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【目的】探究暗管排水对鄂尔多斯市达拉特旗重度盐碱地土壤盐分运移的影响机制。【方法】按照暗管间距18 m、吸水管埋深1.2~1.5 m、集水管埋深1.8~2.0 m的参数，铺设暗管进行田间小区试验，利用空间插值、线性回归、主成分分析等统计方法，对2019年5—10月暗管排水条件下1 m土层的土壤含盐量、地下水埋深、地下水矿化度、灌排水水质、盐分离子等数据及其相互关系进行分析。【结果】①试验区1 m土层的盐分空间分布属于中等变异（25%~75%），土壤盐分类型为表聚型。②铺设暗管使地下水埋深增加了50~60 cm，试验结束时土壤盐分较试验开始时土壤盐分平均降低10%左右。③暗管铺设条件下，土壤盐分的主导离子为K++Na+、SO42-和Cl-，地下水中主导离子为K++Na+、Cl-和HCO3-。④暗管铺设下黄河水灌溉后，土壤中HCO3-量增加56%，而其他盐离子量降低，SO42-、K++Na+、Cl-降幅较大分别为36%、34%、31%；灌水淋洗后，排水、地下水中各离子量均增加，排水矿化度增加幅度是地下水矿化度增加幅度的3.43倍。【结论】重度盐碱地铺设暗管，能控制地下水埋深，并降低土壤含盐量，有效促进土壤中SO42-、K++Na+、Cl-的淋洗排出，但同时要注意黄河水灌溉中HCO3-可能引起的土壤碱化问题。

Nitrous oxide (N₂O) produced from dissolved nitrogen (N) compounds in agricultural runoff water must be accounted for when reporting N₂O budgets from agricultural industries. Constructed ('artificial') water bodies within the farm landscape are the first aquatic systems that receive field N losses, yet emission accounting for these systems remains under-represented in Intergovernmental Panel on Climate Change (IPCC) emission factor (EF) guidelines and global N₂O budgets. Here, we examine the role of artificial waters as indirect sources of agricultural N₂O emissions, identify research gaps, and explore the challenge of predicting these emissions using default EFs. Data from 52 studies reporting dissolved N₂O, nitrate (NO₃), and EFs were synthesised from the literature and classified into four water groups; subsurface drains, surface drains, irrigation canals, and farm dams. N₂O concentration varied significantly between artificial waters while NO₃ did not, suggesting functional differences in the way artificial waters respond to anthropogenic N loading. EFs for the N₂O-NO₃-N concentration ratio were highly skewed and varied up to three orders of magnitude, ranged 0.005%-2.6%, 0.02%-4.4%, 0.03%-1.33%, and 0.04%-0.46% in subsurface drains, surface drains, irrigation canals, and farm dams, respectively. N₂O displayed a non-linear relationship with NO₃, where EF decreased exponentially with increasing NO₃, demonstrating the inappropriateness of the stationary EF model. We show that the current IPCC EF model tends to overestimate N₂O production in response to NO₃ loading across most artificial waters, particularly for farm dams. Given their widespread existence, there is a need to: (a) constrain their global abundance and distribution; (b) include artificial waters in the global N₂O budget, and (c) expand the study of N processing in artificial waters across a geographically diverse area to develop our biogeochemical understanding to the level that has been achieved for rivers and lakes.

Purpose While well-designed drainage systems could improve crop growth and yield by mitigating waterlogging and salinity stresses, field evidence of the yield responses to changes in plant-water relations and ion concentrations in leaves is scarce. We investigated the changes in ion concentrations in leaves and plant-water relations of sunflower caused by drainage in waterlogged saline soil, and their relationships to growth and yield. Methods Over two growing seasons, we tested four drainage treatments: undrained, surface drains (SD; 0.1 m deep, 1.8 m apart), subsoil drains (SSD; 0.5 m deep, 4.5 m apart) and SSD + SD. All plots were inundated (2–3 cm depth; water salinity, EC w , 1.5–2.5 dS m –1 ) for 24 h at vegetative emergence and at the 8-leaf stage before opening drains. Results Relative to the most drained treatment (SSD + SD), the undrained treatment caused higher waterlogging at 0–30 cm depth, and decreased solute potential (Ψ s ) of soil at 7.5 cm to 52–374 kPa, leaf K + by 5–20%, stomatal conductance by 5–37% and leaf greenness by 12–25%, but increased leaf Na + by 25–70%, Na + /K + ratio by 38–100% and leaf water potential by 90–250 kPa throughout the cropping season; these changes were closely related to reduced growth and yield. Conclusions The improved yield from the combination of shallow surface and sub-surface drains was attributed to an alleviation of salinity-waterlogging stress early in the season and to increased soil water late in the season that increased Ψ s and decreased Na + /K + ratio in leaves.

