Large Irrigation Districts Research Articles

Estimating distributed autumn irrigation water use in a large irrigation district by combining machine learning with water balance models

Autumn irrigation (AI) after crop harvesting is vital for agricultural production in cold arid regions, typically requiring more water than regular irrigation during crop growth phases. Understanding distributed AI water use (AIWU) is key to optimizing irrigation strategies and refining agro-hydrological simulations. However, field-scale monitoring of irrigation water use is challenging, particularly in large districts, and quantification methods for distributed AIWU have seldom been investigated. Given this, we proposed a remote sensing-based model to estimate AIWU from water balance analysis, with key components derived through machine learning algorithms. Specifically, to address the unique challenge of short water ponding duration during AI, we selected MODIS as the primary data source, supplemented with Sentinel-2 data to enrich field-level details. A Sentinel-2 empirical bathymetry model, informed by in-situ measurements, served as a foundation for constructing a MODIS bathymetry model using random forest. This MODIS bathymetry model was essential for determining water ponding depths, which allowed for accurate estimation of infiltration rates when applied to the MODIS-based water index time series. By integrating these estimates into the water balance framework, we achieved a more precise estimate of AIWU. The proposed model was applied to Hetao Irrigation District (HID), the largest irrigation district in the arid region of China. The estimated steady infiltration rate and canal efficiency align with similar studies. The results indicate that the average AIWU per unit AI area in the HID remains stable during 2010–2020 with substantial spatial variability at the pixel level, providing new insights into the local hydrological cycle. The AIWU of HID has been decreasing by 19 million m3 per year during this period mainly due to the reduction in AI area, with a more prominent trend from 2010 to 2017. Our study deepens insights into the spatial–temporal dynamics of AIWU in arid regions, contributing to better irrigation management.

Long-term responses of the water cycle to climate variability and human activities in a large arid irrigation district with shallow groundwater: Insights from agro-hydrological modeling

Climate variability and human activities are the two major driving forces of changes in the hydrological cycle. However, how these two factors separately influence the water cycle in arid irrigation districts with shallow groundwater tables is still unknown. This study aimed to investigate the long-term changes in the water cycle and then quantify the individual contributions of climate variability and human activities to the changes in the water cycle in a large irrigation district. The Hetao Irrigation District (HID), located in the upper Yellow River Basin in Northwest China, was selected as the case study area, and an agro-hydrological model (SWAT-AG), which has powerful capabilities to model complex agro-hydrological processes, was employed as the simulation tool. The results showed that from 2000 to 2018, evapotranspiration (ET) of growing season in the HID remained relatively stable, but there were significant decreases in evaporation and dramatic increases in transpiration. For the subsurface hydrological processes, deep percolation and capillary rise of growing season both showed a decreasing trend, while groundwater drainage showed an increasing trend. The water exchange between the atmosphere layer and the root zone (i.e., ET, including evaporation and transpiration) was more significantly dominated by climatic conditions. In contrast, the subsurface hydrological processes were mainly affected by human activities, e.g., irrigation management, canal seepage, farmland area and cropping pattern. Climate variability contributed to approximately 60% of the changes in ET, whereas human activities introduced over 70% of the changes in deep percolation, capillary rise, and groundwater drainage, respectively. This study provides valuable insights into the impacts of climate variability and human activities on the changes in the water cycle and enhancements of water management practices in arid irrigation districts with shallow groundwater tables.

