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Soil salinization is the major factor affecting the sustainable development of agriculture in the Kaidu River Irrigation District (KRID) in Xinjiang Province, China. To better understand water and salt variation regularity in the KRID, this study established a watershed-scale distributed water and salt transport model for the KRID based on the new Soil and Water Assessment Tool with a salinity module (SWAT-Salt). Its point source salt module and irrigation water salt module were modified in the study to obtain more accurate simulation results. Based on evaluation indices, the simulation results of streamflow, salt loading, and crop yield showed that the modified model can more accurately depict water and salt transport processes in the KRID and has good applicability. According to the simulation results, the average annual amount of water and salt entering Bosten Lake through the drainage canal accounted for 15 % and 51 %, reaching 4.29 × 108 m3 and 57.87 × 104 t respectively. The drainage and salt discharge during winter irrigation accounted for 69 % and 74 %, reaching 2.95 × 108 m3 and 42.47 × 104 t, respectively. However, the regions along Bosten Lake had high groundwater and soil salinity, facing a risk of increased salinization and necessitating land or water management to decrease salinization. In conclusion, the modified SWAT-Salt can be applied in agricultural irrigation areas with more diverse irrigation sources and is a useful tool for investigating and assessing watershed-scale salinization, as well as implementing targeted management strategies to reduce salinization. ● The point source and irrigation water salt module of the SWAT-Salt model was modified. ● The model can be applied in agricultural irrigation areas with more diverse irrigation sources. ● The model can effectively analyze the water-salt process at the watershed scale. ● The regions around Bosten Lake in the KRID are at risk of increased salinization. [ABSTRACT FROM AUTHOR]

A large amount of continuous input data is used to estimate groundwater level (GWL) by using machine learning models. However, data collection is very difficult and costly in undeveloped countries. Therefore, obtaining a general model and using less input data is the key to popularizing the application of machine learning models for estimating groundwater levels. This study evaluated the potential of the kernel-based nonlinear extension of the Arps decline model (KNEA), long short-term memory network (LSTM) and gated recurrent unit (GRU) for accurately estimating GWL in the Hetao Irrigation District in China. All models were developed using monthly records from 143 monitoring wells between 1990 and 2015. Eight input combinations (including the one-month prior GWL, air temperature, global solar radiation, precipitation and amount of irrigation) were applied to explore the possibility of improving model accuracy using less input data. In addition, the general performance of the models was evaluated by cross validation. The results showed that the KNEA model was superior to the LSTM and GRU models for all input combinations using the local application. For cross-district application, the average statistical results indicated that the LSTM (RMSE = 0.45 m and R2 = 0.78) and GRU (RMSE = 0.48 m and R2 = 0.76) models performed better than the KNEA model (RMSE = 0.70 m and R2 = 0.62), and the LSTM model achieved the highest accuracy and stability. For input data, these three models had difficulty obtaining satisfactory monthly GWLs using meteorological and irrigation data without pervious GWL data. Adding meteorological data on the basis of the historical GWL greatly improved the accuracy of the models. Compared with PREC and GSR, adding temperature input had the best improvement. However, adding large-scale average irrigation data did not significantly improve the accuracy of the models. In addition, the LSTM model and input data of the historical GWLs and temperature were recommended in arid and semiarid agricultural areas with limited data. • The ability of the LSTM, GRU, and KNE models was investigated for groundwater-level estimation. • The LSTM models outperformed the GRU and KNEA models in cross-validation. • Eight input combinations were applied to explore appropriate input. • Adding temperature improved the model accuracy on the basis of the pervious GWL. [ABSTRACT FROM AUTHOR]

Drip irrigation under film mulch (DIUFM) and subsurface pipe drainage (SPD) are important measures to cope with water shortages and soil salinization in arid areas of northwest China. To investigate the coordinated operation mode of DIUFM and SPD, a new H2DSWAP model was developed based on coupling the HYDRUS-2D with a Soil–Water–Atmosphere–Plant model (SWAP). In the H2DSWAP model, real-time evapotranspiration, simple root growth, and the interaction between crops and soil water and salt are considered to improve the simulation accuracy. The model was calibrated and validated using parameter estimation and uncertainty analysis software (PEST) using field experiment datasets in 2019 and 2020 respectively. Compared with the original HYDRUS-2D model, the simulation accuracy of the H2DSWAP for soil water (root mean square error (RMSE) = 0.011 cm3·cm−3; mean absolute error (MAE) = 0.008 cm3·cm−3; determinant coefficient (R2) = 0.869) and soil salt (RMSE = 0.296 g·kg−1; MAE = 0.231 g·kg−1; R2 = 0.959) contents has been greatly improved. In addition, the simulation of the leaf area index (LAI) and yield also fitted well with field observations. The calibrated model was used to predict salt transport at depths of 0–100 cm and the change in maize yield under DIUFM. The results indicated that maize yield decreased yearly, and soil salinity increased yearly under DIUFM without SPD. Based on the obtained results, the management strategies of winter irrigation (WIR) under different drip irrigation water amounts, namely 600 mm (S1), 540 mm (S2), 480 mm (S3), and 420 mm (S4), were further investigated. Water productivity (WP) and yield were used as evaluation indices. The WIR was carried out every 4–5 years when the drip irrigation amount of the maize field was S1 and S2 was recommended. However, it was recommended to carry out WIR every 2 and 3 years under S3 and S4, respectively. Overall, the H2DSWAP model can be used as a useful tool to guide the operation mode of drip irrigation under mulch and subsurface pipe drainage in saline soils. • A new coupled H2DSWAP model was developed based on the HYDRUS-2D and SWAP. • The H2DSWAP model was calibrated and validated using experimental observations. • The soil salinity within 0–100 cm increased yearly without leaching measures. • Using the coupled H2DSWAP model to investigate optimized winter irrigation modes. [ABSTRACT FROM AUTHOR]

