Optimal Allocation Method of Irrigation Water from River and Lake by Considering the Field Water Cycle Process

Hydrochemical variations and driving mechanisms in a large linked river-irrigation-lake system

A linked river-irrigation-lake system exhibits intricate and dynamic hydrochemical variations, closely related to changes in natural conditions and anthropogenic activities. However, little is known about the sources, migration and transformation of hydrochemical composition, and the driving mechanisms, in such systems. In this study, the hydrochemical characteristics and processes in the linked Yellow River-Hetao Irrigation District-Lake Ulansuhai system were studied, based on a comprehensive hydrochemical and stable isotope analysis of water samples collected during spring, summer, and autumn. The results showed that the water bodies in the system were weakly alkaline with a pH range of 8.05-8.49. The concentrations of hydrochemical ions showed an increasing trend in the water flow direction. Total dissolved solids (TDS) were less than 1000 mg/L (freshwater) in the Yellow River and the irrigation canals, and increased to more than 1800 mg/L (saltwater) in the drainage ditches and Lake Ulansuhai. The dominant hydrochemical types varied from SO₄•Cl-Ca•Mg and HCO₃-Ca•Mg types in the Yellow River and the irrigation canals to Cl-Na type in the drainage ditches and Lake Ulansuhai. The ion concentrations in the Yellow River, the irrigation canals, and the drainage ditches were highest during summer, while ion concentrations in Lake Ulansuhai were highest during spring. The hydrochemistry of the Yellow River and the irrigation canals was mainly affected by rock weathering, while evaporation was the principal controlling factor in the drainage ditches and Lake Ulansuhai. Water-rock interactions including the dissolution of evaporites and silicates, the precipitation of carbonates, and cation exchange were the main sources of hydrochemical compositions in this system. Anthropogenic inputs had a low impact on the hydrochemistry. Therefore, greater attention should be paid in future to hydrochemical variations, especially salt ions, in the management of linked river-irrigation-lake system water resources.

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

A GA-BP Neural Network Regression Model for Predicting Soil Moisture in Slope Ecological Protection

In this study, based on a highway project in Zhejiang, China, the meteorological factors and soil moisture of high side slopes were monitored in real time by a meteorological data monitoring system, and the correlation between soil moisture and meteorological factors was investigated using the obtained data of soil moisture and total solar radiation, atmospheric temperature, soil temperature, relative humidity, and wind speed. Based on the correlation and the influence of meteorological factors on soil moisture lag, a back propagation (BP) neural network regression model optimized with genetic algorithm (GA) was proposed for the first time and applied to soil moisture prediction of high side slopes. The results showed that the BP neural network regression model and the GA-BP neural network regression model were used for soil moisture prediction in two cases without and with lags, respectively, and both prediction methods showed a more significant improvement in prediction accuracy considering their lags compared with those without lags; the GA-BP neural network regression model outperformed the BP neural network regression model in terms of accuracy. V-fold cross-validation eliminated the effect of random errors, indicating that the model can be applied to soil moisture prediction for ecological conservation. Using the soil moisture prediction results as the basis for screening ecological slope protection vegetation is of great significance to the safety and reliability of road construction.

Interval-Parameter Two-Stage Stochastic Programming (IPTSP) Model of Ecological Water Replenishment Scheme in the National Nature Reserve for Improved Suitable Habitat for Rare and Endangered Migrant Birds

In this work, an interval-parameter two-stage stochastic programming (IPTSP) model of water resources allocation was established for maximizing the restored habitat area of large, rare, and endangered water birds by adjusting the recommended scheme of water replenishment under different scenarios and constraints. The established model can efficiently deal with the uncertainties, such as the interval parameters and random variables, in the management system of water resources simultaneously. A case study was conducted in the Momoge National Nature Reserve (MNNR) in northeast China to maximize the restored habitat area of large, rare and endangered water birds based on limited water resources. According to the previous studies, a water area with a depth of 0–40 cm is a suitable habitat area in the MNNR for the Siberian crane, oriental stork, and red-crowned crane. The results of the present work show that the habitat area restored by water replenishment schemes under low, medium, and high flood flow scenarios after optimization increased in comparison to 13.36 × 103 ha of the recommended scheme, with an increase of [0.62, 5.23], [1.49, 6.42], and [2.43, 7.17] × 103 ha, respectively (the two numbers within each bracket represent the lower and upper bounds of the restored habitat areas). As a result, the carrying capacity of suitable habitat areas increased by [0.82, 6.88], [1.96, 8.45], and [3.21, 9.43] × 103 birds, correspondingly. The restored wetland area of the project recommendation scheme was 34.23 × 103 ha, and that of the optimal water replenishment schemes was [29.35, 41.01], [31.02, 44.13], and [33.88, 46.04] × 103 ha, respectively under the three flood flow scenarios. The results reveal that the model constructed in this work realizes the optimization and adjustment of the initial scheme to an increased restored wetland and habitat area with an increase in the flow level. Here, the upper bound of the interval value mentioned above is significantly higher than the lower bound value, which indicates that a feasible decision space was provided for decision makers to optimize and adjust the recommended scheme on the basis of the actual situation. The model-optimized schemes significantly improved the utilization of limited water resources. The results of this study can provide valuable theoretical support for the restoration and protection of rare and endangered water bird habitats and planning and management of water resources.

