Unprecedented large-scale aquifer recovery through human intervention

Domain: earth_science
Published: 2025-08-07
Editorial outcome: major_revision

Abstract
Groundwater depletion is a critical global challenge, particularly in intensively cultivated drylands, with few documented cases of successful recovery. Here, we report a striking reversal of long-term groundwater decline in the North China Plain, one of the world’s most severely depleted aquifers. Based on a comprehensive analysis of groundwater levels from over 2000 monitoring wells spanning the past two decades, we show that groundwater levels have risen at an average rate of ~0.7 m year−1 since 2020, surpassing 2005 levels by 2024. This recovery is driven by a combination of large-scale surface water diversion from the humid south and stringent groundwater pumping regulations, further amplified by wet years (e.g., 2021). From 2005 to 2023, these policies reduced annual groundwater abstraction by ~12 km3 and increased environmental water allocations to over 7 km3 since 2021, promoting aquifer recharge and restoring environmental flows. Our findings demonstrate that rapid, large-scale groundwater recovery is achievable through integrated water management and targeted policy interventions across extensive regions (~130,000 km2).

Introduction
Groundwater depletion is a critical global challenge 1–4, particularly in intensively cultivated drylands 5. Continued declines in groundwater levels will have wide-ranging impacts, including land subsidence 6,7, the desiccation of rivers and wetlands 8,9, seawater intrusion 10, intense economic pressure on rural communities 11, and threats to the viability of global food supplies 1. To mitigate groundwater depletion, various strategies have been implemented worldwide, such as reduced groundwater abstraction 12,13, substitution of groundwater pumping with surface water from reservoirs 14, managed aquifer recharge (MAR) 12,15,16, and inter-basin water diversions 17–19. Despite these efforts, a recent study found that rapid groundwater level declines (>0.5 m year−1) remain widespread, with only a small fraction of globally productive aquifers showing signs of recovery (aquifers with rising trends >0.2 m year−1 constituting ~6% over the past two decades) 5. Cases of groundwater recovery, when reported, are often spatially limited and exhibit only modest rates of recovery (e.g., Mancha Occidental aquifer (Spain) 20, Abbas-e Sharghi basin (Iran) 14, Eastern Malwa Plateau alluvial aquifer (India) 21, Bangkok basin (Thailand) 13, Taiyuan basin (China) 19, and parts of California and Arizona (US) 18; Fig. 1). Cultivated drylands are particularly vulnerable to rapid and accelerating groundwater level declines due to inadequate management strategies 5. In this context, the North China Plain (NCP), an intensively cultivated region spanning ~130,000 km2, presents a compelling case study. Here, we use the NCP as an example to demonstrate the feasibility of achieving rapid groundwater recovery (~0.7 m year−1) at an unprecedented spatial scale through human intervention.

The NCP, which forms the plain portion of the Hai River basin (HRB), has been identified as a global hotspot for groundwater depletion 1. This region, which is responsible for ~10% of China’s grain production, supports a population of ~130 million people and is characterized by severely limited water resources, with <500 m3 of renewable water resources available per capita annually 22,23. To meet the escalating demands of socioeconomic development, groundwater in the NCP has been heavily exploited. By the early 2000s, groundwater constituted >65% of the total water supply, with irrigation accounting for ~70% of the total water usage. As a result, groundwater levels in the NCP declined at a rate of 1–2 m year−1 by the end of the 20th century 3,24, leading to an estimated cumulative depletion of ~60 km3 from the 1960s to 2008 25. In response to the acute water shortage in North China, the South-to-North Water Diversion (SNWD) project was initiated in 2002, diverting water from the humid Yangtze River basin. The high-quality diverted water has primarily been used to replace groundwater pumping for municipal and industrial purposes 17,26. Additionally, excess diverted water has been used to replenish rivers and lakes and increase aquifer storage through MAR. Alongside strict groundwater pumping policies (e.g., shutting off wells and restricting groundwater pumping for irrigation when surface water is available), these measures have contributed to the stabilization and local recovery of groundwater levels in the NCP 17,27. While conceptual and numerical models have indicated the potential for groundwater recovery across the NCP 28,29, the actual extent of this recovery has remained uncertain due to a lack of in-situ observations, which are often further hampered by spatiotemporal inconsistencies among monitoring well records.

In this study, we compiled and analyzed the most comprehensive dataset of in-situ groundwater level measurements to date across the NCP, from 2005 to 2024, with ~190,000 measurements from over 2000 wells. Our analysis reveals a significant recovery trend, with groundwater levels rising by ~0.7 m year−1 on average across the NCP during 2020–2024. This recovery is primarily attributed to water diversions and active aquifer restoration policies. These findings challenge a previous study 5, which may have underestimated the extent of groundwater recovery (Fig. 1f). Overall, our results provide critical insights into the potential for addressing groundwater depletion in cultivated drylands globally.

Results
To investigate groundwater recovery in the NCP, we compiled an extensive dataset of in-situ groundwater level measurements from both unconfined and confined aquifers in urban and rural areas within the NCP. The establishment of China’s national groundwater monitoring project in 2018 markedly improved the spatiotemporal coverage and quality of in situ measurements. Prior to 2018, there were many temporal discontinuities in the monitoring data, but the advent of an automatic monitoring system thereafter provided continuous data. Post-2018, there was a notable increase in monitoring well density in the NCP, particularly in the southeastern portions and in confined aquifers (Supplementary Fig. 1). For unconfined aquifers, groundwater levels were deepest (≥40 m) in the piedmont plain in the western portion of the NCP and shallowest (≤10 m) in the eastern portion of the NCP (Supplementary Fig. 1a–d). For confined aquifers, groundwater heads were deepest (≥70 m) in the cities of Tianjin, Cangzhou, and Hengshui (Supplementary Fig. 1e–h). Rapid shallowing trends have been observed across the NCP in both unconfined and confined aquifers (Fig. 2). We calculated Theil-Sen slopes for depth to groundwater at monitoring wells during 2020–2024 (Fig. 2m) because 2020 marked the point of reversal in the average groundwater depth in the NCP. In unconfined aquifers, the most pronounced reversal in groundwater levels was found in the piedmont plain in the western portion of the NCP, with trends exceeding 1.5 m year−1 during 2020–2024. In confined aquifers, rapid shallowing trends in groundwater heads (≥1.5 m year−1) were evident in the piedmont areas, as well as in the cities of Tianjin, Cangzhou, and Hengshui. Additionally, wells with continuous measurements indicated recovery in both urban and rural areas (Fig. 2b–l).

The groundwater level data indicate that the reversal of deepening trends in the average groundwater depth anomalies in both unconfined and confined aquifers occurred around 2020 (Fig. 3a). By the end of 2024, the average groundwater depth was ~11 m and ~27 m in unconfined and confined aquifers, respectively. In general, depths to groundwater in unconfined and confined aquifers are greatest in summer and smallest in winter, driven primarily by the combined effects of groundwater pumping for irrigation (peaking in spring) and recharge from precipitation (summer monsoonal rainfall accounting for ~60% of annual precipitation). Seasonal variability in groundwater depth within a year is ~1.5 m in unconfined aquifers and ~4 m in confined aquifers. To mitigate the impact of seasonal variability, we isolated the long-term trends in the groundwater depth anomaly time series after removing the seasonal variability using STL decomposition. Our analysis shows that the rate of deepening in groundwater depth in unconfined aquifers slowed before the reversal in 2020 compared to the deepening trend in the late 20th century. Historically, groundwater levels in unconfined aquifers in the Piedmont Plain declined at a rate of 1–2 m year−1 during 1984–2005 24. The deepening trend decreased to 0.2 m year−1 during 2005–2019, with relatively humid conditions in 2012 and 2016 contributing to mild recoveries. After the reversal in 2020, a rapid recovery rate of 0.7 m year−1 occurred during 2020–2024, with the average groundwater depth anomaly in unconfined aquifers in 2024 nearing levels last seen in 2005. For confined aquifers, we found a deepening trend of ~0.5 m year−1 during 2005–2019, followed by a recovery of ~4 m during 2020–2024. By 2024, the average groundwater head had returned to levels comparable to those in 2013. Notably, the recovery rates for both unconfined and confined aquifers decreased after 2021. Additionally, numerous sources have indicated a recovery in groundwater levels, as shown by the mitigation of land subsidence and re-emergence of springs in regions such as Beijing and Xingtai (see Methods).

Beijing experienced the first reversal of deepening groundwater levels in unconfined aquifers, followed by other cities in the NCP (Fig. 3b). Monitoring data from Beijing, the city with the highest quality data in the NCP, indicated the earliest reversal in 2016, followed by a recovery rate of 1 m year−1 during 2016–2024. Since 2021, Beijing’s groundwater level has been shallower than it was in 2005. In Baoding, the reversal occurred in 2020, with a recovery rate of 1.5 m year−1 during 2020–2024. By 2024, the groundwater level in Baoding was shallower than in 2005. The recovery of groundwater levels in Hengshui and Tangshan has been ongoing since 2021, albeit at a slower rate of 0.4 m year−1 and 0.3 m year−1, respectively. The lower uncertainty in average groundwater depth anomalies for Beijing and the NCP, compared to Baoding, Hengshui, and Tangshan, underscores the importance of dense in-situ monitoring networks and comprehensive regional analysis in improving the reliability of groundwater assessments.

We identified and analyzed trends in groundwater depth at continuously monitored wells during 2005–2017 and 2018–2024 (Fig. 3c). A well displaying a monotonic deepening trend followed by a monotonic rebound was labelled as having a reversal of the deepening trend. For other wells, we assessed whether there was a significant deepening or shallowing trend (p<0.05, MK test). Our findings show that significant deepening trends were predominant during 2005–2017, whereas significant shallowing trends became prevalent during 2018–2024. Approximately 62% and 83% of the monitoring wells in unconfined and confined aquifers show significant deepening trends during 2005–2017, respectively. Approximately 6% of the monitoring wells in unconfined aquifers, mainly in Beijing, experienced a reversal of the deepening trend during this period. In contrast, during 2018–2024, over half of the monitoring wells exhibited a significant shallowing trend, and about a quarter showed signs of a deepening trend reversal. These results align with the first reversal observed in Beijing in 2016 and the overall reversal at the NCP scale in 2020.

Methods
Groundwater level data collection
The reform of China’s groundwater monitoring system around 2018 led to a discontinuity in the availability of depth to groundwater data for the NCP, with most studies only including data before 2019 5,26–28 or after 2017 60. Since 2005, the China Institute for Geo-Environment Monitoring published the Yearbooks of Groundwater Levels, providing the first publicly available and most comprehensive dataset of groundwater depth across China. However, before the establishment of the national groundwater monitoring project in 2018, monitoring wells were sparse, data quality was inconsistent, and only a limited number of wells provided continuous data, with observation intervals varying from daily to monthly. Since 2018, newly installed monitoring wells have automatically recorded groundwater depth at 1‒4 hour intervals, resulting in continuous time series data and improving the reliability of monthly averages used in this study. Monthly average groundwater depth data for 2005–2017 were digitized from the printed Yearbooks of Groundwater Levels, while data from 2018 to 2024 were obtained from the China Institute for Geo-Environment Monitoring’s online platform (https://geocloud.cgs.gov.cn).

Data preprocessing
Quality control and interpolation procedures were applied before aggregating groundwater depth data at regional scales. Three parameters were defined for preprocessing: (1) maximum allowable gap for linear interpolation (intrplGap=3 months), (2) minimum length of time series required (minLen=48 months), and (3) moving window size for outlier detection (windowOL=24 months). First, linear interpolation was applied to internal data gaps not exceeding intrplGap. Second, wells with fewer than minLen months of valid observations were excluded from the analysis. Third, a moving average filter with a window length of windowOL months was used to identify and mask outliers, defined as values deviating by more than three standard deviations from the moving average. These thresholds were selected based on data characteristics and preliminary sensitivity analyses to ensure robust spatiotemporal coverage while minimizing the influence of outliers.

Analyses of depth to groundwater in the NCP
The Quaternary aquifers in the NCP are typically divided into four aquifers, with the shallowest being unconfined and the remaining three confined 25. In the piedmont plain, water is primarily pumped from the unconfined aquifer, whereas in other regions, it mainly comes from the confined aquifers because of the presence of saline and brackish water in the unconfined aquifer 24,61.

We aimed to develop a consistent understanding of groundwater depth dynamics across various regional scales over the past two decades, particularly under the reformed groundwater monitoring system around 2018. First, we calculated the change in groundwater depth between consecutive months and applied a weighted average to the groundwater depth changes at each available monitoring well, using the corresponding areas of Thiessen polygons as the basis for weighting:

ΔDph = Σ (Si × ΔDph i,j) / Stot (1)

where ΔDph i,j and ΔDph i,j-1 represent the groundwater depth in months j and j−1 at each monitoring well, Si is the area of the Thiessen polygon associated with the well, and Stot is the area of the target region (each well was associated with a unique polygon). This equation means that only wells with valid groundwater depth measurements in both months j and j–1 were included in the spatial averaging. Second, we summed the changes to obtain the cumulative change:

ΔDph_cum = Σ ΔDph j (2)

Groundwater depth anomalies were then calculated as anomalies of the cumulative change (ΔDph_cum). Finally, we employed the Seasonal and Trend decomposition using Loess (STL) to decompose the average depth series into trend, seasonal, and remainder components. The Theil-Sen slope 62,63 was used to assess the trends in the STL components because it is robust against outliers and effective for analyzing trends in groundwater depth 5.