Waterlogging and salinity can occur together in salinised landscapes and restrict crop production. Drainage can alleviate waterlogging and salinity, but previous research suggested the need for deep drains, which may not be acceptable to smallholder farmers. Consequently, for the first time to our knowledge, we tested the usefulness of shallow drains in sunflower cultivation for smallholder farmers in a salt-affected, waterlogged coastal clay soil in the Ganges Delta. Experimental treatments were as follows: undrained, open surface drains (SD; 0.1 m deep, 1.8 m apart), slotted-pipe subsoil drains (SSD; 0.5 m deep, 4.5 m apart) and SSD+SD. All plots were inundated (2–3 cm above the soil surface) for 24 h before opening drains, at vegetative emergence and then at the V8 stage of plants. Relative to the most-drained (SSD+SD) treatment, the SD and SSD treatments gave 15–29% less yield, while the undrained treatment depressed yield by 48%. Soil water content (SWC) at 0–60 cm depth early in the season was 6–21, 4–10 and 3–5% less in SSD+SD treatment than in the undrained, SD and SSD treatments, respectively, while from flowering to harvest, SWC in SSD+SD was 2–4, 4–8 and 4–10% higher than in the undrained, SD and SSD treatments, respectively. In addition, soil electrical conductivity EC 1:5 at 0–60 cm depth in SSD+SD treatment was 31–52, 16–38 and 11–32% lower than in the undrained, SD and SSD treatments, respectively. Across all treatments, the increases in yield due to drainage were associated with decreases in waterlogging (in the 0–30 cm layer) early in the growing season, increases in SWC late in the growing season and decreases in EC 1:5 throughout the cropping season. While the shallow surface drains alone increased the yield, additional shallow subsoil drains further increased crop yield on coastal saline soils.

Artificial neural networks are a tool for modeling of nonlinear systems in various engineering fields. These networks are effective tools for modeling the nonlinear systems. Each artificial neural network includes an input layer, an output layer between which there are one or some hidden layers. In each layer, there are one or several processing elements or neurons. The neurons of the input layer are independent variables of the understudy issue, and the neurons of the output layer are its dependent variables. Artificial neural system, through exerting weight on inputs and by suing an activation function attempts to achieve a desirable output. In this research, in order to calculate the drain spacing in an unsteady state in a region situated in the north east of Ahwaz, Iran with different soil properties and drain spacing, the artificial neural networks have been used. The neurons in the input layer were: Specific yield, hydraulic conductivity, depth of the impermeable layer, height of the water table in the middle of the interval between the drains in two-time steps. The neurons in output layer were drain spacing. The network designed in this research was included a hidden layer with four neurons. The distance of drains computed via this method had a good agreement with real values and had a high precision in compare with other methods.

In semi-arid irrigated environments, agricultural drainage is at the heart of three agro-environmental issues: it is an indicator of water productivity, it is the main control to prevent soil salinization and waterlogging problems, and it is related to the health of downstream ecosystems. Crop water balance models combined with subsurface models can estimate drainage quantities and dynamics at various spatial scales. However, such models' precision (capacity of a model to fit the observed drainage using site-specific calibration) and accuracy (capacity of a model to approximate observed drainage using default input parameters) have not yet been assessed in irrigated areas. To fill the gap, this study evaluates four parsimonious drainage models based on the combination of two surface models (RU and SAMIR) and two subsurface models (Reservoir and SIDRA) with varying complexity levels: RU-Reservoir, RU-SIDRA, SAMIR-Reservoir, and SAMIR-SIDRA. All models were applied over two sub-basins of the Algerri–Balaguer irrigation district, northeastern Spain, equipped with surface and subsurface drains driving the drained water to general outlets where the discharge is continuously monitored. Results show that RU-Reservoir is the most precise (average KGE (Q0.5) of 0.87), followed by SAMIR-Reservoir (average KGE (Q0.5) of 0.79). However, SAMIR-Reservoir is the most accurate model for providing rough drainage estimates using the default input parameters provided in the literature.

