Based on the large amount of agricultural irrigation in Inner Mongolia, the shortage of fresh water resources, the abundance of saline-alkali land resources, and the abundance of brackish water resources that can be developed and utilized, exploring a reasonable brackish water irrigation system is an important direction of water saving. In order to explore the effect of saline water freezing irrigation on soil water, heat and salt changes under different irrigation amounts. Taking the severe saline-alkali land as the research object, a two-year experiment on saline water freezing irrigation was conducted in Dalate Banner, Inner Mongolia. The irrigation amount was 140 mm (T1), 180 mm (T2), 220 mm (T3) and the control group without irrigation treatment (CK). The salinity of irrigation water was about 8~10 g/L, and it was mixed with Yellow River water to maintain an irrigation water salinity of 3~5 g/L. By monitoring the changes of soil moisture content, soil temperature, eight ions and other indicators during the test period, the changes of soil water, heat and salt during saline water freezing irrigation were explored. In the second year, the soil moisture content increased as the irrigation amount increased. The soil heat preservation effect was positively correlated with the irrigation amount. With the increase of continuous freezing irrigation years, the soil desalination rate gradually increased. The water content of 0~20 cm soil layer before sowing was 57%~74%, which was 55%~66.1% higher than that of CK group. Saline water freezing irrigation had a positive effect on soil desalination in spring, and the desalination rate was T2 (73.5%)>T1 (68.05%)>T3(42.1%), and the CK group showed salt accumulation. When the irrigation amount of saline water freezing irrigation is 180 mm, it can ensure the maximum reduction of soil salinity on the basis of good soil moisture content and temperature.

In order to reveal the effect of biochar application on soil moisture content, soil structure and grain filling process of maize, a field experiment was conducted with spring maize as the test crop. Four biochar dosages were set up: BC0(0 t/hm²), BC7.5(7.5 t/hm²), BC15(15 t/hm²) and BC22.5(22.5 t/hm²). The results showed that the application of biochar within a certain range could increase the soil moisture content of 0-120 cm soil layer, but excessive application of biochar would decrease the soil moisture content. With the increase of biochar application, the soil bulk density decreased and the porosity increased. The application of biochar can reduce the solid phase volume fraction of soil and increase the gas phase and liquid phase volume fraction of soil. The generalized soil structure index (GSSI) increases with the increase of carbon application amount, and the soil three-phase structure distance index (STPSD) first decreases and then increases with the increase of carbon application amount, reaching the optimal value in BC7.5 treatment (94.94, 7.92). At the same time, the deviation value R of the soil is the smallest (11.21), and the three-phase comparison is the closest to the ideal state. Compared with BC0 treatment, each carbon treatment extended the time to reach the maximum grouting rate by 1.341~1.783 d, the maximum grouting rate decreased by 1.416~2.168 g/d, the average grouting rate increased by 0.081~0.124 g/d, and the active period of grouting increased by 5.703~7.347 d. Grouting validity increased 1.704~2.266 d; The application of biochar is mainly to improve the grouting quality of spring maize by increasing the grouting rate of each stage, extending the duration of each stage, active period of grouting and effective period of grouting. From the perspective of economic benefits and sustainable agricultural development, 15 t/hm² is a more reasonable and effective biochar application.

Dissolved organic carbon (DOC) is a crucial component of the global carbon cycle, while total nitrogen (TN) concentration serves as a key indicator of nitrogen cycling. The distribution of DOC and TN in coastal drainage ditches significantly affects the quality of coastal agricultural environments. This study systematically analyzed changes in DOC and TN concentrations, along with their influencing factors, in typical drainage ditch systems within the coastal reclamation areas of Jiangsu Province. The results highlighted the distribution patterns of carbon and nitrogen in various levels of drainage ditches. DOC and TN concentrations in the Beibatan drainage system ranged from 3.90~18.36 mg/L and 2.32~7.10 mg/L, respectively, with a clear spatial distribution pattern. The concentration order in different ditch levels was lateral ditch > branch ditch > main ditch. Pollutant concentrations first increased and then decreased from inland to coastal sections. A significant positive correlation between DOC and TN suggested that they share common pollution sources and runoff pathways. Moreover, environmental factors such as temperature (T), dissolved oxygen (DO), and electrical conductivity (EC) greatly influenced the variations in carbon and nitrogen concentrations in water bodies, particularly the salinity changes in brackish water areas, which play a key role in the removal processes of DOC and TN. This study provides a theoretical foundation for controlling agricultural non-point source pollution and protecting the mudflat ecological environment in reclamation areas, and offers insights for enhancing the environmental management of drainage ditch systems.