Our results show that the selection of preprocessing parameters minimally impacted the main findings (Supplementary Fig. 5a, b). To assess the uncertainty introduced, we tested a range of values: intrplGap from 1 to 5, and minLen and windowOL of 12, 24, 36, 48, and 60, ensuring that windowOL did not exceed minLen. Although aggregating monthly changes in groundwater depth introduced some uncertainty, the influence of preprocessing selections remained limited, as indicated by the shaded areas in Supplementary Fig. 5a, b. The number of monitoring wells available increased significantly after 2018 (Supplementary Fig. 5c). For unconfined aquifers, well coverage rose from ~400 to ~500 in 2010 to ~500 to ~700 in 2018. For confined aquifers, the number increased from ~120 wells before 2018 to ~700 after 2018. We analyzed changes in groundwater depth rather than absolute levels, which reduced the sensitivity of regional trends to data gaps or inconsistencies in well continuity. Groundwater heads in different confined aquifer layers followed two general patterns (Supplementary Fig. 5d). First, deeper layers typically had greater depths to groundwater. Second, due to intensive pumping and vertical leakage through aquitards, groundwater heads across layers were often similar. We did not analyze the dynamics of groundwater heads across different confined layers at the regional scale, primarily due to the limited number of monitoring wells in the second and third confined layers, particularly before 2018.

We assessed the uncertainty associated with the spatial distribution of the monitoring network and the application of the Thiessen polygon method using Monte Carlo simulations. In each of the 10,000 simulations, 95% of available monitoring wells were randomly selected to calculate the monthly average groundwater depth. This approach accounts for the ~5% month-to-month fluctuation in well availability.

From the simulation results D, we computed the 90% confidence interval (CI) and standard deviation (σ) as follows:

CI = [D5%, D95%] (3)

σ = √Var(D) (4)

where D5% and D95% represent the 5th and 95th percentiles of the simulation ensemble.

Trend significance was evaluated using the Mann–Kendall test at a significance level of p<0.05 (two-tailed) 64. For individual wells (Fig. 2b–l), trends were estimated using the Theil-Sen slope applied to raw time series data to accommodate data discontinuities. At the city scale (Fig. 3b), we selected four cities, Beijing, Baoding, Hengshui, and Tangshan based on data availability sufficient to generate continuous time series before and after 2018. For trend classification at the well level (Fig. 3c), we considered only wells with complete data for both the 2005–2017 and 2018–2024 periods. A “reversal” of the groundwater deepening trend was identified only when a monotonic decline was followed by a monotonic rebound. Wells with non-monotonic recovery patterns were categorized as “shallowing”.

Statistic data of water resources
We compiled data on precipitation, renewable groundwater resources, water productivity, and water supply and use from the Hai River Water Resources Bulletins (http://www.hwcc.gov.cn/wwgj/xxgb/szygb/) and the China Water Resources Bulletins (http://mwr.gov.cn/sj/tjgb/szygb/). The data include detailed breakdowns of water sources, except for certain years where some information was missing. For example, the breakdown of diverted water in 2016 and 2017 was interpolated based on data from adjacent years, considering the relatively stable water diversion from the Yellow River. Regional data were collected from the Water Resources Bulletins of Beijing (https://swj.beijing.gov.cn/zwgk/szygb/index.html), Tianjin (https://swj.tj.gov.cn/zwgk_17147/xzfxxgk/fdzdgknr1/tjxx/), Hebei (http://slt.hebei.gov.cn/a/zfxxgk/), and Shandong (http://wr.shandong.gov.cn/zwgk_319/fdzdgknr/tjsj/szygb/). Economic output values of water use were adjusted to 2023 constant prices using GDP deflator data from the World Bank (https://data.worldbank.org/indicator) and Producer Price Index (PPI) data from the National Bureau of Statistics of China (https://www.stats.gov.cn/sj/). Additionally, desalination capacity data were obtained from the National Seawater Use Bulletin (https://www.mnr.gov.cn/sj/sjfw/).

Relationship between precipitation and changes in groundwater depth
We analyzed the relationship between precipitation and changes in groundwater depth to assess the impact of human intervention on groundwater recovery. To present precipitation (vertical axes of Fig. 5b, d), we used the 12-month moving average, as its influence on aquifer recharge can be long-lasting:

Pr_Avg12(t) = Σ i=t-11 to t Pr(i) / 12 (5)

where Pr_Avg12(t) is the 12-month moving average precipitation for month t, and Pr(i) is the precipitation in month i.

To represent changes in groundwater depth (horizontal axes in Fig. 5b, d), we used the month-to-month difference in the trend component of aggregated groundwater depth anomalies:

ΔDph_trend(t) = Dph_trend(t) - Dph_trend(t-1) (6)

where ΔDph_trend(t) is the change in the STL trend component of groundwater depth anomalies for month t, and Dph_trend(t) is the STL trend component itself for month t.

Estimation of average specific yield
The average specific yield in the NCP was estimated with a water balance method 34 that uses the relationship between changes in groundwater depth within unconfined aquifers and groundwater abstraction minus recharge. The following equation was applied:

ΔDph = (Pumping - Recharge) / (Sy_fit × Area_NCP) + C (7)

where ΔDph is the annual groundwater depth change in unconfined aquifers, Sy_fit is the fitted specific yield, Area_NCP is the area of the NCP, and C is a constant.

Other evidence of groundwater recovery
In addition to the recovery in groundwater depth, land subsidence in the NCP has also stabilized or begun to recover. Land subsidence in the NCP has worsened since the 1950s 65, but there have been instances of stabilization and recovery in Beijing 6, Tianjin 66, and Cangzhou 67. Springs have re-emerged in Mentougou in Beijing (https://www.beijing.gov.cn/renwen/jrbj/202304/t20230419_3059190.html), and in Bai Springs in Xingtai (http://slt.hebei.gov.cn/a/2022/07/22/7BACE2B982F842A387544296E12E9A0F.html). The thickening of the unsaturated zone due to depletion has likely significantly reduced recharge 68, so a recovery in groundwater levels could potentially lead to increased recharge.

Limitations
Despite using the best available in situ groundwater depth data for the NCP, several limitations remain. First, continuous observations were available for only a limited number of wells, particularly prior to 2018. Data before 2017 were often irregular, with varying measurement frequencies and sparse spatial coverage, particularly in the southeastern NCP. These limitations in groundwater monitoring introduce uncertainty when applying spatial interpolation using the Thiessen polygon method. In areas with sparse data, wells receive disproportionately high weights, which may affect the representativeness and reliability of regional estimates. Second, more rapid recovery observed in urban areas compared to rural regions, largely driven by the replacement of municipal groundwater pumping with water from the SNWD project, could introduce bias when well distribution is uneven. To minimize uncertainty from spatial aggregation, we adopted the non-parametric Thiessen polygon method. While this approach incorporates spatial weighting, it remains sensitive to uneven well distribution. Third, due to the absence of detailed hydrogeological parameters, we were unable to estimate changes in groundwater storage at fine spatial resolutions. This is particularly relevant for confined aquifers, where recovery may be delayed due to long-term compaction effects and limited vertical recharge. Finally, this study focused solely on the NCP due to the intensive effort required to collect and digitize data from printed hydrogeological yearbooks. Expanding this analysis to other regions would require a similar investment in data acquisition and quality control. Nonetheless, our uncertainty analysis (Fig. 3a, b) suggests that these limitations do not compromise the core finding of rapid and regionally significant groundwater recovery in the NCP.

Data availability
GRACE TWSA products were accessible from the Jet Propulsion Laboratory (JPL, https://grace.jpl.nasa.gov) and Center for Space Research (CSR, https://www.csr.utexas.edu). Soil moisture products were accessible from the Global Land Data Assimilation System (GLDAS, https://ldas.gsfc.nasa.gov/gldas/). All other data used in this study and source data for generating figures are accessible from Zenodo (https://doi.org/10.5281/zenodo.15797080) 69.