Analysis of the Scale Effect and Temporal Stability of Groundwater in a Large Irrigation District in Northwest China

The depth to groundwater table (DGT) and the stability sites of groundwater were closely related parameters in groundwater research. Controlling the DGT and identifying stability sites of DGT were of great significance to prevent soil salinization and improve groundwater monitoring. In this study, using DGT data from the Hetao Irrigation District (HID) from 1991 to 2015, combined with spatial interpolation and coefficient-of-variation methods, this study explored the spatiotemporal variation characteristics and scale-effect problems of DGT from four hierarchical scales: the irrigation district, irrigation subdistrict, main canal, and branch canal. The Spearman correlation coefficient, average relative difference, and standard deviation were also used to further clarify the characteristics of groundwater time stability and its periodic variation rule. The results indicated that the spatiotemporal variation in DGT in the HID, and showed moderate variation characteristics, consistent with scale-effect features, which was deeply influenced by the regional climate and human activities. The DGT in the HID showed different temporal stabilities before and after 2000 caused by the application of Water-saving practices (WSPs). The stability sites were not entirely the same in different years or time periods, but they were all at the moderate DGT level in the HID. The results of this study can provide more insights for improving soil salinization and groundwater monitoring and provide more information for agricultural water-use efficiency and management.

Deep Learning-based Near-real-time Monitoring of Autumn Irrigation Extent at Sub-pixel Scale in a Large Irrigation District

Flood irrigation is widely applied in harvested croplands of arid regions and can be classified as autumn/winter irrigation (AI) depending on the time of application. Due to its high water consumption, real-time monitoring of the AI extent is crucial to improve its scheduling. We proposed a remote sensing-based long short-term memory (LSTM) model for near-real-time monitoring of AI extent at sub-pixel scale. The model loosely coupled MODIS data with Sentinel-2 data to solve the mixed pixel issue of MODIS data, and calibrated Sentinel-2 thresholds for AI identification by a random forest (RF) module to extract large-scale reference data with high temporal frequencies. The variable importance estimated by RF is used as a reference for feature screening in the LSTM model. As Sentinel-2 images are not available daily, LSTM models trained with incomplete sequences were validated using multiple validation approaches. The model was applied to the Hetao Irrigation District, the largest irrigation district in arid region of China, and delivered reasonable performance with coefficient of determination of over 0.82 and mean absolute error of around 10.7%. Classification of irrigation patterns using simulated time series of irrigation area fractions revealed eight different irrigation patterns from 2010 to 2020 in the study region. Results indicate that the maximum fraction of AI area upstream is closely related to the cropland distribution pattern. The AI patterns changed significantly over the 11 years, with a more pronounced reduction in irrigation duration for individual pixels in the downstream area. These changes are associated with two important land policies implemented in many regions of China, land consolidation and land transfer. The proposed model demonstrates the potential for near-real-time monitoring of autumn irrigation extent within large irrigation districts, which can aid in AI scheduling and provide insight into irrigation patterns and practices.

The multi-point hydraulic control method for advanced controller design of the open canal irrigation systems

Abstract Most studies about the automatic control of open canal irrigation systems only focus on the distant downstream water level, which ignores the fact that the offtakes may be located anywhere along the canal. Such a simplified control strategy is likely to result in uncontrollable and inefficient water delivery. Therefore, a multi-point hydraulic control method is proposed, in which a simplified Saint-Venant model is formulated to describe the hydraulic states of multiple controlled points. Then, it is underlined that the controlled points with and without the offtakes may have different control objectives. It is suggested to implement soft constraints to the downstream end when there is no offtake, meaning that moderate water level fluctuations are acceptable. By comparing with the common model predictive control (MPC) controller, where the Integrator Delay model and hard constraint are used for distant downstream water level control, the proposed MPC controller successfully improved the water level control stability before the offtakes and the water supply reliability by 91 and 69.5% under the conventional condition and by 54.9 and 27.1% under the water-deficient condition. Accordingly, the proposed multi-point hydraulic control method shows great potential for the precision irrigation of large irrigation districts.

