Irrigation and Drainage最新文献

In recent years, traditional sprinkler irrigation scheduling scenarios have no longer been applicable to modern agriculture because of the increase in energy prices and the rapid development of smart agriculture. This paper proposes a new irrigation scheduling scenario in which a sprinkler is used as the minimum optimization unit for sectoring design. The main challenge of the proposed approach lies in obtaining the most energy-efficient sectoring and pump operating frequencies, and the high complexity of the optimization problem requires considerable computational effort. To compare the irrigation performance before and after optimization, seven scheduling scenarios are established to analyse the performance of the unified control method, branch scheduling method and sprinkler scheduling method. Through numerical calculations and experimental verification, it was found that sprinkler scheduling can not only meet the pressure requirements of sprinklers without using pressure-regulating valves but also minimize energy consumption. Compared with optimal branch pipe scheduling, optimal sprinkler scheduling can reduce the sprinkler pressure variance from 792 to 180 kPa² and reduce the irrigation cost by approximately 18%. In addition, by analysing the uniformity coefficient and distribution uniformity under different scenarios, it was found that sprinkler scheduling optimization does not substantially improve irrigation uniformity.

It is necessary to use different planning models, including decision support systems (DSSs), to allocate water resources. For this purpose, in this study, an irrigation decision support system (IDSS) was developed to improve irrigation management in the farming fields of Mahabad Plain located to the south-east of Lake Urmia. Next, the compatibility of the IDSS with the conditions of the Mahabad irrigation and drainage network, water and soil resources, meteorological data and soil moisture (SM) were investigated. The statistical indices of coefficient of determination (R²), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (EF) and Wilmot agreement (d) were used to evaluate the adaptability of the IDSS. The results showed that the IDSS has reasonable compatibility with soil and water resources, crop yield and meteorological data. Irrigation scheduling provided by the IDSS led to a 13.9% reduction in water consumption and a 6.7% increase in crop yield. The IDSS estimated minimum and maximum temperature and sunshine hours to a satisfactory degree and relative humidity with an acceptable degree (NRMSE = 0.72–0.77) compared to regional synoptic station data. The performance of the IDSS in simulating SM is ranked from good to well (NRMSE = 0.75–0.83). The results indicate that the IDSS has a sufficient performance in estimating meteorological and soil moisture data with R² = 0.90, RMSE = 4.65, NRMSE = 0.78, EF = 0.76 and d = 0.80. In addition, the IDSS provides the optimal irrigation schedule by considering the ability to deliver water from the irrigation and drainage network to the third-grade canal and agricultural fields as the upstream condition.

Straw mulch has been widely used to inhibit soil evaporation in semi-arid regions, but little attention has been given to exploring optimal straw mulch thicknesses for suppressing soil evaporation under different meteorological conditions in seasonally frozen soil regions. By combining field observations and numerical modelling, the optimal straw mulch thickness for inhibiting soil evaporation under different meteorological conditions was determined. Field experiments indicated that the cumulative soil evaporation associated with straw mulch thicknesses of 1–3 cm was 40%, 53% and 65% lower than that of bare land during freeze–thaw cycles. Compared with that of bare fields, the cumulative soil evaporation simulated by SHAW (simultaneous heat and water) decreased from 9% to 82% and from 36% to 88% during the 2017–2018 and 2018–2019 periods, respectively, when the straw mulch thickness ranged from 1 to 20 cm. The cumulative soil evaporation tended to stabilize until the straw mulch thickness reached 14.3 cm under weather conditions with low humidity, high wind speed and sunshine and 14.5 cm under weather conditions with high or moderate humidity, low or moderate wind speed and sunshine from 1987 to 2017. The results have implications for reducing nonproductive soil evaporation and improving agricultural water management in seasonally frozen regions.

The suitability of a given water source for irrigation warrants the diagnosis of potential salinity, sodicity and toxicity hazards. Region-specific irrigation water quality standards and guidelines formulated worldwide are too conservative. After a comprehensive assessment of newer insights from re-analysis of the available data and examination of the associated evidence covering diversified conditions of saline water use, existing guidelines are updated to adjust for site-specific conditions. The updated versions for rating both saline and alkali waters include the chemical composition of irrigation waters, soil type (texture, clay mineralogy), salt tolerance rating of crops and rainfall for broader adaptability. As modified by soil texture and rainfall, crop salt tolerance is the decisive factor for fixing upper salinity limits in hyper-arid, arid, semi-arid and semi-humid regions. The customized recommendations include conjunctive use, leaching/rainfall management, irrigation methods, ionic ratios, fertilizers and manures. Guidelines for sodic/alkali waters are based on both crop responses and sodicity-induced water infiltration problems. The amendment requirements stand defined. In view of the increasing water scarcity, the established guidelines are expected to promote saline and alkali water irrigation in arid and semi-arid regions while safeguarding the environment.

Cascade pumping stations (CPS) consume a large amount of energy every year in lifting districts. To obtain a reasonable and feasible operation scheme for CPS, this paper proposes an optimal operation model for CPS based on simulations. A one-dimensional unsteady flow model of open canals was coupled with the optimal operation model of CPS, and an energy-dominated optimization was proposed with the water level of the inlet pool as the coordinated variable. The optimization model was solved by using the catch-up method and the nondominated sorted genetic algorithm II (NSGA-II). The optimal operation method was validated and implemented in the first-stage and second-stage pumping stations of the Zuncun Irrigation Project by lifting water from the Yellow River in Shanxi province. The results showed that the proposed optimization model can reduce the energy consumption of the CPS by 4% compared with the actual operation. In addition, the optimal operation model of the CPS coupled with simulation can realize the dynamic balance of flow by stabilizing the inlet pool level to operate within a safe range. The energy consumption of the CPS can be reduced by keeping the water level of the intake pool as high a level as possible.

