Rising groundwater table due to restoration projects amplifies earthquake induced liquefaction risk in Beijing

Domain: earth_science
Published: 2025-02-08
Editorial outcome: minor_revision

Abstract
Groundwater restoration is increasingly common to mitigate groundwater overexploitation, which proves effective in resolving urban water scarcity and regional unsustainable development. China’s South-to-North Water Diversion Project is one of the largest water transfer projects to restore groundwater and resolve water shortage in Beijing. However, how the rapidly restored groundwater of this magnitude changes regional seismic stability is largely unknown. Here, we explore the relation between elevated groundwater table and seismic ground liquefaction based on the case of Beijing under the impact of the South-to-North Water Diversion Project. We collect groundwater table depth records and use them to drive three-dimensional geotechnical models that generate ground liquefaction hazard maps. We find a remarkable increase in coverage and severity of liquefaction due to groundwater table rise. Infrastructures built during the rapid urbanization process are often under low groundwater table and thus ill y prepared for this increased seismic risk. These findings highlight the necessity to consider the seismic consequence of large-scale groundwater restoration projects.

Introduction
Water scarcity threatens major regions of North Africa, North America, Australia, North China, and other areas1–4. Globally, 750-800 km3/year of groundwater (GW) is drawn from aquifers to increase water supply for municipal, industrial, and agricultural needs in megacities5. GW overexploitation to meet socioeconomic demand results in GW table drop and can cause serious ecological problems including river dry-up, lake shrinkage, and land subsidence6–8. Therefore, transportation of water from relatively wet regions to dry regions has become an often sought to alternative to increase water supply and reduce the demand for GW. More than 350 inter-basin water transportation projects have been completed by 2021 across the world, in more than 40 countries and regions, including USA, Canada, India, Pakistan, China etc9. These water transportation projects have helped to achieve GW restoration10–12, as evidenced by the rise of GW table, which contributes to ecosystems by sustaining base flow to rivers, lakes, and wetlands13. Albeit the positive influence of GW table rise, it could also lead to unanticipated seismic consequences14 (e.g., through soil liquefaction).

Soilliquefactionoccurswhensoiltransformsfromasolidstatetoa fluid-like state due to excess pore pressure buildup under external loading. This excess pore pressure is built up mainly in the saturated soil at shallow subsurface depths15,16 and GW table, marking the boundary between saturated and unsaturated soil, is therefore a critical factor for liquefaction. Earthquake induced soil liquefaction is frequently observed in major earthquake, with more than 1000 cases of liquefaction reported over the past 80 years17 (Fig. 1), and is a common cause for seismic ground failure and structural damage18,19. In light of the extensive GW restoration projects in the developing and developed countries, the potential seismic liquefaction risk caused by GW table rise becomes a global concern. In some areas with notable liquefaction risk, including the USA, Japan, and China, GW restoration projects have been initiated with plans for ongoing restoration of GW tables in the foreseeable future20–22. For example, GW restoration in the Santa Clara Valley Basin at the southern end of San Francisco Bay initiated in the 1960s and has resulted in gradual GW table depth. In the 1989 Loma Prieta earthquake, many sites in the area suffered from liquefaction hazards23. Since then, GW table has continued to rise, which indicates that the area could be even more vulnerable to liquefaction induced damages now, should a similar earthquake strike. Similarly, in Hyogo Ken, Japan, mass-liquefaction was observed in the 1995 Kobe earthquake24,25. Since then, GW table depth has significantly decreased from 12 m to 6 m, indicating greater liquefaction risk26. Unfortunately, so far, the impact of GW restoration on seismic liquefaction has been largely overlooked.

Here, we study the seismic liquefication potential for the case of Beijing City, China under the impact of one of the world’s largest GW restoration projects9, the South-to-North Water Diversion Project (SNWDP). The GW table depth in Beijing increased from 11.35 m in 1996 to 28.19 m in 2015 due to rapid economic development and urbanization27,28. SNWDP was initiated in 2002 to transport water from the Yangtze River through three canal and pipeline systems to major northern cities in China29,30. Specifically, with the implementation of the middle route of SNWDP in 2014, a total of 8.4 km3 of fresh water was diverted to Beijing up to 2023. The middle route flows from the Danjiangkou Reservoir to Beijing with the long-term goal of diverting 13 km3 of water annually31 (Fig. 2A). Monitoring results indicate that the average GW table in Beijing has gradually risen since the implementation of SNWDP (Fig. 2B). The key factors contributing to this decrease in GW table depth include water diversion, climate variability and reductions in water consumption20,27, with SNWDP playing the dominant role in reversing the long-term trend of GW table drop. Here, we evaluate the potential impact of SNWDP on the seismic risk of Beijing City, China. The soil layers in Beijing are rich with liquefaction prone sand and silt, while the city lies at the intersection of three seismically active zones with geological settings for severe earthquake. Beijing has recorded in history 18 earthquakes with magnitude greater than 4.5 and 6 earthquakes with magnitude greater than 6.0. The largest surface rupture event was a magnitude 8.0 earthquake that occurred in the Sanhe-Pinggu area in 167932. The combination of liquefaction prone soil and high seismicity in Beijing means that GW table rise due to SNWDP could potentially become a major trigger for earthquake hazard. What make matters more complicated is that intense infrastructure development, indicated by the annual residential construction area, has occurred in Beijing during the past two decades concurrently with the economic boom and rapid urbanization, especially during 2000 to 2015 when GW table dropped to its lowest33. The rapid increase in annual residential construction area synchronized with the drop in GW table (Fig. 2B) since the beginning of this century. Therefore, these infrastructures are likely to be ill y prepared for the potential increased seismic risk when GW table rises. Moreover, such risk could also affect other megacities around the world with liquefaction prone soils and high seismicity that are planning for GW restoration, as intense infrastructure development and increase in GW depth often coevolve during rapid urbanization. This is especially relevant to regions that have suffered liquefaction in past earthquakes and recently experienced significant GW table change in Fig. 1.

To assess this previously unanticipated seismic risk, we collected and compiled GW table and soil profile data to investigate the long-term impact of GW table rise on liquefaction potential distribution in Beijing under the influence of SNWDP as a typical case. We obtained the spatial distribution of Beijing GW table depth based on a dataset containing GW table from 122 monitoring wells, covering the period from 1996 to 2023, and predicted the GW table depth distribution for 2025 and 2030. We constructed three-dimensional geotechnical models for the main urban plain area of Beijing, with a spatial resolution of 300 m. Liquefaction hazard maps, displaying the spatial variation of liquefaction potential, are developed in terms of the liquefaction potential index (LPI) using standard penetration test (SPT) based liquefaction triggering procedures, for the period from 1996 to 2030. We then assessed the seismic consequence of GW table rise for underground structures of existing subway lines constructed during the period with low GW table, as a typical example for the influence on infrastructure. This important implication sends an alarm for seismic safety considerations not only for Beijing, but also for other areas in the world subjected to GW table rise due to water restoration projects and other contributing factors.

Results
Spatial distribution of GW table depth from 1996 to 2030
The study area covers the main urban area of Beijing, where data from boreholes and monitoring wells are relatively well documented. It ranges from 116°13’ to 117°23’E and 39°46’ to 40°26’N, with a total coverage of 8386 km2, constituting 51% of the city’s total land area and 81% of resident population (Fig. 2A). Regression analysis was performed to establish the relationship between the average GW depth and the observed GW table depth in the wells (Fig. 2C), which was then used to obtain the projected GW table spatial distribution. Further details of the monitoring data of 122 wells and projected GW table are provided in the supplementary information.

Spatial variability of GW table depth from 1996 to 2030 was obtained (Figs. 2D and Supplementary Fig. 1). From 1996 to 2023, the average GW table depth of Beijing first exhibits an overall increasing trend followed by decrease after 2015 (Fig. 2B). Result shows that GW table depth in the study area remain between 0 and 65 m, gradually decreasing from the northeast to southeast. Local GW table depth in most regions show a positive correlation with the average GW table depth in Beijing, where local GW table depth increases as average GW table depth increases from 1996 to 2015 and decreases after 2015. However, GW table depth in some areas of the southeast corner (Tongzhou district) shows a different trend, decreasing during 2005 to 2015 and increasing from 2015 to 2020. This is mainly attributed to the naturally high GW table in this area, which is less affected by the average GW table depth. For example, for the Houjianping station in Fig. 2C, a quadratic polynomial regression function best fits between the GW table depth of the monitoring well and the average GW table depth in Beijing. Therefore, when the average GW table depth decreases between 2015 to 2020, from 28.19 m to 23.30 m, the GW table depth in this area actually increases, visible from the plots for 2015 and 2020 in Fig. 2D.

A previous study predicted the highest and lowest average GW table scenarios for 2025 and 203020. For the highest GW table scenarios, the average GW table depth in 2030 would reach 10.22 m, similar to the 1996 level. Here, we mainly discuss the change in seismic risk under the highest GW table scenario for 2025 and 2030, while results for other scenarios are provided in the supplementary information. This is reasonable as even for the lower GW table scenarios, though the rise in GW table is projected to be slower, it would eventually reach levels similar to those of the highest scenario at a later time after 2025-2030, as long as the SNWDP continues to supply water to Beijing.

Another justification for the choice of the high GW scenario is that by the end of 2023, the observed average GW table reached 15.56 m, which is comparable to the highest projected value for 2025, at 15.87 m. Considering that the projections for the highest GW table better match the observed data, our analysis primarily focuses on the liquefaction potential under the highest GW table scenario. The spatial distribution of GW table depth in 2023 is very similar to that projected for 2025, with both showing a decreasing trend from the northwest to the southeast. In the Tongzhou district, the majority of GW table depth is within 15 m in both years, with some area exhibiting depth of less than 5 m. Minor discrepancies are observed locally, such as the western part of the study area showing higher GW table compared to 2025, while a small portion of the northern region displays a lower GW table.

Three-dimensional geotechnical models
Owing to natural deposit histories and human activities, the stratigraphic model is a layered structure in the study area with anthropogenic deposits covering the surface and Quaternary deposits lying beneath34,35. Liquefaction generally occurs at shallow subsurface depths, especially in saturated sand and silt36. The shallow soils in Beijing consist of Quaternary Holocene deposits, with formations dating back ~11,700 years34. Considering the prolonged sedimentation history and the relatively brief GW table change over the past 20–30 years, the impact of aging on these soils is expected to be minor for the period concerned in this study37,38. Furthermore, the absence of significant seismic events or violent geological changes in Beijing in recent decades also does not point to significant aging. For the sake of simplicity, we classified the soil in Beijing within 20 m below the surface into three categories, which are liquefaction prone sand and silt, and other not liquefaction prone soils, including anthropogenic deposits, clay, and gravel. This is a significant simplification of realistic stratigraphic structure, but reflects the basic features related to liquefaction and enables regional scale liquefaction assessment. The depth of 20 m is adopted as liquefaction mainly occurs in such shallow depths36. The three-dimensional stratigraphic model was constructed by Ordinary Kriging with a spatial resolution of 300 m (Fig. 3A). Slices at several cross-sections (39.8°N and 116.6°E) of the model are shown in Fig. 3B and Fig. 3C. From west to east, the thickness of sand layers remains largely unchanged regardless of the overall soil thickness. From north to south, silt and sand layers become thicker. The substantial presence of silt and sand poses a potential risk for seismic liquefaction.

SPT blow count (N), density (ρ), and saturated density (ρsat) are three essential indices for assessing site liquefaction potential39. N is obtained from in-situ SPT tests. ρ and ρsat are obtained from laboratory tests. The spatial distribution of N, ρ and ρsat are determined via the distribution-based Kriging method and visualized in Fig. 3D, Fig. 3E, and Fig. 3F. N value ranges from 4 to 40, with an increasing trend along the depth. In general, N values are smaller in the east, north, and south parts of the region, where GW table is relatively higher. Spatial variation of ρ and ρsat is also observed.

Liquefaction hazard mapping
The Chinese Code for the Seismic Design of Buildings (GB50011-2010, referred to as CC) provides the mandated method for liquefaction assessment in China, while the improved simplified procedure for liquefaction assessment proposed by the National Center for Earthquake Engineering Research (NC) is widely adopted globally40 (see “Methods”). Based on the analysis of historical earthquake-induced liquefaction data in China, it has been found that the CC method overestimates liquefaction severity, while the NC method tends to underestimate liquefaction potential41,42. Seismic design of structures should consider input ground motions with 10% and 2% exceedance probability in 50-year hazard levels. Therefore, we adopted both methods, for corroboration, to quantify the liquefaction potential in the study area, under 0.2 and 0.4 g peak ground acceleration (PGA), corresponding to the two hazard levels specified for Beijing, respectively43. The unit “g” refers to the gravitational acceleration, equivalent to 9.81 m/s2. Seismic liquefaction potential in terms of liquefaction potential index (LPI) is determined at 90,000 interpolation points across the study area, the hazard maps were generated for the period from 1996 to 2030 (Fig. 4B, Fig. 4C and Supplementary Figs. 2, 3).

For CC, liquefaction severity is recognized as none with LPI=0 and low, medium, and high with 0<LPI<6, 6<LPI<18, and LPI>18, respectively (Figs. 4A and Supplementary Fig. 2). Overall, the projected liquefaction area in 2025 and 2030 shows a remarkable increase compared to those from 2005 to 2020. Under 0.4 g PGA, between 2005 and 2020, the liquefaction area initially increases and then decreases, within the range of only 15% to 18%. Although significant fluctuations in average and local GW table depth were observed from 2005 to 2020, the local GW table depth is generally below 20 m in many areas in west and north Beijing. As a result, soils above 20 m remained unsaturated and not susceptible to liquefaction, insensitive to GW table change during the period. In 2025, with GW table rise, the liquefaction area reaches 44%, with medium level liquefaction severity area accounting for 11%. In 2030, liquefaction area covers 75% of the study area, with medium level liquefaction severity area covering more than 30%. The liquefaction area in 1996 is close to 2030, accounting for 72% and 75% of the region, respectively, as GW in 2030 is projected to be restored to near 1996 level.

For NC, the liquefaction severity is recognized as low, medium, and high with 0<LPI<5, 5<LPI<15, LPI>15, respectively44. Under 0.4 g PGA, the liquefaction area is generally lower than that obtained by CC, though the same general trend for liquefaction area change is observed, especially in terms of the significant increase in liquefaction area in 2025 and 2030.