Early Yield Forecasting of Maize by Combining Remote Sensing Images and Field Data with Logistic Models

Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for maize that was explored by incorporating remote-sensing-derived land surface temperature (LST) and field in-season data into a series of logistic models with only a few parameters. Continuous observation data of maize were utilized to calibrate and validate the corresponding logistic models for regional biomass estimating based on field temperatures (including crop canopy temperature (Tc)) and relative dry/fresh biomass accumulation. The LST maps from MOD11A1 products, which are considered to be matched as Tc in large irrigation districts, were assimilated into the validated models to estimate the biomass accumulation. It was found that the temporal-scale difference between the instantaneous LST and the daily average value of field-measured Tc was eliminated by data normalization method, indicating that the normalized LST could be input directly into the model as an approximation of the normalized Tc. Making one observed biomass in-season as the driving force, the maximum of dry/fresh biomass accumulation (DBA/FBA) at harvest could be estimated. Then, grain yield forecasting could be achieved according to the local harvest index of maize. Silage and grain yields were evaluated reasonably well compared with field observations based on the regional map of LST values obtained in 2017 in Changchun, Jilin Province, China. Here, satisfactory grain and silage yield forecasting was provided by assimilating once measured value of DBA/FBA at the middle growth period (early August) into the model in advance of harvest. Meanwhile, good results were obtained in the application of this approach using field data in 2016 to predict grain yield ahead of harvest in the Jiefangzha sub-irrigation district, Inner Mongolia, China. This study demonstrated that maize yield can be forecasted accurately prior to harvest by assimilating remote-sensing-derived LST and field data into the logistic models at a regional scale considering the spatio-temporal scale extension of ground information and crop dynamic growth in real time.

Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area

The optimized agricultural water schedule and management, especially in the large irrigation district along the river basins, were closely related to the spatial and temporal runoff variations. However, the impacts of climate change and human activities leads to non-linear and non-stationary monthly runoffs. Under this condition, effectively capturing the variation of the runoff time series and improving the accuracy of the prediction model are of vital importance. However, some existing monthly data-driven prediction studies mainly focus on the model structure and calculation load, ignoring the influence of each frequency component of runoff and the fluctuation of runoff series caused by meteorological factors, which may not effectively capture the potential change process. In this paper, a novel method (CEEMD-MPE-EMD-GRU) for the non-stationary monthly runoff prediction was proposed. It fully took advantages of the complementary ensemble empirical mode decomposition (CEEMD), multi-scale permutation entropy (MPE), empirical mode decomposition (EMD) and gated recurrent unit (GRU). The combined model in general is a data-driven model, and compared with the traditional mechanism model, its most notable advantage is that it successfully overcomes the redundant information of the prediction model. In addition, atmospheric input factor analysis is added on the basis of fully decomposing and identifying non-stationary pseudo-components. The hydrological runoff data (1956–2014) obtained from the Manas River locating at Xinjiang, China were used for prediction. The results indicated that the new CEEMD-MPE-EMD-GRU model reached higher accuracy, as its Nash-Sutcliffe efficiency coefficient (0.960) was significantly larger than those of the GRU model (0.813) and the CEEMD-GRU model (0.889). Meanwhile, the root mean square error and the absolute relative error of the CEEMD-MPE-EMD-GRU model decreased to 0.279 and 0.195, respectively. The new runoff prediction model established in this paper would provide more precise evaluation of the monthly runoff prediction and better guidelines for high-efficiency agricultural water scheduling in the irrigation district.

An Adaptive Predictive Control Algorithm for Comprehensive Dendritic Canal Systems

Automatic canal control is essential to improve water utilization efficiency in irrigation districts. Water level error in the model used by the control algorithm significantly impacts performance. The integrator delay model, which has a linear structure, is widely used in multi-input/multioutput control algorithms, including linear-quadratic control and model predictive control. However, the integrator delay model is suboptimal for large irrigation districts; the canals are long, the flows are large, there are many scattered offtakes, and the operating conditions vary. We developed an adaptive predictive control mode for the Qinhan Canal in Ningxia Province, China. We used a segment integrator delay model for this large irrigation district and employed a differential evolution algorithm for online system identification; this minimizes error. We analyzed and controlled for various uncertainties (i.e., flow observation noise, geometric and hydraulic parameter errors, and accidental interference with the automatic control system). Simulations indicated that the water level fitting errors were significantly reduced compared with the original model, and water level regulation was significantly improved, especially in terms of the maximum absolute error. Adaptive predictive control was better than linear-quadratic control and model predictive control approach for water level. Adaptive predictive control coped well with several uncertainties. The main factors affecting water level control were the flow observation and gate opening control accuracy, and the extent of real-time data coverage. These features require careful attention when automating and modernizing irrigation districts.

Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia

Water shortage and soil salinization are the key limiting factors in agricultural production of arid and semi-arid regions. Located in western Inner Mongolia of China, the Hetao Irrigation District (HID) is one of the top three largest irrigation districts in China. Irrigation water overuse and high level of soil salinity have curbed the agricultural productivity, adversely affected farmers’ revenues, and threatened long-term sustainability of irrigated farming in the HID. Nevertheless, opportunities still exist to improve the situation. Irrigation water allocation, salt accumulation and leaching, crop productivity and farming decisions are intrinsically connected and thus require taking a holistic approach to investigate into the interactions among all those factors and devise appropriate technological, management and policy interventions. Towards this goal, we develop an integrated hydro-agro-economic optimization model to reconcile agricultural net revenue, irrigation practices, and environmental sustainability in the HID. Positive Mathematical Programming is used for model calibration to ensure the model can replicate the base year observations of crop acreage, making the model suitable for evaluating alternative scenarios. Scenario analyses are conducted to analyze the effects of water supply reduction, reducing winter irrigation, water-saving irrigation, and crop commodity price change on optimal agricultural water management practices. Results show that water supply reduction without complementary measures increases land fallow, exacerbates soil salinization, and reduces net benefits. Winter irrigation can conserve soil moisture and increase the net salt leaching in the root zone, and a reduction in winter irrigation will incur a benefit loss to the HID. Water-saving irrigation can stabilize planting areas under water shortage but exacerbate soil salinization. Price increase of a cash crop, if it has a large area share, tends to “crowd out” grain crops growing in the same season. These results provide a holistic perspective and useful insights for water management and policy in the HID.

A monthly distributed water and salt balance model in irrigated and non-irrigated lands of arid irrigation district with shallow groundwater table

Water scarcity and soil salinization are two major problems threatening the sustainability of irrigation districts in arid regions with shallow groundwater table. To study water and salt balances in arid irrigation districts, we developed a monthly distributed water and salt balance model for arid irrigation district (DAHMID-S) with shallow groundwater table by integrating models of major hydrological processes and the associated salt transport processes, including irrigation, evapotranspiration, soil water and groundwater and salt balances, drainage of water and salt through ditches, and interior drainage of water and associated dry drainage of salt between irrigated and non-irrigated lands. The model was successfully applied to the Hetao Irrigation District (HID), the largest irrigation district in the arid region of China. Results indicate that evapotranspiration is the dominant water consumption component in the HID and accounts for about 95% of the total irrigation and precipitation, while drainage through ditches accounts for 10.5% of the total irrigation. Only 49.4% of the introduced salt is discharged from the HID through drainage ditches. Consequently, most parts of the HID are in the state of salt accumulation except the northern Wulate sub-irrigation district, and the problem of salt accumulation is more serious in the western and southern HID under more favorable conditions of water use. Dry drainage plays a crucial role in salt balance of both irrigated and non-irrigated lands, which results in desalinization in most irrigated lands while salt accumulation in non-irrigated lands. Appropriate measures should be taken to solve water and salt-related problems to improve the sustainability of the HID. In the western HID, irrigation water diversion should be decreased, and the drainage system should be further improved to decrease the salt accumulation to a lower level. Meanwhile, the irrigation water diversion can be increased in the southeastern HID by planting more corn and wheat. Besides, irrigation management should be improved to reduce the surface water return flow to increase the groundwater drainage capacity, especially in the southwestern HID.