Code availability
Data processing and analysis were conducted using MATLAB. The related code can be found on Zenodo (https://doi.org/10.5281/zenodo.15797080) 69.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "After reviewing the submission by Long et al., I find that the study aligns well with the aim and scope of Nature Communications, presenting a compelling analysis of groundwater recovery trends following water management strategies in China, with a particular focus on the North China Plain (NCP). The research is relevant and timely, contributing valuable insights into large-scale aquifer recovery through human intervention.\nHowever, before the manuscript can be considered for acceptance, several issues need to be addressed. I recommend a major revision to improve clarity, methodological transparency, and the contextualization of findings.\nSpecific Comments\n1. Lines 47-51: The manuscript references previous cases of groundwater recovery (e.g., Bangkok, Taiyuan, Upper Guadiana) but does not provide quantitative comparisons of recovery rates or specify the management strategies employed in each case. A comparative analysis summarizing recovery rates and key intervention strategies would help contextualize the uniqueness of the NCP recovery.\n2. Lines 75-79: The manuscript states that data before 2018 was \"sparse and inconsistent\" but does not quantify how much of the dataset belongs to the pre-2018 vs. post-2018 period. Please specify the percentage of data from each period to highlight any potential biases. Clarify the temporal resolution of the automatic monitoring system after 2018 (e.g., hourly, daily, monthly). If interpolation was used to address missing data, provide details on the methodology and its impact on results.\n3. Lines 173-176: The study analyzes data from 2005 onward, but the rationale for this choice is unclear. Was 2005 selected due to a significant policy shift or the onset of intensified depletion? Are earlier groundwater records available that could provide a longer historical context? A brief justification of the historical significance of this period would improve clarity.\n4. Lines 214-219 & 254-258: The study discusses double cropping reduction and reclaimed water use but does not quantify their contribution to groundwater recovery. Provide an analysis of how much of the observed recovery can be attributed to land use changes versus engineering interventions (e.g., compare recovery trends in areas with strict policy enforcement vs. those without).\n5. Lines 392-398: The limitations section does not address potential spatial biases in the distribution of monitoring wells. Discuss whether the placement of wells (e.g., concentration in urban areas vs. rural regions) might overestimate or underestimate recovery trends.\n6. The manuscript is not difficult to read, but some sections could be simplified. A light English revision focusing on conciseness and clarity is recommended.", "reviewer_2": "GENERAL COMMENTS\nThis manuscript provides a detailed analysis of groundwater levels in the North China Plain (NCP) from more than 2,000 monitoring sites over the past couple decades to show substantial groundwater depletion trends from 2005-2017 largely reversed during 2018-2024. The authors attribute observed trend reversals to human interventions including surface-water diversions and active aquifer restoration policies. The content and scope of the article are appropriate for a high-impact journal such as Nature as these findings could have important implications for groundwater management policies worldwide. Additionally, the data analysis is comprehensive and nicely presented. However, I have major concerns regarding attribution of groundwater level recoveries to specific management policies, which need to be satisfactorily addressed for this article to be considered for publication. Additionally, I have some more minor concerns regarding statistical evaluation and presentation of groundwater-level data.\nMAJOR CONCERNS\nThe manuscript essentially presents two detailed analyses: (1) groundwater-level trends and (2) water supply and use budgets. The major recovery of groundwater levels since 2020 is largely attributed to changes in water management policies that include increased diversions of Yangtze River water and restoration of environmental flows, which ramped up after 2015. A major conceit of the paper is the recovery of groundwater levels is caused by aggregate changes in groundwater management policy. However, this is difficult to prove and it is uncertain the magnitude of the effect. Additionally, it is extremely difficult to say which water management strategies contributed the most to observed recovery due to their complex and interrelated natures across a large area.\nAlthough the recoveries in groundwater levels during 2020-2024 are certainly compelling, as the authors note extremely high precipitation in 2021 clearly played a large hand in the recovery. I understand that management policy may amplify the effects of precipitation on recharge, and subsequent groundwater level recoveries, but the magnitude of this effect appears highly uncertain. In other words, would the recovery have been as persistent and dramatic if 2021 hadn’t had nearly twice the average precipitation of the past 20 years? This needs to be more centrally addressed.\nThe authors only discuss the effect of the large precipitation in 2021 in lines 266-270 and use continued recovery (at much lower rates) in the following years as proof that precipitation was not the sole driver of recovery, but this argument needs to be strengthened. For example, reversal of soil moisture deficits from above average rainfall can potentially result in more effective recharge for subsequent years of relatively average precipitation conditions. To be clear, I do believe the authors are most likely correct that management is playing a role, but the argument needs to be strengthened and backed with more rigorous quantification.\nThere are two potential avenues this reviewer sees to accomplish this, but the authors may have other ideas which I would welcome. The first is to better quantify and present the timing of trend reversals. One of the stronger arguments presented is that trend reversals began, especially in Beijing, before the record high year of precipitation in 2021, which amplified recovery rates. However, right now it is hard to see in the figures and text how the timing of trend reversals in different areas correspond to shifts in management in those areas.\nThe other analysis that could potentially strengthen the argument is to calculate a “relative recharge efficiency” based on the ratio of precipitation (or precipitation minus PET) to observed, aggregate changes in unconfined groundwater levels. Although this would be an extremely broad analysis, it could effectively serve as a “back of the envelope” to help normalize recovery rates by precipitation amounts. One way to go about this could be to calculate total precipitation and unconfined aquifer head changes in a 3-year moving window. If the management actions that ramped up after 2015 have had an effect, we should see an increase in the relative recharge efficiency after 2015. An analysis like this, or discussion and some basic calculations to this effect, would greatly strengthen the central argument of the manuscript.\nFinally, it should be recognized, that it is difficult to attribute the relative effects of varying management strategies on the observed recoveries, which likely differ across the large study region. However, it struck me upon reading that the substantial increase of diversions into environmental flows is a very important aspect of the shift in management strategy. I feel that if a direct line could be drawn to this increase of diverted water to environmental flows to the observed recoveries, it could have important global implications for water resources management strategies and policies. Discussion around the efficacy of environmental flows in recharging groundwater should be strengthened, if possible.\nMINOR CONCERNS\nI have a more minor but important concern with the way aggregate regional groundwater trends are computed and presented. I understand that the Thiessen polygon weighted average trends are computed for unconfined and confined aquifers in different regions and subregions. I agree the weighted mean of the different depth to groundwater measurements should be sensitive to aggregate trends over time if the distribution of data is not greatly skewed. However, I think a more effective way to calculate and present the aggregate trends would be to center individual time series by subtracting the mean before aggregation. This would ensure that no artifacts are introduced by wells with stronger or weaker trends being located in areas of lower or higher groundwater levels, and focuses computations on relative changes as opposed to absolute depths to water. Additionally, this would aid in the presentation of results in figure 2a,c by removing the effects of absolute groundwater level depth and focusing on relative changes. Figure 2a,c is also somewhat misleading because of the different scales of the x and y axes. Normalizing water level records by subtracting the mean could aid in putting all of these data on a single measurement scale. The average mean of both the unconfined and confined records could be calculated, and applied to offset the confined aquifer measurements below the unconfined on a relative scale, with unconfined measurements centered at zero and confined measurements centered at about -15 m. Additionally, it would be helpful to show that the confined measurement timeframe occurs in the latter part of the period displayed for the unconfined aquifer (this could also be accomplished by showing that fig 2c as a “zoomed in” portion of the period displayed for figure 4a).\nMINOR COMMENTS\nLine 47: add “globally productive” before “aquifers showing” ?\nLine 51: You mention California’s Central Valley, but the areas shown in figure 1t are not in the Central Valley. I understand the cited literature here (Scanlon et al., 2016) is not on what the cited map is based. Maybe just say “parts of California and Arizona” to cover your bases.\nLine 55: add “spatial” before “scale”\nLine 60: Is the “less than 500 m^3” based on precipitation divided by population?\nLine 63: Change “were declining” to “declined”\nLine 288: What kind of wells were closed, mostly municipal used for public drinking water supply?\nFigure 4: This is a very nicely presented figure, but I am afraid it is trying to do too much and it becomes fairly confusing are hard to read / connect to the main points of the paper. It might be helpful is figure 4 were used to more effectively highlight the different timings of trend reversals compared to shifts in management in different areas or the change of relative recharge efficiencies prior to and following 2015.", "reviewer_3": "This is an interesting paper that relates a reversal in the trend of increasing groundwater depth in a large aquifer to changes in water supply and use and supports the benefits of groundwater management responses that combine supply and demand interventions. Given the scale of the reversal, the study is of wider significance.\nThe main weakness of the paper is the lack of any attempt to quantitatively demonstrate that the changes in groundwater storage are consistent with the changes in groundwater abstraction and recharge. For example “Subbasins II and III, the primary recipients of SNWD-C water, showed changes in average groundwater depth corresponding to the amount of SNWD-C water used (Fig. 4c)” but the connection is circumstantial. This could potentially be explored by quantitatively integrating GRACE data within the figures and analysis (given large-scale of this study); by estimating volume of storage changed using Storage Coefficient (based on best available data, notwithstanding that they “could not evaluate changes in groundwater storage at fine spatial scales”); or by evaluating the indicative confined / unconfined Storage Coefficient from the volumetric recovery and volumetric reduction in groundwater abstraction.\nOther comments\n• The reductions in agricultural water use appear to be associated with significant reductions in crop area and intensity (e.g. shift from double to single cropping). Whilst there was a reported decrease in irrigation intensity (km3/km2) it would be helpful to see if food (calorie) production has been maintained or whether a large decrease in food production is a significant dis-benefit of the improved groundwater management\n• Water productivity (GDP per m3) increased but no evidence as to whether that is due to more efficient usage or is associated with China’s continuing economic transition (agricultural to industry; primary to tertiary production)\n• There are three confined aquifers – why wasn’t data sub-divided by aquifer to ensure that changing relative proportions in each didn’t affect tends\n• A few cases where there lacks detail: (1) Figure 2- green shadows represent the “uncertainty” – more detail required in caption; (2) unspecified “Quality control measures and interpolations were applied”\n• Consistency in terminology between Figure 3 (e.g. “Diverted(Yellow)”) and text (e.g. SNWD-C”) would be helpful\n• L203 “This greater reduction in confined aquifer use is primarily due to the better water quality in confined aquifers” doesn’t make sense\n• The methodology appears sound, but the authors should justify the use of the Theissen polygon approach (and its advantages / disadvantages) given it magnifies the weight given to wells in data-sparse areas (which have higher uncertainty in their spatial reliability)? Address in limitations?"}, "author_response": "Dear reviewers:\nWe are grateful for your insightful suggestions and have made substantial revisions to the manuscript based\non your suggestions. Below, we provide a detailed, point-by-point response to the reviewers’ comments,\nwith the comments highlighted in blue and our responses in black. Note that line numbers in our responses\nto reviewers’ comments refer to those in the clean version of the manuscript.\nThe reviewers’ comments primarily focused on three key aspects: methodological transparency, influence\nof human intervention on groundwater recovery, and quantitative analysis of the groundwater balance along\nwith comparisons to previous studies. In response, we have made significant changes as listed below. This\nis followed by a point-by-point response to each comment and suggestion.\n(1) Methodological adjustments: Following Reviewer 2’s instruction, we shifted our focus to relative\nchanges rather than absolute water depths, reducing the sensitivity of regional trends to data gaps or\ninconsistencies in well continuity. The revised methodology yielded results that remain consistent with\nthe original findings, underscoring the reliability of our study. Furthermore, we have provided a detailed\nexplanation of the preprocessing methodology and demonstrated the robustness of our results against\nvariations in the preprocessing parameters. With the inclusion of open-sourced code, we believe that\nour methodology offers transparency to both reviewers and future readers.\n(2) Analysis of human interventions: We strengthened our argument that sustained human interventions\nhave contributed to groundwater recovery by incorporating the reviewers’ valuable guidance. Key\nenhancements include introducing a “relative recharge efficiency” metric reflecting the contribution of\nactive management strategies, analyzing the timing of reversals in different regions, and presenting\nevidence of recovery linked to river replenishment and changes in agricultural land use. Furthermore,\nwe demonstrated, using soil moisture data, that the impact of extremely high precipitation in 2021 on\namplified recovery rate was temporary. These refinements enhance the discussion on groundwater\nrecovery.