Greater liquefaction potential is generally detected in the north and southeast parts of the study area. Under 0.4 g PGA, both the liquefaction area coverage and severity level show significant increase in the southeast region for both the CC and NC methods. Significant increase in liquefaction potential is also observed in the north region in 2025 and 2030. These findings indicate that the restoration of GW from the SNWDP could significantly increase the seismic liquefaction risk in Beijing, especially in the southeast and north parts of the city. The relatively high liquefaction potential in 2015 in parts of the southeast region is mainly caused by the nonmonotonic trend between local GW table depth in Tongzhou and the average GW table depth in Beijing shown in Fig. 2C. The results for 0.2 g PGA and other projected GW table scenarios are provided in the supplementary information.

The liquefaction area coverage and severity level in the observed case of 2023 are similar to those projected in 2025, with ~40% of the study area affected in both years. Liquefaction primarily occurs in the northern and southeastern regions, in accordance with the GW table distribution. The low-level liquefaction severity area in 2023 is slightly smaller than in 2025 under both methods, mainly due to a lower GW table in the northern region. For CC, significant liquefaction occurs in the southeastern region in both years, with a slightly larger affected area in 2023 compared to the projected 2025.

Methods
Data collection
GW data from 1996 to 2023 was reported by the Beijing Water Authority. GW table depth of 122 wells distributed in 14 districts were compiled. To ensure that the well measurements accurately reflect the GW table, data from confined wells were excluded, leaving a dataset of 122 wells located in the urban area. The long-term GW table monitoring data from these 122 wells, spanning from December 1996 to December 2023, were used to determine both the average and spatial distribution of GW table depth within the study area. The average GW table depth for 1996 to 2023 was calculated using the area weighted average of the monitoring data from the wells. Projections of average GW table depth for 2025 and 2030 were obtained from a previous study which simulated the impact of SNWDP under four scenarios, each incorporating different combinations of precipitation and GW usage20. All four scenarios are considered in this study. The results of the projected highest GW table are presented in the main text, while the details and outcomes for the other three scenarios are provided in the supplementary materials. GW table depth trend near the liquefaction sites globally in the past 80 years is obtained from a previous study58, the GW table depth trend before and after earthquake is obtained from previous study and website21,26.

Geotechnical data investigated at field sites were collected from various construction projects, including subways, airports, roads, commercial structures, and residential structures. 26 boreholes were obtained from previous publications and 73 boreholes were provided by various companies and institutions. These data cover construction projects in 14 districts and along 16 subway lines in Beijing completed from 1985 to 2023. To create a three-dimensional stratigraphy model, geological data including age, sequence, and elevation of stratum within the depth of 20 m below ground surface were extracted from site investigation reports. Result of in-situ tests (SPT) and laboratory tests were collected for site characterization.

Geostatistical analysis
To meet the requirements for geotechnical engineering design, boreholes have been used in various projects59–61. We collected data from 99 boreholes scattered across the study area. The uneven distribution is mainly due to uneven urban development (Fig. 2A). Boreholes analyzed in this study contain all geotechnical data needed for liquefaction hazard assessment including the stratigraphic structure, necessary laboratory test results, and SPT blow count. The thickness of Quaternary sediments shows a gradual increasing then decreasing trend from the west to the east part of Beijing, with mainly sand and gravel in the west and sand, silt, and clay in the east35.

We applied a best-fit distribution-based Kriging method for spatial interpolation of stratigraphic structure and geotechnical properties. Ordinary Kriging, the most frequently used geostatistical method, was adopted to provide the best-unbiased estimate with minimum variance when the properties can be seen as stationary Gaussian random field62,63. However, it should be noted that the Gaussian distribution is usually adopted without validation, which has a strong impact on the effectiveness and accuracy of the Ordinary Kriging method64. As improvement, we used the Akaike Information Criterion (AIC) to select the best-fit distribution out of Truncated Normal, Lognormal, Truncated Gumbel, and Weibul distribution:

AIC= \sum_{i=1}^{N} 2 ln g(x_i,p,q) + 2k

where g(.) is the probability function; N is the size of input dataset; xi is one observation data; p,q are the parameters of the candidate distribution; k is the number of distribution parameters. The candidate distribution of the minimum AIC value was recognized as the best-fit distribution, which was then transferred to normal distribution through the equal probability criterion (EPC). The value of random variables at unobserved locations is believed to be correlated with surveyed sites nearby:

zc_0 = \sum_{i=1}^{n} \lambda_i z_i

where zc_0 is the estimated value of the unobserved location; \lambda_i is the weight coefficient; zi is one of the observation data nearby. To obtain the weight coefficient, semi-variogram is commonly used to characterize the spatial structure, which makes it critical to select the appropriate function for geostatistical analysis65. We applied the cross-validation test to select the best-fit function with linear, power, spherical, and Gaussian model as candidates. Ordinary Kriging was eventually constructed with transferred data following procedures mentioned above. EPC was adopted to transfer the estimated data back to the selected best-fit probability space.

Assessment of seismic soil liquefaction potential
Two SPT-based method were adopted to determine the liquefaction potential of borehole locations in Beijing66. CC is a representative method widely adopted in China. In the CC framework, liquefaction occurs when observed SPT N value is smaller than the calculated threshold value:

N_{cr} = N_0 \beta \ln(0.6d_s + 1.5) - 0.1d_w / \rho_c

where N_{cr} is the critical value of N, N_0 and \beta are determined by design basic seismic acceleration of the region, d_s is the soil depth, d_w is the depth of the GW table, and \rho_c is the clay content. \rho_c is considered as 3 when less than 3 in a sandy soil layer. The liquefaction severity index for sand and silt deposits is calculated based on:

LPI = \sum_{i=1}^{n} (1 - N_i / N_{cr,i}) d_i W_i

where LPI is an index indicating the severity of liquefaction, d_i is the layer thickness, W_i is a weight function value W_i takes 10 when depth is below 5 m, takes 20 at 20 m and is taken by linear interpolation from 5 to 20 m. Liquefaction hazard severity is then classified into four levels, which are none, low, medium, and high under the circumstances of LPI=0, 0<LPI \le 6, 6<LPI \le 18 and LPI > 18, respectively.

The NCEER method is also widely used internationally for liquefaction assessment40. The cyclic stress ratio (CSR) and the cyclic resistance ratio (CRR) are the two variables required for evaluation of liquefaction resistance of soils. CSR represents for the seismic demand on a soil layer, which is determined by peak horizontal acceleration, vertical stresses, and stress reduction coefficient:

CSR = 0.65 \alpha_{max} \sigma_{vo} / \sigma'_{vo} \gamma_d

where \alpha_{max} is the peak horizontal acceleration, g is acceleration of gravity, \sigma_{vo} and \sigma'_{vo} are total and effective vertical stresses, \gamma_d is stress reduction coefficient related to soil depth. CRR represents for the capacity of the soil to resist liquefaction, which is estimated using corrected N value smaller than 30. CRR is CRR_{7.5} for magnitude 7.5 earthquakes:

CRR_{7.5} = 1 / (34 - N_{1,60}) + N_{1,60} / 135 + 50 / (10 \cdot N_{1,60} + 45)^2 - 1 / 200

N_{1,60} is corrected N value. Liquefaction potential at certain depth of a site is quantified by the factor of safety against liquefaction (FS) with the consideration of magnitude:

FS = CRR_{7.5} / CSR \cdot MSF

where MSF is the magnitude scaling factor. A soil layer with FS<1 is generally classified as liquefiable. LPI is a single-valued parameter applied to evaluate liquefaction severity at a liquefaction-prone site67:

LPI = \sum_{i=1}^{n} \omega_i F_i H_i

where \omega_i is the weighting factor (=10-0.5z_i), z_i is the depth of i-th layer, F_i is liquefaction severity. F_i equals to 1-FS_i for FS_i less than 1, otherwise it equals to 0. H_i is the thickness of the discretized soil layers. The liquefaction severity is classified with LPI=0, 0<LPI<5, 5<LPI<15 and LPI>15, respectively. LPI_{CC} and LPI_{NC} were both referred as LPI in this study for convenience. LPI of both methods can be calculated using the code provided.

