Building damage risk in sinking Indian megacities

Land subsidence in Indian megacities

Utilizing 1,299 Sentinel-1 SAR images, including 15 frames, we generated high-resolution VLM rates for 5 Indian megacities (Fig. 1): Delhi (2015–2023), Mumbai (2015–2023), Bengaluru (2019–2023), Chennai (2015–2020) and Kolkata (2017–2023). The dataset encompasses ~0.5 million elite pixels (Fig. 2 and Supplementary Table 2). The SAR images were processed using an advanced multitemporal InSAR algorithm to generate high-resolution maps of VLM rates for each city (Methods).

a–e, InSAR VLM rate maps observed in Delhi (a; 2015–2023), Mumbai (b; 2015–2023), Bengaluru (c; 2019–2023), Chennai (d; 2015–2020) and Kolkata (e; 2017–2023). The maps include ~0.5 million pixels each, corresponding to an average ~75 m resolution. f, The empirical cumulative distribution function of VLM rates for all observed data pixels in each megacity. g, VLM distribution in each megacity, showing the percentage of area falling within specific VLM categories. h, Boxplot statistics of VLM rates for each Indian megacity. Boxplots display the median (central line), interquartile range (IQR; 25th–75th percentiles, box bounds), whiskers extending to 1.5 × IQR and outliers as individual points. Minimum and maximum values are also indicated. The sample sizes were n = 182,817 (Delhi), n = 146,648 (Mumbai), n = 64,286 (Kolkata), n = 94,376 (Chennai) and n = 63,464 (Bengaluru). Mean rates of VLM for Delhi, Mumbai, Bengaluru, Chennai and Kolkata are −0.8 ± 2.2 mm yr⁻¹, −0.8 ± 1.2 mm yr⁻¹, −0.7 ± 1.7 mm yr⁻¹, −0.7 ± 2.8 mm yr⁻¹ and −2.2 ± 1.8 mm yr⁻¹, respectively. Maximum, minimum and mean VLM rates for the smallest administrative divisions in India, namely wards, are provided in Supplementary Tables 3–7. i,j, Treemap charts showing population exposed to different VLM categories estimated using the WorldPop 100-m gridded population dataset⁷⁴. Almost 1.9 million (M) people in Indian megacities are exposed to subsidence of more than 4 mm yr⁻¹. Maps in a–e generated with MATLAB R2023a. Credit: basemaps in a–e, Esri, HERE, Garmin, Foursquare, METI/NASA, USGS.

We found widespread subsidence in all cities, with maximum rates of 51.0 mm yr⁻¹, 31.7 mm yr⁻¹, 26.1 mm yr⁻¹, 16.4 mm yr⁻¹ and 6.7 mm yr⁻¹ for Delhi, Chennai, Mumbai, Kolkata and Bengaluru, respectively (Fig. 2g). Additionally, we identified subsidence hotspots in most cities (Supplementary Fig. 1 and Supplementary Tables 3–6). For instance, rates of up to 28.5 mm yr⁻¹, 38.2 mm yr⁻¹ and 20.7 mm yr⁻¹ affected areas of Bijwasan, Faridabad and Ghaziabad, in Delhi (Fig. 2a). In Chennai, the fastest subsidence rates surround flood plains of the Adyar River and city centre areas, including Valasaravakkam, Kodambakkam, Alandur and Tondiarpet (Fig. 2d). We also identified localized uplift in some cities, such as areas near Dwarka, Delhi that are rising at a rate of 15.1 mm yr⁻¹ (Fig. 2a). The VLM empirical cumulative distributions (Fig. 2f) indicate that 31,373 (17.16%, 196.27 km²), 41,937 (28.59%, 262.36 km²), 19,240 (30.31%, 120.37 km²), 12,198 (12.92%, 76.31 km²) and 35,632 (55.42%, 222.91 km²) pixels in Delhi, Mumbai, Bengaluru, Chennai and Kolkata, respectively, are affected by subsidence after accounting for uncertainty (one standard deviation) (Fig. 2f). Only pixels with VLM values more negative than the uncertainty threshold are classified as subsiding. Figure 2g,h provides the statistics for VLM results obtained for each city, and Fig. 2i,j displays the population exposed to different levels of VLM. The seasonal amplitude of VLM is shown in Supplementary Fig. 2.