Ecological flow has been treated as a critical index to ensure the sustainable development of the ecological environment in the basin. In order to scientifically determine the ecological flow of the Dadu River, based on the daily runoff data of Luding hydrology station from 1993 to 2022, eight hydrological methods, including the Tennant method, Low Water Frequency method, and Monthly Guarantee Rate method, among others, were used to calculate the ecological flow of Dadu River. The applicability of different methods in Dadu River was comprehensively evaluated using composite index based on deviation index and standard attainment rate. Results showed that: ① The runoff at the Luding Station on the Dadu River exhibited a slight but statistically insignificant increasing trend. The degree of the hydrological regime was low, and the runoff series demonstrated good consistency. ② The ecological flow evaluation results of various hydrological methods were of varying quality, with the monthly assurance rate method and RVA method having the highest comprehensive index and good hydrological rhythm representation. Therefore, the monthly assurance rate method and RVA method are recommended as the ecological flow calculation methods for the Dadu River at Luding Station. ③ The recommended minimum and maximum ecological flow rates based on the monthly assurance rate method are 167.1 m3/s and 1 942 m3/s, respectively, with an average of 801.8 m3/s; the recommended minimum and maximum ecological flow rates based on the RVA method are 161.9 m3/s and 1 217 m3/s, respectively, with an average of 549.6 m3/s.

This article briefly describes different methods for calculating ecological flow, and asserts that hydrological regimes play a major role in determining the biotic compoisition, which exhibit distinct regional characteristics. Ecological flow calculation methods should be adapted to regional characteristics. This study aims to propose a more suitable ecological flow calculation method for regional hydrological regimes, using the Le'an River in Jiangxi Province as a case study. By analyzing the hydrological regimes of rivers in southern mountainous regions，six commonly used hydrological methods were used to calculate monthly ecological flow. The advantages and disadvantages of the six methods were compared and analyzed, and guarantee level and deviation coefficient were used to determine the appropriate ecological flow of rivers. The study proposed that the ecological flow calculation of rivers in southern mountainous areas should use the 7Q10 method from March to July, and the Texas method should be used in other months. These calculation results better align with the hydrological regimes of rivers in southern mountainous regions.

In order to investigate the adsorption characteristics of heavy metals As(V) and Hg(II) on loess with different particle sizes, this paper takes five different particle sizes of loess as the research object, and studies the effects of reaction time, heavy metal concentration, solid-liquid ratio, pH and other factors on the adsorption characteristics of As(V) and Hg(II) on five different particle sizes of loess. The results showed that the adsorption of As(V) and Hg(II) by loess reached saturation at 480 min and 60 min. The adsorption kinetics process was most consistent with the quasi-second-order kinetic model, and the adsorption process was mainly controlled by chemical adsorption. The smaller the particle size of loess, the better the adsorption effect. The Langmuir model, Freundlich model, and D-R model can all explain the isothermal adsorption of As(V) and Hg(II) by loess well. The Langmiur model calculated that the maximum adsorption capacity of loess for As(V) and Hg(II) was 0.360 and 1.096 mg/g. The calculation results of the D-R model show that the average adsorption free energy of the five types of loess for As(V) and Hg(II) adsorption ranges from 8.01 to 10.11kJ/mol and 7.55~11.17 kJ/mol, and the adsorption mechanism is primarily ion exchange. Appropriately increasing the solid-liquid ratio can significantly increase the adsorption efficiency of loess, with removal rates of As(V) and Hg(II) reaching up to 81.9% and 93.6%. Acidic conditions(pH=3~7) are more favorable for the adsorption of As(V), and the adsorption capacity of Hg(II) reaches the highest value at pH=5.