Although photovoltaic (PV) irrigation systems are widely used in China, feasibility assessment of these systems is important because of differences in the distribution characteristics of solar resources and crops. In this study, kiwifruit planting in Shaanxi province was considered, and a calculation model for PV irrigation system evaluation was developed. Based on the geographical distribution of kiwifruit planting, as well as the spatial and temporal distributions of solar energy in Shaanxi province, the application potential of PV irrigation for kiwifruit was investigated comprehensively from the perspectives of technology, economy and irrigation feasibility. The results showed that the proportion of the PV module scale to the irrigation scale in all the kiwifruit planting areas in Shaanxi province was far less than 1.5%, and there were no technical obstacles. In Baoji, Weinan, Hanzhong and Ankang, the annual cost of the PV irrigation system was greater than that of the diesel pump irrigation system. Regarding irrigation feasibility, farmlands with a slope of 0%–8.75% were considered highly suitable for installing a PV irrigation system. The results revealed 32,269 ha of farmland appropriate for PV irrigation among the 66,371 ha of kiwifruit in Shaanxi province.

One hundred years ago, irrigation development began in Ethiopia, but the efficiency and sustainability of the developed scheme have remained very low. As a result, this study aimed to assess the factors that most frequently affect a scheme's performance. The study analysed general physical and social performance indicators. Structural and social factors were used to identify the most common causes of underperformance of the schemes in the Omo Gibe river basin. Frequency analysis was conducted using Statistical Package for the Social Sciences (SPSS). The relative operational scheme performance and relative irrigated area were 32 and 31%, respectively. The beneficiary to target performance was 63%. Structural and social factors such as flooding and subsequent damage to the under sluice and lack of participatory approaches during scheme development were the most common determinants of irrigation scheme performance. Hence, catchment development and community participation in scheme development are crucial for mitigating irrigation scheme underperformance.

Evapotranspiration (ET) and actual evapotranspiration (AET) serve as critical parameters in the water vapour exchange between terrestrial surfaces and the atmosphere. ET denotes the theoretical maximum evapotranspiration achievable under ideal conditions, whereas AET represents the actual evapotranspiration observed, factoring in the constraints imposed by available water resources. Precise estimation of AET is imperative for the optimization of water resource management and the advancement of sustainable development initiatives. In recent years, deep learning techniques have been extensively utilized in AET estimation. However, traditional deep learning models often lack the incorporation of essential physical constraints. We proceeded to enhance the loss function of the temporal convolutional network (TCN) by taking into account the physical relationships that exist among soil water content (SWC), potential evapotranspiration (PET) and AET, thereby introducing a novel physically coupled deep learning model (AET, SWC after kernel principal component analysis, PET, TCN and AKP-TCN), and checked the rationality of the model with the FLUXNET 2015 dataset. These findings underscore that the AKP-TCN model exhibits heightened sensitivity to peak fluctuations in AET under the imposition of physical constraints. This approach notably enhances the precision of AET simulations in areas marked by complex and variable climatic conditions, such as the Mediterranean climate zone and Oceania, achieving determination coefficient (R²) values surpassing the threshold of 0.900. Compared to traditional models, which include long short-term memory (LSTM), convolutional neural networks (CNN) and TCN, the AKP-TCN delivers substantial R² improvements of 16%, 16% and 9%, respectively. This advancement offers a novel perspective for coupling deep learning with physical mechanisms.

Small-scale irrigation (SSI) plays a large role in rural livelihoods and the economy in Ethiopia. Despite considerable investment, overall SSI performance is disappointingly poor. The sedimentation of canals and intakes leads to low performance and the abandonment of systems. Livestock roaming in the command area and around riverbanks are an important contributor to sedimentation. Commonly proposed solutions, including technocratic fixes, institutional arrangements between irrigators and livestock farmers, and collective action by irrigators, have not yielded satisfactory results. Based on three case studies from Ethiopia, we illustrate why existing solutions are not effective and why collective action is not straightforward. Using in-depth interviews and focus-group discussions, we examine the complex intertwined relationships between irrigators, livestock farmers and local government. Without understanding the interrelationship and accounting for the links between irrigation and livestock in the design and governance structures of SSI, the proposed technical and organizational fixes are unlikely to be successful. Addressing conflicting interests and building consensus and trust among irrigators and livestock farmers are prerequisites for solving the performance concerns of many Ethiopian SSIs.

Rapid and accurate field evaluation of hydraulic performance is critical for the operation of a microirrigation system. However, the optimal sample size and the specific locations of the emitters selected in one subunit for field tests have not been determined. A model (Hydraulic Analysis of Pressurized Irrigation System,HAPIS) was constructed for hydraulic analysis of a pressurized irrigation system by coupling MATLAB and EPANET. The random sampling method (RSM) and uniform sampling method (USM) were optimized for emitters selected through simulation, aiming to achieve higher estimation accuracies of the mean emitter discharge rate of the subunit ($�\overline{q}�)$ and the Christiansen uniformity coefficient (CU) while minimizing the number of emitters tested. In addition, a linear sampling method at predetermined emitter locations (LSMPE) was developed to simplify the evaluation process using a genetic algorithm (GA). The results indicate that the appropriate sample size range for RSM was 20–40, in which the maximum percentage difference between $�\overline{q}$ and CU was maintained at ±10%. For the USM, a sample size of approximately 18 can provide relatively accurate estimations of $�\overline{q}$ and CU, while it is recommended that the sampled emitters be distributed over three to five laterals. The optimal sample size of LSMPE could be decreased to approximately 10, and the selected emitters were arranged along the sampling line. The absolute relative estimation error of $�\overline{q}$ and CU could be maintained at <1%.