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Groundwater plays a critical role in the global water cycle and is the drinking source for almost half of the world’s population. However, exact quantification of its storage change remains elusive due primarily to limited ground observations in space and time. The Gravity Recovery and Climate Experiment (GRACE) twin-satellite data have provided global observations of water storage variations at monthly sampling for over a decade and a half, and is enable to estimate changes in groundwater storage (GWS) after removing other water storage components using auxiliary datasets and models. In this paper, we present an overview of GWS changes in three main aquifers within China using GRACE data, and conduct a comprehensive accuracy assessment using in situ ground well observations and hydrological models. GRACE detects a significant GWS depletion rate of 7.2 ± 1.1 km3/yr in the North China Plain (NCP) during 2002–2014, consistent with ground well observations and model predictions. The Liaohe River Basin (LRB) experienced a pronounced GWS decline during 2005–2009, at a depletion rate of 5.0 ± 1.2 km3/yr. Since 2010, GRACE-based GWS reveal a slow recovery in the LRB, with excellent agreement with ground well observations. For the whole study period 2002–2014, no significant long-term GWS depletion is found in the LRB nor in the Tarim Basin. A case study in the Inner Tibetan Plateau highlights there still exist large uncertainties in GRACE-based GWS change estimates.

The Sino-German research collaboration project, “Recycling of organic residues from agricultural and municipal origin in China” (2008–2012), comprising different interdisciplinary research groups, and also German small and medium-sized enterprises, aimed at developing integrated strategies and solutions for the recycling of organic residues in China. In an intensive crop-livestock agricultural region in the Shunyi District of Beijing, five typical cropping systems were investigated. The research was conducted in the form of analyses of phosphorus (P) in soil, plants, animal feed, animal products, manures, mineral and organic fertilizers and the derivation of the corresponding nutrient balances and P flows. The mean annual P balance surplus was 492 kg·ha−1·yr−1 P for the vegetable production system, significantly higher (P<0.05) than that for orchards (130 kg·ha−1·yr−1P) and cereal crops (83 kg·ha−1·yr−1 P). Plant-available P (Olsen-P) concentrations of topsoils (0–20 cm) had good correlations with the amounts of P applied (from mineral and organic sources). Compared to results from the Second Chinese National Soil Survey of 1981, mean concentrations of available P in soils of 19 plots investigated in Shunyi District increased 10-fold (from 7.3 to 60 mg·kg−1) from 1981 to 2009. On average, the critical limit for Olsen-P concentrations (>30 mg·kg−1) that can lead to increased risk of P loss was exceeded in all five cropping systems. With feed additives, the “natural background value” (Chinese Environmental Quality Standard for Soils) of copper and zinc in topsoils was exceeded at several sites. Screening for several substances in the veterinary antibiotic classes of sulfonamides, tetracyclines, and fluoroquinolones revealed widespread topsoil contamination. Calculated livestock densities were 10.6 livestock units per ha arable land in 2007. Animal husbandry is increasingly conducted in large operations, making traditional ways of reuse difficult to apply. Comparing three management systems for treatment of organic residues from a pig farm via aerobic (composting) or anaerobic (biogas) treatment in a life cycle assessment, the resulting cropland demand for a sustainable land application of biogas effluent varied between 139 and 288 ha·yr−1, well above the cropland area owned by the farm (10 ha). The mismatch problems in the above context between business-as-usual and improving performance are framed and discussed as (1) the mismatch between centralized animal husbandry and smallholder farming, (2) the mismatch between livestock density and cropland, (3) nutrient (including P) recycling and increasing organic matter content versus energy production, (4) subsidies for compost production and biogas, as well as (5) advances in the regulatory framework in China.