In order to systematically analyze the water quality status and spatio-temporal distribution characteristics of the main rivers in Sichuan Province, this paper adopts the water quality identification index for water quality evaluation based on the monitoring data of 86 water quality monitoring sections in 2021, compares the differences in the results of the three empowerment methods of the average coefficient method, the coefficient of variation method and the entropy method. This paper also analyzes the spatial characterization of water quality in Minjiang and Tuojiang rivers. Water quality during the year was characterized by the presence of Class IV water sections in June, with pollution indicators of ammonia nitrogen and COD_Mn, and only a few Class III water sections in the remaining months. The WQI of the Minjiang River was 1.48 at the upstream section and 2.15 at the downstream section, while the Tujiang River fluctuated between 2.08 and 2.32. It can be concluded that the province's water quality in Sichuan Province in 2021 was excellent, indicating that the results of water environment management in recent years are remarkable, but there are still sections that exceed the standard during the summer period. The pollutant content of the river will increase significantly after flowing through a large city, and the WQI value of the downstream section will then rise, while the pollutants will decay during the flow process, both of which together affect the spatial distribution of the river water quality.

To investigate precise forecasting methods of short-term reference evapotranspiration (ET ₀) in Hubei, this study compares two approaches: the HS model and the PMF model. Based on ET ₀ calculated by FAO56-PM and historical data from 18 meteorological stations, the HS model was calibrated and validated. Forecasts showed strong alignment with the benchmark, with average r of 0.85 for the HS and 0.84 for the PMF models, and the average MAE of 0.65 mm for the HS and 0.64 mm for the PMF models, and similar RMSE (0.87 mm). The TOPSIS method indicates that the HS model is recommended for ET ₀ forecasting at 11 stations. Overall, the HS model has higher prediction accuracy across all stations in Hubei Province, making it recommended for forecasting ET ₀ to support irrigation predictions and decision-making.

In the field of agricultural water resource management, accurate prediction of crop transpiration is crucial for efficient utilization of irrigation water. The current daily prediction method fails to fully utilize the dynamic changes within the day, which limits the accuracy of the prediction. To address the issue, this study proposes a bidirectional long short term memory network (BiLSTM) based on external attention mechanism (EA), which optimizes model hyperparameters using the Sand Cat Swarm Optimization (SCSO) algorithm for hourly reference crop transpiration prediction. Firstly, the SCSO method was used to optimize the EA-BiLSTM model. The optimized algorithm converged after 70 epochs, and the average coefficient of determination increased to 0.750. Furthermore, five characteristic parameters, historical ET ₀, solar radiation, air temperature, air humidity, and maximum wind speed, were selected from the 10 features. In this paper, the method was verified in the field planting area of the national precision Agriculture research Demonstration base in Changping, Beijing. In the prediction of the next 7 hours, the coefficient of determination was increased from 0.619 to 0.644, achieving better prediction results. Finally, through the interpretability analysis of the model, it was confirmed that historical ET ₀ had the highest contribution to the prediction, reaching 0.043, followed by air humidity and total radiation. The comparison results with DT, Lasso, LMP, CNN and other forecasting methods show that the prediction results using EA -BiLSTM-SCSO have achieved the lowest error values in both MAE and MSE indicators. In terms of coefficient of determination, the EA-BiLSTM-SCSO model achieved an average of 0.722, which was 12.6% higher than the CNN model. The study validated the advantages of deep learning and feature engineering in improving the hourly prediction accuracy of reference crop transpiration. The method is used to estimate the water demand of crops in intelligent irrigation, which can realize the accurate prediction of future irrigation, so as to formulate reasonable irrigation plans, improve the efficiency of irrigation water utilization, and carry out effective irrigation water scheduling.