Dynamic analysis method
Plane strain soil-fluid couple analysis is conducted using the OpenSees finite element program68. Soil is simulated using a u-p formulation quadrilateral Four Node QuadUP soil-fluid coupled element based on Biot’s theory69. Three types of soil are all modeled using the unified plasticity model for large post-liquefaction deformation of sand developed by Wang et al (CycLiq)70. The CycLiq model incorporates critical state soil mechanics to achieve a unified description of the mechanical behavior of sand from pre- to post liquefaction stages, which has been validated against a wide range of laboratory element and model tests and successfully applied to various geotechnical earthquake engineering studies71. The underground structure is simulated using combined elements that consist of both quadrilateral elements and nonlinear fiber beam elements to accurately reflect the volume and stiffness of the structure. The steel reinforcement is modeled using the Giuffre-Menegotto-Pinto model to simulate the Bauschinger effect, and concrete is modeled using the Kent-Scott-Park model53. The simulation procedure follows that of Wang et al.72 The parameters for the underground structure and the constitutive model are provided in the supplementary information.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The main message of the manuscript “Restored groundwater via South-to-North Water Diversion amplifies seismic risk of Beijing City, China” is that groundwater level rise imposes a liquefaction risk for infrastructures planned and built during a low groundwater level period, before the initiation of the groundwater level restoration project. The methods and results are very standard. The work presented supports the conclusions and claims. Through the example of the Beijing region, the authors demonstrate the importance of re-evaluating liquefaction potential as part of a groundwater level restoration project. The spatial extent of the water level and stratigraphy data is vast and allows robust regional study. The societal impact of this report is clear, both for the Beijing area and as a call for thorough thought on any similar consequences of nature restoration projects around the world.\nFollowing the widely used liquefaction potential evaluation procedures (e.g., the NCEER method), the manuscript assumes that liquefaction potential is a matter of state. The risk would change if the soil is saturated or dry at a specific point in space. Hence, the fact that a shallower groundwater level increases liquefaction potential is a priori known. Although this is not the scope of this manuscript, one must wonder if the unique aspects of groundwater restoration will induce significant dynamic effects. To name a few interesting questions: A. What is the impact of the rate of water level changes compared to natural water level fluctuations? B. Does the restoration process induce significant groundwater fluid flow that can bring the soil closer or further from a critical point? (see, for example, Manga et al., 2012) C. Is there any effect for the duration that a soil layer was dry before becoming saturated again (Does “aging” come into play?)\nMinor comments:\n1. Figure 1: The maps in panels B-C lack scale or coordinate marks. This is especially important since the GW depth vs. time is measured locally, but the area of the map is large. This will help the reader assess the GW measurements' relevance to the presented liquefaction events. The case for panel C would be much more convincing if the measuring point of GW depth were closer to the liquefaction events location (especially because Kobe 1995 liquefaction was mainly of coastal areas and reclaimed lands, where shallow groundwater levels are generally trivial).\n2. Figure 1B-C: It is not immediately apparent that the red triangle marks the time of the liquefaction event.\n3. Figure 2: It would be beneficial to mark the location of the five monitoring wells on panel D maps (whose data is presented in panel C). I could not spot the location of the abnormal Luozhuang station described in the main text lines 122-126.\n4. Figure 5B: This panel is complicated to understand. Polar diagrams are a good choice when directional or periodical information is to be depicted. Is this the case here? I could not figure it out.\n5. Lines 52-53: missing a reference for GW depth (ref. 33 ?).\n6. Lines 133-134: “it would eventually reach similar levels as the highest scenario”. Do you mean sometime after 2025-2030? Better to be explicit.\nReferences:\nManga, M., Beresnev, I., Brodsky, E.E., Elkhoury, J.E., Elsworth, D., Ingebritsen, S.E., Mays, D.C. and Wang, C.Y., 2012. Changes in permeability caused by transient stresses: Field observations, experiments, and mechanisms. Reviews of Geophysics, 50(2).", "reviewer_2": "08/21/2022\nBy Guoquan Wang\nReview Comments:\nThis manuscript addresses a critical and timely issue concerning the seismic risks , specifically liquification, associated with the South-to-North Water Diversion Project (SNWDP) in Beijing, China. The authors have effectively demonstrated how the rapid restoration of groundwater (GW) levels, facilitated by the SNWDP, could amplify the risks of seismic-induced liquefaction, particularly for infrastructure that was constructed during periods of low GW levels.\nThe topic is of high importance, given the increasing reliance on large-scale water transfer projects to mitigate water scarcity. The potential unintended consequences, such as increased seismic risks, make this study highly relevant. The manuscript is well-written, logically structured, and the results are presented in a manner that is both clear and impactful.\nOverall, this manuscript makes a significant contribution to understanding the complex interactions between large-scale water management projects and seismic risks (e.g., liquification). I strongly recommend its publication, with minor revisions to address the suggestions outlined below.\nTitle Suggestion:\nThe current title, \"Restored groundwater via South-to-North Water Diversion amplifies seismic risk of Beijing City, China,\" effectively captures the essence of your study. However, I would like to suggest a couple of improvements for greater precision and clarity.\nFirstly, the term \"seismic risk\" in the title could be more specific. Since your manuscript specifically addresses earthquake-induced liquefaction risk, it would be beneficial to reflect this in the title to align with the content of the paper.\nPublic Understanding of \"Seismic Risk\":\nFor many in the general public, the term \"seismic risk\" is often associated with the risk or likelihood of earthquakes occurring. This could lead to the misunderstanding that your study suggests the South-to-North Water Diversion Project might increase the frequency or intensity of earthquakes themselves, rather than just the potential damage from an earthquake, such as liquefaction.\nTechnical Understanding of \"Seismic Risk\":\nIn technical contexts, \"seismic risk\" often refers to the potential for damage or adverse effects resulting from an earthquake, encompassing a broad range of impacts, including ground shaking, liquefaction, landslides, and more. However, this nuance might not be immediately clear to non-experts.\nIn summary, the use of \"seismic risk\" in the title could indeed be misleading. Instead, a more specific term like \"earthquake-induced liquefaction risk\" would better convey the precise nature of the risks being discussed in the paper. This change would ensure that the title is both accurate and less likely to be misunderstood by readers who might interpret \"seismic risk\" more broadly as an increase in earthquake frequency or intensity.\nSecondly, the term \"Beijing City\" might not be the most precise descriptor, as your study covers a larger area within the Beijing region. Using just \"Beijing\" would be more appropriate and concise.\nWith these considerations in mind, I propose the following revised title:\nIncreasing Groundwater Level via South-to-North Water Diversion Amplifies Earthquake-Induced Liquefaction Risk in Beijing, China\nTerminology Suggestion:\nI noticed that the term \"groundwater level\" is used throughout the manuscript. While this term is commonly understood, it is important to note that in Hydrology, \"groundwater level\" typically refers to the hydrologic head within confined aquifers. The term \"groundwater table,\" on the other hand, specifically refers to the boundary between dry soils and wet soils (water-saturated soils or rock below), which is the focus of your study.\nGiven that your research is centered on the seismic risks associated with changes in the groundwater table in response to the South-to-North Water Diversion Project, I would suggest using the term \"groundwater table\" consistently throughout the manuscript. This adjustment would enhance clarity and precision, particularly for readers from the hydrological community.\nWhile it is not incorrect to use \"groundwater level,\" switching to \"groundwater table\" would align more closely with hydrological terminology and might prevent potential confusion.\nComment on Well Data and Groundwater Level Representation:\nThe use of long-term groundwater level data from 122 wells spanning 1996 to 2021 is a valuable aspect of this study. However, I noticed that while the authors provide a map showing the location of these wells, the specific depths of the wells are not included. This omission raises an important concern regarding the interpretation of the groundwater levels reported. \nWhen the authors mention that groundwater depth ranges from 0 to 65 meters, it is crucial to clarify whether these measurements represent the groundwater table or the hydraulic head in confined aquifers. Given the geological context of the Beijing area, where there is not a strict boundary between unconfined and confined aquifers, wells with depths greater than about 50 meters may be recording the hydraulic head of confined aquifers rather than the local groundwater table.\nThis distinction is particularly significant for the study, as the hydraulic head in confined aquifers can fluctuate more dramatically than the groundwater table in unconfined aquifers. Understanding whether the data primarily reflects changes in the groundwater table or the hydraulic head is foundational to the conclusions drawn about seismic risks related to groundwater restoration.\nTherefore, I strongly suggest that the authors provide detailed information (e.g., as supplement information) about the wells used in their study, including the location, well depth, and the specific groundwater levels recorded. This additional data will not only enhance the transparency of the study but also ensure that the conclusions about groundwater level changes and their implications for seismic risk are robust and accurately interpreted.\nComment on Figure 2 and Terminology Consistency:\nRegarding Figure 2, I have a couple of suggestions that could enhance the clarity and interpretability of the figure:\nColor Scheme: To better represent the data visually, I recommend using a color gradient where shallow groundwater table levels are depicted with warm colors (e.g., light blue), while deep groundwater table levels are shown with cold colors (e.g., deep blue or even red). This intuitive color scheme will help readers quickly distinguish between areas of shallow and deep groundwater levels.\nTerminology Consistency: Throughout the manuscript, I noticed some inconsistency in the use of the terms \"groundwater level\" and \"groundwater depth.\" It’s important to use one term consistently to avoid confusion. Additionally, please ensure that the directional changes are accurately described. For example, an increase in groundwater level corresponds to a decrease in groundwater depth. Mixing up these terms and directions can lead to significant confusion. I also noticed a few instances in the text where \"increase\" and \"decrease\" were mistakenly used in the wrong context.", "reviewer_3": "The manuscript evaluates the seismic liquefaction potential in Beijing, focusing on groundwater (GW) level rise. It examines how this rise affects the seismic liquefaction risk, particularly for liquefaction-prone soils in this seismically active area. The study highlights increased risks for subway infrastructure, which was constructed when GW levels were lower, suggesting it may not withstand higher liquefaction hazards. Overall, it addresses the often-overlooked risk of increased infrastructure vulnerability due to rising GW levels. However, the analysis does not explicitly link to the South-North Water Diversion Project (SNWDP). I believe the manuscript would benefit from addressing the following comments.\nThe rising groundwater level should not be directly attributed to the SNWDP, as discussed in Long et al. (2019). Instead, increased precipitation in recent years may significantly contribute to the restored GW levels. Furthermore, the future GW level projections in this study were obtained based on a wet climate scenario. Therefore, many statements in the manuscript, including the title, need revision to accurately reflect the causes of groundwater level changes and to avoid misinterpretation.\nThe authors should elaborate on how liquefaction potential due to GW levels was considered in the design of underground structures, such as the subway system in Beijing. It is crucial for readers to understand the implications of this study, particularly regarding the seismic response of structures if groundwater level changes were not incorporated into the design.\nThe calculation of liquefaction potential was not verified. In particular, a significant increase in liquefaction potential is projected for 2025 compared to 2020. The authors could consider verifying this projection using data from 2023 or 2024 to enhance the study's credibility.\nThere are large differences in liquefaction hazard maps produced by different methods. It would be better to discussion the uncertainty between different methods, and what implications may have on the findings.\nPlease clarify why the liquefaction area shows minimal changes, despite substantial fluctuations in GW levels from 2005 to 2020 (as depicted in Figures 4A and 5C). Notably, the percentages of liquefaction area in 2005 and 2020 are lower than those in 2010 and 2015 when groundwater levels were at their lowest.\nLine 25: Please correct the citation error.\nFigure 1 appears to be unrelated to the primary analysis and may need re-evaluation for inclusion.\nFigure 2B: The residential area data could be omitted since it is not utilized in the analysis.\nFigure 2C: It would improve clarity to indicate well locations on the map (Figure 2A). Additionally, please explain the purpose of the regression lines and the method used to calculate average groundwater depth.\nLine 145: The section titled “Three-Dimensional Geotechnical Models” should be moved to the Methods section.\nLine 188: An explanation of the unit \"g\" is needed to avoid confusion with the gram.\nFigure 4A: Please clarify the reason for using two colors in the bar graphs.\nLine 305: Please specify which scenario data were utilized in this study.\nLine 320: Kindly provide the full name for \"OK.