【目的】研究不同断面结构的暗管在非饱和土壤中的排水排盐效果。【方法】选用了4种断面结构的暗管在室内进行土柱滴灌排水试验；其中，T1为底部不透水的圆形暗管，T2为底部不透水的等边三角形暗管，T3为底部带不透水翼的圆形暗管，T4为底部带不透水翼的等边三角形暗管；各暗管均由金属丝网构成，外裹无聚酯长丝针刺无纺土工布作滤层。供试土壤为砂土，每个土柱灌水7 L，每个处理设置5个重复。利用MATLAB平台对4种暗管周围的非饱和土壤水分运动进行了模拟。【结果】T2处理的拦截面宽度大于T1处理，其对水分的吸持能力是T1处理的2倍；且对土壤水分绕流现象的抑制作用比T1处理的性能略好。T2处理的暗管出水时间比T1处理的提早7.45 h；对暗管增加底翼后，可增强其抑制土壤水绕流的能力，提高其排水排盐效果；其中，T3、T4处理的暗管底部50 cm处的土壤含水率分别为17.02%±0.37%和16.62%±0.77%，均小于T1、T2处理同位置处的土壤含水率；T3、T4处理的排水量分别比T1、T2处理的值增加119.8 mL和119.7 mL，排盐量增加16.76 g和18.83 g；T3、T4处理的暗管出水时间分别比T1、T2处理的出水时间提前9.79 h和3.47 h。通过数值模拟进一步验证了T2处理可以抑制绕流；暗管增加底翼后，可进一步提高其抑制绕流的能力。【结论】在非饱和土壤中，三角形断面暗管抑制土壤含水率绕流的作用好于圆形断面暗管的同类能力；暗管增加底翼后，可以进一步提高其对绕流现象的抑制作用，提高其排水排盐能力。

Soil salinization poses a significant challenge to agricultural productivity worldwide, particularly in the rice-wheat belt of the western Indo-Gangetic Plains (IGP), where excess sodium salts (sodicity) degrade soil health and threaten crop production. The cut-soiler, a farm machine developed in Japan, is an innovative and cost-effective solution. The cut-soiler constructs residue-filled, shallow subsurface drains, enhancing lateral and vertical water movement through the soil and improving soil conditions and crop productivity. Unlike previous studies confined to semi-controlled experimental settings, this research uniquely evaluates the effectiveness of cut-soiler technology on local farms severely affected by salinization, specifically addressing subsurface sodicity and recurrent waterlogging conditions that hinder agricultural profitability. From 2019 to 2023, feasibility trials were conducted in farmers’ fields across Punjab and Haryana, India, where gypsum was applied alongside crop residue to enhance subsurface soil reclamation. The analysis reveals significant improvements in the rice and wheat yields. The findings suggest that the application of a cut-soiler over an area of 20 hectares produces a positive net present value (NPV > 0), a benefit-cost ratio (BCR > 1), and an internal rate of return (IRR > 10%), thus supporting the financial viability of the investment for the reclamation of sodicity-affected regions. In addition, the cut-soiler reduces crop residue burning, contributing to environmental sustainability. The substantial yield increases observed in both experimental and conventional farming settings highlight the potential of cut-soiler technology to transform agricultural practices in salt-affected regions, improve crop productivity, and boost economic returns for local farmers.

This study examines the effectiveness of hydraulic perforated subsurface stormwater drain, tested as a drainage component in a laboratory flume at the REDAC, Universiti Sains Malaysia. The experiment measured Manning’s roughness coefficient (n) along the pipe to analyse its relationship with flow velocity, discharge, and the Froude number under simulated runoff conditions. The analysis focused on a scenario with a partially open gate, a longitudinal slope of 1:500, and a water depth of 15 cm. The results revealed predominantly turbulent flow within the pipe, with increasing discharge and velocity observed at the outlets. Manning’s n values, ranging from 0.016 to 0.018, showed an inverse linear relationship with flow velocity, indicating that greater roughness reduces flow velocity. These findings show the potential for reducing peak flows and improving stormwater management in urban subsurface drainage systems. Incorporating flow characteristics and behaviour during system design can help mitigate flash flood risks effectively.

The development of an integrated theory of subsurface drainage based on hydrology and hydrogeology concepts is presented. The historical context, the main hypothesis derived from the Boussinesq equation and the validation of the model predictions are discussed. Theoretical developments of this equation demonstrate that a single parameter ( σ )—a combination of soil and drainage system properties—is sufficient for predicting the dynamics of subsurface drain flow rates. We also demonstrate that these drain flow rates are a function of the level of water replenishment in the system (classically the water table elevation), of the recharge intensity of the aquifer and of a buffer function related to the swelling or deflation of the water table shape during recharge events. For values of σ > 1 , the buffer role of the water table is negligible. In that case approx. 13% of the water table recharge contributes to the flow rate, which is shown to explain the observed disconnection between water table elevations and peak flow rates at the outlet of classic agricultural drainage systems and to predict these peak flow rates accurately. A modelling approach based on this theory and validated experimentally (SIDRA model) allowed us to test the quality of the peak flow prediction. The SIDRA model also includes a surface runoff module and has been coupled to different modelling tools and used to analyse the impacts of subsurface drainage on water quality. The approach contributed towards the development of tools that helped to connect better the drainage systems to the hydrological functioning of watersheds.