In order to accurately estimate the ET of winter wheat in the Huaibei Plain, the 2017-2021 meteorological observation data, weighing evapotranspiration meter data and high-precision meteorological station data of Wudougou Experimental Station were selected, and the generalized nonlinear complementary correlation model was constructed to estimate the ET by adopting the principle of generalized complementarity of ET and exploring the applicability of the two αe-annual statistical models based on the aridity coefficient (AI), Liu method and Brutsaert method; and the correlation between ET and meteorological factors during the growing period of winter wheat was also studied. Liu method and Brutsaert method); and correlation analysis between ET and meteorological factors during the growing period of winter wheat, to clarify the degree of ET influencing factors at each fertility stage of wheat; using the stepwise method, we constructed a multivariate linear model of ET and meteorological factors through stepwise regression analysis, and compared it with the generalized nonlinear complementary correlation model, to indentify the ET prediction models of higher accuracy. Results showed that the statistical model using the Brutsaert method to calculate αe based on AI was more accurate than the Liu method. The prediction accuracy of the Liu method at 1 m and 2 m burial depths differed across growth stages, with the order of accuracy being Heading-Maturity > Greening-Jointing > Emergence-Branching > Branch-Overwintering for 1 m burial depth. For the Brutsaert method at 2 m burial depth, accuracy followed the order of Heading-Maturity > Emergence-Branching > Greening-Jointing > Branch-Overwintering. Net radiation and average air temperature during the heading and maturity stages had the strongest influence on evapotranspiration, both promoting ET. The linear regression model of daily ET and meteorological factors during the reproductive period showed higher accuracy compared to the generalized nonlinear complementary correlation model, making it a reliable tool for ET estimation in winter wheat under conditions with limited meteorological data availability.

In order to improve the irrigation water utilisation efficiency of pressed-sand melon in Ningxia, we explored the effects of using non-conventional drip irrigation tapes with drip head spacing of 1.6m on the growth characteristics of mulch-sand melon under different irrigation parameters. Four irrigation quotas of 15 m³/hm² (W1), 30 m³/hm² (W2), 45 m³/hm² (W3), and 60 m³/hm² (W4), and three irrigation frequencies of 4 times (S1), 5 times (S2), and 6 times (S3) were set up, and a conventional drip irrigation tape with a drip head spacing of 0.3 m was used as the control to determine the growth of pressed-sand gourd at different fertility periods and the final yield, quality indexes. The results showed that the length of the vine at the stage of extension, late flowering and fruiting, late fruiting, ripening and extension increased by 14.50% in the T3 treatment compared with the CK; and decreased by 14.50%, 22.88%, 13.91% and 16.72% in the T6, T9 and T12 treatments compared with the CK. The transverse and longitudinal diameter of pressed sand melon increased with the increase of irrigation quota when the number of irrigation was the same. The yield of mulch-sand melon increased with the increase of irrigation quota, and the average single melon weight of pressure sand melon was the largest in T5 treatment, and the irrigation water use efficiency of each treatment was higher than that of CK by 248.73~27.14 kg/m³. The total acid, soluble sugar, vitamin C and soluble solids indexes were the largest under different treatments in T8 treatment, T8 treatment, T5 treatment, and T11 treatment, respectively, which were higher than that of CK by 273.91%, 22.14%, and 273.91%. 273.91%, 22.01%, 76.70% and 25.77%, respectively. Considering from the perspective of quality, the optimal treatment irrigation method is treatment T8 irrigation quota is 60m³/hm², the number of times of irrigation is 5 times, and the irrigation quota is 270 m³/hm². Combining the three indicators of yield, irrigation water use efficiency and quality of pressed sand melon, the experiment concluded that the optimal treatment is T5, that is, the whole life cycle of 5 times of irrigation, in which the seedling irrigation quota is 30 m³/hm², and the remaining 4 times of irrigation quota is 15 m³/hm², which is 15 m³/hm². The combination of 15 m³/hm² and 90 m³/hm² was the optimal irrigation system.