\n(3) Groundwater balance and comparison with previous studies: We quantitively analyzed changes in\ngroundwater levels, abstraction, and recharge, illustrating their consistency with hydrogeological\nconditions. In addition to in-situ observations, GRACE data indicate recovery in total water storage\nafter 2020, aligning with our findings. These all strengthen the reliability of our core findings. We also\nprovided detailed information on previously reported cases of groundwater recovery, highlighting the\nuniqueness and significance of the North China Plain case.\nReviewer #1 (Remarks to the Author):\nComment 1: After reviewing the submission by Long et al., I find that the study aligns well with the aim\nand scope of Nature Communications, presenting a compelling analysis of groundwater recovery trends\nfollowing water management strategies in China, with a particular focus on the North China Plain (NCP).\nThe research is relevant and timely, contributing valuable insights into large-scale aquifer recovery through\nhuman intervention.\n1/23\nHowever, before the manuscript can be considered for acceptance, several issues need to be addressed. I\nrecommend a major revision to improve clarity, methodological transparency, and the contextualization of\nfindings.\nResponse to R1C1: We greatly value the time and effort you have invested in reviewing our manuscript,\nand we appreciate your constructive comments. To address methodological transparency, we have included\na comprehensive description of the method employed, demonstrating that our results remain robust and\nunaffected by variations in data preprocessing. In line with your guidance, we also enhanced the discussion\non the contributing factors to groundwater recovery. Furthermore, we revised the original map of global\ngroundwater recovery cases, incorporating key intervention strategies employed. A detailed list of responses\nis provided below.\nSpecific Comments\nComment 2: Lines 47-51: The manuscript references previous cases of groundwater recovery (e.g.,\nBangkok, Taiyuan, Upper Guadiana) but does not provide quantitative comparisons of recovery rates or\nspecify the management strategies employed in each case. A comparative analysis summarizing recovery\nrates and key intervention strategies would help contextualize the uniqueness of the NCP recovery.\nResponse to R1C2: Based on the original Fig. 1n-t, we created a new figure (Fig. R1) that incorporates the\nareas of aquifers, recovery rates, and key intervention strategies identified in the literature. This updated\nfigure now serves as the new Fig. 1 in the introduction. Additionally, we discovered that the term “Mancha\nOccidental aquifer” is more commonly used than “Upper Guadiana Basin” in the literature, and therefore,\nwe have replaced “Upper Guadiana basin” with this name.\nFig. R1 Comparisons with previously reported cases of recovery. a, Previously reported groundwater\nrecovery trends in the 21st century, including the Mancha Occidental aquifer in Spain (b), Abbas-e Sharghi\n2/23\nbasin in Iran (c), Eastern Malwa Plateau alluvial aquifer in India (d), Bangkok basin in Thailand (e), Taiyuan\nbasin in China (f), East Salt River and Upper Santa Cruz basins in the United States (g). The trends\npreviously reported for the North China Plain are also highlighted in f5. Positive trends (blue) indicate\nshallowing groundwater, while negative trends (red) indicate deepening groundwater. The main drivers for\neach recovery case are also labelled.\nComment 3: Lines 75-79: The manuscript states that data before 2018 was \"sparse and inconsistent\" but\ndoes not quantify how much of the dataset belongs to the pre-2018 vs. post-2018 period. Please specify the\npercentage of data from each period to highlight any potential biases. Clarify the temporal resolution of the\nautomatic monitoring system after 2018 (e.g., hourly, daily, monthly). If interpolation was used to address\nmissing data, provide details on the methodology and its impact on results.\nResponse to R1C3: Generally, ~40% of the monitoring wells were pre-2018 and the remaining ~60% were\npost-2018 (Fig. R2c). For unconfined aquifers, well coverage rose from ~400 to ~500 in 2010 to ~500 to\n~700 in 2018. For confined aquifers, the number increased from ~120 wells before 2018 to ~700 after 2018.\nRecognizing the potential biases introduced by changes in the monitoring network, we revised our data\nprocessing approach following Reviewer 2’s suggestion. By analyzing changes in groundwater depth rather\nthan absolute levels, we reduced the sensitivity of regional trends to data gaps or inconsistencies in well\ncontinuity. These revisions are described in the revised Methods.\nRevised lines 416–420:\nThe number of monitoring wells available increased significantly after 2018 (Supplementary Fig. 5c). For\nunconfined aquifers, well coverage rose from ~400 to ~500 in 2010 to ~500 to ~700 in 2018. For confined\naquifers, the number increased from ~120 wells before 2018 to ~700 after 2018. We analyzed changes in\ngroundwater depth rather than absolute levels, which reduced the sensitivity of regional trends to data gaps\nor inconsistencies in well continuity.\nThe temporal resolution of the automatic monitoring system after 2018 used in this study is monthly. We\nclarified this in the Methods.\nRevised lines 373–375:\nSince 2018, newly installed monitoring wells have automatically recorded groundwater depth at 1‒4 hour\nintervals, resulting in continuous time series data and improving the reliability of monthly averages used\nin this study.\nLinear interpolation was applied to address short-term data gaps. Through testing various preprocessing\nparameters, we demonstrated that our results remained robust and unaffected by the selection of\npreprocessing methods (Fig. R2a-b). We included a detailed description in the Methods.\nRevised lines 380–389:\nQuality control and interpolation procedures were applied before aggregating groundwater depth data at\nregional scales. Three parameters were defined for preprocessing: (1) maximum allowable gap for linear\n3/23\ninterpolation (intrplGap, = 3 months), (2) minimum length of time series required (minLen, = 48 months),\nand (3) moving window size for outlier detection (windowOL, = 24 months). First, linear interpolation was\napplied to internal data gaps not exceeding intrplGap. Second, wells with fewer than minLen months of\nvalid observations were excluded from the analysis. Third, a moving average filter with a window length of\nwindowOL months was used to identify and mask outliers, defined as values deviating by more than three\nstandard deviations from the moving average. These thresholds were selected based on data characteristics\nand preliminary sensitivity analyses to ensure robust spatiotemporal coverage while minimizing the\ninfluence of outliers.\nRevised lines 411–416:\nOur results show that the selection of preprocessing parameters minimally impacted the main findings\n(Supplementary Fig. 5a-b). To assess the uncertainty introduced, we tested a range of values: intrplGap\nfrom 1 to 5, and minLen and windowOL of 12, 24, 36, 48, and 60, ensuring that windowOL did not exceed\nminLen. Although aggregating monthly changes in groundwater depth introduced some uncertainty, the\ninfluence of preprocessing selections remained limited, as indicated by the shaded areas in Supplementary\nFig. 5a-b.\nFig. R2 Groundwater depth data preprocessing and monitoring wells for multiple aquifers. a,\nUncertainty in groundwater depth anomalies in unconfined aquifers due to varying preprocessing\n4/23\nparameters. The green line shows the anomaly used in the main results, and the green shaded area reflects\nthe range of values from different parameter settings. b, Same as (a), but for confined aquifers. The purple\nline and shaded area denote the central estimate and uncertainty bounds, respectively. c, Monthly number\nof available monitoring wells after preprocessing. Light blue and yellow indicate unconfined and confined\naquifers before 2018, respectively; dark blue and red indicate unconfined and confined aquifers after 2018.\nd, Examples of monitoring wells observing multiple confined aquifers. The purple, yellow, and red lines\nshow the groundwater depth in the first (shallowest), second, and third (deepest) confined aquifers,\nrespectively.\nComment 4: Lines 173-176: The study analyzes data from 2005 onward, but the rationale for this choice is\nunclear. Was 2005 selected due to a significant policy shift or the onset of intensified depletion? Are earlier\ngroundwater records available that could provide a longer historical context? A brief justification of the\nhistorical significance of this period would improve clarity.\nResponse to R1C4: Thank you for this comment. We selected 2005 as the starting point for this study\nbecause the first publicly available groundwater depth dataset was published in the Yearbooks of\nGroundwater Levels that year. This rationale was outlined in the Methods. Historical groundwater records\nwere sparse prior to 2005 in the literature, with monitoring across the NCP conducted only occasionally\n(e.g., in 1980 and 2005) and long-term records available for only a limited number of wells.\nRevised lines 369–371:\nSince 2005, the China Institute for Geo-Environmental Monitoring published the Yearbooks of\nGroundwater Levels, providing the first publicly available and most comprehensive dataset of groundwater\ndepth across China.\nReferences (in Chinese):\nWang, S., Song, X., Wang, Q., Xiao, G. & Liu, C. Dynamic Features of Shallow Groundwater in North\nChina Plain. Acta Geographica Sinica63(5), 435-445 (2008).\nYang, H. et al. Evolution of groundwater level in the North China Plain in the past 40 years and suggestions\non its overexploitation treatment. Geology in China48(4), 1142-1155 (2021).\nComment 5: Lines 214-219 & 254-258: The study discusses double cropping reduction and reclaimed water\nuse but does not quantify their contribution to groundwater recovery. Provide an analysis of how much of\nthe observed recovery can be attributed to land use changes versus engineering interventions (e.g., compare\nrecovery trends in areas with strict policy enforcement vs. those without).\nResponse to R1C5: Thank you for this suggestion. Groundwater levels in areas that experienced significant\nreductions in double cropping during 2013–2018 demonstrated earlier recoveries compared to the average\ngroundwater levels across the NCP. For example, rural regions between Beijing and Langfang, where the\ngreatest decline in double cropping occurred, showed groundwater level recovery starting in 2013–2014\n(Fig. R3c). These revisions were incorporated into the manuscript. Reclaimed water is primarily used to\n5/23\nreplenish rivers and lakes, along with applications in sanitation, landscape irrigation, and industry. However,\nquantifying the contribution of reclaimed water use to groundwater recovery remains challenging due to\nthe absence of detailed data on its specific usage breakdown.\nRevised lines 187–188:\nReclaimed water is primarily used for river and lake replenishment, as well as for sanitation, landscape\nirrigation, and industry.\nRevised lines 265–270:\nIn parallel, changes in irrigation practices also contributed to partial recovery. The NCP’s dominant double\ncropping system (winter wheat and summer maize rotation) requires intensive spring irrigation, historically\nsustained by groundwater pumping. Between 2013 and 2018, the area under double cropping declined by\n~10,000 km2. In rural areas between Beijing and Langfang, where the reduction was the greatest,\ngroundwater recovery began as early as 2013–2014 (Fig. 6c).\nReferences:\nDong, L. et al. Shifting agricultural land use and its unintended water consumption in the North China Plain.\nScience Bulletin (2024).\nFig. R3 Engineering and policy approaches for reversing groundwater depletion. a, Water diversion\nand river replenishment in the NCP. The green line represents the boundary of the NCP. Blue polygons\n6/23\nrepresent lakes and reservoirs. Blue, orange, and dark red lines represent rivers, SNWD-C, and SNWD-E,\nrespectively. b, Groundwater depth in unconfined aquifers near replenished rivers. Green, purple, yellow,\nand blue lines correspond to four monitoring wells located between the Sha and the Hutuo rivers from2018\nto 2024. c, Groundwater depth in unconfined aquifers in areas where double-cropped acreage declined\nsubstantially between2013 and 2017. Green and purple lines represent two representative monitoring wells.\nd, GRACE-derived total water storage anomalies (TWSA) in the NCP. The green line shows the average\nof the JPL and CSR mascon products; the shaded green area shows their spread. The thick blue line\nrepresents the trend (based on STL decomposition). e, Annual groundwater balance in the NCP from 2005\nto 2023. The x-axis shows net groundwater abstraction (abstraction minus recharge) based on Hai River\nWater Resources Bulletins (km3). The y-axis represents the annual change in the trend in groundwater depth\n(based on STL decomposition) in unconfined aquifers. S indicates the fitted specific yield derived from\ny_fit\nthe slope of the balance-depth relationship.\nComment 6: Lines 392-398: The limitations section does not address potential spatial biases in the\ndistribution of monitoring wells.\nDiscuss whether the placement of wells (e.g., concentration in urban areas vs. rural regions) might\noverestimate or underestimate recovery trends.\nResponse to R1C6: This is an excellent question. We acknowledged that potential spatial biases could arise\nfrom the distribution of monitoring wells. Rapid recovery rates observed in urban areas compared to rural\nareas, largely driven by replacement of municipal groundwater pumping with water from the SNWD project,\ncould introduce bias when well distribution is uneven. To minimize uncertainty from spatial aggregation,\nwe adopted the non-parametric Thiessen polygon method. This consideration, along with other uncertainties\nfrom variations in well distributions, has been incorporated into the limitation section in the Methods.\nRevised lines 471–480:\nFirst, continuous observations were available for only a limited number of wells, particularly prior to 2018.\nData before 2017 were often irregular, with varying measurement frequencies and sparse spatial coverage,\nparticularly in the southeastern NCP. These limitations in groundwater monitoring introduce uncertainty\nwhen applying spatial interpolation using the Thiessen polygon method. In areas with sparse data, wells\nreceive disproportionately high weights, which may affect the representativeness and reliability of regional\nestimates. Second, more rapid recovery observed in urban areas compared to rural regions, largely driven\nby replacement of municipal groundwater pumping with water from the SNWD project, could introduce\nbias when well distribution is uneven. To minimize uncertainty from spatial aggregation, we adopted the\nnon-parametric Thiessen polygon method. While this approach incorporates spatial weighting, it remains\nsensitive to uneven well distribution.\nComment 7: The manuscript is not difficult to read, but some sections could be simplified. A light English\nrevision focusing on conciseness and clarity is recommended.\nResponse to R1C7: Thank you for this suggestion. We have thoroughly revised our manuscript to enhance\n7/23\nits conciseness and clarity, and we hope these improvements meet your expectations.