An analysis on influencing factors of irrigation water effective utilization coefficient – A case study of Jiangsu province, China

Abstract The irrigation water effective utilization coefficient (IWEUC) is a critical indication of agricultural water use efficiency. To improve water-saving potential, the inter-annual variation of IWEUC from 2014 to 2021 in Jiangsu Province was analyzed. Taking consideration of natural factors, planting structure, management levels, and water-saving engineering, the primary influencing factors of IWEUC were investigated through principal component analysis. The results revealed that IWEUC in Jiangsu Province was higher than the annual national level and showed an insignificant increasing trend. IWEUC and its trend were negatively correlated with irrigation district size. Water-saving irrigation areas had an extremely significant impact on IWEUC (P < 0.01). The positive load of water-saving engineering investment was rated first. Furthermore, economic and water-saving benefits for different irrigation district scales based on the TOPSIS model were evaluated. Despite restricted government support, the economic gains for large irrigation districts were superior to those for small irrigation districts. In the past decade, agricultural water declined while agricultural water conservation rose. The completion of integrated agricultural water pricing reform, as well as the improvement of water-saving engineering and optimization of management level, had a significant beneficial influence on IWEUC. Jiangsu's IWEUC has been efficiently implemented, and provides guidelines in other regions.

Multi-year mapping of flood autumn irrigation extent and timing in harvested croplands of arid irrigation district

ABSTRACT Flood irrigation after crop harvest, e.g. autumn irrigation (AI), is a common irrigation practice in arid and semi-arid regions like Hetao Irrigation District (HID) in Northwest China to increase soil moisture and leach soil salt. Detailed information about the extent, timing, and amount of AI is imperative for modeling agro-hydrological processes and irrigation management. However, little attention is given to the identification of the above AI factors. There are basically three major difficulties in estimating the annual changes in AI, including a suitable index to identify AI, temporal instability of thresholds, and an effective validation method for irrigation timing. Therefore, this study proposes a simple and effective threshold-based method to extract the extent and timing of AI in the HID using MODIS water indices at a daily timescale. The Multi-Band Water Index (MBWI) time series is first reconstructed using an adaptive weighted Savitzky-Golay filter and then used to identify the AI extent and time. The proposed model has a stronger generalization capability both in time and space due to robust thresholds selected from the Z-score normalized feature variable. The model is validated both at pixels generated by the segmentation of Sentinel-derived MBWI using a threshold-based model and at sampling points from the field survey. Results show that the model performed well with an overall accuracy of more than 90.0% for the irrigation area. The overall accuracies of irrigation timing are 76.4% and 91.7% based on the middle-to-late and whole irrigation periods, respectively. We found a decreasing trend in the AI area and a gradual delay in the starting time of AI in the HID, mainly due to changes in cropping patterns, climate, and irrigation fees. Overall, the model is promising in identifying flood irrigation extent and timing in large irrigation districts and is helpful for irrigation scheduling.

Damage Simulation Analysis of Canal Concrete Lining Plates Based on Temperature-Stress-Water Load Coupling

Forming an important component of water conservation infrastructure, canal concrete linings are often subjected to damage to different degrees under the combined action of many factors (temperature, stress, water load, etc.) during construction or later operation. Here we explore the temperature and stress changes in the lining plate under different temperatures and water loads and determine the most unfavorable position (where the stress is more concentrated or the stress value is the largest) of the whole canal lining plate to provide guidance for the subsequent design, construction, and maintenance of canal linings. This paper takes a large irrigation district canal lining project in Henan Province, China as an example and uses ABAQUS finite element software to simulate the temperature and stress fields of the canal concrete lining plate under the combined actions of temperature, stress and water load. The results show that under both conditions of no water or water load, the temperature distribution is more uniform in the middle area of the canal bottom slab, and the temperature of the sunny side slope is higher than that of the shady side slope. The stress values of the lining plate and the bottom plate at the slope foot of the canal are large. Under the action of water load, the maximum stress of the right slope foot of the canal concrete lining plate reaches 2.38 MPa. Furthermore, the validity of the model is verified by comparing the error values, and parameters such as the elastic modulus and Poisson’s ratio were found to have a large influence on the sensitivity of the model. The results can be used as a reference for further research on canal concrete lining construction quality control.