To comprehensively evaluate the performance of different micro-irrigation filters and achieve optimal selection, a fuzzy comprehensive evaluation model for micro-irrigation filters based on the AHP method-entropy weight method is proposed. Eleven evaluation indicators were selected across four dimensions: rated performance, operational performance, water quality filtration performance, and price, and a hierarchical evaluation index system was constructed; the AHP method and entropy weight method were adopted respectively to determine the indexes' weights in the way of subjective and objective combination assignment, and the fuzzy comprehensive evaluation method was applied to obtain the evaluation results of various micro-irrigation filters.Taking four common micro-irrigation single filters and two combination filters as examples, the fuzzy comprehensive evaluation model is used to carry out comprehensive evaluation, and the results show that the evaluation result of the sand filter is "poor", the evaluation result of the swirl filter is "general", and the evaluation result of the screen filter is "general", the evaluation result of the disc filter is "good", the evaluation result of the sand-screen combined filter is "good", and the evaluation result of the swirl-screen combined filter is "good". Based on the fuzzy mathematical comprehensive evaluation model of micro-irrigation filter which combines the AHP and entropy weight method to determine the weights, the performance of the filter is evaluated by considering the four dimensions of the filter comprehensively, and the evaluation results are more objective and reasonable compared with the selection based on experience or the flow rate alone, which can provide a scientific guidance for the selection of micro-irrigation filters in different situations.

The purpose of this study was to explore the effcts of combined irrigation of brackis water and fresh water on the salt leaching in the topsoil of paddy fields, the physiological growth characteristics and quality of rice in the coastal mudflat of the Yellow River Delta. Used ‘Shengxiang 1826’ as experimental material. Set up two irrigation systems for the main area: freshwater irrigation (FI, irrigatied freshwater throughout the entire growth period), brackish water-freshwater combined irrigation (BFI, irrigating freshwater from transplanting to tillering, and supplementing brackish water from tillering to maturity). Set up five fertilization methods for the cracking area: T1 (conventional fertilization, locally customary fertilization of N 300 kg/hm²), T2 (reducing N20%, N240 kg/hm²), T3 (reducing N20%, increasing spike fertilizer K, N240 kg/hm²+K36 kg/hm²), T4 (reducing N20%, increasing tillering fertilizer Ca, N240 kg/hm²+Ca18 kg/hm²), T5 (reducing N20% and increasing K and Ca, N240 kg/hm²+K36 kg/hm²+Ca 18 kg/hm²).The results showed that BFIT5 treatment can significantly prolong the nutritional and reproductive growth periods of rice plants. Ca fertilizer supplementation reduces Na⁺content by 24.35% (P<0.05), increases K⁺content by 20.20%, decreases Na⁺/K⁺, increases catalase activity (CAT) content by 13.06%, increases proline (Pro) content by 20.69%, and increases soluble sugar content by 9.19%, improving plant resistance. BFIT5 treatment increased the effective number of panicles in rice by 30.1%, the total number of grains per panicle by 7.2%, the number of grains per panicle by 38.2%, the seed setting rate by 28.9%, the yield by 38.2%, the taste value of rice by 6%, the disintegration value, the reduction value, and the cooking quality. Starting to irrigate brackish water during the tillering stage, reducing the conventional nitrogen by 20% and increasing the K and Ca fertilizers, which can saves fresh water for rice fields while improving the utilization efficiency of brackish water resources, which has important practical significance.

The current state has not yet released the farmland water conservancy project construction project budget quota standard. In practice, regional project fee standards are primarily borrowed from those of construction, water conservancy, and land industries. This practice has directly caused confusion in the cost management of the national farmland water conservancy industry. this study employs Principal Component Analysis (PCA) to screen and downscale the influencing factors of such projects' labor budget unit prices. Additionally, the Dung Beetle Optimizer (DBO) is utilized to optimize the weights and thresholds of the BP neural network model, thereby constructing a labor budget unit price prediction model based on PCA-DBO-BP. Then, considering the construction characteristics of irrigation engineering for farmland, based on field survey and research data, we employed the relative comparison method in conjunction with three proximity measures: fuzzy proximity, Euclidean proximity, and gray correlation, to determine the comprehensive similarity. Subsequently, grounded in gray fuzzy theory, we establish a cost measurement model for both the engineering measures and overhead costs of irrigation engineering for farmland. An example study was conducted using data samples from Liaoning Province spanning from 2004 to 2023. The results indicated that: ① Compared to linear fitting and BP neural network models, the predicted value of the PCA-DBO-BP model was closest to the actual value. The model evaluation indices, R ², RMSE and MAE, were 0.978, 1.676, and 1.211, respectively, outperforming both the BP and linear fitting models, and achieving optimal values across all metrics. The screening of influencing factors, optimization of algorithms, and comparison of different models demonstrate that the PCA-DBO-BP model for predicting manual budget unit prices exhibits higher prediction accuracy and generalization ability. ② Using the grey fuzzy model, the comprehensive similarity was calculated, with a maximum similarity of 0.853 and a minimum of 0.528. Based on these calculations, the final determination of the engineering measures rate for the irrigation engineering for farmland was set at 3.90%, while the indirect cost rate was determined to be 7.83%. Furthermore, the relative error method was applied to calculate the relative errors, yielding 1.53% for the engineering measures rate and 2.02% for the indirect cost rate, respectively. These results demonstrate that the model is both accurate and reasonable, possessing significant theoretical and practical value. By establishing a comprehensive calculation model for labor budget unit price, measure cost, and overhead cost, this study has not only improved the theoretical system of engineering cost but also provided theoretical support for scientifically determining the productivity level of irrigation engineering for farmland and evaluating the reasonability of the engineering budget fee standard.