\" However, it may be advisable to avoid using \"OK\" as an abbreviation.\nLines 350-351: The phrase “For sand and silt deposits…” is incomplete and needs clarification.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewers\nWe are very grateful to the reviewers for their positive and careful comments and\nsuggestions, which have encouraged and guided us to improve the quality of the article.\nIn the following, we address the reviewers’ comments one by one. The original\ncomment is first presented in italic and our response is then provided. For the\nconvenience of the reviewer, we have also provided our revisions in the manuscript for\neach comment after our response, highlighted in grey and presented in red in the revised\nmanuscript.\nReviewer #1\nReviewer:\nThe main message of the manuscript “Restored groundwater via South-to-North Water\nDiversion amplifies seismic risk of Beijing City, China” is that groundwater level rise\nimposes a liquefaction risk for infrastructures planned and built during a low\ngroundwater level period, before the initiation of the groundwater level restoration\nproject. The methods and results are very standard. The work presented supports the\nconclusions and claims. Through the example of the Beijing region, the authors\ndemonstrate the importance of re-evaluating liquefaction potential as part of a\ngroundwater level restoration project. The spatial extent of the water level and\nstratigraphy data is vast and allows robust regional study. The societal impact of this\nreport is clear, both for the Beijing area and as a call for thorough thought on any\nsimilar consequences of nature restoration projects around the world.\nFollowing the widely used liquefaction potential evaluation procedures (e.g., the\nNCEER method), the manuscript assumes that liquefaction potential is a matter of state.\nThe risk would change if the soil is saturated or dry at a specific point in space. Hence,\nthe fact that a shallower groundwater level increases liquefaction potential is a priori\nknown. Although this is not the scope of this manuscript, one must wonder if the unique\naspects of groundwater restoration will induce significant dynamic effects.\nResponse:\nWe appreciate the evaluation as well as the detailed comments from the reviewer and\nhave revised our manuscript accordingly. We hope the revision can adequately address\nthe important issues raised by the reviewer. In response to the reviewer’s following\ncomments, we have added some brief discussions on the possible dynamic effects of\ngroundwater restoration.\nReviewer:\nTo name a few interesting questions: A. What is the impact of the rate of water level\nchanges compared to natural water level fluctuations?\nResponse:\nWe appreciate the reviewer for raising the question regarding the impact of rate of water\nlevel changes compared to natural water table fluctuations. A previous study has shown\nthat during 2014-2018 when the South-to-North Water Diversion (SNWD) began\nfunctioning, the groundwater table changes in Beijing have primarily been influenced\nby the South-to-North Water Diversion Project (SNWDP), agricultural water use policy,\nand precipitation. 40% of the groundwater table rise in Beijing was attributed to the\nSNWDP, while 30% was due to natural precipitation (Long et al., 2020), as shown in\nFig. R1. Without SNWDP, even considering the change in water use policy, the\ngroundwater table in Beijing would still continue to drop.\nFrom 2020 to 2023, this trend has continued as shown in Table R1. In 2021, the high\nlevel of precipitation and SNWDP diverted water led to the greatest rise in groundwater\ntable. In other years with less precipitation, incoming SNWDP water still caused\nsignificant groundwater table rise. We have clarified the multiple factors influencing\nthe rise of groundwater table in Beijing in Line 63-66, with particular emphasis on the\ncritical role of the SNWDP. Additionally, in Line 357-359, we have highlighted that\nother factors could also lead to similar outcomes. We have also revised the title to\n“Rising Groundwater Table Considering South-to-North Water Diversion Amplifies\nEarthquake-Induced Liquefaction Risk in Beijing, China”.\nTo further compare the impact of groundwater table rise with natural fluctuations, and\nprovide a more solid response to the reviewer’s comment, we assumed that from 2020\nto 2030 the change in precipitation accounts for 30% of groundwater recovery. Under\nthis assumption, considering only precipitation variability, the average groundwater\ntable depth in Beijing by 2030 would be 19.25m (referred to as the 2030_N case). If\nonly natural water fluctuations are considered, the groundwater table in most areas\nwould be much lower than those projected for 2030, with the difference being\nespecially significant in the northern and southeastern regions (Fig. R2). The\nliquefaction area and severity level in 2030_N are much less compared to 2030. In\n2030_N, the liquefaction area accounts for 19% of the study area, much less than the\n75% in 2030 (Fig. R3). These analyses indicate that the projected increase in\nliquefaction risk in Beijing by 2030 is largely due to the SNWDP water in comparison\nwith natural water level fluctuations.\nThe possible influence of rapid water table recovery on the properties of soil is also\ndiscussed in response to the following comments.\nFig. R1 | Groundwater depth in Beijing under different scenarios from 2014 to 2018.\nS1 removes the benefits of the SNWDP, S2 removes the benefits of agriculture water\nuse policy based on S1, S3 removes the increased groundwater recharge due to\nprecipitation variability based on S2. (Adapted from Long et al., 2020)\nFig. R2 | Spatial distribution of groundwater table depth in the study area. 2030_N\nrepresents the scenario where groundwater level affected only by natural water\nfluctuations, 2030 represents the scenario with the highest projected groundwater level.\nFig. R3 | Liquefaction hazard assessment of the study area using The Chinese Code for\nthe Seismic Design of Buildings method under 0.4g PGA.\nTable R1 Groundwater depth, annual precipitation and incoming water volume of the\nSNWDP from 2020 to 2023\nYear Groundwater depth (m) Annual precipitation Incoming water volume\n(mm year-1) (km3)\n2020 23.30 560 8.8\n2021 17.71 924 12.5\n2022 16.66 482 11.1\n2023 15.56 727 9.8\nLine 63-66:\nMonitoring results indicate that the average GW table in Beijing has gradually risen\nsince the implementation of SNWDP (Fig. 2B). The key factors contributing to this\ndecrease in GW table depth include water diversion, climate variability, and reductions\nin water consumption20,27, with SNWDP playing the dominant role in reversing the\nlong-term trend of GW table drop.\nLine 357-359:\nThis study focuses on the GW table rise primarily caused by SNWDP, while changes\nin climate and water usage policies could lead to similar elevations in GW table in other\nregions, which could also lead to increase in seismic liquefaction risk.\nLong, D. et al. South-to-North Water Diversion stabilizing Beijing’s Groundwater\nlevels. Nat. Commun. 11, 1-10 (2020).\nReviewer:\nB Does the restoration process induce significant groundwater fluid flow that can bring\nthe soil closer or further from a critical point? (see, for example, Manga et al., 2012)\nReferences:\nManga, M., Beresnev, I., Brodsky, E.E., Elkhoury, J.E., Elsworth, D., Ingebritsen, S.E.,\nMays, D.C. and Wang, C.Y., 2012. Changes in permeability caused by transient stresses:\nField observations, experiments, and mechanisms. Reviews of Geophysics, 50(2).\nResponse:\nWe appreciate the reviewer for highlighting the potential dynamic impact of significant\nflow on soil liquefaction. We have studied the article suggested by the reviewer, as well\nas two related references cited within it (Wang, 2007 & Cox et al., 2012). One argument\npresented in these studies is that stress-induced oscillations, such as those caused by\nearthquakes, may lead to significant changes in the permeability and fluid flow\ncharacteristics of the ground. For instance, during the 1999 Chi-Chi earthquake,\nmonitoring data revealed a step-like increase of 3-5 meters in the water levels of three\naquifers at a station. This abrupt rise in water level is indicative of an increase in the\nvertical permeability of the aquifer during the earthquake, resulting in the pore pressure\nto spread from a source to soil sites, which in turn triggered liquefaction in intermediate\nfield.\nIn our study, based on monitoring data of wells spanning the period from 2015 to 2023\nwhen SNWDP began functioning, the annual average fluctuation in groundwater level\nwas 1.58m, corresponding to an average seepage flow velocity of 5.01×10-8 m s-1.\nAssuming a relatively low permeability of 1×10-6 m s-1 for the liquefiable soil layer,\nthe hydraulic gradient would still only be approximately 0.05, significantly lower than\ntypical critical hydraulic gradient for sand and silt. Therefore, under these conditions,\nit is unlikely that the liquefiable soil layer would undergo seepage-induced deformation\nor failure on a large scale. However, we do acknowledge that upward water flow may\nlead to local soil loosening and an increase in the vertical permeability coefficient.\nAlthough this aspect is not within the main scope of this study, we have supplemented\nour discussion on the potential influence of significant water flow in Line 336-346.\nLine 336-346:\nAnother interesting aspect for the potential influence of groundwater table rise due to\nSNWDP is the seepage flow. Based on monitoring data of wells spanning the period\nfrom 2015 to 2023 when SNWDP began functioning, the annual average fluctuation in\ngroundwater level was 1.58m, corresponding to an average seepage flow velocity of\n5.01×10-8 m s-1. Assuming a relatively low permeability of 1×10-6 m s-1 for the\nliquefiable soil layer, the hydraulic gradient would still only be approximately 0.05,\nsignificantly lower than typical critical hydraulic gradient for sand and silt. Therefore,\nunder these conditions, it is unlikely that the sand and silt layers would undergo\nseepage-induced deformation or failure on a large scale. However, there exists the\npossibility that upward water flow may lead to local soil loosening, thereby affecting\nthe liquefaction potential of local soil layers56-58. The impact of seepage flow on local\nliquefaction susceptibility should be analyzed in future studies.\n56. Manga, M. et al. Changes in permeability caused by transient stresses: Field\nobservations, experiments, and mechanisms. Rev. Geophys. 50 (2012).\n57. Wang, C.Y. Liquefaction beyond the near field. Seismol. Res. Lett., 78, 512–517\n(2007).\n58. Cox, S. C. et al. Hydrological effects of the Darfield (Canterbury) Mw 7.1\nearthquake, 4 September 2010, New Zealand. N. Z. J. Geol. Geophys. 55, 231-247\n(2012).\nReviewer:\nC. Is there any effect for the duration that a soil layer was dry before becoming\nsaturated again (Does “aging” come into play?)\nResponse:\nWe highly value the issue related to aging raised by the reviewer. Aging of soil typically\nspans from hundreds to millions of years (Mulilis et al., 1977; Bwambale & Andrus,\n2019), during which water chemistry and cyclic loading (such as earthquakes and\nrepeated groundwater table fluctuations) lead to changes in soil properties. However,\nover the past 20-30 years in Beijing, the impact of aging is considered minor for the\nfollowing reasons:\nFirst, the soil layers considered in this study mainly consist of Quaternary deposits, with\nformations dating back around 11700 years. During this deposition process, these soils\nhave generally undergone significant aging. Therefore, the 20-30 years timespan is a\nvery small increment compared to the history of the soil.\nSecond, while cyclic groundwater table change has been reported to affect soil aging,\nthe past 20-30 years has mainly been a period of constant groundwater table drop for\nBeijing, without significant cyclic processes.\nThird, no significant seismic events or violent geological changes have occurred in the\narea in recent decades, which further reduces the possible influence of aging.\nWe have supplemented the manuscript with relevant explanations in Line 170-175.\nLine 170-175:\nThe shallow soils in Beijing consist of Quaternary Holocene deposits, with formations\ndating back approximately 11,700 years35. Considering the prolonged sedimentation\nhistory and the relatively brief GW table change over the past 20-30 years, the impact\nof aging on these soils is expected to be minor for the period concerned in this study38,39.\nFurthermore, the absence of significant seismic events or violent geological changes in\nBeijing in recent decades also does not point to significant aging.\nReferences:\n38. Mulilis, J. P., Chan, C. K., Seed, H. B., & Mori, K. Resistance to liquefaction due\nto sustained pressure. Proc. ASCE 103, 793-797 (1977).\n39. Bwambale, B., & Andrus, R. D. State of the art in the assessment of aging effects\non soil liquefaction. Soil Dyn. Earthq. Eng. 125, 105658 (2019).\nReviewer:\nMinor comments:\n1. Figure 1: The maps in panels B-C lack scale or coordinate marks. This is especially\nimportant since the GW depth vs. time is measured locally, but the area of the map is\nlarge. This will help the reader assess the GW measurements' relevance to the\npresented liquefaction events. The case for panel C would be more more convincing if\nthe measuring point of GW depth were closer to the liquefaction events location\n(especially because Kobe 1995 liquefaction was mainly of coastal areas and reclaimed\nlands, where shallow groundwater levels are generally trivial).\nResponse:\nWe have followed the reviewer’s suggestion to add the coordinates and scale to the\nfigure. Also, there are liquefaction sites of Kobe earthquake which are closer to the\nmeasure point of groundwater table depth (Shibata et al., 1996). Figure 1 has been\nrevised (Line 85).\nFig.1 | Global observations of liquefaction since 1944 and GW table depth trends.\nA Reported liquefaction sites17,25 and corresponding Groundwater (GW) table depth\ntrend in the twenty-first century21,26,34. The scattered circle point represents the location\nof the liquefaction site, and the color of the points indicate the trend of GW table depth.\nB Liquefaction sites in the San Francisco Bay area, USA, observed for the Loma Prieta\nearthquake, and GW table depth change21 (at the red square location in the map). C\nLiquefaction sites in Hyogo Ken, Japan, observed for the Kobe earthquake17,25, and GW\ntable depth change26 (at the red square location in the map).\n25. Shibata, T., Oka, F., & Ozawa, Y. Characteristics of ground deformation due to\nliquefaction. Soils Found. 36, 65-79 (1996).\nReviewer:\n2. Figure 1B-C: It is not immediately apparent that the red triangle marks the time of\nthe liquefaction event.\nResponse:\nThe figure has been revised to better show the time of the liquefaction event (see our\nresponse to the previous comment).\nReviewer:\n3. Figure 2: It would be beneficial to mark the location of the five monitoring wells on\npanel D maps (whose data is presented in panel C). I could not spot the location of the\nabnormal Luozhuang station described in the main text lines 122-126.