\nReviewer #1 (Remarks on code availability):\nNo comments\n8/23\nReviewer #2 (Remarks to the Author):\nComment 1: GENERAL COMMENTS\nThis manuscript provides a detailed analysis of groundwater levels in the North China Plain (NCP) from\nmore than 2,000 monitoring sites over the past couple decades to show substantial groundwater depletion\ntrends from 2005-2017 largely reversed during 2018-2024. The authors attribute observed trend reversals\nto human interventions including surface-water diversions and active aquifer restoration policies. The\ncontent and scope of the article are appropriate for a high-impact journal such as Nature as these findings\ncould have important implications for groundwater management policies worldwide. Additionally, the data\nanalysis is comprehensive and nicely presented. However, I have major concerns regarding attribution of\ngroundwater level recoveries to specific management policies, which need to be satisfactorily addressed for\nthis article to be considered for publication. Additionally, I have some more minor concerns regarding\nstatistical evaluation and presentation of groundwater-level data.\nResponse to R2C1: We sincerely appreciate the time and effort you have dedicated to reviewing our\nmanuscript, as well as your valuable and constructive suggestions. We revised the processing of\ngroundwater depth data by aggregating the relative changes in groundwater depth rather than absolute\ndepths. This approach reduced the sensitivity of regional trends to data gaps or inconsistencies in well\ncontinuity. To attribute groundwater recovery, we highlighted the effectiveness of management policies by\nillustrating the varying timings of trend reversals and the changes in relative recharge efficiencies, as per\nyour guidance. Regarding river replenishment, our findings show that wells adjacent to replenished rivers\ndisplayed more rapid recovery rates compared to others. We also demonstrated, using soil moisture data,\nthat the impact of extremely high precipitation in 2021 on amplified recovery rate was temporary. Thanks\nto your insightful feedback, we believe that our methodology and results have been substantially improved.\nA detailed list of responses is provided below.\nMAJOR CONCERNS\nComment 2: The manuscript essentially presents two detailed analyses: (1) groundwater-level trends and\n(2) water supply and use budgets. The major recovery of groundwater levels since 2020 is largely attributed\nto changes in water management policies that include increased diversions of Yangtze River water and\nrestoration of environmental flows, which ramped up after 2015. A major conceit of the paper is the\nrecovery of groundwater levels is caused by aggregate changes in groundwater management policy.\nHowever, this is difficult to prove and it is uncertain the magnitude of the effect. Additionally, it is extremely\ndifficult to say which water management strategies contributed the most to observed recovery due to their\ncomplex and interrelated natures across a large area.\nAlthough the recoveries in groundwater levels during 2020-2024 are certainly compelling, as the authors\nnote extremely high precipitation in 2021 clearly played a large hand in the recovery. I understand that\nmanagement policy may amplify the effects of precipitation on recharge, and subsequent groundwater level\nrecoveries, but the magnitude of this effect appears highly uncertain. In other words, would the recovery\nhave been as persistent and dramatic if 2021 hadn’t had nearly twice the average precipitation of the past\n9/23\n20 years? This needs to be more centrally addressed.\nThe authors only discuss the effect of the large precipitation in 2021 in lines 266-270 and use continued\nrecovery (at much lower rates) in the following years as proof that precipitation was not the sole driver of\nrecovery, but this argument needs to be strengthened. For example, reversal of soil moisture deficits from\nabove average rainfall can potentially result in more effective recharge for subsequent years of relatively\naverage precipitation conditions. To be clear, I do believe the authors are most likely correct that\nmanagement is playing a role, but the argument needs to be strengthened and backed with more rigorous\nquantification.\nThere are two potential avenues this reviewer sees to accomplish this, but the authors may have other ideas\nwhich I would welcome. The first is to better quantify and present the timing of trend reversals. One of the\nstronger arguments presented is that trend reversals began, especially in Beijing, before the record high year\nof precipitation in 2021, which amplified recovery rates. However, right now it is hard to see in the figures\nand text how the timing of trend reversals in different areas correspond to shifts in management in those\nareas.\nThe other analysis that could potentially strengthen the argument is to calculate a “relative recharge\nefficiency” based on the ratio of precipitation (or precipitation minus PET) to observed, aggregate changes\nin unconfined groundwater levels. Although this would be an extremely broad analysis, it could effectively\nserve as a “back of the envelope” to help normalize recovery rates by precipitation amounts. One way to\ngo about this could be to calculate total precipitation and unconfined aquifer head changes in a 3-year\nmoving window. If the management actions that ramped up after 2015 have had an effect, we should see an\nincrease in the relative recharge efficiency after 2015. An analysis like this, or discussion and some basic\ncalculations to this effect, would greatly strengthen the central argument of the manuscript.\nFinally, it should be recognized, that it is difficult to attribute the relative effects of varying management\nstrategies on the observed recoveries, which likely differ across the large study region. However, it struck\nme upon reading that the substantial increase of diversions into environmental flows is a very important\naspect of the shift in management strategy. I feel that if a direct line could be drawn to this increase of\ndiverted water to environmental flows to the observed recoveries, it could have important global\nimplications for water resources management strategies and policies. Discussion around the efficacy of\nenvironmental flows in recharging groundwater should be strengthened, if possible.\nResponse to R2C2: Thank you for your suggestions. We utilized both of your proposed approaches to\nstrengthen our argument that sustained human intervention significantly influences groundwater recovery\n(Fig. R4). First, we compared groundwater depth and precipitation in Beijing and the NCP (Fig. R4a, c).\nThe earlier recovery observed in Beijing coincided with the earlier substitution of pumping with diverted\nwater, compared to the other regions in the NCP. Notably, Beijing’s recovery remained resilient even during\ndroughts in 2019 and 2022 (Fig. 5c). After 2020, SNWD-C deliveries expanded to other parts of the NCP,\nreaching over 4 km3 per year and supporting a broader reversal of regional groundwater declines (Fig. 5a).\nThe coupling between precipitation and groundwater depth anomalies further highlights the impact of these\ninterventions (Fig. 5b, d). A systematic leftward shift in precipitation-groundwater scatter plots indicates\nincreasingly shallow groundwater conditions, even under comparable rainfall, signaling enhanced\nrecharge efficiency. These shifts were evident both in drought years (e.g., 2014, 2019) and wet years (e.g.,\n2016, 2023–2024) (Fig. 5b), suggesting that the interventions have strengthened the system’s resilience. A\nsimilar pattern is observed in Beijing (Fig. 5d), where 2024 groundwater depths significantly improved\ncompared to 2005. Although the extreme rainfall in 2021 temporarily accelerated groundwater recovery,\nits effect was short-lived, as soil moisture returned to baseline levels by early 2022 (Supplementary Fig. 3).\nIn contrast, the continued groundwater rise in Beijing (2016–2020) and the ongoing NCP-wide recovery\nsince 2022 underscore the effectiveness and durability of policy-led groundwater restoration efforts.\nReferences:\nZheng, W., Wang, S., Sprenger, M., Liu, B. & Cao, J. Response of soil water movement and groundwater\nrecharge to extreme precipitation in a headwater catchment in the North China Plain. J Hydrol 576, 466-\n477 (2019).\n11/23\nFig. R4 Groundwater depth response in unconfined aquifers to precipitation variability. a, Annual\nprecipitation in the HRB (white bars) and average groundwater depth anomalies in unconfined aquifers\nacross the NCP. Background shading denotes dry (orange), normal (gray), and wet (blue) years. The red\nline shows the long-term trend in average groundwater depth anomalies, derived via STL decomposition.\nb, Scatter plot relating 12-month average precipitation in the HRB (Pr) to the corresponding change in the\nSTL trend component of groundwater depth anomalies in the NCP. Both axes use logarithmic scales. Point\ncolors indicate months, and labels such as “2014Q2” denote the year and calendar quarter. c, Same as panel\na, but for Beijing: annual precipitation and groundwater depth anomalies in unconfined aquifers. d, Same\nas panel b, but for Beijing: precipitation-groundwater trend relationships based on STL decomposition.\nFig. R5 Soil moisture response to precipitation variability. The blue bars represent monthly precipitation\n12/23\nin the HRB. The red line represents root zone soil moisture anomalies in the NCP from the GLDAS CLSM\nv2.2 dataset.\nWe used the groundwater depth of four wells located between the Hutuo River and Sha River to demonstrate\nthe effectiveness of river replenishment (Fig. R3b). The Hutuo River became part of the experimental\nreplenishment effort in 2018, followed by the inclusion of the Sha River in 2019 as another key target of\nthe initiative. Our analysis revealed that wells adjacent to replenished rivers displayed more rapid recovery\nrates compared to those distant from replenished rivers, highlighting the success of river replenishment in\nrecharging the aquifer through losing streams. Details of this analysis have been incorporated into the\nrevised manuscript.\nRevised lines 256–265:\nManaged aquifer recharge (MAR) and reduced irrigation-related pumping are critical strategies for\nrestoring depleted aquifers (Fig. 6a-c). For years, rivers in the NCP had dried up due to upstream reservoir\ninterception and declining groundwater levels. Experimental river replenishment began in 2018 in the\nFuyang, Hutuo, and Juma rivers. Since then, over 10 km3 of water from the SNWD project has been\nallocated to restore rivers across the NCP by 2024. Additional contributions came from Yellow River\ndiversions and local reservoirs, benefiting rivers (e.g., the Yongding River) and lakes (e.g., the Baiyangdian\nLake). These efforts have rejuvenated drying rivers, restored riparian ecosystems, and enhanced aquifer\nrecharge. Wells near replenished rivers exhibited more rapid groundwater recovery rates (Fig. 6b),\nconfirming the effectiveness of river-based MAR. Other MAR methods, including flood-restoring recharge\nponds and injection wells targeting confined aquifers, were employed but at smaller scales.\nMINOR CONCERNS\nComment 3: I have a more minor but important concern with the way aggregate regional groundwater\ntrends are computed and presented. I understand that the Thiessen polygon weighted average trends are\ncomputed for unconfined and confined aquifers in different regions and subregions. I agree the weighted\nmean of the different depth to groundwater measurements should be sensitive to aggregate trends over time\nif the distribution of data is not greatly skewed. However, I think a more effective way to calculate and\npresent the aggregate trends would be to center individual time series by subtracting the mean before\naggregation. This would ensure that no artifacts are introduced by wells with stronger or weaker trends\nbeing located in areas of lower or higher groundwater levels, and focuses computations on relative changes\nas opposed to absolute depths to water. Additionally, this would aid in the presentation of results in figure\n2a,c by removing the effects of absolute groundwater level depth and focusing on relative changes. Figure\n2a,c is also somewhat misleading because of the different scales of the x and y axes. Normalizing water\nlevel records by subtracting the mean could aid in putting all of these data on a single measurement scale.\nThe average mean of both the unconfined and confined records could be calculated, and applied to offset\nthe confined aquifer measurements below the unconfined on a relative scale, with unconfined\nmeasurements centered at zero and confined measurements centered at about -15 m. Additionally, it would\nbe helpful to show that the confined measurement timeframe occurs in the latter part of the period displayed\n13/23\nfor the unconfined aquifer (this could also be accomplished by showing that fig 2c as a “zoomed in” portion\nof the period displayed for figure 4a).\nResponse to R2C3: We followed your instructions and focused on relative changes instead of absolute\ndepths (Fig. R6), which reduced the sensitivity of regional trends to data gaps or inconsistencies in well\ncontinuity, particularly with the introduction of new monitoring wells in 2010 and 2018. Because the\nobservation periods of the wells vary, using the mean groundwater depth at each well as a benchmark would\nlead to temporal discrepancies. Instead, we aggregated the groundwater depth change at each well to\ncompute groundwater depth anomalies. The revised groundwater depth anomalies for unconfined aquifers\nin the NCP are generally consistent with the original average groundwater depths, except for deviations\nobserved in 2010 and 2018. The increased number of available wells in 2010 and 2018 misleadingly\nintroduced shallowing and deepening effects in the original results, respectively. For confined aquifers, the\nrevised results align with the original results post-2018, while also offering an estimation of the water table\nprior to 2018. Following your recommendations, the x-axis of Fig. R6a now shares the same scale for\nunconfined and confined aquifers. Additionally, we noted that the average groundwater depths for\nunconfined and confined aquifers were ~11 m and ~27 m in 2024, respectively. However, we retained the\ndiffering scale on the y-axis for clarity, as using a uniform y-axis scale would obscure the trends in\nunconfined aquifers due to their smaller variation. Details of the revised methodology have been\nincorporated into the Methods.\nRevised lines 395–406:\nWe aimed to develop a consistent understanding of groundwater depth dynamics across various regional\nscales over the past two decades, particularly under the reformed groundwater monitoring system around\n2018. First, we calculated the change in groundwater depth between consecutive months and applied a\nweighted average to the groundwater depth changes at each available monitoring well, using the\ncorresponding areas of Thiessen polygons as the basis for weighting:\n∑𝑖𝑆𝑖 ×(𝐷𝑝ℎ𝑖,𝑗 −𝐷𝑝ℎ𝑖,𝑗−1)\n∆𝐷𝑝ℎ𝑗 = (1)\nwhere Dph\ni,j\nand Dph\ni,j-1\nrepresent the groundwater dept𝑆h𝑡 𝑜i𝑡n months j and j - 1 at each monitoring well, S\ni\nis the area of the Thiessen polygon associated with the well, and S is the area of the target region (each\ntot\nwell was associated with a unique polygon). Second, we summed the changes to obtain the cumulative\nchange:\n𝑖\n∆𝐷𝑝ℎ_𝑐𝑢𝑚𝑖 = ∑∆𝐷𝑝ℎ𝑗 (2)\n𝑗=1\nGroundwater depth anomalies were then calculated as anomalies of the cumulative change (ΔDph_cum).\ni\n14/23\nFig. R6 Time series and trends in groundwater depths across the NCP. a, Time series of average\ngroundwater level anomalies in unconfined and average groundwater head anomalies in confined aquifers.