Aerosol effects on convective precipitation is critical for understanding human impacts on extreme weather and the hydrological cycle. However, even their signs and magnitude remain debatable. In particular, aerosol effects on vertical structure of precipitation have not been systematically examined yet. Combining 6‐year space‐borne and ground‐based observations over the North China Plain, we show a boomerang‐shape aerosol effect on the top height of convective precipitation, from invigoration to suppression. Further analyses reveal that the aerosols play distinct effects on precipitation rate at different layers. Particularly, near surface precipitation rate shows no significant responses to aerosol and precipitation‐top height due to strong evaporation. The competition of energy between released from condensation and freezing and absorbed by evaporation contributes to different responses of precipitation‐top height to aerosol and can explain the boomerang‐shape aerosol effect. Plain Language Summary Aerosol particles in the atmosphere can alter precipitation efficiency and modulate the hydrological cycle, while their impacts on the cloud and precipitation vertical profiles remain poorly understood. Using 6‐year multi‐source observation data along with reanalysis meteorology, we find that aerosols exert distinct effects on precipitation rate at different layers. The observations show that aerosols enhance precipitation‐top height first and then suppress it under various dynamics and thermodynamics conditions, with a turning point at medium aerosol amount. In contrast, the response of near surface precipitation rate to aerosol perturbation is complex due to varying evaporation efficiency. These findings challenge the previous studies that suggested that the characteristics of cloud and precipitation at high altitude are closely correlated with precipitation rate near the surface. Key Points Observations show a boomerang‐shape aerosol effect on the top height of convective precipitation from invigoration to suppression Aerosols impose distinct effects on precipitation rate at different layers, with no significant impact near surface Energy change within conversion processes between hydrometeors and water vapor explains different responses of precipitation to aerosol

【Objective】Understanding the seasonal dynamics of vegetation and the factors driving these changes is essential for sustainable land management and ecological protection. This study investigates the seasonal variations in vegetation cover across North China and identifies the dominant natural and anthropogenic factors influencing these patterns. 【Method】Multi-source data, including the normalised difference vegetation index (NDVI), nighttime light intensity, meteorological data, and soil and terrain attributes, were used to analyse the spatiotemporal dynamics of NDVI from 1982 to 2021. Trend analysis, the Hurst index, and interpretable machine learning methods were employed to detect long-term NDVI trends and quantify the influence of key environmental and human drivers. 【Result】The average NDVI in the study area increased significantly in all seasons from 1982 to 2021. Spatially, about 60% of the region exhibited NDVI values above 0.6 in summer, while winter and spring NDVI values remained relatively low. Areas showing significant NDVI increases were concentrated in winter and spring, accounting for 97.70% and 86.94% of the total area, respectively. In contrast, only 1.06% and 2.22% of the region experienced significant NDVI declines in spring and summer. Future projections suggest that NDVI in approximately 82% of the region may decline. NDVI in non-cropland areas generally increased, whereas croplands showed a continuous decline. The dominant drivers of NDVI variation differed by land-use type: NDVI in croplands was mainly influenced by elevation and potential evapotranspiration, while in non-cropland areas, it was primarily affected by precipitation and soil moisture. 【Conclusion】The NDVI in North China exhibited clear spatiotemporal heterogeneity driven by both natural and anthropogenic factors. Sustainable land management strategies should account for these differences, targeting croplands and non-croplands separately to enhance vegetation resilience and ecosystem stability.

Since the implementation of market oriented economic reform in 1978, China has been on the track of rapid urbanization. The unprecedented urbanization in China has resulted in substantial cultivated land loss and rapid expansion of urban areas. The cultivated land loss due to urbanization not only threatens food security in China, but has also led to ecological system degradation to which close attention should be paid. Therefore, we examined the effects of the conversion from cultivated to urban areas on the ecosystem service in the North China Plain on the basis of a net primary productivity based ecosystem service model (NESM) and a buffer comparison method. Cultivated land loss due to urbanization in the North China Plain led to a total loss of ecosystem service value of 34.66% during the period 1988–2008. Urban expansion significantly decreased the ecosystem service function of water conservation (–124.03%), nutrient cycling (–31.91%), gas regulation (−7.18%), and organic production (–7.18%), while it improved the soil conservation function (2.40%). Land use change accounted for 57.40% of the changes in ecosystem service and had a major influence on the changes in nutrient cycling and water conservation. However, climate change mainly determined the changes in gas regulation, organic production, and soil conservation.