\nResponse:\nWe appreciate the reviewer’s suggestion and have changed and marked the location of\nthe five monitoring wells on panel A for improved clarity (Line 155). Note Luozhuang\nstation has been replaced by Houjianping station for better representative coverage of\nthe study area, which also shows the anomaly.\nFig.2 | Geological map of Beijing and GW table depth from 1996 to 2030. A\nGeological map of Beijing. The pink rectangle shows the study area. The red line in the\nsubplot is the middle route of SNWDP. The locations of representative monitoring\nwells in C are presented. B Average GW table depth and annual residential construction\narea in Beijing. C Representative regression models used to estimate the correlation\nbetween local GW table depth and Beijing average GW table depth. Scatter points and\nlines are data from monitoring wells and the best fit curves, respectively.\nReviewer:\n4. Figure 5B: This panel is complicated to understand. Polar diagrams are a good\nchoice when directional or periodical information is to be depicted. Is this the case\nhere? I could not figure it out.\nResponse:\nAgreed. We have revised the polar diagrams in the figure to bar plots as following (Line\n285).\nFig.5 | Liquefaction potential of subway stations in the year of completion and 2030.\nB Change in liquefaction potential for each station from the year of completion to 2030\nusing the Chinese Code for the Seismic Design of Buildings method (CC). The dashed\nlines distinguish different liquefaction severity levels. The full name of the stations:\nWL-Wenyanglu, DL-Daoxianghulu, TD-Tundian, YF-Yongfeng, YN-Yongfengnan,\nXW-Xibeiwang, MW- Malianwa, NS-Nanshao, SY-Shahegaojiaoyuan, SH-Shahe,\nGC-Gonghuacheng, CK-Changpingxibeikou, SQ-Shisanlingjingqu, CP-Changping,\nCG-Changpingdongguan, BW-Beishaowa, LC-Lucheng, DY-Dongxiayuan, HF-\nHaojiafu, BD-Beiyunhedong, BX-Beiyunhexi, TM-Tongyunmen, TG-\nTongzhoubeiguan, WL-Wuzixueyuanlu, TQ-Tuqiao, LL-Linheli, LY-Liyuan, JS-\nJiukeshu, GY-Guoyuan, TY-Tongzhoubeiyuan, CQ-Ciqu, CN-Ciqunan, JL-Jinghailu,\nTL-Tongjinanlu.\nReviewer:\n5. Lines 52-53: missing a reference for GW depth (ref. 33 ?).\nResponse:\nWe apologize for the missing reference and have added it.\nLine 52-53:\nSince then, GW table depth has significantly decreased from 12m to 6m, indicating\ngreater liquefaction risk26.\n26. Ministry of Land, Infrastructure, Transport and Tourism. Water Information System\n(2024). Accessed on July 2024 from http://www1.river.go.jp/.\nReviewer:\n6. Lines 133-134: “it would eventually reach similar levels as the highest scenario”.\nDo you mean sometime after 2025-2030? Better to be explicit.\nResponse:\nAgreed. For the predicted lower groundwater table rise scenarios, even though its rise\nis more gradual, the average groundwater depth will eventually reach levels comparable\nto the highest scenarios’ groundwater table in 2025 and 2030 at a later time, if the\nSNWDP continues to supply water to Beijing. We have revised the manuscript as\nfollows (Line 143-145).\nLine 143-145:\nThis is reasonable as even for the lower GW table scenarios, though the rise in GW\ntable is projected to be slower, it would eventually reach levels similar to those of the\nhighest scenario at a later time after 2025-2030, as long as the SNWDP continues to\nsupply water to Beijing.\nReviewer #2\nReviewer:\nThis manuscript addresses a critical and timely issue concerning the seismic risks,\nspecifically liquification, associated with the South-to-North Water Diversion Project\n(SNWDP) in Beijing, China. The authors have effectively demonstrated how the rapid\nrestoration of groundwater (GW) levels, facilitated by the SNWDP, could amplify the\nrisks of seismic-induced liquefaction, particularly for infrastructure that was\nconstructed during periods of low GW levels.\nThe topic is of high importance, given the increasing reliance on large-scale water\ntransfer projects to mitigate water scarcity. The potential unintended consequences,\nsuch as increased seismic risks, make this study highly relevant. The manuscript is well-\nwritten, logically structured, and the results are presented in a manner that is both\nclear and impactful.\nOverall, this manuscript makes a significant contribution to understanding the complex\ninteractions between large-scale water management projects and seismic risks (e.g.,\nliquification). I strongly recommend its publication, with minor revisions to address the\nsuggestions outlined below.\nResponse:\nWe are extremely grateful for the encouraging comments from the reviewer, and have\nimproved the manuscript based on the comments and suggestions.\nReviewer:\nTitle Suggestion:\nThe current title, \"Restored groundwater via South-to-North Water Diversion amplifies\nseismic risk of Beijing City, China,\" effectively captures the essence of your study.\nHowever, I would like to suggest a couple of improvements for greater precision and\nclarity.\nFirstly, the term \"seismic risk\" in the title could be more specific. Since your manuscript\nspecifically addresses earthquake-induced liquefaction risk, it would be beneficial to\nreflect this in the title to align with the content of the paper.\nPublic Understanding of \"Seismic Risk\":\nFor many in the general public, the term \"seismic risk\" is often associated with the risk\nor likelihood of earthquakes occurring. This could lead to the misunderstanding that\nyour study suggests the South-to-North Water Diversion Project might increase the\nfrequency or intensity of earthquakes themselves, rather than just the potential damage\nfrom an earthquake, such as liquefaction.\nTechnical Understanding of \"Seismic Risk\":\nIn technical contexts, \"seismic risk\" often refers to the potential for damage or adverse\neffects resulting from an earthquake, encompassing a broad range of impacts,\nincluding ground shaking, liquefaction, landslides, and more. However, this nuance\nmight not be immediately clear to non-experts.\nIn summary, the use of \"seismic risk\" in the title could indeed be misleading. Instead, a\nmore specific term like \"earthquake-induced liquefaction risk\" would better convey the\nprecise nature of the risks being discussed in the paper. This change would ensure that\nthe title is both accurate and less likely to be misunderstood by readers who might\ninterpret \"seismic risk\" more broadly as an increase in earthquake frequency or\nintensity.\nSecondly, the term \"Beijing City\" might not be the most precise descriptor, as your\nstudy covers a larger area within the Beijing region. Using just \"Beijing\" would be\nmore appropriate and concise.\nWith these considerations in mind, I propose the following revised title:\nIncreasing Groundwater Level via South-to-North Water Diversion Amplifies\nEarthquake-Induced Liquefaction Risk in Beijing, China\nResponse:\nWe appreciate the detailed suggestion and have revised our title (Line 1-2). The\nprevious choice of “seismic risk” instead of “liquefaction risk” was mainly to appeal\nbetter to the broader audience, but we do agree that being more specific would improve\nclarity. The reviewer correctly pointed out that “city” is not really needed in the title,\nwhich has now been removed. We have also changed “via” in the previous title to\n“considering” to correctly reflect that factors other than SNWDP also contribute to\ngroundwater table rise.\nLine 1-2:\nRising Groundwater Table Considering South-to-North Water Diversion Amplifies\nEarthquake-Induced Liquefaction Risk in Beijing, China\nReviewer:\nTerminology Suggestion:\nI noticed that the term \"groundwater level\" is used throughout the manuscript. While\nthis term is commonly understood, it is important to note that in Hydrology,\n\"groundwater level\" typically refers to the hydrologic head within confined aquifers.\nThe term \"groundwater table,\" on the other hand, specifically refers to the boundary\nbetween dry soils and wet soils (water-saturated soils or rock below), which is the focus\nof your study.\nGiven that your research is centered on the seismic risks associated with changes in\nthe groundwater table in response to the South-to-North Water Diversion Project,\nI would suggest using the term \"groundwater table\" consistently throughout the\nmanuscript. This adjustment would enhance clarity and precision, particularly for\nreaders from the hydrological community. While it is not incorrect to use \"groundwater\nlevel,\" switching to \"groundwater table\" would align more closely with hydrological\nterminology and might prevent potential confusion.\nResponse:\nWe sincerely appreciate the reviewer’s suggestion regarding the terminology for\ngroundwater level. In response, we have revised the manuscript by replacing\n“groundwater level” with “groundwater table” and “groundwater depth” with\n“groundwater table depth” throughout the text for consistency and clarity.\nReviewer:\nComment on Well Data and Groundwater Level Representation:\nThe use of long-term groundwater level data from 122 wells spanning 1996 to 2021 is\na valuable aspect of this study. However, I noticed that while the authors provide a map\nshowing the location of these wells, the specific depths of the wells are not included.\nThis omission raises an important concern regarding the interpretation of the\ngroundwater levels reported.\nWhen the authors mention that groundwater depth ranges from 0 to 65 meters, it is\ncrucial to clarify whether these measurements represent the groundwater table or the\nhydraulic head in confined aquifers. Given the geological context of the Beijing area,\nwhere there is not a strict boundary between unconfined and confined aquifers, wells\nwith depths greater than about 50 meters may be recording the hydraulic head of\nconfined aquifers rather than the local groundwater table.\nThis distinction is particularly significant for the study, as the hydraulic head in\nconfined aquifers can fluctuate more dramatically than the groundwater table in\nunconfined aquifers. Understanding whether the data primarily reflects changes in the\ngroundwater table or the hydraulic head is foundational to the conclusions drawn\nabout seismic risks related to groundwater restoration.\nTherefore, I strongly suggest that the authors provide detailed information (e.g., as\nsupplement information) about the wells used in their study, including the location,\nwell depth, and the specific groundwater levels recorded. This additional data will not\nonly enhance the transparency of the study but also ensure that the conclusions about\ngroundwater level changes and their implications for seismic risk are robust and\naccurately interpreted.\nResponse:\nThis is indeed a very important issue. The groundwater data from the Beijing Water\nAuthority website includes records from phreatic wells, confined wells, and mixed\nwells. To obtain the groundwater table that this study is interested in, rather than the\nhydraulic head of the confined aquifers, we excluded data from confined wells in our\nanalysis, such as Dongxiaojing well in Chaoyang district. We have followed the\nreviewer’s suggestion and provided the locations of 122 monitoring wells along with\ngroundwater table depth from 1996 to 2023 in Supplementary Data 1. We have also\nrevised the manuscript as following (Line 113-114).\nLine 113-114:\nTo ensure that the well measurements accurately reflect the GW table, data from\nconfined wells were excluded, leaving a dataset of 122 wells located in the urban area.\nSupplementary Data 1: Groundwater table depth of monitoring wells in Beijing during 1996-2023.\nYear\nWell Longitude (°) Latitude(°)\n1996 1998 1999 2000 2002 2003 2005 2006 2007 2008 2010 2011 2012 2014 2015 2016 2017 2018 2019 2020 2021 2023\nJingliyuan 116.80 40.32 8.00 12.50 18.00 16.00 25.00 27.50 27.50 29.13 30.13 44.13 26.63 30.13 41.13 34.13 39.13 41.63 31.63 29.13 32.92 34.21 19.42 20.48\nXitiangezhuang 116.79 40.40 16.00 21.00 17.50 26.00 25.00 32.00 30.00 33.56 36.06 39.06 33.56 36.06 31.06 28.56 33.56 39.06 31.06 34.56 24.58 25.77 15.89 23.72\nTongjunzhuang 116.76 40.34 9.00 12.50 16.00 18.00 25.00 30.00 28.00 27.48 32.48 44.98 27.48 21.48 37.48 37.48 47.48 52.48 39.98 32.48 28.76 31.30 15.79 17.12\nXikanggezhuang 116.73 40.43 11.00 12.50 17.00 21.00 20.00 20.00 13.50 35.85 35.85 35.85 35.85 54.35 30.85 30.85 35.85 35.85 33.35 28.35 13.85 16.86 9.94 14.40\nMiyunshahe 116.85 40.39 14.00 17.50 21.00 26.00 25.00 27.50 25.00 36.42 36.42 37.42 36.42 36.42 31.42 31.42 36.42 36.42 33.92 28.92 22.10 23.43 14.30 20.45\nShilibao 116.79 40.36 15.00 17.50 17.50 24.00 25.00 29.00 29.00 29.10 31.60 40.10 28.10 26.60 27.10 24.10 29.10 34.10 29.10 29.10 24.10 24.44 15.87 18.98\nZhangzikou 116.69 40.31 4.00 3.00 7.50 9.00 15.00 25.00 24.50 27.95 35.45 38.95 37.95 12.95 43.95 47.95 42.95 47.95 42.95 33.95 23.69 25.30 9.49 10.05\nLiulianghe 116.65 40.28 3.00 3.00 4.00 5.00 12.00 17.50 20.00 18.43 29.43 29.43 33.43 40.43 38.43 43.43 33.43 38.43 38.43 28.43 22.30 23.29 8.44 7.72\nTaoshan 116.61 40.26 4.00 6.00 6.50 5.00 9.00 22.00 15.00 21.13 26.13 23.63 23.63 13.63 27.13 31.13 31.13 26.13 31.13 26.13 12.77 13.08 11.18 9.50\nQijianfang 116.17 40.22 18.00 17.00 17.50 25.00 20.00 30.00 10.00 31.93 34.43 33.93 33.43 44.43 37.43 34.43 36.93 39.43 34.43 40.43 36.52 37.83 36.84 32.40\nNanhaozhuang 116.21 40.22 16.00 12.00 12.00 21.00 14.00 30.00 10.00 33.90 35.90 35.40 33.90 36.40 30.40 34.40 35.40 38.90 31.40 36.40 32.28 31.60 29.09 16.76\nAsuwei 116.36 40.17 12.50 11.00 17.00 12.50 14.00 22.50 14.00 16.14 15.14 15.14 15.14 10.14 18.64 21.14 21.14 21.14 19.14 28.64 30.08 29.78 23.02 22.09\nHengqiao 116.20 40.18 12.50 10.00 10.00 15.00 5.00 15.00 12.50 18.31 24.31 22.81 19.81 28.31 26.81 31.81 31.81 32.81 30.81 30.81 32.18 31.37 28.60 20.15\nJingwentun 116.27 40.19 7.50 7.50 9.00 7.50 10.00 14.00 11.00 20.66 22.66 21.16 19.16 29.16 27.66 25.16 20.66 21.66 16.66 22.66 21.13 23.47 16.29 12.46\nXingshou 116.42 40.22 7.50 5.00 12.50 4.50 10.00 25.00 10.00 20.63 20.63 22.63 19.13 24.63 55.63 28.13 26.63 25.63 22.63 27.63 17.51 18.75 15.32 14.88\nLafeite 116.46 40.13 24.00 24.00 31.00 27.00 31.00 27.50 22.50 16.06 26.06 31.06 26.06 26.06 5.06 32.06 18.56 31.06 29.06 36.06 25.14 21.86 17.10 15.61\nDongguanshi 116.15 40.14 12.00 10.00 11.00 17.50 20.00 20.00 13.50 19.94 26.44 26.44 27.94 32.94 29.44 32.94 32.44 27.94 30.44 35.44 33.14 32.95 29.90 21.58\nDongzhacun 116.21 40.15 7.50 8.00 7.50 10.00 16.00 12.50 13.50 15.00 21.50 21.50 21.50 26.50 40.00 27.50 29.00 28.00 17.00 30.00 36.00 29.48 26.60 18.46\nDongxiaokou 116.37 40.05 24.00 27.50 20.00 10.00 15.00 18.00 32.00 28.90 33.90 28.90 39.90 37.90 33.90 41.40 42.90 33.90 41.40 43.90 18.90 14.54 10.48 6.93\nZhenggezhuang 116.38 40.12 21.00 25.00 22.00 22.00 30.00 20.00 25.00 26.72 29.22 34.72 29.72 33.22 26.72 34.22 34.72 36.72 34.22 43.72 31.72 28.42 23.74 20.05\nZhuxinzhuang 116.30 40.10 17.50 17.00 16.50 17.50 20.00 16.00 22.50 25.84 26.84 33.84 35.84 34.84 43.84 34.34 34.34 33.84 34.34 36.84 29.84 24.68 20.62 16.21\nChanglinzhuang 116.77 40.26 4.00 7.50 10.00 12.00 25.00 23.50 25.00 30.50 32.00 40.50 36.50 42.50 40.50 41.50 45.50 47.50 43.00 35.50 28.66 28.21 12.79 11.35\nDongyinjiafu 116.90 40.09 17.50 10.00 17.50 12.50 17.50 22.00 22.50 32.34 35.84 31.84 41.84 41.84 37.34 39.84 39.84 