\nThe green and purple lines represent the average groundwater depth anomalies in unconfined and confined\naquifers, respectively. The thick blue and red lines illustrate long-term trends in unconfined and confined\naquifers derived from STL decomposition, respectively. The green and purple shadows represent the\nuncertainty associated with the spatial distribution of the monitoring network and the application of the\nThiessen polygon method in unconfined and confined aquifers, respectively. The dashed brown lines\nrepresent the timing of reversals. b, Average groundwater level anomalies in unconfined aquifers at the city\nscale. Same legend as in panel a. c, Proportion of monitoring wells exhibiting deepening (p < 0.05, red),\nshallowing (p < 0.05, light blue), reversing (dark blue), and no significant trend (orange) during 2005–2017\nand 2018–2024.\nMINOR COMMENTS\n15/23\nComment 4: Line 47: add “globally productive” before “aquifers showing” ?\nResponse to R2C4: We made the recommended change.\nRevised lines 46–49:\nDespite these efforts, a recent study found that rapid groundwater level declines (> 0.5 m year-1) remain\nwidespread, with only a small fraction of globally productive aquifers showing signs of recovery (aquifers\nwith rising trends > 0.2 m year-1 constituting ~6% over the past two decades).\nComment 5: Line 51: You mention California’s Central Valley, but the areas shown in figure 1t are not in\nthe Central Valley. I understand the cited literature here (Scanlon et al., 2016) is not on what the cited map\nis based. Maybe just say “parts of California and Arizona” to cover your bases.\nResponse to R2C5: We revised the manuscript following your suggestion.\nRevised lines 49–52:\nCases of groundwater recovery, when reported, are often spatially limited and exhibit only modest rates of\nrecovery (e.g., Mancha Occidental aquifer (Spain), Abbas-e Sharghi basin (Iran), Eastern Malwa Plateau\nalluvial aquifer (India), Bangkok basin (Thailand), Taiyuan basin (China), and parts of California and\nArizona (US); Fig. 1).\nComment 6: Line 55: add “spatial” before “scale”\nResponse to R2C6: We made this change.\nRevised lines 55–57:\nHere we explore the NCP as an example of the feasibility of achieving rapid groundwater recovery (~0.7 m\nyear-1) at an unprecedented spatial scale through ongoing human intervention.\nComment 7: Line 60: Is the “less than 500 m^3” based on precipitation divided by population?\nResponse to R2C7: The 500 m3 was derived by dividing the annual renewable water resources (20–45\nkm3), as reported in the Hai River Water Resources Bulletins, by the population (~150 million). In unusually\nwet years like 2021, the annual renewable water resources can reach up to 73 km3. As a result, we used the\nterm “less than 500 m3” in the manuscript. We clarified this in the manuscript and included the annual\nrenewable water resources in the open dataset.\nRevised lines 66–68:\nThis region, which is responsible for ~10% of China’s grain production, supports a population of ~130\nmillion people and is characterized by severely limited water resources, with less than 500 m3 of renewable\nwater resources available per capita annually.\nComment 8: Line 63: Change “were declining” to “declined”\n16/23\nResponse to R2C8: We revised it in the manuscript.\nRevised lines 71–73:\nAs a result, groundwater levels in the NCP declined at a rate of 1–2 m year-1 by the end of the 20th century,\nleading to an estimated cumulative depletion of about 160 km3 from the 1960s to 2008.\nComment 9: Line 288: What kind of wells were closed, mostly municipal used for public drinking water\nsupply?\nResponse to R2C9: No data were available regarding the specific types of wells that were closed. The most\nreliable information to date comes from a news conference hosted by the Ministry of Water Resources,\nwhich reported that over 27,000 wells had been closed by 2023\n(http://www.mwr.gov.cn/hd/zxft/zxzb/fbh20230228/). Based on our experience, we conclude that most of\nthe closed wells were for municipal use. Additionally, some wells for irrigation were also closed,\ncorresponding to the adoption of dryland farming practices and seasonal fallowing. We have updated the\nnew data in the manuscript.\nRevised lines 322–324:\nTogether with stringent pumping regulations (e.g., closure of more than 27,000 wells by 2023 according to\nthe Ministry of Water Resources), these activities effectively curbed the potential for the Jevons Paradox,\nwhere increased efficiency could have led to higher overall water consumption.\nComment 10: Figure 4: This is a very nicely presented figure, but I am afraid it is trying to do too much\nand it becomes fairly confusing are hard to read / connect to the main points of the paper. It might be helpful\nis figure 4 were used to more effectively highlight the different timings of trend reversals compared to shifts\nin management in different areas or the change of relative recharge efficiencies prior to and following 2015.\nResponse to R2C10: We now present two figures (Fig. R3 and Fig. R4) that replace the original Fig. 4.\nThe first integrates the effectiveness of river replenishment and agricultural land use change, along with a\ncomparison to GRACE data, reflecting both your feedback and that of other reviewers. The second\nillustrates the different timings of trend reversals and the changes in relative recharge efficiencies,\nincorporating your suggestions. We hope these revised figures are clear and easy to understand.\n17/23\nReviewer #3 (Remarks to the Author):\nComment 1: This is an interesting paper that relates a reversal in the trend of increasing groundwater depth\nin a large aquifer to changes in water supply and use and supports the benefits of groundwater management\nresponses that combine supply and demand interventions. Given the scale of the reversal, the study is of\nwider significance.\nResponse to R3C1: We sincerely appreciate the time and effort you have dedicated to reviewing our\nmanuscript and providing constructive suggestions. Under your thoughtful guidance, we incorporated a\nquantitative comparison of groundwater recovery and reductions in groundwater abstraction. Additionally,\nGRACE data have been integrated into the manuscript. Your suggestions regarding uncertainty induced by\nThiessen polygons, water productivity, food production, and terminology consistency are also invaluable.\nWe believe that these revisions have significantly enhanced the manuscript. A detailed list of responses is\nprovided below.\nComment 2: The main weakness of the paper is the lack of any attempt to quantitatively demonstrate that\nthe changes in groundwater storage are consistent with the changes in groundwater abstraction and recharge.\nFor example “Subbasins II and III, the primary recipients of SNWD-C water, showed changes in average\ngroundwater depth corresponding to the amount of SNWD-C water used (Fig. 4c)” but the connection is\ncircumstantial. This could potentially be explored by quantitatively integrating GRACE data within the\nfigures and analysis (given large-scale of this study); by estimating volume of storage changed using\nStorage Coefficient (based on best available data, notwithstanding that they “could not evaluate changes in\ngroundwater storage at fine spatial scales”); or by evaluating the indicative confined / unconfined Storage\nCoefficient from the volumetric recovery and volumetric reduction in groundwater abstraction.\nResponse to R3C2: We observed a strong linear relationship (R2=0.87) between changes in groundwater\ndepth in unconfined aquifers and net water balance deficits, calculated as annual groundwater abstraction\nminus recharge from the Hai River Water Resources Bulletins (Fig. R3e). The fitted specific yield (S ) of\ny_fit\n0.07 generally matches reported values of ~0.12 in the piedmont plains and ~0.04 in other regions of the\nNCP, supporting the reliability of the water balance approach. Satellite observations from GRACE and\nGRACE-FO show recovery in total water storage anomalies (TWSA) beginning around 2020 (Fig. R3d).\nDiscrepancies between GRACE TWSA data and in-situ groundwater trends likely stem from variations in\nsoil and surface water storage, uncertainties in specific yield and storage coefficients, or inherent limitations\nin GRACE resolution. A detailed analysis has been included in the manuscript.\nRevised lines 289–303:\nGroundwater depth recovery observed in monitoring wells is consistent with both satellite-based and water\nbalance estimates (Fig. 6d-e). Interannual changes in groundwater depth in unconfined aquifers show a\nstrong linear relationship (R2=0.87) with net water balance deficits, calculated as annual groundwater\nabstraction minus recharge from the Hai River Water Resources Bulletins (Fig. 6e). The fitted specific yield\n(S ) of 0.07 generally matches reported values of ~0.12 in the piedmont plains and ~0.04 in other regions\ny_fit\nof the NCP, supporting the reliability of the water balance approach (see Methods). Satellite observations\n18/23\nfrom GRACE and GRACE-FO show a recovery in total water storage anomalies (TWSA) beginning around\n2020 (Fig. 6d), yet this rebound compensates for only about half of the ~49 km3 of total water storage\ndepletion between 2005 and 2019. Based on a 3 m decline in groundwater level and the estimated S ,\ny_fit\nunconfined aquifer storage alone decreased by ~27 km3 during this period. Confined aquifers,\ncharacterized by much smaller storage coefficients (0.0004–0.004), contributed relatively little to the total\ndepletion, indicating that the majority of groundwater loss occurred in unconfined systems. Discrepancies\nbetween GRACE TWSA data and in-situ groundwater trends likely stem from variations in soil and surface\nwater storage, uncertainties in specific yield and storage coefficients, or inherent limitations in GRACE\nresolution. A full attribution of these differences is beyond the scope of this study.\nRevised lines 455–460:\nThe average specific yield in the NCP was estimated with a water-balance method that uses the relationship\nbetween changes in groundwater depth within unconfined aquifers and groundwater abstraction minus\nrecharge. The following equation was applied:\n𝑃𝑢𝑚𝑝𝑖𝑛𝑔−𝑅𝑒𝑐ℎ𝑎𝑟𝑔𝑒\n∆𝐷𝑝ℎ = +𝐶 (5)\nwhere ΔDph is the annual groundwater depth c𝑆h𝑦a_n𝑓g𝑖𝑡e ×in 𝐴u𝑟n𝑒c𝑎on𝑁f𝐶i𝑃ned aquifers, S is the fitted specific yield,\ny_fit\nArea is the area of the NCP, and C is a constant.\nNCP\nOther comments\nComment 3: The reductions in agricultural water use appear to be associated with significant reductions in\ncrop area and intensity (e.g. shift from double to single cropping). Whilst there was a reported decrease in\nirrigation intensity (km3/km2) it would be helpful to see if food (calorie) production has been maintained\nor whether a large decrease in food production is a significant dis-benefit of the improved groundwater\nmanagement\nResponse to R3C3: Food production in Beijing, Tianjin, and Hebei gradually increased during 2005–2023\n(Fig. R7) according to the China Rural Statistical Yearbook, primarily aided by increased yield per unit area.\nAdditionally, further decline in double-cropped area has slowed since 2018, as diverted water allowed local\nsurface water to be redirected for irrigation. A detailed analysis has been included in the manuscript.\n19/23\nFig. R7 Annual food production in Beijing, Tianjin, and Hebei during 2005–2023.\nRevised lines 270–275:\nSince 2018, further decline in double-cropped area has slowed, as diverted water has allowed local surface\nwater to be redirected for irrigation. In contrast, the southeastern NCP retained stable cropping patterns\ndue to reliance on Yellow River diversions. Despite the cropping adjustments, regional food production in\nBeijing, Tianjin, and Hebei rose from ~28 million tons in 2005 to ~42 million tons in 2023 (Supplementary\nFig. 4), aided by increased yield per unit area, helping to ensure local food security.\nReferences:\nDong, L. et al. Shifting agricultural land use and its unintended water consumption in the North China Plain.\nScience Bulletin (2024).\nComment 4: Water productivity (GDP per m3) increased but no evidence as to whether that is due to more\nefficient usage or is associated with China’s continuing economic transition (agricultural to industry;\nprimary to tertiary production)\nResponse to R3C4: We believe the increase in GDP per m3 is linked to more efficient water usage, as the\nindustry added value per m3 also increased more than fivefold between 2005 and 2023. Coupled with\ndecreasing irrigation intensity and increasing food production (see Response to R3C3), this indicates that\nwater use efficiency is improving in both agricultural and industrial sectors. We added the industry added\nvalue per m3 data to the manuscript.\nRevised lines 318–320:\nAs a result, water productivity in the NCP has increased more than fivefold between 2005 and 2023. In\n2023, economic output per unit of water use reached ~$50 m-3 based on gross domestic product (GDP) and\n~$120 m-3 based on industrial added value, compared to just ~$9 m-3 and ~$19 m-3, respectively, in 2005.\n20/23\nComment 5: There are three confined aquifers – why wasn’t data sub-divided by aquifer to ensure that\nchanging relative proportions in each didn’t affect tends\nResponse to R3C5: We did not analyze the regional dynamics of groundwater heads across different\nconfined aquifer layers at the regional scale, primarily due to the limited number of monitoring wells in the\nsecond and third confined layers, particularly before 2018. To give readers a general understanding of\ngroundwater heads in these layers, we included groundwater depths from three representative wells (Fig.\nR2d). The details have been incorporated into the manuscript.\nRevised lines 420–425:\nGroundwater heads in different confined aquifer layers followed two general patterns (Supplementary Fig.\n5d). First, deeper layers typically had greater depths to groundwater. Second, due to intensive pumping and\nvertical leakage through aquitards, groundwater heads across layers were often similar. We did not analyze\nthe dynamics of groundwater heads across different confined layers at the regional scale, primarily due to\nthe limited number of monitoring wells in the second and third confined layers, particularly before 2018.\nComment 6: A few cases where there lacks detail: (1) Figure 2- green shadows represent the “uncertainty”\n– more detail required in caption; (2) unspecified “Quality control measures and interpolations were applied”\nResponse to R3C6: The legend for the green and purple shadows in the original Fig. 2 (Fig. R6) has been\nupdated to “Uncertainty (Thiessen)”, representing the uncertainty associated with the spatial distribution of\nthe monitoring network and the application of the Thiessen polygon method. Additionally, we have included\na comprehensive description of the method employed, including the preprocessing steps you highlighted.\nFor further information, please refer to Response to R1C3.\nRevised lines 380–389:\nQuality control and interpolation procedures were applied before aggregating groundwater depth data at\nregional scales. Three parameters were defined for preprocessing: (1) maximum allowable gap for linear\ninterpolation (intrplGap, = 3 months), (2) minimum length of time series required (minLen, = 48 months),\nand (3) moving window size for outlier detection (windowOL, = 24 months). First, linear interpolation was\napplied to internal data gaps not exceeding intrplGap. Second, wells with fewer than minLen months of\nvalid observations were excluded from the analysis. Third, a moving average filter with a window length of\nwindowOL months was used to identify and mask outliers, defined as values deviating by more than three\nstandard deviations from the moving average. These thresholds were selected based on data characteristics\nand preliminary sensitivity analyses to ensure robust spatiotemporal coverage while minimizing the\ninfluence of outliers.