A monthly distributed agro-hydrological model for irrigation district in arid region with shallow groundwater table

Agro-hydrological processes in arid irrigation districts mainly include precipitation, water diversion, irrigation, drainage, evapotranspiration (ET), and soil water and groundwater flow, which interact with each other and are controlled by complex natural and anthropogenic drivers. To better understand the agro-hydrological processes in arid irrigation districts with shallow groundwater table, we developed a novel monthly distributed agro-hydrological model for irrigation districts (DAHMID) based on the concepts of canal command area (CCA) and sub-drainage command area (SDCA). The DAHMID model is driven by meteorology, irrigation, and evapotranspiration (ET) estimated by remote sensing-based ET model, and considers soil water and groundwater balances in both irrigated and non-irrigated lands and interior drainage between them. The model was applied to Hetao Irrigation District (HID), the largest irrigation district in arid region of China with a total irrigated area of 0.68 million ha. The DAHMID model was calibrated with groundwater table depth measurements in 13 CCAs of HID from 2008 to 2010, and validated from 2012 to 2013. Results depicted that the root mean square errors (RMSEs), normalized RMSEs (NRMSEs), Nash-Sutcliffe efficiency coefficients (NSEs), and coefficients of determination (r2) of groundwater table depth in both irrigated and non-irrigated lands for all CCAs were in the ranges of 0.19–0.34 m, 0.10–0.25, 0.30–0.82, and 0.68–0.91, respectively. The simulation results from 2008 to 2014 indicated that interior drainage from irrigated land to non-irrigated land is an important approach of drainage in HID, which is about 14.3% of total irrigation water diversion and 34.9% more than the drainage through ditches. The interior drainage process is basically similar to irrigation and ditch drainage processes, all reaching their peaks in May and October. ET is the major water consumption in HID, which is about 95% of total irrigation water diversion and precipitation in average. The net capillary rise of irrigated land is significantly less than that of non-irrigated land due to the impact of irrigation infiltration. The DAHMID model has less parameters and requires less inputs, and can be better applied to continuous simulation of agro-hydrological processes in irrigation districts in medium and long periods with satisfactory simulation accuracy.

Application of Budyko framework to irrigation districts in China under various climatic conditions

AbstractBudyko's framework has been widely used to study basin‐scale water balance. In this study, we focus on the extended application of Fu's equation (one formulation of the Budyko‐type curves) to 371 large irrigation districts in China over a period of 2010–2017. Water balance method was used to estimate actual evapotranspiration in the irrigated areas. Considering the contribution of shallow groundwater to , the water availability in the Budyko framework defined as equivalent precipitation for irrigation areas is the sum of irrigation water , precipitation and groundwater evaporation . Results showed that the relationships between evapotranspiration , water availability and energy supply can be accurately described by the Budyko's curves. The optimal values of Budyko parameter ω for each irrigation district was obtained with multi‐annual data using least square method. The Fu's equation performed better in humid and semi‐humid regions than arid and semi‐arid regions. The comparison between and confirmed the relative effect of water availability and energy supply on according to the variation of climatic conditions. Normalized difference vegetation index (NDVI) and soil properties (denoted by the proportion of clay and sand) were selected to develop empirical equation for parameter ω using multiple linear regression analysis method. This study shows that the Budyko framework can be extended to irrigation areas and provides useful information on evapotranspiration to assist in water management in irrigation areas.