37.84 42.84 41.84 34.47 34.11 31.89 30.58\nDashigezhuang 116.89 40.05 17.50 8.00 17.50 16.00 18.00 20.00 22.00 29.73 32.73 29.23 36.73 37.23 32.23 36.23 37.23 35.73 38.73 39.73 32.66 32.92 31.46 31.07\nShizhongwu 116.83 40.23 7.50 15.00 20.00 20.00 25.00 30.00 32.50 31.51 34.51 44.01 36.51 44.01 54.01 44.01 44.01 49.01 49.01 49.01 15.82 16.53 15.19 19.23\nZhaoquanying 116.59 40.21 7.50 7.50 15.00 14.50 10.00 12.00 14.00 34.95 44.95 48.95 37.45 50.95 49.95 62.45 55.95 54.95 49.95 52.95 38.85 36.87 25.23 21.57\nWangjiachang 116.69 40.08 11.00 10.50 17.50 15.50 10.00 24.00 25.00 26.07 15.07 27.57 25.07 30.07 37.07 27.57 30.07 28.07 30.07 23.07 25.85 23.65 18.30 16.16\nQianliwu 116.71 40.19 4.50 3.00 5.00 12.00 14.00 19.00 24.50 26.77 29.77 30.27 38.27 36.77 15.77 39.27 39.27 31.77 39.27 44.27 27.35 25.64 9.71 6.82\nAnlezhuang 116.78 40.14 5.50 3.00 5.00 7.50 15.00 23.00 24.50 25.38 22.88 15.88 27.88 18.88 36.88 16.88 17.88 20.38 15.38 15.88 30.32 27.16 18.42 13.13\nXiaotianzhu 116.58 40.05 24.50 32.00 32.00 31.00 25.00 32.50 35.00 30.75 25.75 21.25 27.25 30.75 28.25 34.25 31.25 30.75 30.75 27.25 26.20 21.97 20.12 17.26\nDongmagezhuang 116.57 40.14 20.00 22.50 28.00 25.00 23.00 25.00 25.00 35.87 45.87 50.87 52.87 51.87 55.87 57.87 53.87 53.37 52.87 48.37 42.17 40.12 30.01 26.01\nMigezhuang 116.64 40.08 17.50 25.00 30.00 25.00 15.00 31.00 31.00 40.98 37.48 39.48 44.48 49.48 29.48 50.48 49.48 47.48 49.48 49.48 46.28 46.11 37.97 35.40\nBeiwu 116.84 40.07 12.50 6.00 14.00 15.00 15.00 20.00 21.00 25.30 25.30 19.30 26.80 25.80 36.80 28.30 24.30 24.30 26.80 26.30 28.17 27.93 24.89 24.04\nShatuo 116.62 40.11 17.50 27.50 30.50 26.00 17.50 25.00 30.00 27.56 19.06 20.06 29.06 32.56 18.56 37.56 34.56 34.06 34.56 29.56 27.22 25.86 15.54 13.31\nWali 116.76 40.17 4.00 3.00 4.00 9.00 15.50 25.00 24.50 27.30 25.30 24.30 34.30 28.30 31.80 24.30 26.80 24.30 24.30 31.80 26.96 25.35 12.92 8.86\nZhanggezhuang 116.97 40.14 10.00 12.50 14.00 20.00 21.00 22.00 23.00 26.36 34.36 35.36 37.36 40.36 39.36 40.36 39.36 39.36 41.36 31.86 29.44 31.73 18.63 17.73\nXiangyangzha 116.69 40.17 12.00 5.00 15.00 16.00 10.50 17.50 22.00 26.73 27.23 27.23 36.23 35.23 32.73 35.23 36.23 35.23 47.23 37.73 27.15 25.20 8.69 6.28\nXixiaoying 116.55 40.19 5.00 10.00 21.00 15.00 20.00 18.00 15.00 19.04 39.04 44.04 34.04 44.04 49.04 54.04 49.04 46.54 44.04 45.04 36.40 34.16 26.21 21.30\nXiapotu 116.63 40.23 10.00 5.00 8.00 12.00 11.00 17.00 20.00 25.15 35.15 37.65 35.15 41.15 45.15 47.65 45.15 45.15 40.15 45.15 33.21 24.78 13.33 12.01\nPingguchengguan 117.14 40.16 10.00 10.00 15.00 18.00 24.00 21.00 21.00 29.74 32.24 37.24 36.24 44.74 35.24 33.24 37.24 40.24 41.24 37.24 26.43 31.53 22.30 24.92\nDaguanzhuang 117.02 40.15 10.00 10.00 15.00 18.50 22.50 21.00 22.50 24.47 32.47 33.47 33.97 36.97 25.47 36.47 37.47 38.97 37.47 28.97 27.36 29.74 20.75 17.46\nXigu 117.10 40.19 10.00 15.00 17.50 20.50 25.00 22.50 22.50 23.39 29.39 32.39 32.39 41.89 26.89 28.39 34.39 37.39 38.39 26.89 27.15 31.02 13.81 16.08\nXuijiawu 117.05 40.19 10.00 13.00 50.00 21.00 25.00 22.50 22.50 21.70 28.70 31.70 31.70 41.70 26.70 33.20 36.70 38.20 38.20 26.70 27.77 31.56 11.71 16.75\nBeidulehe 117.21 40.19 10.00 16.00 18.00 22.00 25.00 22.50 22.00 48.56 55.06 58.56 58.56 68.56 53.56 58.56 61.06 64.06 63.56 48.56 43.02 47.64 26.15 32.46\nYukou 117.01 40.19 10.00 13.00 20.00 22.50 25.00 23.00 23.00 26.71 34.21 38.21 36.71 46.71 31.71 41.71 43.21 45.21 43.21 33.21 32.09 35.84 19.61 21.74\nTuotousi 116.97 40.18 10.00 14.00 20.00 23.00 25.00 24.00 24.00 26.62 33.62 38.62 36.62 46.12 31.12 42.62 43.62 43.62 44.62 36.12 31.41 33.62 19.53 19.95\nAngu 117.16 40.11 10.00 7.50 15.00 12.00 19.00 20.00 20.00 44.32 50.32 55.32 50.32 56.82 50.32 51.32 54.32 54.32 54.32 41.82 47.32 43.46 32.97 33.73\nDawangwu 117.10 40.08 10.00 10.50 15.00 12.00 18.00 17.50 20.00 20.78 28.28 33.28 24.78 31.78 28.28 29.28 30.78 30.78 33.28 35.78 27.78 29.46 18.49 17.25\nGetatou 116.98 40.11 10.00 11.00 15.00 16.00 23.00 18.00 22.50 27.51 33.51 32.01 37.51 36.01 33.51 40.01 37.51 37.51 39.51 32.51 34.01 37.87 19.26 20.92\nJianglizhuang 116.97 40.06 10.00 11.00 15.00 16.50 21.00 17.50 22.00 26.40 30.90 30.40 33.90 30.40 33.90 37.90 36.40 36.40 37.40 33.90 35.40 31.23 25.14 22.48\nNandulehe 117.23 40.17 10.00 12.50 16.00 21.00 25.00 22.50 21.50 40.79 46.79 52.79 50.29 60.79 45.79 53.29 51.29 56.29 55.79 40.79 34.84 47.26 22.42 30.18\nBeiwu 116.24 39.98 18.00 17.50 19.00 18.00 12.50 25.00 24.00 28.95 28.95 27.95 31.45 30.45 28.45 31.45 31.45 31.45 26.45 29.95 25.18 21.04 12.88 8.85\nWuluju 116.29 39.94 21.00 18.50 23.00 20.00 24.00 27.50 27.50 28.53 25.53 34.53 34.53 34.53 32.53 31.03 35.53 32.53 29.53 31.53 28.00 25.27 19.11 14.26\nZhufang 116.32 40.04 19.00 18.50 18.00 14.00 16.00 25.00 27.50 31.59 31.59 25.59 36.59 30.59 28.59 26.59 29.09 26.59 26.59 22.59 21.14 23.53 17.29 13.37\nTangjialing 116.29 40.07 17.50 17.00 17.00 17.00 17.00 20.00 25.00 32.04 34.04 38.04 40.04 38.04 34.04 38.04 38.04 37.04 38.04 30.54 23.83 21.98 17.35 14.55\nJingqiaodaxue 116.20 40.12 10.00 8.00 11.00 7.50 18.50 12.50 15.00 24.91 27.91 32.41 27.91 32.91 30.91 35.91 35.91 35.91 35.91 35.91 36.42 33.35 30.39 22.49\nNan’anhe 116.13 40.05 12.50 11.00 14.00 17.50 17.50 20.00 20.00 23.91 28.91 27.91 33.91 36.91 32.91 38.91 38.91 37.91 37.41 38.91 31.06 31.17 24.31 11.10\nChanglexincun 116.19 40.10 12.50 10.00 12.50 7.50 18.00 15.00 17.00 17.53 20.53 24.03 21.53 26.53 23.53 28.03 27.53 27.53 27.53 26.53 28.53 28.23 22.24 15.49\nXiaonanzhuang 116.30 39.97 20.00 18.00 22.00 17.50 22.00 28.00 27.00 31.94 35.94 34.94 34.94 33.44 33.44 31.94 35.94 33.44 30.94 31.94 26.43 22.75 18.34 11.43\nBeianhe 116.13 40.07 10.00 9.00 11.00 17.50 18.00 20.00 15.00 19.79 23.79 23.79 27.79 30.79 26.79 32.79 29.79 27.79 27.79 34.29 21.79 30.10 17.99 3.04\nHoushajian 116.14 40.12 11.00 9.00 10.00 18.50 20.00 25.00 13.00 20.04 26.54 29.54 27.54 33.04 29.04 33.54 28.04 25.04 25.04 36.54 26.09 32.74 28.88 19.04\nWangjing 116.49 40.00 31.00 31.00 31.00 32.00 32.50 32.50 33.00 33.59 42.09 38.09 38.59 39.59 39.59 45.59 45.59 39.59 45.59 33.59 36.99 37.02 33.71 30.13\nWangsiying 116.53 39.88 17.56 16.89 22.95 14.72 22.14 21.10 19.96 21.57 20.92 17.06 18.59 16.80 15.12 17.94 14.03 14.31 17.68 19.63 17.65 16.00 15.13 13.98\nXinzhuang 116.49 39.95 25.78 28.51 29.40 30.68 32.15 28.26 27.56 31.40 30.53 24.35 30.73 22.73 25.96 23.24 12.01 8.02 6.09 12.55 27.81 25.83 24.12 25.30\nHuanggang 116.48 40.06 32.50 30.50 31.50 32.00 31.00 32.50 37.50 40.27 45.27 40.27 46.27 44.77 49.27 54.27 55.27 46.77 50.27 43.27 36.19 33.87 30.43 26.57\nMaquanying 116.51 40.04 32.50 32.50 32.50 32.50 32.50 32.50 36.00 41.95 39.45 38.45 40.45 43.45 38.95 48.45 48.45 44.95 43.95 45.95 47.29 45.01 42.70 38.27\nDongliu 116.57 39.89 16.27 20.11 21.60 18.37 19.29 19.47 19.48 21.06 20.67 16.19 16.62 17.98 10.22 17.29 9.13 11.69 17.93 16.02 18.77 16.86 15.19 15.21\nHengjiezishuizhan 116.50 39.83 14.52 9.69 18.45 9.06 17.56 19.26 15.78 19.73 19.99 19.92 19.34 19.00 22.56 20.41 21.60 22.54 21.13 21.35 28.79 27.66 25.67 21.50\nFulicheng 116.45 39.90 22.58 22.72 26.84 24.23 30.47 26.59 22.93 28.26 22.30 22.37 25.98 25.26 25.92 25.61 17.88 18.80 17.14 24.31 27.11 26.68 25.09 21.58\nZhaolaiseenlingongyuan 116.45 40.03 32.00 32.00 30.50 28.00 30.00 32.50 36.00 39.48 44.48 42.98 49.48 44.48 47.48 51.98 56.98 49.48 55.48 46.98 41.45 39.76 36.88 29.67\nMayucun 116.13 39.93 22.50 31.00 27.00 32.50 32.50 22.50 25.00 60.73 60.73 60.73 62.23 53.23 52.23 58.23 58.23 58.23 53.23 58.23 28.23 38.85 28.84 25.34\nGucheng 116.18 39.91 22.50 26.00 26.00 28.50 31.00 22.50 30.00 44.02 49.02 48.02 49.02 44.02 39.02 44.02 46.52 43.02 49.02 46.52 29.02 33.89 23.25 20.06\nLianshihu 116.16 39.90 22.00 29.00 26.00 26.50 30.00 22.00 25.00 55.54 55.54 55.54 55.54 46.54 45.54 51.54 51.54 51.54 47.54 55.54 39.54 39.99 30.86 27.15\nXihuangcun 116.21 39.94 20.50 20.00 25.00 25.00 28.00 24.00 30.00 33.66 39.16 40.66 39.66 45.16 40.16 40.66 45.16 39.16 38.16 39.16 23.16 28.05 18.00 14.83\nShiliuzhuanggongyuan 116.42 39.84 17.53 17.61 20.28 16.67 25.48 23.42 19.60 18.51 18.67 18.43 20.12 20.78 25.66 25.04 23.56 23.65 22.70 23.67 18.65 16.91 13.31 7.93\nLuyinggongyuan 116.38 39.78 14.00 17.00 16.00 17.00 22.50 22.50 22.50 22.97 24.47 23.97 24.47 24.47 28.97 28.97 27.97 25.97 25.47 26.97 23.85 21.70 18.34 12.85\nCaihuying 116.31 39.86 19.43 16.55 19.55 19.19 24.85 24.83 26.69 22.63 24.45 23.82 26.69 26.82 26.77 26.35 26.54 25.51 25.10 28.29 22.47 19.98 18.01 12.09\nKejiyuanqu 116.29 39.84 20.00 14.00 18.00 17.50 24.00 23.00 27.50 27.40 27.90 28.40 28.90 28.40 27.40 29.40 28.90 28.40 28.40 31.40 24.17 22.86 18.26 11.71\nFengyiqiao 116.31 39.87 19.76 17.39 20.00 19.57 24.93 25.08 26.89 22.54 24.27 23.72 26.45 26.58 26.42 25.79 26.49 24.89 24.57 27.93 23.28 20.15 17.98 12.16\nXinfadi 116.33 39.81 17.00 14.50 17.50 17.50 22.50 24.00 27.00 27.48 30.48 28.48 30.98 30.98 32.48 31.48 30.48 30.48 18.48 31.48 25.42 22.79 19.27 14.04\nTiyuzhongxin 116.28 39.87 19.49 17.91 19.98 19.50 25.84 24.23 28.40 23.83 26.46 26.21 28.32 28.13 26.49 27.34 28.47 25.30 25.66 29.96 17.72 16.36 14.13 12.78\nZhangyicun 116.23 39.87 18.86 18.96 20.38 19.59 26.93 22.87 29.51 30.29 31.50 32.06 32.45 32.27 30.02 27.73 32.57 26.40 30.64 38.44 20.31 14.95 11.67 11.32\nBaotai 116.27 39.80 15.00 12.50 17.00 14.00 22.00 21.00 27.50 30.48 25.98 25.98 26.48 23.48 23.48 28.48 28.48 23.48 25.98 30.98 18.62 14.31 12.89 10.88\nCaoqiao 116.35 39.86 19.80 18.55 20.32 19.98 24.78 25.53 24.66 21.97 22.88 22.29 25.59 26.03 27.08 25.65 24.93 25.35 23.82 27.95 22.32 20.67 17.44 12.43\nShuangbutou 116.69 39.97 10.21 7.45 14.20 9.18 9.49 12.33 14.59 13.94 6.13 6.02 6.85 9.00 7.24 11.03 9.80 12.50 7.12 6.83 10.26 10.26 9.29 9.20\nMafang 116.84 39.83 25.63 27.26 23.30 22.25 24.21 28.84 29.03 16.75 18.93 16.09 18.81 17.19 16.20 18.41 17.49 15.97 18.33 5.95 8.10 7.53 5.77 7.19\nXinglongzhuang 116.83 39.62 2.44 5.05 5.34 5.55 7.81 9.48 6.22 0.00 5.19 4.40 5.81 5.50 4.65 5.69 5.69 4.84 5.46 4.60 7.30 4.11 2.22 2.70\nXiaolinpailaozhan 116.91 39.78 2.18 2.83 4.34 4.84 5.35 8.25 6.46 5.14 7.58 4.46 4.38 4.58 3.25 4.89 5.63 5.05 7.35 4.53 4.13 5.29 3.05 2.66\nHoujianping 116.88 39.73 2.09 4.60 4.61 4.92 5.72 8.47 9.11 9.32 6.57 5.11 5.12 4.77 4.93 3.42 3.51 4.93 5.72 7.34 7.82 7.79 4.12 4.63\nLuozhuang 116.87 39.68 2.07 4.21 5.01 5.11 6.41 8.84 5.98 4.57 9.59 9.06 9.91 8.82 7.89 9.58 10.37 9.22 9.75 8.63 10.44 9.35 6.80 6.09\nHaojiafu 116.72 39.90 6.09 5.09 7.92 6.88 6.36 10.01 8.91 10.83 5.92 5.07 5.75 3.19 2.76 9.62 4.60 5.50 4.51 6.34 10.67 10.09 10.20 10.00\nYizhuangshididong 116.59 39.78 7.57 6.13 8.18 5.25 6.57 11.26 9.94 9.32 10.16 12.61 10.57 7.38 7.78 7.69 8.00 8.44 8.47 10.06 10.82 10.59 10.57 10.58\nCaofang 116.77 39.75 2.97 3.55 4.27 4.90 5.23 8.70 6.40 3.78 6.37 4.99 4.64 4.82 3.75 5.27 5.21 4.60 5.56 5.59 8.14 8.63 7.32 8.01\nYulinzhuang 116.79 39.80 3.07 3.20 4.10 4.79 4.86 8.49 6.62 5.74 7.04 4.70 4.39 3.75 3.42 5.60 2.69 4.99 4.89 5.30 9.53 7.35 5.63 4.68\nZhangjiawanzhazhan 116.71 39.85 5.41 4.82 6.11 5.74 5.21 9.57 8.16 6.11 3.89 3.45 2.87 0.30 0.28 6.44 0.61 5.59 4.91 4.62 6.75 7.91 5.50 5.93\nMacun 116.58 39.72 5.82 5.21 4.44 5.86 4.33 10.19 8.71 5.64 5.42 9.69 8.28 3.55 3.76 4.48 4.46 4.91 5.36 7.20 5.04 8.13 7.10 9.52\nQishanzhuang 116.69 39.83 5.40 4.80 5.32 5.18 4.77 9.59 8.13 7.62 6.97 7.03 5.75 3.19 3.21 8.52 3.21 8.91 9.38 7.76 10.98 8.98 6.76 6.85\nYinghaishuichang 116.44 39.76 10.00 11.00 13.50 10.00 14.00 18.00 16.00 20.29 20.79 19.79 20.29 19.29 24.79 24.29 24.29 24.29 22.29 19.29 21.81 20.30 17.63 13.62\nNandundai 116.38 39.58 15.00 16.00 16.00 17.50 17.50 16.00 17.50 20.84 25.34 25.34 24.84 25.84 24.84 25.84 23.84 21.84 23.34 24.34 19.18 18.42 17.72 15.05\nXin’anzhuang 116.33 39.47 10.00 8.00 10.00 12.00 12.50 16.00 17.50 20.79 22.29 21.79 22.79 22.79 20.79 23.29 20.79 20.79 20.79 23.29 19.51 16.08 13.60 11.54\nJiajiatun 116.42 39.46 10.00 8.00 10.00 12.00 12.50 16.00 16.50 13.96 17.96 18.96 18.96 19.96 18.96 18.96 16.46 18.96 17.96 18.96 15.34 14.24 12.73 11.22\nTianjiaying 116.50 39.56 12.50 9.00 14.00 12.00 15.00 15.00 15.00 12.60 20.10 19.10 16.10 18.10 17.60 18.60 17.60 17.60 17.60 17.60 20.18 19.94 18.15 14.97\nXiaoyiying 116.55 39.69 6.32 6.31 4.95 7.58 4.74 10.34 9.27 5.38 5.10 9.49 8.51 3.17 2.90 3.41 3.42 4.19 3.93 6.12 6.85 7.03 5.61 6.39\nQiandaying 116.36 39.69 8.00 7.50 9.00 12.50 15.00 16.00 19.00 17.36 19.36 19.36 19.36 19.36 20.86 24.86 23.36 19.36 19.36 20.86 18.25 17.38 15.88 13.47\nXimagezhuang 116.27 39.54 8.00 10.00 8.00 10.00 12.00 14.00 16.00 16.55 19.05 17.55 19.05 19.05 19.05 21.05 21.05 19.05 19.55 20.05 21.56 20.67 19.94 18.57\nKangying 116.68 39.62 4.10 5.17 5.86 6.81 8.69 9.90 7.45 6.24 12.48 10.69 12.55 12.36 9.93 12.57 10.90 9.74 11.22 11.99 14.92 14.23 10.98 11.06\nXilushuichang 116.46 39.62 14.00 12.50 15.00 15.00 13.00 12.50 15.00 15.04 18.04 19.04 17.54 17.04 17.04 20.54 16.04 15.04 16.04 16.04 16.74 15.99 15.14 13.32\nBaoshan 116.49 39.80 12.26 9.14 15.62 8.19 13.71 17.33 14.42 15.56 16.15 17.15 16.36 15.48 20.13 18.10 18.93 20.93 18.62 17.03 21.45 19.56 16.70 13.16\nWuzibushushejing 116.33 39.92 22.50 22.00 25.50 22.50 22.50 27.50 27.50 24.00 26.50 25.50 25.50 26.50 26.00 23.00 26.00 25.00 16.50 26.00 20.00 21.10 20.13 16.14\nZhongshangongyuan 116.38 39.91 24.00 22.50 26.00 24.00 25.00 29.00 25.00 26.23 21.73 23.73 26.23 25.73 26.23 23.73 21.23 23.73 16.23 25.23 22.73 27.07 24.84 18.15\nBeijingshishaoniangong 116.43 39.93 27.00 25.00 27.50 28.00 28.00 31.00 26.00 25.19 21.19 17.69 24.19 21.19 21.19 20.19 12.69 14.19 12.69 12.69 23.19 22.87 20.86 16.44\nZhongshangongyuan 116.39 39.91 24.50 22.50 26.50 24.50 26.00 28.50 25.00 26.73 21.23 23.23 26.23 24.73 25.73 23.73 18.73 21.23 16.23 25.23 23.73 27.07 24.84 18.15\nTuotou 115.99 39.66 9.00 5.50 12.50 7.00 13.00 9.00 8.00 0.00 6.48 7.48 5.48 1.48 0.00 3.98 3.98 0.00 1.48 7.48 4.76 4.10 2.12 2.98\nDinggezhuang 116.10 39.64 11.00 7.50 11.00 12.00 12.00 12.50 10.00 5.55 12.55 8.55 12.55 12.55 10.05 10.05 10.05 10.05 10.05 12.55 9.48 9.34 7.04 6.87\nXiying 115.97 39.62 11.00 6.00 10.00 14.00 16.00 10.00 9.00 7.99 10.99 13.99 11.49 8.99 6.49 10.99 10.99 7.49 8.99 14.49 15.73 15.25 10.27 8.67\nBeiguangyangcheng 116.19 39.76 9.00 11.00 12.50 7.50 10.00 11.00 15.00 10.46 10.46 11.96 10.96 4.96 2.96 7.96 7.96 7.96 6.46 17.96 9.55 8.30 5.02 3.16\nGuandao 116.14 39.68 9.00 7.50 8.00 10.00 10.00 12.00 11.00 8.73 8.73 8.73 8.73 12.73 6.23 8.73 8.73 7.73 8.73 15.73 11.60 11.79 9.65 8.94\nLiangquanshuichang 116.14 39.72 7.50 10.00 7.50 9.00 10.00 9.00 10.00 7.12 7.12 8.12 8.12 0.62 0.00 4.62 2.12 4.62 2.12 12.62 9.65 7.79 8.90 7.15\nSucun 116.05 39.67 9.00 7.50 12.00 11.00 12.50 11.00 8.00 1.40 8.40 5.90 8.40 8.40 5.40 9.40 6.40 3.40 5.90 7.40 7.84 7.68 5.64 5.77\nChanggouwushuichulichang 115.90 39.57 14.00 9.50 8.00 15.00 17.50 12.50 12.00 5.80 4.30 5.80 5.30 4.30 1.30 4.30 2.30 1.80 3.30 6.30 3.00 1.95 1.34 1.43\nXiaotao 116.17 39.61 10.00 8.00 7.50 8.00 7.50 10.00 13.00 11.18 12.18 11.68 13.18 12.18 10.68 13.18 13.18 12.18 14.18 17.18 18.18 14.56 12.28 10.58\nLiulihe 116.09 39.60 11.50 7.50 8.50 9.00 11.50 12.50 11.00 3.62 10.62 6.62 10.12 10.62 6.12 6.62 6.62 6.62 7.62 9.62 14.18 5.44 3.55 3.79\nReviewer:\nComment on Figure 2 and Terminology Consistency:\nRegarding Figure 2, I have a couple of suggestions that could enhance the clarity and\ninterpretability of the figure:\nColor Scheme: To better represent the data visually, I recommend using a color\ngradient where shallow groundwater table levels are depicted with warm colors (e.g.,\nlight blue), while deep groundwater table levels are shown with cold colors (e.g., deep\nblue or even red). This intuitive color scheme will help readers quickly distinguish\nbetween areas of shallow and deep groundwater levels.