Agricultural Water Resources Research Articles

Optimal allocation of soil and water resources in agriculture under the total regional evapotranspiration indicator: A case study in Guangping, China

There is a mismatch between crop water demand, the spatial and temporal distribution of natural rainfall, surface water sources, and arable land resources in the North China Plain. This has been compensated for by massive groundwater exploitation, and as a result, ground water levels have continuously declined and groundwater landing funnels have formed in some areas. Consequently, there is an urgent need for the rational allocation of soil and water resources. In this study, regional soil types and the corresponding crops in Guangping County, Hebei Province, China, alongside the regional annual average total water consumption were used to determine reasonable allocations for crop planting structures, with an aim of maximizing the water utilization efficiency and economic benefits while also achieving a regional groundwater dynamic equilibrium. Daily meteorological data from 1973 to 2021, including precipitation, temperature, sunshine hours, wind speed, and humidity, as well as crop data, including irrigation systems and crop growth status, were collected. Water consumption models for eight soil types and seven typical crops forming 56 calculation partitions were established to obtain annual average water consumption values for the different crops. The maximum economic benefit and water-use efficiency were then used as the objective functions and total water consumption as the control condition for the non-dominated sorted genetic algorithm-Ⅱ (NSGA-II), to optimize the allocation of soil and water resources in the region; the adjusted quantities for the different crops cultivated under different soil types in the study area were obtained. The areas growing sweet potato and vegetable-fruits were found to increase by 0.5255 km2 and 4.4452 km2, respectively, when compared to the pre-optimization period, whereas the areas with winter wheat-summer maize, millet, peanut, cotton, and summer maize decreased by 1.3481 km2, 0.1812 km2, 0.7316 km2, 2.0896 km2, and 0.62 km2, respectively. The economic benefit increased from 810.37 to 853.64 million yuan, while the water-use efficiency increased from 1.5426 kg/m3 to 1.5859 kg/m3. The results provide a basis for soil and water resource management in the region and a technical reference for similar areas to improve agricultural water-use efficiency and economic benefits.

Ripples of change: Assessing the impact of water resource tax reform pilot on the green growth of China's coastal cities

Coastal cities, despite being close to expansive oceans, are still susceptible to water constraints. The water resource tax reform pilot has been instrumental in promoting water conservation and effective utilization, so contributing significantly to the green growth of the coastal cities where it has been implemented. Utilizing data from prefecture-level cities in China's coastal areas from 2007 to 2019 and adopting the Green Total Factor Productivity as a measure of green growth, this study is dedicated to exploring the impact of water resources tax reform on the green growth of coastal cities. It treats the water resource tax reform pilot as a quasi-natural experiment and utilizes multi-period DID and PSM-DID methodologies to examine the effects of this reform on Green Total Factor Productivity. The findings indicate that the water resource tax reform pilot has made a significant contribution to green growth, primarily by reducing water consumption and enhancing governmental focus on marine environmental regulation. Expanding upon these findings, the article provides suggestions in several domains like the establishment of distinct tax rates, allocation of tax money, government oversight, and implementation of taxes on agricultural water resources. These findings can serve as a scientific benchmark for the development and execution of water resource tax policies in other areas.

Gender roles and ethnicity in agricultural water management: a case study of Tinh Bien District, An Giang Province, Vietnam

Women form a crucial part of Vietnam’s agricultural labor force and contribute to national economic development. Still, often the nature of their contribution is inadequately recognized by broad sectors of society. Due to entrenched gender roles and norms, women’s influence over agricultural activity decision-making at the household and institutional levels is limited and poorly acknowledged. This paper explores women’s participation at a household and local level in agricultural water resources management. This research draws upon field studies conducted in Tinh Bien district, An Giang province, Vietnam, a predominantly wetlands environment but with some prominent low hills that present different challenges for water management. The study employed focus group discussion, in-depth interviews, and participant observations to gather data at households and higher levels of governance. The findings highlight that despite being central to socio-economic production across multiple aspects of water wetlands management, women’s voices are overlooked for inclusion within management institutions. It was noted that Khmer minority women tend to be better represented at the household level than Kinh women, but both Khmer and Kinh women’s decision-making power over wetlands and water management at higher institutional levels is still limited.

Spatial Insights into Drought Severity: Multi-Index Assessment in Małopolska, Poland, via Satellite Observations

This study focuses on the assessment of drought severity, employing a comparative analysis between the normalized multi-band drought index (NMDI; calculated using Sentinel-2 imagery) and the combined drought indicator (CDI). The research aims to pinpoint the most accurate and reliable remote sensing techniques, which reflect ground-based measurements, thereby enhancing the precision of drought monitoring systems. By investigating the specific area of Małopolska, the study not only contributes to the global knowledge base on drought assessment methodologies but also addresses the regional needs for improved drought management practices. Through an exhaustive analysis correlating satellite-derived indices with in situ meteorological data, this research elucidates the potential of integrating NMDI and CDI for a comprehensive understanding of drought dynamics in Małopolska. In particular, the indices depict different dynamics of drought levels, as well as the location of regions more prone to its occurrence. The findings are poised to advance drought monitoring capabilities, offering significant insights for agricultural sustainability and water resource management in the region.

GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

Assessing the impact of incentive coordination effect on the equilibrium of agricultural water usage by China's South-to-North Water Diversion Middle Route Project.

In recent years, the sustainable development of agricultural water resources has received much attention. The mismatch between agricultural water distribution patterns, land resources, and socioeconomics threatens food production, especially in vast water-scarce plains. Therefore, monitoring agricultural water spatial equilibrium (AWRSE) is necessary. Based on equilibrium theory and information entropy, in this study, the AWRSE evaluation model is constructed from three aspects: agricultural water resources, land resources, and socioeconomics. In addition, the relationship between social factors with cropping pattern as the primary explanatory variable and AWRSE was examined in conjunction with the extended STIRPAT model and applied to the water-receiving area of the Middle Route of South-to-North Water Diversion Project (MR-SNWDP). The results show that compared with the pre-diversion period, the AWRSE of 75% of the water-receiving cities has been significantly improved by the MR-SNWTP water supply. The MK test z value of the overall regional AWRSE has changed from - 0.328 to - 2.65, and the AWRSE development has shifted from not significantly better to significantly better. The cropping pattern shows a positive response to this development, and this effect can be mitigated in the late stage of water transfer; when the proportion of food crop cultivation increases by 1%, the sub-regional AWRSE value will increase by 0.347%. The evaluation model demonstrates a broad range of inclusiveness and application potential; it provides novel insights for examining agroecological, social, and economic stability.

The influence of technology perceptions on farmers' water-saving irrigation technology adoption behavior in the North China Plain

Abstract The application and promotion of water-saving irrigation technology are of great significance for maintaining food security and the sustainable development of agricultural water resources. Based on the characteristics of integrated agricultural technologies, a binary logistic model was used to analyze the impact of technology perception on farmers' adoption behavior regarding water-saving irrigation technologies using data from surveys of 775 wheat and maize farmers in the North China Plain. The results show the following: the perceived ease of use of technology significantly contributes to farmers' water-saving irrigation technology adoption behavior, but the effect of the perceived usefulness of technology is not significant. Government regulation plays a moderating role in the impact of the perceived ease of use of technology on the adoption of water-saving irrigation technologies by farmers. In addition to the perceived ease of use of technology and technology training, large-scale farmers are influenced by government advocacy and technology subsidies, while smallholders are mainly influenced by the perceived usefulness of technology. Therefore, the focus of future work should be on improving farmers' perceptions of the ease of use of water-saving irrigation technologies, expanding the scope of technical training and technical subsidies, and strengthening government advocacy and education.

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.

Analysis of Crop Irrigation Water Requirements and Water Scarcity Footprint in the Beijing–Tianjin–Hebei Region Based on the GeoSim–AquaCrop Model

To reduce crop-related water consumption and enhance agricultural water resource efficiency in the Beijing–Tianjin–Hebei region, this study employed the AquaCrop model to simulate crop yield and irrigation water requirements and calculated the water scarcity footprint (WSF). The results were as follows: (1) The AquaCrop model exhibited strong applicability, with R2, RMSE (Root Mean Square Error), EF (Nash–Sutcliffe model efficiency coefficient) and d values of 0.9611, 6.6%, 0.91, and 0.98 (winter wheat), and 0.9571, 5.5%, 0.95, and 0.99 (summer maize) for canopy cover simulation. Similarly, aboveground biomass simulation yielded values of 0.9661, 0.8 t/ha, 0.93, and 0.98 (winter wheat), and 0.9087, 1.3 t/ha, 0.90, and 0.98 (summer maize). Winter wheat soil moisture content simulation showed an R2 of 0.9706, RMSE of 3.7 mm, EF of 0.93, and d of 0.98. (2) The AquaCrop model simulated the winter wheat and summer maize yields and irrigation water requirements for the years 2009, 2014, and 2019, validating the scalability and spatial visualization capabilities of GeoSim in extending AquaCrop simulations. (3) Integrating the water footprint and the water resources system, this study assessed the WSFs of winter wheat and summer maize. From 2009 to 2019, winter wheat production in the region increased by 25.08%, and summer maize production increased by 37.39%. The WSF of winter wheat decreased, whereas the WSF of summer maize increased. It is recommended to reduce crop cultivation areas in regions such as Daming County, Ningjin County, and Dingzhou City while further improving irrigation water efficiency, which would facilitate the sustainable utilization of water resources in the area.

Soil Moisture Prediction Model Based on Improved GRU Recurrent Neural Network

Soil moisture plays a crucial role in land water and energy cycles, and has a certain impact on weather and climate change. In agricultural production, crop moisture status can be determined based on soil moisture, and timely and effective irrigation strategies can be formulated to ensure grain yield while saving water resources, maximizing the value of agricultural water resource utilization, and achieving sustainable development. Therefore, the accuracy of soil moisture prediction has important research value for many fields such as agriculture and climate. In this paper, the super parameters of GRU Recurrent neural network are optimized by intelligent seagull optimization algorithm using a small number of influencing factors, namely, atmospheric temperature, atmospheric humidity, rainfall and soil moisture data, and a soil moisture prediction model is established. The model was used to predict soil moisture for the next 12 hours, 24 hours, 36 hours, and 48 hours, respectively. The final experiment showed that the model in this paper had better predictive effect on soil moisture, with the best predictive evaluation index data being MAPE (12h) = 4.4120%, R2 (12h) = 0.94605, and RMSE (12h) = 1.9998; By comparing the prediction results of multiple time steps vertically, it was found that the prediction accuracy of the model in this paper decreased more smoothly, meeting the requirements of soil moisture prediction.

Effects of land use changes on green water along upper and middle reaches of Huaihe River Basin

Study regionThe Upper and Middle Reaches of the Huaihe River Basin Study focusThe examination of the ramifications of alterations in land utilization on terrestrial water resources holds paramount importance in the realm of agricultural water resource management. Despite progress in understanding the cumulative impacts of land use on terrestrial water bodies, the understanding of the exact impacts of a single land use on terrestrial water bodies is still insufficient. In this investigation, the SWAT model was employed to meticulously delve into the ramifications of modifications in land use on the quantity of green water in the middle and upper stretches of the Huaihe River, with a focus on simulating the hydrological cycle. Furthermore, this inquiry introduces a novel coefficient of impact to scrutinize the repercussions of specific land use changes on terrestrial water bodies. New hydrological insights for the regionThe outcomes of the study unveil that albeit the spatial expanse of certain land use categories is restricted, their repercussions on terrestrial water bodies reverberate extensively. Ultimately, the classification of forest land and agricultural acreage underwent refinement, incorporating considerations of vegetation cover and crop typology. The inquiry establishes that the extent of forest land and the genre of crops cultivated on agricultural land stand as pivotal determinants of terrestrial water dynamics.

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca2+, Mg2+, Na+, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.

Editorial on Hydrology and Water Resources in Agriculture and Ecology

The agricultural sector uses the largest share of freshwater, accounting for over 70% of the global freshwater withdrawals, and this proportion can be up to 90% in arid and semiarid regions [...]

Impacts of recent rainfall changes on agricultural productivity and water resources within the Southern Western Ghats of Kerala, India.

Significant changes in rainfall patterns are critical to agriculture, and the dependency of cropping systems on rainfall variability would engender appropriate farming practices and agriculture policies for a climate-resilient agriculture system. This study analyses the significance of rainfall variability on agriculture productivity in the Wayanad district of Kerala (India) using time series data on rainfall (1989-2019) and crop yield (2000-2019). The spatial variability of rainfall patterns reveals a dichotomy between the rain gauge stations in the northern and southern parts of the region. Despite the absence of statistically significant trends in the monthly, seasonal and annual rainfall, based on the Mann-Kendall trend analysis, an increase in the yield of many crops (e.g., winter paddy, banana) is evident, which emphasises the critical role of irrigation in driving the crop productivity. As an adaptation strategy to changing rainfall patterns, irrigation would meet the additional crop water requirement for sustainable agricultural production under the varying rainfall distributions. However, the increase in the area under irrigation in recent years has had significant implications for both surface water and groundwater resources. The conclusive findings suggest that the region requires climate-resilient agriculture, focusing on optimising irrigation and developing sustainable agriculture and water conservation strategies.

Agricultural Water Poverty Index: A Tool for Agricultural Water Resources Management in District Dir Lower, Khyber Pakhtunkhwa, Pakistan

Agricultural Water Poverty Index: A Tool for Agricultural Water Resources Management in District Dir Lower, Khyber Pakhtunkhwa, Pakistan

2009–2016年中国农业大学石羊河实验站玉米日耗水量数据集

A good understanding of the variation in crop water consumption in farming systems is essential for studying the patterns of water consumption by crops at various growth stages. Based on this, this dataset provides the maize evaporation and precipitation intensity data measured by a large-scale lysimeter at Shiyanghe Experimental Station of China Agricultural University from 2009 to 2016, with a temporal resolution of daily scale. The irrigation method is border irrigation. The data acquisition and processing process strictly follows the instrumental data quality control and specifications. After data processing, quality control and evaluation, the data are made public on a daily scale with a view to providing a basis for the efficient use of regional agricultural water resources.

2017–2019年宁夏兰山骄子葡萄园日蒸散发观测数据集

The grapevine is one of important economic crops. Accurate measurements of daily grapevine evapotranspiration (ET) measurements are essential for improving the understanding of crop water demand and optimizing water consumption efficiency. Unfortunately, there is a relative scarcity of daily grapevine ET values in China. This dataset presents daily ET values obtained through using the Bowen ratio energy balance method and the surface renewal method in Lanshan Jiaozi Vineyard on the outskirts of Yinchuan City, spanning the period from 2017 to 2019. Abnormal Bowen ratio values have been removed during the estimation of ET and the quality of each daily ET measurement has been thoroughly assessed. The dataset is freely available, with the primary goal of establishing a foundation for the efficient use of agriculture water resources. Additionally, it is aimed to assess the applicability of the surface renewal method in a highly heterogonous vineyard.

The water-retaining functional slow-release fertilizer modified by carboxymethyl chitosan

To solve the problem of shortage of agricultural water resources and low utilization rate of fertilizer, a slow-release fertilizer based on chitosan modified water retention function was developed. Solution polymerization and semi-interpenetrating network technology were used to load urea aldehyde into carboxymethyl chitosan superabsorbent resin network. This technology realizes the simultaneous slow release of nutrients and water by using modified chitosan, which has important implications for the application of chitosan in agriculture to regulate the soil water and fertilizer conditions. The optimal preparation conditions were: MBA 0.07 %, KPS 0.8 %, AM to AA mass ratio of 0.3:1, CMC content of 10 %, AA neutralization degree 85 %, UF 20 %, AA+AM mass sum of 10 g, reaction temperature 70 °C and reaction time 2 h. The maximum water absorption rate of the optimized NC reached 172.3 g/g. The cumulative release of nitrogen in 30 days was 83.67 %. The application of NC in sandy soil promoted seed germination and growth. The comprehensive results indicate that NC has broad application prospects in arid areas based on its excellent water retention and nutrient release performance.

Optimization of agricultural planting structure in irrigation areas of Heilongjiang province considering the constraints of water resource system resilience

From the perspective of sustainable development in irrigation areas, imperfect constraints, which have negatively impacted previous agricultural planting structure optimization models, tend to produce unreasonable optimization results. Thus, in this paper, attempts are made to incorporate the resilience of water resource systems into the constraint set and explore the optimal layout of agricultural planting structures in irrigation areas. We selected 12 sample irrigation areas in Heilongjiang Province, China, including Chahayang, Fuyu, and Tailai, as the research areas and considered their actual situations. Then, we established a multiobjective optimization model for an agricultural planting structure with rice, soybean, and corn planting areas as decision variables; socioeconomic benefits, agricultural production benefits, ecological benefits, and water resource benefits as objective functions; and water resource system resilience and net crop output value and other factors as constraints. Fifteen scenarios were assessed through the combination of objective functions, and the nondominated sorting genetic algorithm III (NSGA-III) was used to optimize and analyze the agricultural planting structure of typical irrigation areas in Heilongjiang Province and to determine the optimal planting plan. The research results found that the function values adjusted by planting area have significantly improved compared to those before optimization. The adjustment trend of agricultural planting area is to increase the planting area of rice and soybean and reduce the planting area of corn. The introduction of constraints on water resource system resilience ensures that other benefits remain basically unchanged; thus, the water resource objective function is significantly better with resilience constraints than without them. Therefore, water resource system resilience can act as an effective constraint; this finding enriches and expands the theoretical and methodological system for optimizing the management of agricultural planting structures in irrigation areas and thus provides important technological support for ensuring regional food security, ecological security, and water security, which has substantial practical significance.

Time-Lagged Response of Streamflow in the Lower Yellow River to the Water Regulation by Xiaolangdi Reservoir: Implication for Efficient Water Supply

The Lower Yellow River (LYR) is a vital water resource for agriculture, industry, and domestic use in the surrounding areas. Understanding the delayed response of local streamflow response to remote reservoir operations is crucial for effective water management and flood control. In this work, we utilize historical hydrological data and statistical analysis techniques to investigate the time-lagged response of streamflow in the LYR to water regulation by the Xiaolangdi Reservoir. The results demonstrate that there is a time lag of 1.98 days, 2.86 days, and 3.93 days between the record of water regulation at Xiaolangdi Reservoir and the arrival time at Gaocun, Aishan, and Lijin stations, respectively. Time lag correction is proven to be crucial when establishing the relationship between the daily streamflows in the LYR and those at Xiaolangdi station. Further analysis reveals that the travel distance of streamflow is the dominant factor determining the lag time, with a time lag coefficient of 0.57 days per hundred kilometers. It is expected that the findings in this study could offer a fundamental basis for decision-makers in water resource management.