Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Land Subsidence
3.2. Groundwater Variation
- The time-series dataset for groundwater levels and subsidence in the Battersea area were aligned for the same time periods. To align the time periods, the missing values were filled using ARIMA (autoregressive integrated moving average) [66], which combines three components: autoregression (AR), differencing (I), and moving average (MA).
- The data were normalised for both variables (groundwater level and subsidence) by converting the values into relative changes or percentage changes. This made the trends of both variables more comparable without being affected by the difference in units.
- The moving average was calculated for both variables to smooth the time-series data and highlight the underlying trends.
- Cross-correlation analysis [67]: The cross-correlation function was computed between the normalised time-series data for groundwater levels and subsidence rates. This helped to assess the strength and direction of the relationship between the two variables, as well as the time lag between them, if any.
- Based on the results of the trend analysis, cross-correlation analysis, and Granger causality test, the relationship between the temporal trends of groundwater levels and subsidence in the Battersea area was interpreted.
3.3. Construction Work Assessment
3.4. Trend Analysis
4. Results and Discussions
4.1. Land Subsidence
4.2. Correlation with Groundwater
4.2.1. Cross-Correlation Analysis
4.2.2. The Granger Causality Test
4.2.3. Correlation of Subsidence with Construction (Piling) Work
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter Measured | Study Area | Observation Period | SAR Sensor/Other Data Used | Result | Reference |
---|---|---|---|---|---|
Land uplift due to groundwater variation | San Bernardino, California | 1992–1993 | ERS 1,2 | 0.87 cm/month | [43] |
Land subsidence due to groundwater variation | Venice | 1971–2002 | ERS-1, 2 and others | 3–5 mm/year | [44] |
Land subsidence and groundwater variation | Kolkata, India | December 1992 to July 1998 | ERS 1,2 | 5 mm/year | [26] |
Subsidence in the geothermal fields | Taupo Volcanic Zone (TVZ), New Zealand | 1996 to 2005 | ERS 1,2 and Envisat | −10 and +15 mm/year | [45] |
Surface deformation monitoring over a hydrocarbon reservoir | Middle East | 2004–2007 | Envisat satellite and Radarsat-1 | Horizontal Deformations (1.8 mm/year), Vertical Deformation (4.8 mm/year). | [46] |
Land subsidence due to groundwater and mining | Pangzhuang mining field, China | September 2004–December 2010 | ALOS PALSAR | 42 ± 15 mm | [47] |
Ground motion over Coalfield | South Wales Coalfield, United Kingdom | 1992 and 1999 | ERS-1/2 | Uplift at centre of coalfield 1 cm/year. | [48] |
Land subsidence and groundwater variation | Wuhan, China | April 2015 to April 2016 | Sentinel-1A | −82 mm/year to 18 mm/year | [49] |
Groundwater depletion and land subsidence | Central Mexico | 2006–2011 | ALOS-1, GRACE, and others | 3620 MCm/year | [50] |
Structurally controlled land subsidence due to groundwater exploitation | Aguascalientes Valley, Mexico | 1996–2020 | ERS-1/2, ENVISAT, Sentinel-1 | −10 cm/year to −14 cm/year | [51] |
Data | Description |
---|---|
InSAR data | 99 Sentinel-1 SLC images, VV polarisation, Frame 422, Descending, IW Beam mode, Resolution: Azimuth: 20 m by Range: 5 m, Repeat Cycle: 12 days, Wavelength: 5.6 cm, C-band, Master Image: 1 November 2018, Time period: October 2016 to October 2020, Digital Elevation Model: SRTM V4, Software Used: ENVI SARscape, ArcGIS (ArcMap 10.2.2) |
Groundwater Data | Borehole groundwater data from the United Kingdom Environment Agency |
Piling Data | Projects by sheet piling and Martello piling |
Location | Area (km2) | No. of PS | PS Density | Land Deformation (mm/year) | |||
---|---|---|---|---|---|---|---|
Max | Min | Mean | St. Dev. | ||||
Battersea | 0.72 | 8124 | 11,745 | 0.5 | −18.71 | −6.8 | 1.6 |
Granger Causality Test | ||||
---|---|---|---|---|
Model 1: Groundwater~Lags (Groundwater, 1:1) + Lags (Subsidence, 1:1) | ||||
Model 2: Groundwater~Lags (Groundwater, 1:1) | ||||
Res.Df | Res.Df | F | P r (>F) | |
1 | 38 | |||
2 | 39 | −1 | 6.5242 | 0.01477 * |
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Agarwal, V.; Kumar, A.; Qin, Z.; Gomes, R.L.; Marsh, S. Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London. Remote Sens. 2023, 15, 3798. https://doi.org/10.3390/rs15153798
Agarwal V, Kumar A, Qin Z, Gomes RL, Marsh S. Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London. Remote Sensing. 2023; 15(15):3798. https://doi.org/10.3390/rs15153798
Chicago/Turabian StyleAgarwal, Vivek, Amit Kumar, Zhengyuan Qin, Rachel L. Gomes, and Stuart Marsh. 2023. "Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London" Remote Sensing 15, no. 15: 3798. https://doi.org/10.3390/rs15153798
APA StyleAgarwal, V., Kumar, A., Qin, Z., Gomes, R. L., & Marsh, S. (2023). Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London. Remote Sensing, 15(15), 3798. https://doi.org/10.3390/rs15153798