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For comprehending the effect of tidal action on nitrogen cycle in silty-clay riparian hyporheic zones, the synchronous monitoring of water level and water quality was carried out along a test transect during a spring tidal period from 21 to 23 October 2021. Moreover, the permeability and chemical composition of soil samples from drilled holes were measured. Subsequently, the spatiotemporal variation of inorganic nitrogen concentrations in the groundwater in the riparian hyporheic zone was investigated during the study period, and the potential reason was discussed. It is shown that the delayed response time of groundwater level in the silty-clay riparian zone to the tide-driven fluctuation of the river stage increased with distance from the shore and reached 3.0 h at the position 3.83 m away from the shore. The continuous infiltration of the river water under tide action contributed to the aerobic and neutral riparian hyporheic zone conductive to nitrification. Within 4 m away from the bank, the dominant inorganic nitrogen form changed from NO 3 − -N to NH 4 + -N, upon increasing the distance from the bank. Additionally, the removal of nitrogen could occur in the riparian hyporheic zone with aerobic and neutral environment under the conjoint control of nitrification, microbial assimilation, and aerobic denitrification. [ABSTRACT FROM AUTHOR]

• Deep learning and ensemble-based method are coupled for efficient data assimilation. • The residual learning is combined with densely-connected convolutional neural network to construct surrogate for groundwater contaminant transport model. • ILUES is used for joint inversion high-dimensional parameters using RDCNN-based surrogate model. • The influence of main parameters of RDCNN on the prediction accuracy is analyzed. Data assimilation for high-dimensional parameter joint inversion of multiple time-varying source strength and hydraulic conductivity fields can be computationally intensive as a large number of forward model runs are usually required. In this study, a deep neural network-based surrogate is proposed to replace the forward model in the data assimilation process to efficiently achieve high-dimensional parameter inversion. The deep convolutional encoding-decoding network architecture is used to leverage the advantages of the convolutional network in processing image-like data, where the high-dimensional input and output fields of the forward model are expressed as images. The encoding network extracts the main features of the multiple time-varying source input image and high-dimensional hydraulic conductivity fields, and the decoding network refines the extracted features to generate the output hydraulic head and contaminant concentration fields. The Residual Dense Convolutional Neural Network (RDCNN) is proposed by combining residual learning with a densely-connected convolutional neural network to solve the image regression problem for surrogate construction. Iterative local updating ensemble smoother (ILUES) is utilized to assimilate hydraulic head and contaminant concentration data to inverse high-dimensional parameters. The proposed method is evaluated by jointly inversing the high-dimensional hydraulic conductivity and source parameters for a synthetic multiple-source contaminated aquifer. The results indicate that compared to the surrogate constructed by Dense Convolutional Neural Network (DCNN) without addressing residual learning, the RDCNN surrogate captures the model input-output relationship better with relatively high training efficiency. The RDCNN surrogate-coupled ILUES achieves state-of-the-art performance in terms of inversion accuracy of 9 pollution source parameters and 255 hydraulic conductivity field parameters and computational efficiency in comparison to the ILUES based on the forward model. [ABSTRACT FROM AUTHOR]

Summary Mulched drip irrigation for cotton field is an effective measure for the utilization of saline water, and the regulation of soil water and salt. However, the reasonable methods for quantifying actual evapotranspiration (ET) and deep percolation of recharge to groundwater are still not very well understood, which restricts the accurate regulation of soil water and salt for cotton growth in oasis. In this paper, a set of experiments of mulched drip irrigation with brackish water were conducted in a typical arid region of Tarim basin in southern Xinjiang, China. The irrigation events were recorded, and ET and fluctuations of groundwater table were carefully measured for two consecutive irrigation periods of flowering and bolling stages. A group of upscaling conversion methods were used to quantify the ET, in which canopy structure was considered to estimate the transpiration from leaf scale to a unit of field scale. The groundwater table had a significant response to the irrigation events, thus the deep percolation was estimated using water-table fluctuation method (WTF). Results showed that during the two irrigation events of flowering and bolling stages, the total ET was 31.1 mm with the soil surface evaporation of only 0.4 mm. The total percolation of recharge to groundwater was 48.2 mm which contributed to the groundwater run-off of 22.1 mm. Transpiration of 30.7 mm accounted for 98.6% of the total ET of 31.1 mm and 34.3% of the irrigation water of 90.6 mm. Compared with transpiration, the deep percolation accounted for 53.2% of irrigation water, indicating a serious excessive irrigation that recharged to groundwater. Soil salt budget showed that the salt leached into groundwater was 1.56 times of the input from brackish irrigation water and fertilization during the two irrigation periods. Even for the irrigation practice with brackish water, the accumulated salt of soil profile could also be leached out under large amount of irrigation water (e.g. 90.6 mm for the two irrigation periods, 10 days). However, the waste of enormous water which for instance occupied 53.2% of the irrigation water in this study was not conducive to the sustainable utilization of water resources in the arid oasis. Furthermore, the methods introduced in this paper for ET and deep percolation calculation of cotton filed could be used to quantify the oasis hydrologic cycle of micro-irrigation, to gain a better understanding of the ecological process. [ABSTRACT FROM AUTHOR]