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Exploring Spatiotemporal Relationships between InSAR-derived Land Subsidence and Satellite-based Hydrological Variables

Zhang, Yixin LU (2021) In Student thesis series INES NGEM01 20211
Dept of Physical Geography and Ecosystem Science
Abstract
Shabestar basin in the East Azerbaijan province, Northwest Iran, where irrigation is the main groundwater consumer, has experienced large-scale subsidence and groundwater deletion, which poses a threat to the local agricultural activities, economic development, and food security. With the emergency of mitigating the risk, satisfying future demand for groundwater, and improving resilience considering climate change, this study proposes a satellite-based approach to explore the spatio-temporal relationships between measured subsidence and hydrological variables in the basin to assist groundwater management strategy.

We investigated ground subsidence in the basin using the SBAS-InSAR technique based on series of Sentinel-1A TOPS Synthetic... (More)
Shabestar basin in the East Azerbaijan province, Northwest Iran, where irrigation is the main groundwater consumer, has experienced large-scale subsidence and groundwater deletion, which poses a threat to the local agricultural activities, economic development, and food security. With the emergency of mitigating the risk, satisfying future demand for groundwater, and improving resilience considering climate change, this study proposes a satellite-based approach to explore the spatio-temporal relationships between measured subsidence and hydrological variables in the basin to assist groundwater management strategy.

We investigated ground subsidence in the basin using the SBAS-InSAR technique based on series of Sentinel-1A TOPS Synthetic Aperture Radar (SAR) images acquired from 22 January 2016 to 21 October 2020 along ascending and descending tracks. The study showed average subsidence rates ranged from -97.5 mm/year to 10 mm/year in the basin after decomposing line-of-sight velocity fields to vertical components. The prominent subsidence was found in the eastern and western portions of the basin, and the maximum average subsidence rate was detected in the eastern part of the basin near Nazarlu.

Correlation analysis between the surface subsidence and potential driving factors, including the actual evapotranspiration (ETa), land surface temperature (LST), the normalized difference vegetation index (NDVI), precipitation (P), and soil water index (SWI), revealed a significant relationship between the first three variables and observed subsidence by InSAR. A multivariate long short-term memory (LSTM) network was established to investigate the importance of the first three variables and predict subsidence in the near future. The result quantitatively revealed that the agricultural practice had a major impact on subsidence occurrence in the basin. Furthermore, our findings indicated that the area is estimated to continue subsiding dramatically in the next five years. This study fills the gap in the local groundwater monitoring system using satellite-based data and artificial intelligence and contributes to the local groundwater management by providing insights into the main drivers of groundwater-induced subsidence. (Less)
Popular Abstract
Groundwater is a critical natural resource. In the mid-latitude arid and semi-arid country, such as Iran lacking adequate surface water supply, groundwater has an important role in providing a water supply for irrigation and drinking water. The increasing groundwater demand in the face of population growth and climate change causes land subsidence, posing a threat to the local agricultural activities, economic development, and food security. In order to mitigate the risk and satisfy future water demand, the study proposes a satellite-based method to explore relationships between measured subsidence and hydrological variables, spatially and temporally, to assist groundwater management strategy.

Shabestar basin, located in East Azerbaijan... (More)
Groundwater is a critical natural resource. In the mid-latitude arid and semi-arid country, such as Iran lacking adequate surface water supply, groundwater has an important role in providing a water supply for irrigation and drinking water. The increasing groundwater demand in the face of population growth and climate change causes land subsidence, posing a threat to the local agricultural activities, economic development, and food security. In order to mitigate the risk and satisfy future water demand, the study proposes a satellite-based method to explore relationships between measured subsidence and hydrological variables, spatially and temporally, to assist groundwater management strategy.

Shabestar basin, located in East Azerbaijan province, Northwest Iran, is selected as the study area. The basin has experienced large-scale subsidence and groundwater deletion in the past decades. We firstly investigated the local land subsidence from 2016 to 2020 using a mature technique called SBAS-InSAR technique. The result showed that mean subsidence rates ranged from -97.5 mm/year to 10 mm/year in the basin. The prominent subsidence was found in the eastern and western parts of the basin, and the largest mean subsidence rate was detected in the eastern part of the basin near Nazarlu town.

Correlation analysis between the surface subsidence and five potential driving factors, including the actual evapotranspiration (ETa), land surface temperature (LST), the normalized difference vegetation index (NDVI), precipitation (P), and soil water index (SWI), indicated a significant relationship between the first three variables and measured subsidence. In order to assess the importance of variables and predict subsidence, in the near future, we constructed a deep learning model called a multivariate long short-term memory (LSTM) network. Our findings quantitatively revealed that the agricultural practice had a primary impact on subsidence occurrence in the basin. Furthermore, the result indicated that the area is estimated to continue subsiding rapidly in the next five years. This study fills the gap in the local groundwater monitoring system using satellite-based products and artificial intelligence and contributes to the local groundwater management by providing insights into the main drivers of groundwater-induced subsidence. (Less)
Please use this url to cite or link to this publication:
author
Zhang, Yixin LU
supervisor
organization
course
NGEM01 20211
year
type
H2 - Master's Degree (Two Years)
subject
keywords
InSAR, Shabestar, subsidence, groundwater, hydrological variables, LSTM, Sentinel-1A, Geomatics
publication/series
Student thesis series INES
report number
546
language
English
id
9057709
date added to LUP
2021-06-23 11:11:37
date last changed
2021-06-23 11:11:37
@misc{9057709,
  abstract     = {{Shabestar basin in the East Azerbaijan province, Northwest Iran, where irrigation is the main groundwater consumer, has experienced large-scale subsidence and groundwater deletion, which poses a threat to the local agricultural activities, economic development, and food security. With the emergency of mitigating the risk, satisfying future demand for groundwater, and improving resilience considering climate change, this study proposes a satellite-based approach to explore the spatio-temporal relationships between measured subsidence and hydrological variables in the basin to assist groundwater management strategy.
 
We investigated ground subsidence in the basin using the SBAS-InSAR technique based on series of Sentinel-1A TOPS Synthetic Aperture Radar (SAR) images acquired from 22 January 2016 to 21 October 2020 along ascending and descending tracks. The study showed average subsidence rates ranged from -97.5 mm/year to 10 mm/year in the basin after decomposing line-of-sight velocity fields to vertical components. The prominent subsidence was found in the eastern and western portions of the basin, and the maximum average subsidence rate was detected in the eastern part of the basin near Nazarlu.

Correlation analysis between the surface subsidence and potential driving factors, including the actual evapotranspiration (ETa), land surface temperature (LST), the normalized difference vegetation index (NDVI), precipitation (P), and soil water index (SWI), revealed a significant relationship between the first three variables and observed subsidence by InSAR. A multivariate long short-term memory (LSTM) network was established to investigate the importance of the first three variables and predict subsidence in the near future. The result quantitatively revealed that the agricultural practice had a major impact on subsidence occurrence in the basin. Furthermore, our findings indicated that the area is estimated to continue subsiding dramatically in the next five years. This study fills the gap in the local groundwater monitoring system using satellite-based data and artificial intelligence and contributes to the local groundwater management by providing insights into the main drivers of groundwater-induced subsidence.}},
  author       = {{Zhang, Yixin}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Student thesis series INES}},
  title        = {{Exploring Spatiotemporal Relationships between InSAR-derived Land Subsidence and Satellite-based Hydrological Variables}},
  year         = {{2021}},
}