In Delhi (Fig. 2a,g), the primary driver of land subsidence is the compaction of alluvial deposits caused by extensive groundwater withdrawals^37,41 (Supplementary Fig. 3). In contrast, the localized uplift in ward Dwarka (Supplementary Fig. 4) is attributed to aquifer recharge, facilitated by strict government regulations implemented between the mid-2000s and 2011, which curtailed groundwater pumping, promoted rainwater harvesting and supported the revival of an old water body between 2012 and 2015, thereby mitigating strain on groundwater resources and enhancing recharge^37,42. In Chennai, the widespread subsidence observed in the city centre can be attributed to the compaction of alluvium deposits from Holocene fluvial sediments, especially in the floodplain of the Adyar River (Fig. 2d), characterized by sandy clay, silt and sand. In addition, the two major hotspots of subsidence (K.K. Nagar and Tondiarpet) in Chennai can be related to groundwater extraction (Supplementary Fig. 5). The VLM rate in Kolkata broadly agrees with that of earlier studies³⁹, and minor discrepancies are likely due to differences in the observation period. The observed subsidence pattern in Kolkata may be attributed to the compaction of Pleistocene and Holocene sediments. In Bengaluru, the widespread presence of igneous and metamorphic rocks such as gneiss, granodiorites and granite may also be responsible for the relatively minimal subsidence witnessed (Fig. 2c,g). However, there was an increase in the extraction of groundwater in Bengaluru towards the end of 2022, which can be derived from a comparison of groundwater level and deformation time series (Supplementary Fig. 6). Subsidence is notably lower in the majority of Mumbai except in economically disadvantaged neighbourhoods (for example, Dharavi) with high-density informal settlements (Supplementary Fig. 7). In addition to the potential compaction resulting from extensive groundwater extraction through millions of unregulated borewells, Indian megacities may also experience subsidence due to the cumulative weight of the urban structures above⁴³.

Angular distortion hazards

Angular distortion hazard, the ratio of differential subsidence to the horizontal distance over which subsidence occurs, can cause damage to infrastructure, increasing the risk of building damage^{19,44,45,46,47}. Here we oversampled the InSAR VLM annual rates to the vertices of individual buildings and computed the angular distortion annual rate (β) for each building and then scaled it with the observation period of 2015–2023 to retrieve the total angular distortion (β_tot); see Methods for details. We displayed the results in four categories: low, medium, high and very high total angular distortion hazards (Supplementary Fig. 8a–e). We found that in Delhi, 2,718 (0.055%) buildings are exposed to medium hazards. In comparison, 167 buildings in Mumbai are affected by a combination of medium, high and very high hazards (Supplementary Table 8). Approximately 39 buildings in Chennai are in the medium hazard category, having β_tot ranging from 0.0006 rad to 0.02 rad. All of Bengaluru and Kolkata fall within the low hazard category.

We created various distortion angle hazard accumulation scenarios, assuming differential subsidence continues sublinearly. For simplicity, we present these scenarios as future projections, assuming subsidence began at the start of the InSAR measurement period for each city. However, they can be similarly applied to any period. To this end, we estimated and categorized β_tot for 10 years (near future; Supplementary Fig. 9a–e), 30 years (intermediate future; Supplementary Fig. 10a–e) and 50 years (far future; Fig. 3a–e) scenarios. For the near-future scenario (Supplementary Fig. 9 and Supplementary Table 8), 6,188 (0.17%) buildings in Delhi, Mumbai and Chennai fall into the medium hazard category, whereas the rest of the buildings, including those in Bengaluru and Kolkata, are categorized as low hazard.

a–e, Total angular distortion (β_tot) projected for the next 50 years in Delhi (a), Mumbai (b), Bengaluru (c), Chennai (d) and Kolkata (e). f, Count of buildings per each hazard category (low to very high) for a 50-year scenario, per megacity. The inset bar chart represents the number of buildings in each megacity with no data. Maps in a–e generated using MATLAB R2023a and QGIS 3.34. Credit: basemaps in a–e, Esri, HERE, Garmin, Foursquare, METI/NASA, USGS.