In order to improve the rationality and practicality of agricultural irrigation water quotas, based on the limitations of the current quotas (Guangxi 2019 version) , combined with the latest national quotas and relevant regulations，the Guangxi agricultural irrigation water quotas revision has been carried out. The natural conditions, water use conditions, engineering facilities and field measures have been considered to construct the water quotas framework. In the revision, 20 new crop types were added, the quotas zoning was partially adjusted, also, a 75% hydrological year type was appended, and the statistical interface position of water quotas was modified. Through studying the long-term irrigation water use patterns of crops at various irrigation stations and the effective utilization coefficient of irrigation water data, as well as the water use per unit irrigated area in each region, the proportional coefficient of irrigation water quotas for different hydrological years and different regions was proposed. Effective utilization coefficients of irrigation water below designated locations in different zones were proposed. Based on this, advanced and standard values for agricultural irrigation water quotas for 60 major crops were formulated. At the same time, the conversion of the quotas between the specified location and the head of the canal and the field was established, which provides a method for calculating quotas in areas with incomplete measurement and lack of data. The revised water quotas were improved in terms of coverage, rationality, practicality, and progressiveness. Recommendations for further improving the methods for revising water quotas are also provided.

In order to reduce the influence of crops on radar scattering signals, explore the response relationship between radar backscattering coefficients and soil moisture with different polarization methods, and realize precise monitoring of soil moisture in winter wheat farmland, both mini synthetic aperture radar (MiniSAR) polarization data and multispectral data acquired via UAVs were used to estimate soil moisture. The impact of vegetation canopy on microwave signals was reduced by integrating vegetation cover fraction into Water Cloud Model (WCM). The adjusted WCM was subsequently employed to calculate the backscattering coefficients of soil under different polarizations. Subsequently, using the soil backscattering coefficient under different polarizations, polarization differences, polarization ratios, alongside Normalized Difference Vegetation Index (NDVI) values, as input variables, soil moisture is estimated using both a linear regression model and a BP neural network model. The results showed that compared with the linear regression model of soil moisture in winter wheat, the soil moisture inversion model based on BP neural network had higher accuracy, and the prediction effect of the model was analyzed by taking the winter wheat planting area in the central part of Jun County, Hebi City, Henan Province as the experimental area. By comparing all soil moisture regression models, it can be concluded that the model achieves its highest accuracy when using the backscattering coefficients derived from the improved WCM under VV and HH polarizations, along with their polarization difference and ratio, as inputs. This configuration yields a high coefficient of determination (R ²) of 0.767, indicating a strong correlation between predicted and actual soil moisture values. Additionally, the model demonstrates a mean absolute error of 0.013 6 cm3/cm3 and a root mean squared error of 0.017 6 cm3/cm3, highlighting its precision in estimating soil moisture content. This finding shows that the combination of the adjusted WCM and the BP neural network for winter wheat soil moisture inversion demonstrates high inversion accuracy, which can provide a novel approach for accurately monitoring winter wheat soil moisture.