\nRevised lines 411–416:\nOur results show that the selection of preprocessing parameters minimally impacted the main findings\n(Supplementary Fig. 5a-b). To assess the uncertainty introduced, we tested a range of values: intrplGap\nfrom 1 to 5, and minLen and windowOL of 12, 24, 36, 48, and 60, ensuring that windowOL did not exceed\n21/23\nminLen. Although aggregating monthly changes in groundwater depth introduced some uncertainty, the\ninfluence of preprocessing selections remained limited, as indicated by the shaded areas in Supplementary\nFig. 5a-b.\nComment 7: Consistency in terminology between Figure 3 (e.g. “Diverted(Yellow)”) and text (e.g. SNWD-\nC”) would be helpful\nResponse to R3C7: The legend “Diverted (Yellow)” refers to water diversion from the Yellow River. The\nSouth-to-North Water Diversion (SNWC) is a project that channels water from the Yangtze River via two\nroutes: the central route (SNWD-C) and the eastern route (SNWD-E). The SNWD-C serves as a vital source\nof imported water for the NCP, whereas SNWD-E supplies only a small amount of water (~0.2 km3 year-1)\nto the southeastern parts of the NCP. We revised the legend in the original Fig. 3 (Fig. R8), aiming to make\nthe terminology clear and easy to understand.\nFig. R8Annual water supply and usage in the HRB. a, Annual water supply from various sources (km3),\nincluding surface water from reservoirs, groundwater from unconfined and confined aquifers, water\ndiversion from the Yellow River, water diversion from the Yangtze River (SNWD), and reclaimed water. b,\nProportions of total water supply in 2005, 2014, and 2023. c, Annual water use across different sectors\n(km3), including municipal, agricultural, industrial, and environmental water usage. d, Proportions of total\nwater use in 2005, 2014, and 2023.\n22/23\nComment 8: L203 “This greater reduction in confined aquifer use is primarily due to the better water quality\nin confined aquifers” doesn’t make sense\nResponse to R3C8: The better water quality in confined aquifers made them a primary source for municipal\nuse, but this role was replaced by SNWD following its implementation. We revised the manuscript to\nhighlight the substitution of groundwater abstraction, ensuring our statements are clear.\nRevised lines 209–214:\nThis greater reduction in confined aquifer use is largely attributed to its greater substitution by water from\nthe SNWD project. Historically, confined aquifers, valued for their high water quality, have been a primary\nsource for municipal use, particularly because unconfined aquifers are more vulnerable to contamination.\nWith the introduction of water from the SNWD project, groundwater from confined aquifers that had been\nused for municipal supplies has increasingly been replaced, thereby reducing groundwater pumping.\nComment 9: The methodology appears sound, but the authors should justify the use of the Theissen polygon\napproach (and its advantages / disadvantages) given it magnifies the weight given to wells in data-sparse\nareas (which have higher uncertainty in their spatial reliability)? Address in limitations?\nResponse to R3C9: You are correct that wells in data-sparse areas, which were more likely to exhibit higher\nuncertainty, were assigned relatively greater weight through the use of Thiessen polygons. We have\nincorporated this disadvantage, as well as the other concerns raised by other reviewers, in the limitation\nsection of the Methods. As for the advantages, we adopted the non-parametric Thiessen polygon method to\nmitigate further uncertainty from spatial aggregation parameters.\nRevised lines 471–480:\nFirst, continuous observations were available for only a limited number of wells, particularly prior to 2018.\nData before 2017 were often irregular, with varying measurement frequencies and sparse spatial coverage,\nparticularly in the southeastern NCP. These limitations in groundwater monitoring introduce uncertainty\nwhen applying spatial interpolation using the Thiessen polygon method. In areas with sparse data, wells\nreceive disproportionately high weights, which may affect the representativeness and reliability of regional\nestimates. Second, more rapid recovery observed in urban areas compared to rural regions, largely driven\nby replacement of municipal groundwater pumping with water from the SNWD project, could introduce\nbias when well distribution is uneven. To minimize uncertainty from spatial aggregation, we adopted the\nnon-parametric Thiessen polygon method. While this approach incorporates spatial weighting, it remains\nsensitive to uneven well distribution.\n23/23"}, {"round": 2, "reviewer_comments": {"reviewer_1": "After reviewing the revised manuscript submitted by Long et al., I find that the authors have adequately addressed all the comments provided in the previous round of review. Therefore, I recommend acceptance of the manuscript after minor revisions.\nSpecifically, I suggest reordering the panels in Figure 2a, as the current arrangement (e.g., d, c, b in the upper section) lacks a logical sequence and may cause confusion for the reader.", "reviewer_2": "Peer-review for manuscript submitted to Nature Communications (Revision 1)\nTitle: Unprecedented large-scale aquifer recovery through human intervention\nAuthors: Long et al.\nGENERAL COMMENTS\nI have reviewed the authors’ revision and am impressed with how they have rigorously addressed reviewer concerns and elegantly integrated those changes in their revision. I feel that the revision is satisfactory for publication, but I have some minor suggestions the authors can integrate if they wish. I also have some minor requests for clarifications.\nMINOR COMMENTS\nFigure 5: Thank you for considering my suggestion and I hope this was a useful exercise for you. I think this is an interesting analysis I am a little confused as to what individual points represent. Are these monthly values of the aggregate, Thiessen-polygon weighted anomalies considering all changes within the year prior? Perhaps a short paragraph on this analysis in the methods will help clarify. I think I count 12 points in the 2021 ovals, so I am assuming this is the aggregate STL decomposed trend at a monthly timestep with a 1 yr lookback window? Or centered in the moving window?\nThe primary trends of increased Pr corresponding to shallowing GWLs are clear. I think if I squint I can see the leftward shift you are describing, but it is hard to know which clusters of points I am comparing to find the leftward shift. Perhaps the data can be binned as “before” (2005-2017) and “after” (2018-2024) large-scale interventions ramped up (if I am correct in my description of the timing). If each of these binned periods can be fit with a line (sen slope?), perhaps we could more easily see the shift of the line, which would represent the median relationship between Pr and GWL changes in each of the two periods. This might have to be a “semi-quantitative” indicator just for illustrative purposes, since there could be statistical overlap between the CIs of the slopes, but it might make it easier to visualize. Please disregard this comment if you find this analysis does not work to make the differences clearer. If there is room on the plot, I would have a left pointing arrow indicating the direction of “enhanced relative recharge efficiency” or similar.\nFigure 6b: It is a little confusing because you use the colors for canals and rivers in the right panel for two of the GWL records. I am thinking you want to have a limited color palette so as not to overload the viewer. However, it got me thinking it might be nice to have the yellow and blue lines represent canal and river diversions, respectively, and plotted on a secondary axis to compare to the GWL records on the time series plot. If it makes it too busy don’t worry about it. Thanks for including these data to strengthen the argument about environmental flows.\nFigure 6e: Perhaps circles (with black borders?) since it is a little hard to see the crosses on the small plot. I think it is nice to include the method of Butler et al., 2016 here. I am wondering did you do a similar regression for Beijing and did it show a different slope? Out of scope for the current paper to dive into those differences so I am not asking to include. You may want to include a horizontal line on the y-axis at 0 and indicate “shallowing” or “deepening” on either side of it. This could also apply to Figs 5b,d (it would be vertical there), but I understand it might make too busy.\nMethods: I am really happy to hear that focusing on the anomalies helped the analysis. I usually subtract the mean, which indicates all points as either “shallower” or “deeper” than the average observed value, but I understand this can be tricky when aggregating discontinuous records. I am having a little trouble understanding how the anomalies were calculated based on the explanation, but the results look as I would think they should, with minimal changes to the findings of the prior analysis. Does the offset simply equate to subtracting the first measurement from each available time series from the subsequent points, indicating shallower or deeper depths from the first observation? Perhaps just one or two more sentences to clarify and provide an intuitive sense of what was done to normalize these data.", "reviewer_3": "This paper provides a robust evaluation of the reversal of groundwater level declines in the North China Plain and provides quantitative evidence of a diversity of supply and demand-side factors that have contributed to the recovery. Whilst the evidence linking actions to recovery are largely circumstantial, the authors make a sufficiently convincing case for this interesting and positive study to be published. The authors have responded comprehensively to the review comments.\nI have two minor comments.\nL240 – the leftward shift is unlikely to indicate “enhanced recharge efficiency” but rather that the relative balance between recharge and groundwater discharge/abstraction has changed (net water balance deficits, as per Figure 6e)\nL319 – clarify whether the economic output values for 2005 are expressed in terms of 2023 value for comparability"}, "author_response": "Dear and reviewers:\nWe are grateful for your thoughtful and constructive suggestions and have made further revisions to the\nmanuscript based on your suggestions. Below, we provide a detailed, point-by-point response to the\nreviewers’ comments, with the comments highlighted in blue and our responses in black. Note that line\nnumbers in our responses to reviewers’ comments refer to those in the clean version of the manuscript.\nThe reviewers’ comments primarily focused on the leftward shifts in the precipitation-groundwater depth\nchange plots and the price benchmarks used to calculate the economic output values of water use. In\nresponse, we have made significant changes as listed below. This is followed by a point-by-point response\nto each comment and suggestion.\n(1) Leftward shifts in the precipitation-groundwater depth change plots: We agree with Reviewer 3’s\ncomment that the leftward shifts in Fig. 5b and Fig. 5d reflect changes in the relative balance of the\naquifers, driven by both increased recharge and reduced abstraction. Accordingly, we revised the\nmanuscript to use the phrase “alter the groundwater balance” instead of “enhance recharge efficiency”.\nFollowing Reviewer 2’s suggestions, we also added red arrows to the figure to clearly highlight these\nleftward shifts.\n(2) Benchmarks of economic output values: The economic output values reported in the original\nmanuscript were taken directly from the annual bulletins published by local water authorities and did\nnot account for year-to-year price fluctuations. We are grateful to Reviewer 3 for highlighting this issue.\nTo ensure comparability, we adjusted all economic output values to 2023 constant prices using GDP\ndeflator data from the World Bank and Producer Price Index (PPI) data from the National Bureau of\nStatistics of China.\nReviewer #1 (Remarks to the Author):\nAfter reviewing the revised manuscript submitted by Long et al., I find that the authors have adequately\naddressed all the comments provided in the previous round of review. Therefore, I recommend acceptance\nof the manuscript after minor revisions.\nSpecifically, I suggest reordering the panels in Figure 2a, as the current arrangement (e.g., d, c, b in the\nupper section) lacks a logical sequence and may cause confusion for the reader.\nReviewer #1 (Remarks on code availability):\nThe authors have included the dataset used in the development of this research, along with instructions for\nits use.\nResponse to R1: We appreciate your efforts in helping to improve the quality of our manuscript. In the\noriginal Fig. 2a, the panels labeled “b” through “j” were arranged in counterclockwise order. We\nacknowledge that this may have caused some confusion, and have therefore exchanged the labels “b” and\n“c” (Fig. R1). We hope the revised panel arrangement is now clearer.\n1/9\nFig. R1Trends in groundwater depths across monitoring wells in the North China Plain and comparisons with previously\nreported cases of recovery. a, Overview of the Hai River basin (HRB). The purple line represents the boundary of the HRB. The\ngreen line represents the northern and western boundary of the North China Plain (NCP), which is the plain portion of the HRB.\nOrange lines represent the central route of the South-to-North Water Diversion (SNWD-C). Blue polygons represent reservoirs.\nBrown lines represent the boundaries of Hebei Province and the provincial-level municipalities of Beijing and Tianjin (Province).\nRed diamonds represent major cities. Purple circles represent the locations of monitoring wells (b-l). b-j, Instances of continuous\ndepth to groundwater measurements in unconfined aquifers during 2005–2024. Green lines represent the depth to groundwater.\nThick blue lines illustrate the long-term trends derived from STL decomposition. k-l, Instances of continuous depth to groundwater\nmeasurements in confined aquifers during 2005–2024. Purple lines represent the depth to groundwater. Thick red lines illustrate\nthe long-term trends derived from STL decomposition. m, Trends in groundwater levels and heads at individual wells during 2020–\n2024. Circles indicate unconfined aquifers, while triangles represent confined aquifers. Positive trends (blue) indicate shallowing\ngroundwater, while negative trends (red) indicate deepening groundwater.\n2/9\nReviewer #2 (Remarks to the Author):\nPeer-review for manuscript submitted to Nature Communications (Revision 1)\nTitle: Unprecedented large-scale aquifer recovery through human intervention\nAuthors: Long et al.\nGENERAL COMMENTS\nI have reviewed the authors’ revision and am impressed with how they have rigorously addressed reviewer\nconcerns and elegantly integrated those changes in their revision. I feel that the revision is satisfactory for\npublication, but I have some minor suggestions the authors can integrate if they wish. I also have some\nminor requests for clarifications.\nResponse to R2C1: Thank you again for your insightful and constructive suggestions. Following your\ncomments, we have made all possible revisions and believe that these changes have improved the clarity\nand overall quality of the manuscript. We hope that the revised version now meets your expectations.\nMINOR COMMENTS\nFigure 5: Thank you for considering my suggestion and I hope this was a useful exercise for you. I think\nthis is an interesting analysis I am a little confused as to what individual points represent. Are these monthly\nvalues of the aggregate, Thiessen-polygon weighted anomalies considering all changes within the year prior?\nPerhaps a short paragraph on this analysis in the methods will help clarify. I think I count 12 points in the\n2021 ovals, so I am assuming this is the aggregate STL decomposed trend at a monthly timestep with a 1\nyr lookback window? Or centered in the moving window?