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 (800 mm yr⁻¹) under the impact of dominant vegetative characteristics of winter wheat–summer maize (W_W-S_M) 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.

The North China Plain (NCP) is experiencing serious groundwater level decline and groundwater nitrate contamination due to excessive water pumping and application of nitrogen (N) fertilizer. In this study, grain yield, water and N use efficiencies under different cropping systems including two harvests in 1 year (winter wheat–summer maize) based on farmer (2H1Y)^FP and optimized practices (2H1Y)^OPT, three harvests in 2 years (winter wheat–summer maize–spring maize, 3H2Y), and one harvest in 1 year (spring maize, 1H1Y) were evaluated using the water-heat-carbon-nitrogen simulator (WHCNS) model. The 2H1Y^FP system was maintained with 100% irrigation and fertilizer, while crop water requirement and N demand for other cropping systems were optimized and managed by soil testing. In addition, a scenario analysis was also performed under the interaction of linearly increasing and decreasing N rates, and irrigation levels. Results showed that the model performed well with simulated soil water content, soil N concentration, leaf area index, dry matter, and grain yield. Statistically acceptable ranges of root mean square error, Nash–Sutcliffe model efficiency, index of agreement values close to 1, and strong correlation coefficients existed between simulated and observed values. We concluded that replacing the prevalent 2H1Y^FP with 1H1Y would be ecofriendly at the cost of some grain yield decline. This cropping system had the highest average water use (2.1 kg m⁻³) and N use efficiencies (4.8 kg kg^–1) on reduced water (56.64%) and N (81.36%) inputs than 2H1Y^FP. Whereas 3H2Y showed insignificant results in terms of grain yield, and 2H1Y^FP was unsustainable. The 2H1Y^FP system consumed a total of 745 mm irrigation and 1100 kg N ha^–1 in two years. When farming practices were optimized for two harvests in 1 year system (2H1Y)^OPT, then grain yield improved and water (18.12%) plus N (61.82%) consumptions were minimized. There was an ample amount of N saved, but water conservation was still unsatisfactory. However, considering the results of scenario analyses, it is recommended that winter wheat would be cultivated at <200 mm irrigation by reducing one irrigation event.

Conventional farming practices not only constrained food security due to low yield but also threatened the ecosystem by causing groundwater decline and groundwater nitrate contamination. A two׹ear field experiment was conducted at the research station of North China University of Water Resources and Electric Power, Zhengzhou. The WHCNS model was used to simulate grain yield, water and nitrogen fertilizer use efficiencies (WUE and FNUEs) of spring maize under border irrigation method, drip irrigation, and rainfed conditions. In addition, a scenario analysis was also performed on different dry and rainy seasons to assess the long-term impact of rainfall variability on spring maize from 2000–2017. The result showed that the model precisely simulated soil water content, N concentration, crop biomass accumulation, and grain yield. The maximum and minimum range of relative root mean squire error (RRMSE) values were 0.5–36.0% for soil water content, 14.0–38.0% for soil nitrate concentrations, 19.0–24.0% for crop biomass and 1.0–2.0% for grain yield, respectively under three irrigation methods. Both the index of agreement (IA) and Pearson correlation coefficient (r) values were close 1. We found the lowest grain yield from the rainfed maize, whereas the drip irrigation method increased grain yield by 14% at 40% water saving than border irrigation method for the two years with the 11% lower evaporation and maintained transpiration rate. Moreover, the drip irrigated maize had a negligible amount of drainage and runoff, which subsequently improved WUE by 27% in the first growing season and 16% in the second rotation than border irrigation. The drip irrigated maize also showed 24% higher FNUE. The reason of lower WUE and FNUEs under the border irrigation method was increased drainage amounts and N leaching rates. Furthermore, scenario analysis indicated that the dry season could result in a 30.8% yield decline as compared to rainy season.

The relationship between water-sediment processes and landscape pattern changes has currently become a research hotspot in low-carbon water and land resource optimization research. The SWAT-VRR model is a distributed hydrological model which better shows the effect of land use landscape change on hydrological processes in the watershed. In this paper, the hydrological models of the Dapoling watershed were built, the runoff and sediment yield from 2006 to 2011 were simulated, and the relationship between landscape patterns and water-sediment yield was analyzed. The results show that the SWAT-VRR model is more accurate and reasonable in describing runoff and sediment yield than the SWAT model. The sub-basins whose soil erosion is relatively light are mostly concentrated in the middle reaches with a slope mainly between 0–5°. The NP, PD, ED, SPIIT, SHEI, and SHDI of the watershed increased slightly, and the COHESION, AI, CONTAG, and LPI showed a certain decrease. The landscape pattern is further fragmented, with the degree of landscape heterogeneity increasing and the connection reducing. The runoff, sediment yield and surface runoff are all extremely significantly negatively correlated with forest, which implies that for more complicated patch shapes of forest which have longer boundaries connecting with the patches of other landscape types, the water and sediment processes are regulated more effectively. Therefore, it can be more productive to carry out research on the optimization of water and soil resources under the constraint of carbon emission based on the SWAT-VRR model.

Accurate determination of soil water contents (SWC) is crucially important in many scientific fields, particularly in agriculture. In this study, the performance of conventional FDR MI2X and New WiTu Technology water sensors was statistically evaluated under the impact of changing vegetative cover type (corn and weeds). A three replicated randomized complete block design (RCBD) field experiment was conducted from late April to August, 2019. The spring corn Nongda-1505 variety was planted and the field was left unsown to allow weeds to grow. To determine the SWC, six WiTu Technology sensors were installed in the middle of each plot. For FDR sensor measurements, PVC tubes were inserted at 30 cm distance from WiTu sensors and soil samples from the 0-20 cm, 20-40 cm and 40-60 cm depths were taken using soil sampling auger for measurement of gravimetric water content. Both FDR and new Technology of WiTu sensors showed varied performance as soil depth increased from the top layer to deep layer. FDR efficiency was significantly high at the topsoil layer (0-20 cm) with an average accuracy level of 98.01% and reduced in the middle soil layer to 91.78%, then increased to 95.13% in the deep soil layer. While, the average accuracy of the WiTu Technology sensor ranged 98.24%, 95.85% and 92.02% for top, middle and deep soil layers, respectively, these results demonstrate that the soil water detection ability of WiTu Technology is high at the soil depth of 0-40 cm, whereas FDR's ability to detect SWC changes is satisfactory in top to deep layers and low in the middle layer. Furthermore, the FDR showed good consistency with WiTu in all soil layers of the corn field, but there was a larger error for the weed field, which indicated a huge difference in the data measured by FDR and WiTu Technology. The SWC estimation of FDR and WiTu technology more fluctuated with changing vegetation cover type. Thus, the findings of this study provide an opportunity to further study on FDR MI2X and New WiTu Technology sensors for deep-rooted crops under the soil and climatic conditions of Zhengzhou, China.