Developing Irrigation Management at District Scale Based on Water Monitoring: Study on Lis Valley, Portugal

Irrigation districts play a decisive role in Portuguese agriculture and require the adaptation to the new water management paradigm through a change in technology and practices compatible with farmers’ technical know-how and economic sustainability. Therefore, improvement of water management, focusing on water savings and increasing farmers’ income, is a priority. In this perspective, an applied research study is being carried out on the gravity-fed Lis Valley Irrigation District to assess the performance of collective water supply, effectiveness of water pumping, and safety of crop production due to the practice of reuse of drainage water. The water balance method was applied at irrigation supply sectors, including gravity and Pumping Irrigation Allocation. The average 2018 irrigation water allocated was 7400 m3/ha, being 9.3% by pumping recharge, with a global efficiency of about 67%. The water quality analysis allowed identifying some risk situations regarding salinization and microbiological issues, justifying action to solve or mitigate the problems, especially at the level of the farmers’ fields, according to the crops and the irrigation systems. Results point to priority actions to consolidate improved water management: better maintenance and conservation of infrastructure of hydraulic infrastructures to reduce water losses and better flow control; implementation of optimal operational plans, to adjust the water demand with distribution; improvement of the on-farm systems with better water application control and maintenance procedures; and improvement of the control of water quality on the water reuse from drainage ditches. The technological innovation is an element of the modernization of irrigation districts that justifies the development of multiple efforts and synergies among stakeholders, namely farmers, water users association, and researchers.

Research on soil moisture prediction model based on deep learning

Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its’ good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control.

The Functioning of a Water Body Within a Fluvio-Lacustrine System as an Effect of Excessive Nitrogen Loading—The Case of Lake Symsar and its Drainage Area (Northeastern Poland)

Generally, in water ecosystems, it is assumed that rivers play a transport role. In turn, lakes have accumulation properties. However, in fluvio-lacustrine systems, each water body located on a river track can disrupt naturally occurring processes. One such process is the nitrogen cycle. An analysis of the nitrogen cycle, at both the global and local levels, is of extreme significance in view of the progressive degradation of aquatic ecosystems. In this study, we attempted to show that the specific properties of reservoirs located in river–lake systems contribute to an adequate reaction of these reservoirs to situations involving an excessive pollution load. Despite the intensive exchange of water in lakes, they were mainly shown to have an accumulation function. In particular, in those located in the lower part of the system, the total nitrogen load transported outside the example reservoir decreased by 4.3%. The role of these reservoirs depends on the morphometric, hydrologic, and meteorological conditions. The actual loading of the water body was shown to be more than double the permitted critical loading. The creation of conditions similar to those occurring in river–lake systems by, for example, delaying the outflow of water, may favor the protection of surface water from the last element of the system, because this limits the transport of pollutants. This study of the functioning and evolution of lakes’ fluvio-lacustrine systems, including the balance of the nutrient load, enables the prediction of the aquatic ecosystem’s responses in the future and their changes.

A Stochastic Optimization Model for Agricultural Irrigation Water Allocation Based on the Field Water Cycle

Agricultural water scarcity is a global problem and this reinforces the need for optimal allocation of irrigation water resources. However, decision makers are challenged by the complexity of fluctuating stream condition and irrigation quota as well as the dynamic changes of the field water cycle process, which make optimal allocation more complex. A two-stage chance-constrained programming model with random parameters in the left- and right-hand sides of constraints considering field water cycle process has been developed for agricultural irrigation water allocation. The model is capable of generating reasonable irrigation allocation strategies considering water transformation among crop evapotranspiration, precipitation, irrigation, soil water content, and deep percolation. Moreover, it can deal with randomness in both the right-hand side and the left-hand side of constraints to generate schemes under different flow levels and constraint-violation risk levels, which are informative for decision makers. The Yingke irrigation district in the middle reaches of the Heihe River basin, northwest China, was used to test the developed model. Tradeoffs among different crops in different time periods under different flow levels, and dynamic changes of soil moisture and deep percolation were analyzed. Scenarios with different violating probabilities were conducted to gain insight into the sensitivity of irrigation water allocation strategies on water supply and irrigation quota. The performed analysis indicated that the proposed model can efficiently optimize agricultural irrigation water for an irrigation district with water scarcity in a stochastic environment.