For the intermediate-future scenario (Supplementary Fig. 10a–e and Supplementary Table 8), 4,804,360 (97.3%), 41,349 (0.84%) and 4,116 (0.083%) of Delhi’s buildings fall into the low-, medium- and high-hazard categories, respectively. The corresponding values for Mumbai are 1,991,849 (93.2%), 20,106 (0.94%) and 353 (0.016%). Additionally, approximately 5,751 (0.36%) and 7,843 (0.31%) are characterized as medium hazards in Bengaluru and Kolkata, respectively. Furthermore, 72,947 (3.3%) and 1,720 (0.079%) of Chennai are located within the medium- and high-hazard zones, respectively.

For the far-future scenario (Fig. 3 and Supplementary Table 8), the low-hazard zone encompasses 4,768,420 (96.5%), 63,913 (91.34%), 1,554,336 (97.6%), 1,959,415 (90.9%) and 2,352,852 (95.8%) buildings in Delhi, Mumbai, Bengaluru, Chennai and Kolkata, respectively. Chennai includes the largest number of buildings 177,347 (8.2%) exposed to moderate hazards. The high hazard affects 15,530 (0.72 %) in Chennai, 17,492 (0.35%) in Delhi, 4,172 (0.19%) in Mumbai, 263 (0.011%) in Kolkata and 221 (0.014%) in Bengaluru.

Subsidence-related building damage risk

We further assess the subsidence-related building damage risk for the above hazard scenarios by considering building densities obtained from the Google Open Buildings (v3) dataset for the five Indian megacities (see Methods for details). We found that during the observation period (Supplementary Figs. 11 and 12 and Supplementary Table 9), in Delhi, Mumbai and Chennai, 2,264, 110 and 32 buildings, respectively, are categorized as under high damage risk. All other megacities collectively have 100% of their buildings in the very low, low and medium risk categories.

For the near-future scenario (Supplementary Figs. 13 and 14 and Supplementary Table 9), 2.1 million, 1.28 million, 0.88 million, 0.67 million and 0.48 million buildings in Delhi, Kolkata, Chennai, Bengaluru and Mumbai, respectively, have medium damage risk. Also, 3,169, 958 and 262 buildings are exposed to a high risk in Delhi, Chennai and Mumbai, respectively.

For the intermediate-future scenario (Supplementary Figs. 15 and 16 and Supplementary Table 9), we estimated that 24,582, 14,249, 2,577, 40,843 and 4,052 buildings are exposed to high damage risk in these cities, respectively, and 3,169, 255 and 958 buildings in Delhi, Mumbai and Chennai are in the very-high-risk category.

For the far-future scenario (Fig. 4 and Supplementary Tables 9), less than 4.6% and 1% of the total area of each megacity experiences high and very high risk levels. We estimated that 11,457, 3,477, 112, 8,284 and 199 buildings are within the very-high-damage-risk zone in Delhi, Mumbai, Kolkata, Bengaluru and Chennai, respectively. Also, in Chennai, 97,946 buildings are exposed to high damage risk, and the corresponding values for Delhi and Kolkata are 38,428 and 30,344, respectively.

a–e, Risk of subsidence-induced building structural damage projected for the next 50 years in Delhi (a), Mumbai (b), Bengaluru (c), Chennai (d) and Kolkata (e). f–j, Percentage of buildings in each risk category for Delhi (f), Mumbai (g), Bengaluru (h), Chennai (i) and Kolkata (j). The middle of each donut chart displays the number of buildings in each risk category. Maps in a–e generated using MATLAB R2023a and QGIS 3.34. Credit: basemaps in a–e, Esri, HERE, Garmin, Foursquare, METI/NASA, USGS.