• A novel nonlinear Arps decline model with salp swarm algorithm (SSA-KNEA) is proposed. • M5 model tree (M5) and the multivariate adaptive regression splines (MARS) was as compared. • The proposed model outperforms other machine learning models in forecasting evaporation. • SSA-KNEA Model with station 51777 was the best general model. Accurate prediction of water surface evaporation (Ep) is important in the fields of both hydrology and irrigation engineering. This study evaluated the potential ability of a new hybrid model based on the salp swarm algorithm (SSA) and the kernel-based nonlinear Arps decline (KNEA) in predicting Ep. Two other common machine learning models, including the M5 model tree (M5) and the multivariate adaptive regression splines (MARS), were also applied in this study for comparison. All models were developed using daily records between 2000 and 2015 from 12 meteorological stations in the arid and semi-arid regions of northwest China. These daily records, including the maximum and minimum temperatures, solar radiation, wind speed and relative humidity, were randomly divided into two parts, with 70% of which used for model calibration and the others applied for validation. Four different parameter input combinations were equipped to explore the possibility of improving model accuracy. Two data application scenarios and five statistical indicators including the root-mean-square-error (RMSE), mean absolute error (MAE), scatter index (SI), d -index and determination coefficient (R2) were used for model evaluation. In the scenario of using local data as inputs for model calibration and validation, the impacts of wind speed and relative humidity on Ep were both greater than that of solar radiation, and SSA-KNEA was consistently superior to MARS or M5 across all the input combinations. In the scenario of using cross-station data, in which models using the best input combination were developed by local data of each station but validated by data from each of the remaining 11 stations, SSA-KNEA models performed better than MARS or M5 models on average. In addition, the SSA-KNEA model established by data from Station 51777 was the most suitable generalized model in this research area. Overall, our findings suggested that the new hybrid algorithm (i.e., SSA-KNEA) has high potential for Ep estimation in the arid and semi-arid regions of China, with local or cross-station data. [ABSTRACT FROM AUTHOR]

Human activities have strongly influenced nitrogen loads; thus, the accurate evaluation of net anthropogenic nitrogen input (NANI) is very important for developing countermeasures to control N pollution. The spatiotemporal distribution and main components of NANI at the city scale in Hubei Province in 2008-2018 were analyzed using the NANI model. Furthermore, the relationships between NANI and socioeconomic factors, namely, the gross industrial output value per unit area (GIOV), gross agricultural output value per unit area (GAOV), grain yield per unit area (GY), fertilizer consumption density (FCD), population density (PD), and cultivated land area per unit area (CLA), were further analyzed. The results show that NANI in Hubei tended to increase from 14,422.66 kg km -2  year -1 in 2008 to 16,779.39 kg km -2  year -1 in 2012 and then fell to 13,415.74 kg km -2  year -1 in 2018. In terms of the spatial distribution, the NANI values in the mid-east region of Hubei, i.e., Xiangyang, Jingmen, Jingzhou, Suizhou, Xiaogan, Wuhan, Ezhou, and Huanggang and counties directly under the jurisdiction of the province, were significantly higher than those in the west, i.e., Shiyan, Yichang, and Enshi autonomous prefecture. The largest 11-year annual NANI, 39,462.03 kg km -2  year -1 , occurred in Ezhou, while Shiyan had the lowest 11-year annual NANI of 6592.32 kg km -2  year -1 . N fertilizer use (N fer ), which accounted for 55.23% of the NANI was the largest N input source, followed by net N import in food and feed (N im ), atmospheric N deposition (N dep ), N fixation (N fix ), and seeding N (N see ). Pearson correlation analysis between the components of NANI and 6 socioeconomic factors revealed FCD as the primary factor responsible for NANI (r = 0.948), followed by GAOV (r = 0.607) and CLA (r = 0.558). The most direct driving factors of N dep , N fer , N see , and N im were GIOV (r = 0.727), FCD (r = 0.966), CLA (r = 0.813), and GAOV (r = 0.746), respectively. All factors had a significant negative impact on N fix . Therefore, the most efficient strategy to decrease NANI is to control the fertilizer application amount and improve agricultural development. Additionally, it is necessary to replace traditional high-polluting industries with ecological industry to reduce industrial pollution. Graphical abstract.