\nResponse:\nWe have followed the suggestion and revised the figure (Line 155).\nFig.2 | Geological map of Beijing and GW table depth from 1996 to 2030. D Spatial\ndistribution of GW table depth in the study area from 1996 to 2030. Solid boundary for\nthe subplots indicates observed data, while dashed boundary indicates predicted data.\nReviewer:\nTerminology Consistency: Throughout the manuscript, I noticed some inconsistency in\nthe use of the terms \"groundwater level\" and \"groundwater depth.\" It’s important to\nuse one term consistently to avoid confusion. Additionally, please ensure that the\ndirectional changes are accurately described. For example, an increase in\ngroundwater level corresponds to a decrease in groundwater depth. Mixing up these\nterms and directions can lead to significant confusion. I also noticed a few instances in\nthe text where \"increase\" and \"decrease\" were mistakenly used in the wrong context.\nResponse:\nWe sincerely appreciate the reviewer’s suggestion regarding the terminology\nconsistency. In response, we have revised the manuscript by replacing “groundwater\nlevel” with “groundwater table” and “groundwater depth” to “groundwater table depth”\nthroughout the text for consistency and clarity We have also checked the entire\nmanuscript for the usage of \"increase\" and \"decrease\" to ensure the consistency in\nterminology.\nReviewer #3\nReviewer:\nThe manuscript evaluates the seismic liquefaction potential in Beijing, focusing on\ngroundwater (GW) level rise. It examines how this rise affects the seismic liquefaction\nrisk, particularly for liquefaction-prone soils in this seismically active area. The study\nhighlights increased risks for subway infrastructure, which was constructed when GW\nlevels were lower, suggesting it may not withstand higher liquefaction hazards. Overall,\nit addresses the often-overlooked risk of increased infrastructure vulnerability due to\nrising GW levels. However, the analysis does not explicitly link to the South-North\nWater Diversion Project (SNWDP). I believe the manuscript would benefit from\naddressing the following comments.\nResponse:\nWe would like to express our sincere gratitude to the reviewer for the comments and\nconstructive suggestions toward our study. In the following we address each of the\nreviewer’s comments point-by-point.\nReviewer:\n1、The rising groundwater level should not be directly attributed to the SNWDP, as\ndiscussed in Long et al. (2019). Instead, increased precipitation in recent years may\nsignificantly contribute to the restored GW levels. Furthermore, the future GW level\nprojections in this study were obtained based on a wet climate scenario. Therefore,\nmany statements in the manuscript, including the title, need revision to accurately\nreflect the causes of groundwater level changes and to avoid misinterpretation.\nResponse:\nWe appreciate the reviewer for raising the important question regarding the influence\nfactors of water level changes. The previous study referred to by the reviewer has shown\nthat during 2014-2018 when the South-to-North Water Diversion (SNWD) began\nfunctioning, the groundwater table changes in Beijing have primarily been influenced\nby the South-to-North Water Diversion Project (SNWDP), agricultural water use policy,\nand precipitation. 40% of the groundwater table rise in Beijing was attributed to the\nSNWDP, while 30% was due to natural precipitation (Long et al., 2020), as shown in\nFig. R1. Without SNWDP, even considering the change in water use policy, the\ngroundwater table in Beijing would still continue to drop.\nFrom 2020 to 2023, this trend has continued as shown in Table R1. In 2021, the high\nlevel of precipitation and SNWDP diverted water led to the greatest rise in groundwater\ntable. In other years with less precipitation, incoming water still caused significant\ngroundwater table rise, especially in 2022 with a low level of precipitation. We have\nnow clarified the multiple factors influencing the rise of groundwater level in Beijing\nin Line 63-66, with particular emphasis on the critical role of the SNWDP. Additionally,\nin Line 357-359, we have highlighted that the other factors could also lead to similar\noutcomes. We have also revised the title to “Rising Groundwater Table Considering\nSouth-to-North Water Diversion Amplifies Earthquake-Induced Liquefaction Risk in\nBeijing, China”.\nThe groundwater level projections for 2020-2030 in Long et al. (2020) were obtained\nbased on four scenarios, each incorporating different combinations of precipitation and\ngroundwater usage. By the end of 2023, the observed average GW table reached\n15.56m, which is comparable to the highest projected value for 2025. Considering that\nthe projections for the highest GW table closely match the observed data, our analysis\nprimarily focuses on the liquefaction potential under the highest GW table scenario,\nwhile the results from the other scenarios are provided in the supplementary materials.\nWe have clarified the projected scenarios in the manuscript (Line 118-120) and the\nsupplementary materials.\nFig.R1 | Groundwater depth in Beijing under different scenarios from 2014 to 2018. S1\nremoves the benefits of the SNWDP, S2 removes the benefits of agriculture water use\npolicy based on S1, S3 removes the increased groundwater recharge due to precipitation\nvariability based on S2. (Adapted from Long et al., 2020)\nTable R1 Groundwater depth, annual precipitation and incoming water volume of the\nSNWDP from 2020 to 2023\nYear Groundwater depth (m) Annual precipitation Incoming water volume\n(mm year-1) (km3)\n2020 23.30 560 8.8\n2021 17.71 924 12.5\n2022 16.66 482 11.1\n2023 15.56 727 9.8\nLine 63-66:\nMonitoring results indicate that the average GW table in Beijing has gradually risen\nsince the implementation of SNWDP (Fig. 2B). The key factors contributing to this\ndecrease in GW table depth include water diversion, climate variability, and reductions\nin water consumption20,27, with SNWDP playing the dominant role in reversing the\nlong-term trend of GW table drop.\nLine 357-359:\nThis study focuses on the GW table rise primarily caused by SNWDP, while changes\nin climate and water usage policies could lead to similar elevations in GW table in other\nregions, which could also lead to increase in seismic liquefaction risk.\nLine 118-120:\nProjections of average GW table depth for 2025 and 2030 were obtained from a\nprevious study which simulated the impact of SNWDP under four scenarios, each\nincorporating different combinations of precipitation and GW usage20.\nSupplementary materials:\nTo construct the spatial distribution of GW table depth, precipitation, and GW use were\nselected as critical inputs to predict average GW table depth in 2025 and 2030 under\nfour scenarios20 (Supplementary Table 1). Low (L): GW pumping as 101 mm and\nprecipitation as 540 mm; Medium_1 (M1): GW pumping as 91 mm and precipitation\nas 540 mm; Medium_2 (M2): GW pumping as 101 mm and precipitation as 580 mm;\nHigh (H): GW pumping as 91 mm and precipitation as 580 mm.\nReviewer:\n2、The authors should elaborate on how liquefaction potential due to GW levels was\nconsidered in the design of underground structures, such as the subway system in\nBeijing. It is crucial for readers to understand the implications of this study,\nparticularly regarding the seismic response of structures if groundwater level changes\nwere not incorporated into the design.\nResponse:\nWe appreciate the reviewer’s insightful question regarding how groundwater levels are\nconsidered in the design of underground structures. As a critical factor in liquefaction\nassessment, groundwater level is indeed considered in the design of underground\nstructures. Eurocode 8 mandates liquefaction assessment for structures, with\nconsideration of groundwater table through the lifetime of structure (Reference 47).\nSimilarly, the Chinese Code for the Seismic Design of Buildings (GB50011-2010)\nrecommend using either the highest average groundwater table over the design\nreference period or the annual highest level in recent years. However, rapid rise in\ngroundwater table induced by large-scale water diversion projects and other natural and\nhuman factors are not usually considered, especially in engineering practice in Beijing.\nIf construction occurs during a low groundwater level period, it becomes challenging\nto define the lifetime groundwater table. Recent mass leakage in underground structures\nin Beijing have highlighted the inadequate consideration for groundwater table rise in\ndesign and construction (Yu et al., 2022). We have added an explanation in the\nmanuscript to clarify the provisions regarding groundwater levels in relevant design\ncodes (Line 269-278).\nLine 269-278:\nGW table, as a critical factor in liquefaction evaluation, is considered in current seismic\ndesign of underground structures. Eurocode 8 mandates liquefaction assessment for\nstructures, with consideration of GW table through the lifetime of structure47. Similarly,\nCC recommend using either the highest average GW table over the design reference\nperiod or the annual highest level in recent years. However, rapid rise in GW table\ninduced by large-scale water diversion projects and other natural and human factors are\nnot usually considered, especially in engineering practice in Beijing. As a reference, the\nrise of GW table has already taken a toll on underground subway lines in Beijing, with\nreports of multiple leaks as GW rises above the underground structure, which have\nresulted in 600 million RMB repair cost in 2022 aloneError! Reference source not found.,\nindicating inadequate consideration for groundwater table rise in design and\nconstruction.\n47. EN 1998-5. Eurocode 8–design of structures for earthquake resistance, part 5:\nfoundations, retaining structures and geotechnical aspects (2004).\n48. Yu, M., Xu, Y., Li, Y., Song, Y. & Feng, Z. Spatial and Temporal Analysis of the\nInfluence of Groundwater on Leakage of Urban Underground Engineering. ISPRS Ann.\nPhotogramm. Remote Sens. Spatial Inf. Sci. 10, 187-192 (2022).\nReviewer:\n3、The calculation of liquefaction potential was not verified. In particular, a significant\nincrease in liquefaction potential is projected for 2025 compared to 2020. The authors\ncould consider verifying this projection using data from 2023 or 2024 to enhance the\nstudy's credibility.\nResponse:\nWe thank the reviewer for the insightful comment, the recent data could indeed serve\nto verify the projections. We have followed the reviewer’s suggestion and incorporated\nthe groundwater level data for 2023 and the corresponding liquefaction results into both\nthe manuscript (Line 146-154, Line 239-245) and the supplementary materials. The\ngroundwater table in 2023 is very close to the projected high groundwater table scenario\nin 2025, and the liquefaction potential for the two are very similar.\nLine 146-154:\nAnother justification for the choice of the high GW scenario is that by the end of 2023,\nthe observed average GW table reached 15.56m, which is comparable to the highest\nprojected value for 2025, at 15.87m. Considering that the projections for the highest\nGW table better match the observed data, our analysis primarily focuses on the\nliquefaction potential under the highest GW table scenario. The spatial distribution of\nGW table depth in 2023 is very similar to that projected for 2025, with both showing a\ndecreasing trend from the northwest to the southeast. In the Tongzhou district, the\nmajority of GW table depth is within 15m in both years, with some area exhibiting\ndepth of less than 5m. Minor discrepancies are observed locally, such as the western\npart of the study area showing higher GW table compared to 2025, while a small portion\nof the northern region displays a lower GW table.\nFig.2 | Geological map of Beijing and GW table depth from 1996 to 2030. D Spatial\ndistribution of GW table depth in the study area from 1996 to 2030. Solid boundary for\nthe subplots indicates observed data, while dashed boundary indicates predicted data.\nLine 239-245:\nThe liquefaction area coverage and severity level in the observed case of 2023 are\nsimilar to those projected in 2025, with approximately 40% of the study area affected\nin both years. Liquefaction primarily occurs in the northern and southeastern regions,\nin accordance with the GW table distribution. The low-level liquefaction severity area\nin 2023 is slightly smaller than in 2025 under both methods, mainly due to a lower GW\ntable in the northern region. For CC, significant liquefaction occurs in the southeastern\nregion in both years, with a slightly larger affected area in 2023 compared to the\nprojected 2025.\nFig.4 | Liquefaction hazard assessment of the study area from 1996 to 2030. A Area\npercentage of different levels of liquefaction severity under 0.4g peak ground\nacceleration (PGA). The yellow series of bars represent the results under the Chinese\nCode for the Seismic Design of Buildings method (CC), while the green series of bars\nrepresent the National Center for Earthquake Engineering Research method (NC). B\nLiquefaction hazard maps generated using CC under 0.4g PGA. C Liquefaction hazard\nmaps generated using NC under 0.4g PGA. The solid outline represents observed data,\nthe dashed outline indicates predicted data.\nSupplementary Materials:\nThe liquefaction area coverage and severity level in the observed case of 2023 are\nsimilar to those projected in 2025, with approximately 20% of the study area affected\nin both years. The low-level liquefaction severity area in 2023 is slightly smaller than\nin 2025 due to a lower GW table in the northern region. Medium level liquefaction\noccurs in the southeastern region in both years, with a larger affected area in 2023\ncompared to the projected 2025.\nSupplementary Figure 2. Liquefaction hazard assessment of the study area from\n1996 to 2030. A. Area percentage of different levels of liquefaction severity under 0.2𝑔\npeak ground acceleration (PGA). B. Liquefaction hazard maps generated using the\nChinese Code for the Seismic Design of Buildings method (CC)under 0.2g PGA.\nReviewer:\n4、There are large differences in liquefaction hazard maps produced by different\nmethods. It would be better to discussion the uncertainty between different methods,\nand what implications may have on the findings.\nResponse:\nAgreed. The Chinese Code for the Seismic Design of Buildings (GB50011-2010) is\nbuilt on the liquefaction investigation data from several major earthquakes in mainland\nChina. The improved simplified procedure for liquefaction assessment proposed by the\nUSA National Center for Earthquake Engineering Research method was developed\nfrom empirical evaluations of field observations and field and laboratory test data.