of the world.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
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