\nThe primary trends of increased Pr corresponding to shallowing GWLs are clear. I think if I squint I can\nsee the leftward shift you are describing, but it is hard to know which clusters of points I am comparing to\nfind the leftward shift. Perhaps the data can be binned as “before” (2005-2017) and “after” (2018-2024)\nlarge-scale interventions ramped up (if I am correct in my description of the timing). If each of these binned\nperiods can be fit with a line (sen slope?), perhaps we could more easily see the shift of the line, which\nwould represent the median relationship between Pr and GWL changes in each of the two periods. This\nmight have to be a “semi-quantitative” indicator just for illustrative purposes, since there could be statistical\noverlap between the CIs of the slopes, but it might make it easier to visualize. Please disregard this comment\nif you find this analysis does not work to make the differences clearer. If there is room on the plot, I would\nhave a left pointing arrow indicating the direction of “enhanced relative recharge efficiency” or similar.\nResponse to R2C2: Thank you for your thoughtful suggestions. We believe that including Fig. 5\nstrengthens the manuscript by visually supporting our key arguments. Regarding the individual points in\nFig. 5, you are correct that each point represents a month. The x-axes of Fig. 5b and Fig. 5d depict the\nmonth-to-month difference in the STL trend component of aggregated groundwater depth anomalies, while\nthe y-axes show the average precipitation over the preceding 12 months. We clarified this in the Methods.\n3/9\nRevised lines 465–475:\nWe analyzed the relationship between precipitation and changes in groundwater depth to assess the impact\nof human intervention on groundwater recovery. To present precipitation (vertical axes of Fig. 5b, d), we\nused the 12-month moving average, as its influence on aquifer recharge can be long-lasting:\n𝑡\n∑𝑖=𝑡−11𝑃𝑟(𝑖)\n𝑃𝑟𝐴𝑣𝑔12(𝑡)= (5)\nwhere Pr (t) is the 12-month moving average precipitation for month t, and Pr(i) is the precipitation in\nAvg12 12\nmonth i.\nTo represent changes in groundwater depth (horizontal axes in Fig. 5b, d), we used the month-to-month\ndifference in the trend component of aggregated groundwater depth anomalies:\nwhere ΔDph\ntrend\n(t) is the cha∆n𝐷g𝑝e ℎin𝑡𝑟 𝑒th𝑛e𝑑 (S𝑡T)L= tr𝐷en𝑝dℎ c𝑡𝑟o𝑒m𝑛𝑑p(o𝑡n)e−nt 𝐷of𝑝 gℎr𝑡o𝑟u𝑒𝑛n𝑑d(w𝑡a−ter1 )depth anomalies for mont(h6 t),\nand Dph (t) is the STL trend component itself for month t.\ntrend\nWe acknowledge that binning the points into two clusters and linearly fitting them helps improve clarity.\nHowever, we are concerned that this approach may be misleading due to the extreme precipitation in 2021.\nDuring such events, infiltration capacity becomes the dominant limiting factor for recharge, resulting in\nreduced recharge per unit precipitation. As an alternative and to improve clarity, we added red arrows to\nhighlight the leftward shifts observed during droughts (Fig. R2b, d). We chose the phrase “alter the\ngroundwater balance” rather than “enhance recharge efficiency” because the leftward shifts are also\ninfluenced by reductions in groundwater abstraction.\nOriginal lines 238–241:\nA systematic leftward shift in precipitation-groundwater scatter plots indicates increasingly shallow\ngroundwater conditions, even under comparable rainfall, signaling enhanced recharge efficiency. These\nshifts were evident both in drought years (e.g., 2014, 2019) and wet years (e.g., 2016, 2023–2024) (Fig.\n5b), suggesting that the interventions have strengthened the system’s resilience.\nRevised lines 238–241:\nA systematic leftward shift in precipitation-groundwater scatter plots indicates increasingly shallow\ngroundwater conditions, even under similar rainfall conditions. These shifts are particularly pronounced\nduring drought years (red arrow in Fig. 5b), suggesting that the interventions have altered the groundwater\nbalance and strengthened the system’s resilience.\n4/9\nFig. R2 Groundwater depth response in unconfined aquifers to precipitation variability. a, Annual precipitation in the Hai\nRiver basin (HRB, white bars) and average groundwater depth anomalies in unconfined aquifers across the North China Plain\n(NCP). Background shading denotes dry (orange), normal (gray), and wet (blue) years. The red line shows the long-term trend in\naverage groundwater depth anomalies, derived via STL decomposition. b, Scatter plot relating 12-month average precipitation (Pr)\nin the HRB to the corresponding change in the STL trend component of groundwater depth anomalies in the NCP. Both axes use\nlogarithmic scales. Point colors indicate months, and labels such as “2014Q2” denote the year and calendar quarter. The red arrow\nindicates the leftward shifts observed during droughts. The dashed gray line indicates a stable trend. c, Same as panel a, but for\nBeijing: annual precipitation and groundwater depth anomalies in unconfined aquifers. d, Same as panel b, but for Beijing:\nprecipitation-groundwater trend relationships based on STL decomposition.\nFigure 6b: It is a little confusing because you use the colors for canals and rivers in the right panel for two\nof the GWL records. I am thinking you want to have a limited color palette so as not to overload the viewer.\nHowever, it got me thinking it might be nice to have the yellow and blue lines represent canal and river\ndiversions, respectively, and plotted on a secondary axis to compare to the GWL records on the time series\nplot. If it makes it too busy don’t worry about it. Thanks for including these data to strengthen the argument\nabout environmental flows.\nResponse to R2C3: Thank you for your thoughtful suggestions. We revised the figure by changing the\ncolors of the two wells to red and gray to avoid confusion (Fig. R3). Regarding the canal and river diversions,\nwe agree that this information would be valuable. However, detailed data on the amount of water used for\nreplenishment by individual rivers are not available. As a result, we are unable to include these values on a\nsecondary axis. We appreciate your understanding.\n5/9\nFig. R3Engineering and policy approaches for reversing groundwater depletion. a, Water diversion and river replenishment\nin the North China Plain (NCP). The green line represents the boundary of the NCP. Blue polygons represent lakes and reservoirs.\nBlue, orange, and dark red lines represent rivers, the central route of the South-to-North Water Diversion (SNWD-C), and the\neastern route of the South-to-North Water Diversion (SNWD-E), respectively. b, Groundwater depth in unconfined aquifers near\nreplenished rivers. Green, purple, gray, and red lines correspond to four monitoring wells located between the Sha and the Hutuo\nrivers from 2018 to 2024. c, Groundwater depth in unconfined aquifers in areas where double-cropped acreage declined\nsubstantially between2013 and 2017. Green and purple lines represent two representative monitoring wells. d, GRACE-derived\ntotal water storage anomalies (TWSA) in the NCP. The green line shows the average of the JPL and CSR mascon products; the\nshaded green area shows their spread. The thick blue line represents the trend (based on STL decomposition). e, Annual groundwater\nbalance in the NCP from 2005 to 2023. The x-axis shows net groundwater abstraction (abstraction minus recharge) based on Hai\nRiver Water Resources Bulletins (km3). The y-axis represents the annual change in the trend in groundwater depth (based on STL\ndecomposition) in unconfined aquifers, with the dashed gray line indicating a stable trend. Sy_fit indicates the fitted specific yield\nderived from the slope of the balance-depth relationship.\nFigure 6e: Perhaps circles (with black borders?) since it is a little hard to see the crosses on the small plot.\nI think it is nice to include the method of Butler et al., 2016 here. I am wondering did you do a similar\nregression for Beijing and did it show a different slope? Out of scope for the current paper to dive into those\ndifferences so I am not asking to include. You may want to include a horizontal line on the y-axis at 0 and\nindicate “shallowing” or “deepening” on either side of it. This could also apply to Figs 5b,d (it would be\nvertical there), but I understand it might make too busy.\nResponse to R2C4: Thank you for your valuable suggestions. To enhance visibility, we added circles\n6/9\naround the crosses using the same color as the original symbols (Fig. R3e). We also tested black borders,\nbut they appeared visually distracting. We added a citation of Butler et al. (2016) in the main text (now\nlisted as reference 34 in the revised manuscript). We agree that comparing the groundwater balance between\nthe NCP and Beijing would be informative. However, the balance presented in Fig. 6e is based on\nunconfined aquifers. In contrast, the pumping data available for Beijing do not distinguish between\nunconfined and confined aquifers. Using total groundwater abstraction would shift the data points in Fig.\n6e rightward, potentially leading to misleading interpretations. We appreciate your understanding for not\nincluding groundwater balance plots for Beijing. We added reference lines indicating zero change in\ngroundwater depth to Fig. 6e (Fig. R3e), Fig. 5b (Fig. R2b), and Fig. 5d (Fig. R2d). “Shallowing” and\n“deepening” are also labelled on either side of the lines. These modifications improve clarity.\nRevised lines 296–298:\nThe fitted specific yield (S ) of 0.07 generally matches reported values of ~0.12 in the piedmont plains\ny_fit\nand ~0.04 in other regions of the NCP25, supporting the reliability of the water balance approach34 (see\nMethods).\nMethods: I am really happy to hear that focusing on the anomalies helped the analysis. I usually subtract\nthe mean, which indicates all points as either “shallower” or “deeper” than the average observed value, but\nI understand this can be tricky when aggregating discontinuous records. I am having a little trouble\nunderstanding how the anomalies were calculated based on the explanation, but the results look as I would\nthink they should, with minimal changes to the findings of the prior analysis. Does the offset simply equate\nto subtracting the first measurement from each available time series from the subsequent points, indicating\nshallower or deeper depths from the first observation? Perhaps just one or two more sentences to clarify\nand provide an intuitive sense of what was done to normalize these data.\nResponse to R2C5: Thank you for your comments. The results presented in this study are based on the\nmonth-to-month difference of groundwater depth at the same monitoring wells (see Equation 1). We did\nnot use the first measurements as benchmarks, due to discontinuities in the monitoring records and the\nvarying timing of initial measurements across wells. We highlighted that only wells with valid groundwater\ndepth measurements in both months j and j – 1 were included in the spatial averaging for month j in the\nMethods (see the last sentence in the excerpt of the revised manuscript below).\nRevised lines 401–410:\nFirst, we calculated the change in groundwater depth between consecutive months and applied a weighted\naverage to the groundwater depth changes at each available monitoring well, using the corresponding\nareas of Thiessen polygons as the basis for weighting:\n∑𝑖𝑆𝑖 ×(𝐷𝑝ℎ𝑖,𝑗 −𝐷𝑝ℎ𝑖,𝑗−1)\n∆𝐷𝑝ℎ𝑗 = (1)\nwhere Dph\ni,j\nand Dph\ni,j-1\nrepresent the groundwater dept𝑆h𝑡 𝑜i𝑡n months j and j - 1 at each monitoring well, S\ni\nis the area of the Thiessen polygon associated with the well, and S is the area of the target region (each\ntot\n7/9\nwell was associated with a unique polygon). This equation means that only wells with valid groundwater\ndepth measurements in both months j and j – 1 were included in the spatial averaging.\nReviewer #3 (Remarks to the Author):\nThis paper provides a robust evaluation of the reversal of groundwater level declines in the North China\nPlain and provides quantitative evidence of a diversity of supply and demand-side factors that have\ncontributed to the recovery. Whilst the evidence linking actions to recovery are largely circumstantial, the\nauthors make a sufficiently convincing case for this interesting and positive study to be published. The\nauthors have responded comprehensively to the review comments.\nResponse to R3C1: Thank you again for the time and effort you have dedicated to providing constructive\nsuggestions to our manuscript. Regarding your remaining comments, please refer to our following\nresponses.\nI have two minor comments.\nL240 – the leftward shift is unlikely to indicate “enhanced recharge efficiency” but rather that the relative\nbalance between recharge and groundwater discharge/abstraction has changed (net water balance deficits,\nas per Figure 6e).\nResponse to R3C2: Thank you for your comments. We acknowledge that the leftward shifts in Fig. 5b and\nFig. 5d reflect changes in the relative balance of the aquifers. These shifts result from both increased\nrecharge and reduced abstraction. Accordingly, we used the phrase “alter the groundwater balance” rather\nthan “enhance recharge efficiency” in the revised manuscript.\nOriginal lines 238–241:\nA systematic leftward shift in precipitation-groundwater scatter plots indicates increasingly shallow\ngroundwater conditions, even under comparable rainfall, signaling enhanced recharge efficiency. These\nshifts were evident both in drought years (e.g., 2014, 2019) and wet years (e.g., 2016, 2023–2024) (Fig.\n5b), suggesting that the interventions have strengthened the system’s resilience.\nRevised lines 239–242:\nA systematic leftward shift in precipitation-groundwater scatter plots indicates increasingly shallow\ngroundwater conditions, even under similar rainfall conditions. These shifts are particularly pronounced\nduring drought years (red arrow in Fig. 5b), suggesting that the interventions have altered the groundwater\nbalance and strengthened the system’s resilience.\nL319 – clarify whether the economic output values for 2005 are expressed in terms of 2023 value for\ncomparability\nResponse to R3C3: Thanks for your suggestion. The economic output values in the original manuscript\nwere directly obtained from the annual bulletins published by local water authorities and did not account\nfor year-to-year price changes. In response to your suggestion, we adjusted these values to 2023 constant\n8/9\nprices using GDP deflator data from the World Bank and Producer Price Index (PPI) data from the National\nBureau of Statistics of China. An exchange rate of 1 USD = 7 CNY was assumed for currency conversion.\nWe revised the results accordingly in both the main text and the Methods section to reflect these updates.\nOriginal lines 318–320:\nAs a result, water productivity in the NCP has increased more than fivefold between 2005 and 2023. In\n2023, economic output per unit of water use reached ~$50 m-3 based on gross domestic product (GDP) and\n~$120 m-3 based on industrial added value, compared to just ~$9 m-3 and ~$19 m-3, respectively, in 2005.\nRevised lines 322–325:\nAs a result, water productivity in the NCP has increased three to four times between 2005 and 2023. In\n2023, economic output per unit of water use reached ~$50 m-3 based on gross domestic product (GDP) and\n~$120 m-3 based on industrial added value, compared to just ~$17 m-3 and ~$27 m-3, respectively, in 2005\n(all values adjusted to 2023 constant prices).\nRevised lines 459–462:\nEconomic output values of water use were adjusted to 2023 constant prices using GDP deflator data from\nthe World Bank (https://data.worldbank.org/indicator) and Producer Price Index (PPI) data from the\nNational Bureau of Statistics of China (https://www.stats.gov.cn/sj/).\n9/9"}]

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