Groundwater is an important natural resource in the North China Plain (NCP) with high economic benefits and social significance. It fulfills 60% of drinking and 70% of irrigation water requirements. In this review, the information is retrieved from high-quality articles published in MEDLINE and other sources. We saw that groundwater is declining faster (>1 m yr−1) and polluting with NO3− (>30 mg L−1) due to excessive water pumping and application of a nitrogen (N) fertilizer, respectively. The water pumping (>600 mm ha−1 yr−1) for agricultural purposes in the region is higher than the recharge amount (<200 mm yr−1). The low recharge is the result of low rainfall (<600 mm yr−1), and high evapotranspiration (>800 mm yr−1) under the impact of dominant vegetative characteristics of winter wheat–summer maize (WW-SM) rotations, covering >80% of the land. Furthermore, N application exceeds the crop assimilation capacity (>250 kg ha−1 yr−1) and leach deep down (>50 kg ha−1) as well as loss in the atmosphere. Presently, Beijing, Tianjin, and Hebei are ecologically the most affected areas. We suggest that excessive water and N fertilizer use for intensive cropping systems should be controlled by paying high attention to groundwater-friendly farming practices. In addition, artificial groundwater recharge options and their safe utilization would be explored across the region to replenish aquifers. This literature review contributes valuable insights to the knowledge bank and offers a foundation for further research and policy development.

To compensate for the coarse resolution of groundwater storage (GWS) estimation by the Gravity Recovery and Climate Experiment (GRACE) satellites and make better use of available observed groundwater‐level (GWL) data in some aquifers, a ground‐based scaling factor (SF) method is proposed here to derive high‐resolution GRACE GWS estimates. Improvement is achieved by using the gridded SF derived from assimilating ground‐based GWL observations. The proposed SF method is tested on the North China Plain (NCP, ∼140,000 km2), where a dense network of observation wells and a consistently estimated specific yield (SY) data set are available, to demonstrate its effectiveness and practical applications. The sensitivities of SF‐estimated GWS accuracy to the specification of SY and the assimilation of GWL observation data are explored through four designed numerical experiments. Results show that this novel ground‐based method can reduce the impact of SY uncertainty on GWS estimates, particularly in regions with more pronounced regional GWS trends. The accuracy of SF‐estimated GWS is primarily determined by whether the assimilated wells can reflect the regionally averaged GWS trend. GWS accuracy is less dependent on the number of available wells assimilated. The estimated GWS trend (2004–2015) in NCP is −32.6 ± 1.3 mm/yr (−4.6 ± 0.2 km3/yr), with contrasting GWS trends found in the west Piedmont Plain (∼54,000 km2, with a loss of −66.8 mm/yr) and the coastal Eastern Plain (∼20,000 km2, and a gain of +7.2 mm/yr). Despite the limitations of regional and time scale dependence inherent in SF method, this study highlights the benefits of assimilating in situ observed GWL data instead of using model simulations in estimating SF to downscale GRACE GWS to the higher‐resolution that is desired by local water resources management. Key Points A new downscaling method based on groundwater‐based scaling factor correction is proposed to improve gravity recovery and climate experiment (GRACE) groundwater storage estimates The proposed method can reduce the impact of specific yield uncertainty on groundwater storage (GWS) estimation due to the GWS information provided by the assimilated wells of groundwater level The accuracy of GWS change based on the proposed method is primarily determined by whether the assimilated wells can reflect the regional‐averaged GWS trend, less dependent on the number of available wells assimilated

Irrigation over the North China Plain (NCP) has been demonstrated to lower temperature by altering the surface energy budget. During past decades, the concurrence of irrigated area variation and reduced irrigation intensity prompted our investigation into whether there has been a temporal change in irrigation cooling effect over the NCP, which is largely unknown. Using historical observations in 1979–2018, we detect a shift in the cooling effect occurring around 1995, when the expansion of irrigated area was going to slow down and water‐conserving irrigation technology was boomingly introduced. After this time, the accelerated process of cooling effect (−0.0045°C year−1) switches to a decelerated one (0.0089°C year−1). Regional climate simulations also show a pronounced slowdown in irrigation‐induced cooling with the rate of 0.0081°C year−1. The irrigation‐induced cooling is expected to be weaker with the persistent reduction in agricultural water use and contribute to a more rapid warming. Plain Language Summary The North China Plain is the largest irrigated area in China. However, the irrigated agriculture situation in this fertile plain has been going through moderated expansion of irrigated area and reduced irrigation intensity. The variations in irrigation‐induced cooling effect along with this irrigation development were thus investigated based on in situ observations and model simulations. The combined analysis clearly supports a shift from enhancement to alleviation in cooling effect during past decades. The slowdown of irrigation feedback is therefore likely to enable more rapid warming in the future. Key Points The local cooling effect of irrigation during past decades is evaluated in observations and model simulations Over the North China Plain, the irrigation‐induced cooling effect has decreased since the mid‐1990s Persistent changes in irrigation water use cannot be overlooked in climate attribution studies

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.