A study on water distribution management and water-saving potential in a large scale irrigation district—Case study of Beni Amir irrigation district, Morocco

A study on water distribution management and water-saving potential in a large scale irrigation district—Case study of Beni Amir irrigation district, Morocco

Evaluating the adaptability of an irrigation district to seasonal water availability using a decade of remotely sensed evapotranspiration estimates

Competition for fresh water is increasing in highly populated areas especially those regions located in arid and semi-arid climates. Water uses are under scrutiny to determine the potential for water savings. Particularly, the agricultural sector is identified because it is the largest consumer of water. However, information is limited on the year-to-year variability of agricultural irrigation management, specifically the variability related to seasonal water availability. Remote sensing is a useful input into models to estimate actual evapotranspiration (ETa) for large irrigation districts and provides an archive of historical data. In this paper, a decade of remote sensing data are used applying the METRIC algorithm and linear interpolation to achieve ETa estimates. Inter-annual variations in seasonal ETa are related to snowpack, weather, irrigation diversions and crop data to understand the factors having an impact. It was found that consecutive (second) years of dry or wet events impacted the seasonal ETa, indicating limitations in the buffer capacity of the irrigation district. This study shows that the irrigation district is capable of buffering dry or wet events during the first year, but need to adapt during a consecutive year of extreme dry or wet weather. Additionally, cropping patterns indicate that the crop choices in the irrigation district do not change during extreme events. In contrast, irrigation diversions are influenced by prolonged dry events. The depleted fraction indicated good performance of the irrigation system, with potential for improvements in dry years. This study demonstrates the dynamic consumptive water use of an irrigation district, thereby indicating the importance for considering multiple years of data for an assessment of the agricultural sectors’ water use. Additionally, the impact of seasonal water availability gives insight on the adaptability of an irrigation district and its’ capability to cope with current and future variability in weather conditions and extreme events.

Evaluation and analysis of irrigation water use efficiency based on an extreme learning machine model optimized by the spider monkey optimization algorithm

To promote the efficient use of water resources and the sustainable development of the agricultural economy, the main evaluation index that links economic production and the effective use of water resources, irrigation water use efficiency (IWUE), is scientifically evaluated and analyzed. To obtain accurate evaluation results, an evaluation model for IWUE based on an extreme learning machine (ELM) model optimized by the spider monkey optimization (SMO) algorithm (i.e., the SMO-ELM model) was proposed. The model was applied to the evaluation and analysis of IWUE in 24 large and medium irrigation districts in Heilongjiang Province. On this basis, the main driving factors of IWUE differentiation in the irrigation districts were identified, and adaptive control strategies were explored. In addition, to thoroughly test the performance of the constructed SMO-ELM evaluation model, a comparative analysis was performed for a BP model, an ELM model, an ELM model with crow search algorithm optimization (CSA-ELM) and the SMO-ELM model. The results show that there are regional differences in the IWUE of the irrigation districts and that the IWUE of the districts in the eastern part of the study area is better than that of the districts in the central and western parts. The irrigation districts with relatively high evaluation grades (IV to V) are distributed mostly in the eastern part of the study area, and most of the irrigation districts with low evaluation grades (I to II) are located near the main branch of the Songhuajiang River. Differences in indexes, such as the soak field quota and proportion of rice sowing area, are the most important factors related to variations in IWUE grades among the irrigation districts, and targeted control strategies such as planting structure optimization are proposed. The rationality and superiority of the SMO-ELM model are verified by comparing the coefficient of determination (R2), mean square error (MSE), mean absolute percentage error (MAPE), and accuracy of classification (accuracy) of the BP, ELM, CSA-ELM and SMO-ELM models. The R2, MSE, MAPE, and accuracy of the SMO-ELM model are 0.9999, 4.33 × 10−10, 0.00039, and 99.8%, respectively, which are much better than those of the other models. The results of complexity analysis verify the stability of the IWUE evaluation results for the irrigation districts. This article provides a new model for the evaluation of IWUE and scientific and technological support for ensuring the safety of water resources and maintaining the sustainable green development of agriculture.

Modeling agro-hydrological processes and analyzing water use in a super-large irrigation district (Hetao) of arid upper Yellow River basin

High water consumption in large irrigation districts has resulted in large river water diversion and serious water use conflicts in the Yellow River basin (YRB). The excessive water applied is also a cause of the dual problems of soil salinity and water logging in the upper YRB, which further negatively impacts crop yields. This study is aimed at modeling and analyzing the agro-ecohydrological processes and water use performance in large irrigation districts of the YRB. The Hetao Irrigation District (Hetao), a super-large irrigation district located in the upper YRB, was selected as a typical research area. A watershed hydrological model (SWAT-AG) with enhanced capacity for simulating regional agro-ecohydrological processes was adopted in this study. We simulated the agro-ecohydrological processes in Hetao and analyzed the regional characteristics of depth to groundwater table, soil moisture and salt content, evapotranspiration and crop yield. Water accounting was further carried out to interpret water cycle processes and evaluate water use performance in Hetao. Results indicated that Hetao was characterized by shallow depth to groundwater table, high soil moisture and high evapotranspiration. For farmland, there was a decreasing trend for both soil moisture and evapotranspiration from the upper to lower regions in Hetao, but this decreasing trend was not found for natural land. Further, results showed soil salt content has strong spatial variations with land use type, crops and shallow groundwater environments. Correspondingly, crop yields presented a decreasing trend from the upper to lower regions of Hetao, with large spatial variabilities closely related to the spatial distribution of soil water and salt conditions across Hetao. It was worth noting that the regional water use efficiency was relatively low in Hetao, especially with large proportions of non-beneficial water consumption to total water consumption. Overall, this study provided a thorough understanding of agro-ecohydrological processes and their regional characteristics, and the current situation of irrigation water use issues in the whole Hetao district, and further provided valuable insights in improving water use and management in large irrigation districts of the upper YRB.

Non-structural modification of agricultural water distribution systems in large scale irrigation districts

The present study's primary objective is to investigate non-structural alternatives' effectiveness in providing a reliable agricultural water distribution in large scale irrigation districts. Accordingly, the following activities were conducted to accomplish the objective: i) developing an operation model of non-structural alternatives in MATLAB; ii) developing a simplified hydraulic flow simulation model in MATLAB, capable of being integrated the operation model. iii) operational performance appraisal of the developed model under the normal and water shortages scenarios, iv) spatial analysis of the obtained results with GIS to determine each alternatives' effectiveness. The developed model was tested on a real test case, extensive irrigation districts located in central Iran. Analysis of the obtained results reveals that employing the first to third non-structural alternative improved the mean value of the daily water distribution adequacy index by 6–8%, 10–11%, and 5–7% in the normal scenario, 4–5%, 8–7%, and 3–4% in the water scarcity scenario, compared to the water distribution process in the status quo. Besides, the alternatives lead to improving water distribution equity even under the water scarcity scenarios, where the equity improved by 10, 12, and 8% for the first through third alternatives compared to the status quo. Besides the technical assessment, a comprehensive evaluation concerning the non-structural alternatives' effectiveness was conducted, based on the environmental, agricultural, and social effects of these alternatives, to provide practical results. The latter assessment reveals that the second alternative's implementation is expected to lead to a remarkable 12% annual water extraction reduction from the aquifer, decommissioning about 1694 of the 14,440 authorized tube-wells within the district, and a 14.3% reduction in energy consumption. The results showed acceptable improvement in irrigation water distribution by employing non-structural alternatives under normal operating conditions and little improvement under water scarcity scenarios. It was shown that, given the limited infrastructure available in the irrigation districts in developing countries, improving operation management does not necessarily need massive investments in renovating and modernizing these districts.