\nWhen applied to Chinese historical earthquake liquefaction data, the CC method has\nbeen reported to be relatively conservative, while the NC method has been observed to\nbe on the dangerous side, generally underestimate the liquefaction potential (Yang et\nal., 2017, Zhang et al., 2020). We have discussed the differences between the two\nmethods in Line 203-205.\nLine 203-205:\nBased on the analysis of historical earthquake-induced liquefaction data in China, the\nCC method has been found to be more conservative, while the NC method tends to\nunderestimate liquefaction potential42, 43.\n42. Yang, Y., Chen, L., Sun, R., Chen, Y. & Wang, W. A depth-consistent SPT-based\nempirical equation for evaluating sand liquefaction. Eng. Geol. 221, 41-49 (2017).\n43. Zhang, Y. F., Wang, R., Zhang, J. M., & Zhang, J. A constrained neural network\nmodel for soil liquefaction assessment with global applicability. Front. Struct. Civ. Eng.\n14,1066-1082(2020).\nReviewer:\n5、Please clarify why the liquefaction area shows minimal changes, despite substantial\nfluctuations in GW levels from 2005 to 2020 (as depicted in Figures 4A and 5C).\nNotably, the percentages of liquefaction area in 2005 and 2020 are lower than those in\n2010 and 2015 when groundwater levels were at their lowest.\nResponse:\nLiquefaction generally occurs at shallow subsurface depths. Therefore, our study\nfocuses on soil within 20m below the surface. From 2005 to 2020, although significant\nfluctuations in average and local groundwater table depth were observed, the local\ngroundwater table depth is generally below 20m in west and north Beijing, in the blue\nbox areas in the figure below. As a result, soils above 20m remained unsaturated and\nnot susceptible to liquefaction, insensitive to groundwater table change during the\nperiod. We have added this explanation in the manuscript in Line 217-220.\nAs for the greater liquefaction area observed for 2010 and 2015, as noted in the\nmanuscript (Line 131-138), the southeastern region of the study area naturally has high\ngroundwater level and was more prone to liquefaction compared to other areas. In 2010\nand 2015, this region experienced relatively higher groundwater levels, resulting in\ngreater liquefaction area than in 2005 and 2020.\nFigure R4 | Groundwater table depth in Beijing from 1996 to 2030.\nLine 217-220:\nAlthough significant fluctuations in average and local GW table depth were observed\nfrom 2005 to 2020, the local GW table depth is generally below 20m in many areas in\nwest and north Beijing. As a result, soils above 20m remained unsaturated and not\nsusceptible to liquefaction, insensitive to GW table change during the period.\nReviewer:\nLine 25: Please correct the citation error.\nResponse:\nWe apologize for the error and have revised in the following (Line 25).\nLine 25:\nWater scarcity threatens major regions of North Africa, North America, Australia,\nNorth China, and other areas1-4.\n1. Konikow, L. F. & Kendy, E. Groundwater depletion: A global problem. Hydrogeol.\nJ. 13, 317-320 (2005).\n2. Qiu, J. China faces up to Groundwater crisis. Nature 466, 308 (2010).\n3. Shi, J. et al. Assessment of deep Groundwater over-exploitation in the North China\nPlain. Geosci. Front. 2, 593-598 (2011).\n4. He, C. et al. Future global urban water scarcity and potential solutions. Nat. Commun.\n12, 4667 (2021).\nReviewer:\nFigure 1 appears to be unrelated to the primary analysis and may need re-evaluation\nfor inclusion.\nResponse:\nFigure 1 aims to illustrate liquefaction and the fluctuations in groundwater table are a\ncommon phenomenon worldwide. Additionally, it highlights two examples\ndemonstrating that groundwater restoration projects has also led to rising groundwater\nlevels in other areas of the world, that had experience liquefaction before. If subjected\nto seismic events, increased liquefaction risks may also be observed in these recharged\nareas. Therefore, the figure suggests that the findings of this study are not only relevant\nto Beijing, but also serves as a call for thorough thought on any similar consequences\nof restoration projects around the world. To further explain the role of Figure 1, we\nhave emphasized the global significance of the liquefaction risk due to groundwater\ntable change in Line 79-83.\nLine 79-83:\nMoreover, such risk could also affect other megacities around the world with\nliquefaction prone soils and high seismicity that are planning for GW restoration, as\nintense infrastructure development and increase in GW depth often coevolve during\nrapid urbanization. This is especially relevant to regions that have suffered liquefaction\nin past earthquakes and recently experienced significant GW table change in Fig. 1.\nReviewer:\nFigure 2B: The residential area data could be omitted since it is not utilized in the\nanalysis.\nResponse:\nWe appreciate the reviewer for raising this issue. The residential area data in Figure 2B\nillustrates that Beijing experienced consistently high annual construction rates during\nthe period of the lowest groundwater levels. The rising trend of the groundwater level\nis expected to significantly influence the liquefaction potential of infrastructures built\nduring this timeframe. We have better explained its purpose in the revision.\nLine 73-77:\nWhat make matters more complicated is that intense infrastructure development,\nindicated by the annual residential construction area, has occurred in Beijing during the\npast two decades concurrently with the economic boom and rapid urbanization,\nespecially during 2000 to 2015 when GW table dropped to its lowest33.\nReviewer:\nFigure 2C: It would improve clarity to indicate well locations on the map (Figure 2A).\nAdditionally, please explain the purpose of the regression lines and the method used to\ncalculate average groundwater depth.\nResponse:\nWe appreciate the reviewer’s suggestion and have marked the location of the five\nmonitoring wells on panel A for improved clarity (Line 155). Additionally, we have\nexplained the purpose of the regression lines and the method used to calculate average\ngroundwater depth (Line 113-124). The average groundwater table depth for 1996 to\n2023 was calculated using the area weighted average of the monitoring data from the\nwells. Regression analysis was performed to establish the relationship between the\naverage GW depth and the observed GW table depth in the wells (Fig. 2C), which was\nthen used to obtain the projected groundwater table spatial distribution. The regression\nanalysis showed five types of regression lines plotted in Figure 2C.\nFig.2 | Geological map of Beijing and GW table depth from 1996 to 2030. A\nGeological map of Beijing. The pink rectangle shows the study area. The red line in the\nsubplot is the middle route of SNWDP. The locations of representative monitoring\nwells in C are presented. B Average GW table depth and annual residential construction\narea in Beijing. C Representative regression models used to estimate the correlation\nbetween local GW table depth and Beijing average GW table depth. Scatter points and\nlines are data from monitoring wells and the best fit curves, respectively.\nLine 113-124:\nTo ensure that the well measurements accurately reflect the GW table, data from\nconfined wells were excluded, leaving a dataset of 122 wells located in the urban area.\nThe long-term GW table monitoring data from these 122 wells, spanning from\nDecember 1996 to December 2023, were used to determine both the average and spatial\ndistribution of GW table depth within the study area. The average GW table depth for\n1996 to 2023 was calculated using the area weighted average of the monitoring data\nfrom the wells. Projections of average GW table depth for 2025 and 2030 were obtained\nfrom a previous study which simulated the impact of SNWDP under four scenarios,\neach incorporating different combinations of precipitation and GW usage20. Regression\nanalysis was performed to establish the relationship between the average GW depth and\nthe observed GW table depth in the wells (Fig. 2C), which was then used to obtain the\nprojected GW table spatial distribution Further details of the monitoring data of 122\nwells and projected GW table are provided in the supplementary information.\nReviewer:\nLine 145: The section titled “Three-Dimensional Geotechnical Models” should be\nmoved to the Methods section.\nResponse:\nWe appreciate the reviewer for the suggestion. The distribution of soil layers is also a\ncrucial factor for liquefaction assessment. Therefore, we have retained the results of the\n3D geological model in the Results section and moved the methodological details to\nthe Methods section.\nLine 381-387:\nTo meet the requirements for geotechnical engineering design, boreholes have been\nused in various projects59-61. We collected data from 99 boreholes scattered across the\nstudy area. The uneven distribution is mainly due to uneven urban development (Fig.\n2A). Boreholes analyzed in this study contain all geotechnical data needed for\nliquefaction hazard assessment including the stratigraphic structure, necessary\nlaboratory test results, and SPT blow count. The thickness of Quaternary sediments\nshows a gradual increasing then decreasing trend from the west to the east part of\nBeijing, with mainly sand and gravel in the west and sand, silt, and clay in the east36.\nReviewer:\nLine 188: An explanation of the unit \"g\" is needed to avoid confusion with the gram.\nResponse:\nThe unit “g” refers to the gravitational acceleration, equivalent to 9.81 m/s2. We have\nfollowed the reviewer’s suggestion and added the definition of “g” in Line 208-209.\nLine 208-209:\nThe unit “g” refers to the gravitational acceleration, equivalent to 9.81 m/s2.\nReviewer:\nFigure 4A: Please clarify the reason for using two colors in the bar graphs.\nResponse:\nThe yellow series of bars represent the results under the Chinese Code for the Seismic\nDesign of Buildings method (CC), while the green series of bars represent the National\nCenter for Earthquake Engineering Research method (NC). We have provided an\nexplanation of the two colors in the revised manuscript.\nLine 249-252:\nThe yellow series of bars represent the results under the Chinese Code for the Seismic\nDesign of Buildings method (CC), while the green series of bars represent the National\nCenter for Earthquake Engineering Research method (NC).\nReviewer:\nLine 305: Please specify which scenario data were utilized in this study.\nResponse:\nWe have specified that the highest GW table scenario was used in the main text, while\nthe results for the other scenarios are provided in the supplementary materials.\nLine 366-368:\nAll four scenarios are considered in this study. The results of the projected highest GW\ntable are presented in the main text, while the details and outcomes for the other three\nscenarios are provided in the supplementary materials.\nReviewer:\nLine 320: Kindly provide the full name for \"OK.\" However, it may be advisable to avoid\nusing \"OK\" as an abbreviation.\nResponse:\nWe appreciate the reviewer’s suggestion and have replaced all abbreviations of “OK”\nin the manuscript with “Ordinary Kriging”.\nReviewer:\nLines 350-351: The phrase “For sand and silt deposits…” is incomplete and needs\nclarification.\nResponse:\nWe apologize for not being clear enough here and related statement was re-written as:\nLine 418-419:\nThe liquefaction severity index for sand and silt deposits is calculated based on:\nReviewer #4\nReviewer:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse:\nWe sincerely appreciate the reviewer’s comments and suggestions. We hope that the\nrevised version has sufficiently addressed the issues raised by the reviewer.\nReviewer #2\nReviewer:\nThe author has addressed all my comments thoroughly and effectively. I recommend\nconsidering the acceptance of this paper.\nResponse:\nWe are glad to hear that we have addressed the reviewer’s comment. Thank you for the\nsupport.\nReviewer #3\nReviewer:\nThank you to the authors for their explanations, which have adequately addressed all\nmy concerns. I have only several minor suggestions. Lines 113–120: These lines\nprimarily describe the methodology. I suggest moving them to the Method section for\nbetter alignment with the manuscript structure.\nResponse:\nWe are extremely grateful for the encouraging comments from the reviewer, and have\nmoved Line 113-120 to the Method section.\nLine 353-361:\nTo ensure that the well measurements accurately reflect the GW table, data from\nconfined wells were excluded, leaving a dataset of 122 wells located in the urban area.\nThe long-term GW table monitoring data from these 122 wells, spanning from\nDecember 1996 to December 2023, were used to determine both the average and spatial\ndistribution of GW table depth within the study area. The average GW table depth for\n1996 to 2023 was calculated using the area weighted average of the monitoring data\nfrom the wells. Projections of average GW table depth for 2025 and 2030 were obtained\nfrom a previous study which simulated the impact of SNWDP under four scenarios,\neach incorporating different combinations of precipitation and GW usage20.\nReviewer:\nLine 203: Could you clarify what is meant by \"conservation”? Consider using a more\nprecise or straightforward term.\nResponse:\nThe Chinese Code for the Seismic Design of Building (GB50011-2010, referred to as\nCC) is built on the liquefaction investigation data from several major earthquakes in\nmainland China during the 1960s and 1970s. When applied to liquefaction assessment\nof soil layers within 20 meters in depth, CC method tends to overestimate liquefaction\npotential. For clarity, we have replaced the term “conservative” with “overestimate”.\nLine 194-196:\nBased on the analysis of historical earthquake-induced liquefaction data in China, it has\nbeen found that the CC method overestimates liquefaction severity, while the NC\nmethod tends to underestimate liquefaction potential42,43.\nReviewer:\nFigure 4A: Is the predicted groundwater table shown here the same as that in Figure\n2B? Please clarify.\nResponse:\nThe projected groundwater table shown in Figure 4A is the same as the highest\nprojected groundwater table in Figure 2B. We have added the clarification in the\ncaption of Fig.4A.\nLine 242-243:\nThe groundwater table depths for 2025 and 2030 are the projected high groundwater\ntable in Fig. 2B.\nReviewer #4\nReviewer:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse:\nWe appreciate the reviewer’s comments."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The author has addressed all my comments thoroughly and effectively. I recommend considering the acceptance of this paper.", "reviewer_3": "Thank you to the authors for their explanations, which have adequately addressed all my concerns. I have only several minor suggestions.\nLines 113–120: These lines primarily describe the methodology. I suggest moving them to the Method section for better alignment with the manuscript structure.\nLine 203: Could you clarify what is meant by \"conservation\"? Consider using a more precise or straightforward term.\nFigure 4A: Is the predicted groundwater table shown here the same as that in Figure 2B? Please clarify.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter