Agricultural Water Resources Research Articles

Automatic Irrigation System Using Wireless Sensor Network

Abstract: The wireless device network consists of a variety of sensors, computers, and wireless communications. These inventions can be used for controlling the crop parameters within the field. WSN is used as part of agriculture for a variety of reasons such as providing high interpretation, increasing crop production, low energy consumption and widely distributed information. good water management plays an important role in agriculture. Lack of water resources and high number of water pumps relieve management in a very important way. Today, the automatic irrigation system (AIS) is being used to improve the use of agricultural water resources to increase productivity. This irrigation system framework allows for development in a variety of waterless areas. In this way, an effective emergence and irrigation system offers the best advantage in rock bottom water

Ruanda'da Su Kaynakları ve Sulama

The main objective of the study is to assess the agricultural water resources for irrigation potential in Rwanda. Rwandan agriculture national reports, irrigation expert documents, and various published articles were used in the preparation of this study. Rwanda is a small, land-locked, and fertile country in East Africa. Rwanda has a total area of 26,338-km2 and a population size of 12.7 million. Agriculture accounts for 24% of the total GDP (Gross Domestic Product) in 2020 and accounted for 64% of the working population. The country has 1.4 million ha of agricultural land, of which 43% (0.6 million ha) is irrigable land. Approximately 10% (63,742 ha) of the irrigable areas in the country are irrigated. Rwandan's average annual precipitation is 1200 mm, and the total amount of usable water is 6.8 billion m3/year. The percentage of the irrigated plots by surface irrigation, sprinkler, and drip irrigation systems with an average of 49.7%, 6.7%, and 2.5%, respectively in 2017 to 2021. In 2021, the agricultural production of cereals, legumes and pulses, tubers and roots, and vegetables-fruits were 491,130 tons, 288,018 tons, 1,826,018, and 184,944 tons, respectively.

Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration

Sub-seasonal forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to generate forecasts with conservative magnitude compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work and code for the ML models are released along with the paper for the benefit of the artificial intelligence community.

The spatially-distributed ANN-optimization approach for water-agriculture-ecology nexus management under uncertainties and risks

water-agricultural-ecology nexus system has spatial heterogeneous characteristics owing to heterogeneities of soil, crop, and weather types. Such complexes aggravate the difficulties in agricultural water, land, and fertilizer resources optimization. To address the above problems, this paper developed the spatially-distributed ANN-multi-objective multi-preference fuzzy credibility constrained programming (distributed ANN-MOMPFCCP) for optimizing water, land, and fertilizer resources. It incorporates the multi-objective programming (MOP), multi-preferences credibility constrained programming (MPFCCP) with the distributed ANN model, and has obvious advantages in improving the calculation efficiency of the traditional spatially-distributed crop simulation-optimization model. Besides, it can address uncertainties of the crop simulation model by introducing stochastic programming. It builds connections between food yields and agricultural ecological effects by introducing negative and positive ecological benefits in study system. Through proposed model, optimal distributed schemes considering spatial heterogeneous net economic and ecological benefit, and associated integrated risks can be generated. Optimal results show that water shortage risk occupied about 20 % of total risks, economic and ecological loss occupied about 20~20.2 % and 20~21 % of total economic and ecological benefits, respectively. Considering uncertainties of crop simulation model was a high effective way to improve robustness of simulation model. The model could offer insight alternatives for regional managers in measuring distributed agricultural-ecological benefit as well as making tradeoff among economic, ecological benefits and risks. Moreover, this approach can help manage multi-type resources under uncertainties, and provide multiple groups of Pareto solutions for managers, so that managers could select best management practices based on respective risk attitudes, and preferences.

Quantitative driving analysis of climate on potential evapotranspiration in Loess Plateau incorporating synergistic effects

Potential evapotranspiration (ET0) is an important part of regional hydrological cycle and energy cycle. In the context of global climate change, it is of great significance to reveal the spatiotemporal variation of ET0 and its response to climate change for further understanding the evolution mechanism of water cycle in changing environment. In this study, the Penman-monteith formula was utilized to calculate ET0 in 6 sub-basins of the Loess Plateau (LP) during 1974–2019 using the daily observed data from 84 meteorological stations. The Mann-Kendall trend test and Kriging interpolation were applied to analyze the temporal and spatial characteristics of ET0 on annual and seasonal scales. In addition, the multivariate adaptive regression splines model, sensitivity analysis and contribution rate were used to identify the driving factors of ET0 variations by considering the synergistic effect of multiple factors. Results showed that (1) The ET0 of LP showed an insignificant increase at a rate of 5.448 mm/10a on the annual scale. At the seasonal scale, ET0 increased in spring and winter, but decreased in summer and autumn. Spatially, the annual average, spring, summer and autumn ET0 showed a trend of higher north–south and lower east–west; (2) The annual ET0 of LP is most sensitive to relative humidity (RH). At the seasonal scale, ET0 was most sensitive to sunshine hours(S) in spring and autumn, average temperature (T) in summer and RH in winter; (3) RH is the main influencing factor of ET0 change in the LP. At the seasonal scale, wind speed (WS) and T dominated the ET0 variation in spring and winter, respectively. S dominated the ET0 variation in summer and autumn; (4) The variation trend and driving factors of ET0 in each sub-catchment were different from those in the LP, and there were significant differences among the sub-catchments. This study provides a reference for the sustainable development of vegetation restoration and the management of agricultural water resources in the LP.

Livelihood of the Folk People in the novel Sool

Culture is a kind of practice and social behaviour of a particular group of people in the society meant for organizing their lives. It can be attained through the four virtues that are moral, material, pleasure and austerity which is connected to the integral and external part of human life. Tamil people have strongly rooted in its classical language, culture and tradition for more than two thousand years and it has been successfully reflected in its literature which is renowned worldwide for having a great impact on other countries (foreigners). This code of culture and tradition is based on the integral and intellectual insight of Tamil people, not depend on their economic values, this kind of values are found in the novel ‘Sool’ written by the Sakhithya Akademi Award (2019) winning writer So.Dharman who has traced out from the life of Urulaikkudi people depicting their agricultural practices and water resource management especially on various forms of their worship, festival, medicine, rituals and conventional beliefs. These are closely connected to their daily routine life and this research paper is briefly focused on the rustic lives, practices of Tamil people described by the writer So. Dharman. Thus, culture is so called identity and inheritance of Tamil people in and around the world.

Agricultural Water Optimal Allocation Using Minimum Cross-Entropy and Entropy-Weight-Based TOPSIS Method in Hetao Irrigation District, Northwest China

Affected by the temporal and spatial changes of natural resources, human activities, and social economic system policies, there are many uncertainties in the development, utilization, and management process of irrigation district agricultural water resources, which will increase the complexity of the use of irrigation district agricultural water resources. Decision makers find it challenging to cope with the complexity of fluctuating water supplies and demands that are critical for water resources’ allocation. In response to these issues, this paper presents an optimization modeling approach for agricultural water allocation at an irrigation district scale, considering the uncertainties of water supply and demand. The minimum cross-entropy method was used to estimate the parameters of hydrologic frequency distribution functions of water supply and demand, which are the basis for agricultural water resources’ optimal allocation and the evaluation of water resources’ carrying capacity in the Hetao Irrigation District. Interval Linear Fractional Programming was used to find water availability, shortage, and use efficiency in different irrigation areas of the Hetao Irrigation District (HID) under different scenarios. The denominator of fractional planning is the environmental goal, and the numerator is the economic goal; so, the objective function of fractional programming is the utility rate required in the post-optimization analysis. Future water availability and shortage scenarios are adopted consistent with the Representative Concentration Pathways’ (RCPs’) framework, and future water use scenarios are developed using the Shared Socioeconomic Pathways’ (SSPs’) framework. Results revealed that under SSP1, the annual water consumption increased from 30 billion m3 to 60 billion m3, almost doubling in Urad. The annual water consumption under SSP2 and SSP3 increased slightly, from 30 billion m3 to about 50 billion m3. The amount of water available for well irrigation in Urad decreased from 300 to 250 billion m3, while the amount of water available for canal irrigation in Urad remained at 270 billion m3 from 2010 s to 2030 s, only dropping to 240 billion m3 in 2040 s. The entropy-weight-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method was applied to evaluate agricultural water resources’ allocation schemes because it can avoid the subjectivity of weight determination and can reflect the dynamic changing trend of irrigation district agricultural water resources’ carrying capacity. The approach is applicable to most regions, such as the Hetao Irrigation District in the Upper Yellow River Basi with limited precipitation, to determine water strategies under the changing environment.

Spatio-Temporal Matching and Nexus of Water–Energy–Food in the Yellow River Basin over the Last Two Decades

There is an important practical significance to understanding and evaluating the nexus of water, energy, and food (WEF) for ensuring regional security. The Yellow River Basin is a typical area displaying the contradiction between water, energy, and food development caused by the difference in resource distribution and production attributes. The nexus of the WEF system in the Yellow River Basin is systematically and innovatively studied from different perspectives. The aim of the research is to analyze the distribution and transfer direction of single resource elements, the matching and evolution of two kinds of resources linked by water resources, and finally the nexus based on Copula function. The results show that water resources, farmland, and energy production are concentrated in different areas, while the center of gravity shifted to the northwest, northeast, and west of the basin, respectively. For the resource balance of energy production and available industrial water resources, the matching degree has become worse in recent years. For the resource balance of farmland areas and available agricultural water resources, the matching degree in the most source area and middle reaches has improved, but the gap among different sub-regions has widened slightly. It is proven that the three-dimensional t Copula is well characterized by the nexus of the WEF system in the Yellow River Basin. The joint not exceeding the probability of the WEF (W ≤ 7.08 × 1010 m3, E ≤ 6.24 × 108 TCE, F ≤ 4.23 × 107 t) is about 0.3–0.4. The WEF system in the Yellow River Basin (Gn = 0.728 and 0.688) may still have certain security risks in the future compared with other regions in the world, which needs to be regulated by more reasonable policies. This study can provide a theoretical basis for ensuring regional water, energy, and food security.

Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer

Reference evapotranspiration (ETo) is one of the most important and influential components in optimizing agricultural water consumption and water resources management. In the present study, an efficient deep learning model, Auto Encode Decoder Bidirectional Long Short-Term Memory (AED-BiLSTM), was applied for the first time in forecasting 1–3 weeks ahead of weekly ETo. Three meteorological stations, including Kermanshah (semi-arid), Nowshahr (very humid), and Yazd (arid), which have different climates, were investigated and evaluated. For this purpose, a statistical period of 20 years (2000 to 2019), of which 15 years (2000–2014) for training and the final five years (2015–2015) for model testing were considered. The generalized regression neural network (GRNN) and extreme gradient boosting (XGBoost) machine learning models were used to compare the performance of the newly developed model. A novel CatBoost-Boruta algorithm was used to determine the critical lags in the forecasting process. The results showed that the newly developed model (AED-BiLSTM) in all three meteorological stations had higher ability and accuracy in forecasting weekly ETo than the GRNN and XGBoost models. The outcomes of testing phase in Kermanshah station ascertained that the AED-BiLSTM in terms of (ETo(t + 1): R (correlation coefficient) = 0.9835, RMSE (root mean square error, mm/week) = 3.4597; ETo(t + 2): R = 0.9805, RMSE = 3.7215; ETo(t + 3): R = 0.9795, RMSE = 3.8857) outperformed the other models. In Nowshahr station AED-BiLSTM model had higher accuracy in terms of (ETo(t + 1): R = 0.9057, RMSE = 4.6786; ETo(t + 2): R = 0.9036, RMSE = 4.7697; ETo(t + 3): R = 0.9024, RMSE = 4.8437). Also, in Yazd station AED-BiLSTM perform better than other models in terms of (ETo(t + 1): R = 0.9827, RMSE = 3.1319; ETo(t + 2): R = 0.9800, RMSE = 3.3998; ETo(t + 3): R = 0.9774, RMSE = 3.5679). The XGBoost and GRNN models performed better after the AED-BiLSTM model, respectively. The effect of climate type on the accuracy of the models showed that the models have higher efficiency and accuracy in arid and semi-arid climates.

Quantifying the Spatial–Temporal Patterns and Influencing Factors of Agricultural Carbon Emissions Based on the Coupling Effect of Water–Land Resources in Arid Inland Regions

Agricultural carbon emissions are supposed to be a fundamental component of the regional carbon cycle in regions with intense agricultural activities. However, the patterns and controls of agricultural carbon emissions based on the water–land resource coupling effect remain poorly understood in arid inland regions. Thus, we constructed a research framework combining water and land resources with agricultural carbon emissions to assess the linkage of water–land–agricultural carbon emissions in arid inland regions. The agricultural carbon emission measurement model and water–land resource coupling coordination degree model (WLCD) were used for exploring the spatial and temporal patterns of agricultural carbon emissions and water–land elements. The interaction between societal , economic , and ecological factors on agricultural carbon emissions was clarified using the logarithmic mean Divisia index (LMDI). The applicability of the method was verified in the Zhangye oasis in northwest China. The results are as follows: 1) from 2012 to 2019, the agricultural carbon emissions in Zhangye increased first and then declined, with 96% of which mainly coming from chemical fertilizers and plowing; 2) the coupling coordination index of water–land resources in Zhangye increased from 0.3715 in 2012 to 0.5399 in 2019, an increase of 45.33%, indicating a conversion from mild disordered to basic coordination; 3) regarding the effects among the factors, the economic output of agricultural water resource, cropland area per capita, and population (the water and land resource ratio and agricultural carbon emission intensity) exerted positive (negative) impacts on agricultural carbon emissions. Accordingly, it is suggested that the green and low-carbon development strategies for oasis agriculture require a high level of integrated planning and management of water and land resources, as well as comprehensive consideration of low-carbon agriculture and elements related to water and land.

Effects on Net Irrigation Water Requirement of Joint Distribution of Precipitation and Reference Evapotranspiration

To establish the uncertain influence that the joint distribution of precipitation and reference evapotranspiration has on net irrigation water requirement, a Copula function–Monte Carlo method (CFMC) was proposed to calculate the probability of irrigation water requirement. Taking the Jingdian Irrigation District in Northwest China as an example, the distribution laws of precipitation and reference evapotranspiration were studied. Furthermore, five typical years under different crop planting structure conditions were selected, and the variation characteristics of net irrigation water requirement in each typical year under the conditions of climate uncertainty were analyzed. The results revealed the optimal distribution functions of precipitation and reference evapotranspiration to be gamma distribution and lognormal distribution. The probability density map of the joint distribution of precipitation and reference evapotranspiration has a “saddle” shape; that is, irrigation water requirement and reference evapotranspiration are usually inversely related. As the probability of the irrigation water requirement increases, the net irrigation water requirement in the irrigation area also increases. The CFMC method can determine the design value of the net irrigation water requirement under a specific probability for typical years under different crop planting structure conditions, which can provide a reference for agricultural water resource allocation in irrigation areas.

Analysis of the regional differences in agricultural water poverty in China: Based on a new agricultural water poverty index

China's agricultural water resource utilization contradiction is prominent, and there are obvious differences in the distribution and utilization of water resources among regions. The theory of agricultural water poverty is of great significance to promote the efficient utilization of agricultural water resources and alleviate the contradiction of agricultural water use. However, the definition of the existing agricultural water poverty theory is quite controversial, and the mainstream agricultural water poverty index (AWPI) for measuring agricultural water poverty has disadvantages such as complex index selection and lack of unified weights. In this regard, this research takes the lead in providing a more complete definition of agricultural water poverty with reference to the definition of water poverty. In terms of method, the research refers to the Social Water Stress/Scarcity Index (SWSI) framework and proposes a new agricultural water poverty index from the two aspects of agricultural water scarcity and agricultural development capability. Based on this index, the regional differences in agricultural water poverty in China are analyzed. The main findings of the study: The agricultural water poverty index proposed in this study has rich connotations, is easy to compare objectively between regions and is applicable in the field of agriculture. The regional differences in agricultural water poverty in China are large, the number of areas with serious agricultural water poverty problems is large, and such problems last for a long time. The distribution of agricultural water poverty in China has spatial autocorrelation rather than a random distribution. China's relative agricultural water poverty index fluctuates around high values, the gap in agricultural water poverty between regions has not narrowed, and the contradiction in relative agricultural water poverty is prominent. The occurrence paths of agricultural water poverty in different regions are different, and the situation in different regions should be identified based on the scarcity of agricultural water resources and the development capability of agricultural production. Finally, this study expects to improve agricultural water poverty theory to effectively alleviate the problem of agricultural water poverty in different regions and promote balanced regional development.

Long-term soil moisture evolution and its driving factors across China’s agroecosystems

Water is a critical resource for sustainable development and stable food production in agroecosystems. China is one of the countries with intensified agricultural activities and extreme water shortages. Climate change is likely to contribute to an increased scarcity of agricultural water resources, which may threaten China’s food security. Therefore, a comprehensive understanding of long-term agricultural soil water variation and its controlling factors at a regional scale is urgently needed. Based on in-situ relative soil moisture (RSM, the ratio of soil water content to field capacity, %) data collected from 238 agricultural stations in China, variations in RSM between 1992 and 2012 and the driving factors were investigated within different climate zones. Overall, the national mean RSM showed a decreasing trend, indicating mild or moderate levels of drought during the study period. The rates and ratios of RSM reduction varied with the soil depth and climate zone. Among the four climate zones, the reduction in RSM was faster and higher in the plateau mountain and temperate continental climate zones; however, there was no significant trend in the temperate humid and subtropical humid climate zones. Both climate and agricultural management activities have made significant contributions to RSM in China’s agroecosystems. In this study, RSM in 58.2% of the selected stations was driven by the combined effects of climate and management activities. For different climate zones, the RSM in the plateau mountain and temperate continental climate zones was mainly controlled by temperature and precipitation, respectively. In the temperate humid climate zone, climate change was the dominant factor controlling RSM. In the subtropical humid climate zone, grain output had a negative effect on the RSM. Our findings provide a theoretical reference for each region to facilitate agricultural water evaluation and agricultural policymaking, and enhance field management for the sustainable use of agricultural water resources.

Estimation of Landsat-like daily evapotranspiration for crop water consumption monitoring using TSEB model and data fusion.

Evapotranspiration (ET) plays an essential role in agricultural water resource management. Understanding regional agricultural water consumption characteristics can be improved by predicting ET using remote sensing. However, due to the lack of high-resolution images on clear-sky days or the limitation of ET reconstruction on cloudy-sky days, it remains challenging to continuously derive ET at the field scale. In this study, the Landsat and MODIS data were initially fused to obtain the Landsat-like vegetation index and land surface temperature on clear-sky days. Then the two-source energy balance (TSEB) model was applied to calculate the daily ET during the clear-sky. A canopy resistance-based gap-filling method was involved in reconstructing regional ET on cloudy days while considering different environmental factors. The estimations were validated by automatic weather system data (AWS) and eddy covariance (EC) measurements in Guantao County. The results demonstrated that the proposed scheme performed well in estimating cropland ET, with an RMSE of 0.86 mm·d−1 and an R2 of 0.65, and the NSE and PBias were 0.61 and -0.29%, respectively. The crop water consumption analysis revealed that the daily ET of winter wheat peaked during the maturation stage. Nevertheless, summer maize water consumption peaked in the middle of the growing season in this area. The temperature during the early development stage and the soil moisture in the mid and late growth stages had the greatest impact on the ET of winter wheat. During the entire growing period, soil moisture had the largest effect on the ET of summer maize. The findings showed that the TSEB model can be effectively applied to field-scale water consumption monitoring in North China through MODIS and Landsat data fusion and ET temporal reconstruction considering environmental factors.

Evaluation of Agricultural Water Resources Carrying Capacity and Its Influencing Factors: A Case Study of Townships in the Arid Region of Northwest China

The water resources carrying capacity (WRCC) strongly determines the agricultural development in arid areas. Evaluation of WRCC is important in balancing the availability of water resources with society’s economic and environmental demands. Given the demand for sustainable utilization of agricultural water resources, we combine the water stress index and comprehensive index of WRCC and use multi-source data to evaluate agricultural WRCC and its influencing factors at the township scale. It makes up for the deficiencies of current research, such as the existence of single-index evaluation systems, limited calibration data, and a lack of a sub-watershed (i.e., township) scale. By applying multi-source data, this study expands the spatial scale of WRCC assessment and establishes a multidimensional evaluation framework for the water resources in dryland agriculture. The results indicate water stress index ranges from 0.52 to 1.67, and the comprehensive index of WRCC ranges from 0.25 to 0.70, which are significantly different in different types of irrigation areas and townships. Water quantity and water management are key factors influencing WRCC, the water ecosystem is an area requiring improvement, and the water environment is not a current constraint. Different irrigation areas and different types of townships should implement targeted measures to improve WRCC.

Measurement of Agricultural Water and Land Resource System Vulnerability with Random Forest Model Implied by the Seagull Optimization Algorithm

To evaluate the state of an agricultural development more comprehensively, a vulnerability assessment is introduced into agricultural water and land resources system, and it is expected that the vulnerability assessment can provide a basis for improving system structure and function and realizing sustainable development. In the study, 27 evaluation indicators are selected from the agricultural water and land resources system (AWLRS), socio-economic system and ecological structure system to construct the evaluation index system for agricultural water and land resource system vulnerability (AWLRSV). Seagull optimization algorithm (SOA) is used to calibrate the parameters of the random forest (RF) model. SOA-RF model is applied to measure the AWLRSV of Heilongjiang Province in China. The results show that the SOA-RF model has higher accuracy and stronger stability than the traditional RF model and DA-RF model. The value of AWLRSV in Heilongjiang Province presents a downward–upward–downward trend from 2008 to 2018. The vulnerability levels are mainly level II and III, and level III is mainly distributed northwest and southeast of Heilongjiang Province. The novelty of this paper is to regard the agricultural water and land resources system as a compound system, put forward the vulnerability assessment framework. The findings may provide reference for regional sustainable development from a new research perspective.

Application of Cosmic-Ray Neutron Sensor Method to Calculate Field Water Use Efficiency

Field water use efficiency is an important parameter for evaluating the quality of field irrigation in irrigated areas, which directly affects the country’s food security and water resource allocation. However, most current studies use point-scale soil moisture (SM) or remote sensing water balance models to calculate the field water use coefficient, which cannot avoid errors caused by the spatial heterogeneity of SM and insufficient spatial resolution of remote sensing data. Therefore, in this study, the cosmic-ray neutron sensor (CRNS), Time-Domain Reflectometers (TDR) and Automatic Weather Stations (AWS) were used to monitor the meteorological and hydrological data such as SM, atmospheric pressure, and precipitation in the experimental area of Jinghuiqu Irrigation District for three consecutive years. The scale of the CRNS SM lies between the point and the remote sensing. Based on the CRNS SM, the calculation method for canal head and tail water was used to calculate the field water use efficiency to evaluate the level of agricultural irrigation water use in the experimental irrigation area. The results showed that CRNS could accurately detect the change in SM, and four irrigation events were monitored during the winter wheat growth period from October 2018 to June 2019; the calculation result of field water use efficiency in the experimental area was 0.77. According to the field water use efficiency of the same irrigation area from October 2013 to October 2015 in other studies, the field water use efficiency during the growing period of winter wheat in this area increased from 0.503 to 0.770 in 2013–2019, indicating a significant improvement in the field water use level. In general, this study not only solves the problem of low calculation accuracy of field water use efficiency caused by the mismatch of SM monitoring scales but also explores the application potential of CRNS in agricultural irrigation management and water resource allocation.

Research on the safe utilization of agricultural water resources in the Yellow River Basin

Abstract Considering the current problems related to the utilization of agricultural water resources in the Yellow River Basin, we propose the definition of the safe utilization of agricultural water resources based on the summary and analysis of recent studies on water resources security. Through the analysis of water resources development and utilization in the basin over the years, an evaluation index system for agricultural water resources security in the basin was constructed, and a weighted cooperative game method combination was used to evaluate the agricultural water resources security in the basin using the weighted TOPSIS model. The evaluation results show that the utilization of agricultural water resources in the areas under the jurisdiction of Sichuan and Shandong Provinces in the basin was in a secure or a relatively higher state, whereas Ningxia was in a relatively unsecure state, and the remaining provinces were in a basic secure state. Based on the evaluation results, we have provided the corresponding countermeasures for the safe utilization of agricultural water resources in terms of water-saving irrigation technology, water resources monitoring system, and water resources management system to support the effective implementation of efficient water-saving irrigation of agricultural water resources in the Yellow River Basin.

Analysis of the Social Network of the Governance of the Integrated Management of Agricultural Water Resources in Mazandaran Province

Analysis of the Social Network of the Governance of the Integrated Management of Agricultural Water Resources in Mazandaran Province

Emergy evaluation of positive and negative benefits of agricultural water use based on energy analysis of water cycle

The current assessment of the positive and negative benefits of agricultural water use lacks a depiction of the overall internal energy flow, which lead to the identification and analysis of agricultural water use benefits are not comprehensive enough. Based on the “water supply - water use and consumption - drainage - pollutant carrying” link in the ecological economic system of agricultural water resources, the energy conversion relationship of each link were analyzed, and the main connotation and composition of the positive and negative benefits of agricultural water use were raised, then a set of emergy quantitative method system of the positive and negative benefits of agricultural water use was built. Taking Zhengzhou city as an example, the positive and negative benefits of agricultural water use in 2017 were calculated. The research results show that the negative benefits of agricultural water resources eco-economic system production account for 37.93% of the positive benefits. The main reason for the negative benefits is non-point source pollution caused by TN, TP and COD.Consequently, developing production and social policies to limit the amount of non-point source pollution caused by pollutants in agricultural production is an effective way to increase the efficiency of agricultural water production while reducing the negative benefits generated.