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Special Issue "Data-Driven Methods for Agricultural Water Management"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics".

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editors

Guest Editor
Dr. Long Wang

Department of Computer Science and Technology, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, China
E-Mail
Interests: machine learning; data mining; computational intelligence; computer vision
Guest Editor
Dr. Dong Wang

The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Website | E-Mail
Phone: 852-3442 4604
Interests: statistical modeling; machine fault diagnosis; prognostics and health management; mechanical signal processing; statistical signal processing; digital/adaptive signal processing; data mining; non-destructive testing; system diagnostics; energy systems
Guest Editor
Dr. Shancheng Jiang

The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
E-Mail
Interests: machine learning; metaheuristic algorithms
Guest Editor
Dr. Chao Huang

1 Department of Computer Science and Technology, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, China
2 Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
E-Mail
Interests: machine learning; computational intelligence; renewable energy systems; complex systems

Special Issue Information

Dear Colleagues,

Various sensors have allowed the collection of large volumes of data related to agricultural water management. However, conventional approaches and strategies lack the ability to take advantage of such large amounts of data and, thus, advanced data-driven methods are highly desired to achieve smart agricultural water management. Emerging advances in computing technologies have boosted the application of data-driven methods in different domains, such as in healthcare, power supply, and energy management. Since data-driven methods can be employed for forecasting, classification, optimization, etc., it is valuable and meaningful to develop and apply data-driven methods to relevant aspects in the agricultural water management domain.

The aim of this Special Issue is to report on recent advances relating to the following themes: (1) data-driven methods for farm-level and regional water management; (2) data-driven methods for irrigation, drainage, and salinity in cultivated areas; (3) data-driven methods for rainwater harvesting and crop water management in rainfed areas; (4) data-driven methods for groundwater management in agriculture and conjunctive use of groundwater and surface water; and (5) data-driven methods for all related fields of agricultural water management.

Dr. Long Wang
Dr. Dong Wang
Dr. Shancheng Jiang
Dr. Chao Huang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • data-driven methods
  • agricultural water management
  • irrigation
  • groundwater

Published Papers (13 papers)

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Open AccessArticle
Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application
Water 2019, 11(7), 1401; https://doi.org/10.3390/w11071401
Received: 7 May 2019 / Revised: 4 July 2019 / Accepted: 4 July 2019 / Published: 8 July 2019
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Abstract
Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection of the downscaling method and the determination of the environmental variables, namely, the [...] Read more.
Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection of the downscaling method and the determination of the environmental variables, namely, the influencing factors of soil moisture. This study attempted to utilize machine learning and data mining algorithms to downscale the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data from 25 km to 1 km and compared the advantages and disadvantages of the random forest model and the Cubist algorithm to determine the more suitable soil moisture downscaling method for the middle and lower reaches of the Yangtze River Basin (MLRYRB). At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable for the downscaling models. In contrast, variables, such as albedo and evapotranspiration, are infrequently applied; nevertheless, this study selected these two environmental variables, which have a considerable impact on soil moisture. Thus, the selected environmental variables in the downscaling process included the longitude, latitude, elevation, slope, NDVI, daytime and nighttime land surface temperature (LST_D and LST_N, respectively), albedo, evapotranspiration (ET), land cover (LC) type, and aspect. This study achieved downscaling on a 16-day timescale based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. A comparison of the random forest model with the Cubist algorithm revealed that the R2 of the random forest-based downscaling method is higher than that of the Cubist algorithm-based method by 0.0161; moreover, the root-mean-square error (RMSE) is reduced by 0.0006 and the mean absolute error (MAE) is reduced by 0.0014. Testing the accuracies of these two downscaling methods showed that the random forest model is more suitable than the Cubist algorithm for downscaling AMSR-E soil moisture data from 25 km to 1 km in the MLRYRB, which provides a theoretical basis for obtaining high spatial resolution soil moisture data. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Response of Landscape and Ecological Characteristics to the Optimal Rainwater Harvesting Dual-Element Mulch Covered Soil Model in Beijing
Water 2019, 11(4), 654; https://doi.org/10.3390/w11040654
Received: 26 February 2019 / Revised: 12 March 2019 / Accepted: 12 March 2019 / Published: 29 March 2019
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Abstract
The implementation of energy conservation and emissions reduction in Beijing prompted yearly increases in the area of urban green space, leading to direct increases in urban water consumption. This aggravated an already tense situation of water shortage. Considering the low irrigation water utilization [...] Read more.
The implementation of energy conservation and emissions reduction in Beijing prompted yearly increases in the area of urban green space, leading to direct increases in urban water consumption. This aggravated an already tense situation of water shortage. Considering the low irrigation water utilization effectives of the urban green space system, the typical urban greening shrub (Ligustrum vicaryi) was selected as the research object of this study. In a pot experiment, three mulch materials were selected: gravel (CH1), pine needles + gravel (CH2), and bark + gravel (CH3). These materials were set to a uniform thickness of 3 cm, and soil water was maintained between 75% and 85% of the field capacity. Using the analytic hierarchy process and fuzzy mathematics model, the physiological and ecological response characteristics of Ligustrum vicaryi were investigated under different combinations of mulch material. The results for various processing, regarding plant growth, showed CH3 > CH2 > CH1 > CK (Control Check). The leaf area, total leaf area, and leaf area index of CH3 were, respectively, 21.4%, 21.9%, and 62.5% larger than those of the control check (CK). Regarding physiological characteristics, photosynthetic rate, evaporation rate, stomatal conductance, and water use efficiency of CH3 were better than for the other treatments. Regarding ecological services, carbon fixation, oxygen release, cooling, and quantity of humidification of CH3 were optimal. Considered comprehensively for the landscape function, physical characteristics, and ecological services of Ligustrum vicaryi, the preliminary thought is that bark and gravel dual-element mulch, with a layer thickness of 3 cm, was the optimal soil cover treatment for the typical city greening shrub Ligustrum vicaryi. Using the analytic hierarchy process (AHP) and the fuzzy mathematical model for the evaluation of the effects of different soil cover treatments on the landscape function, ecological service function, and physiological characteristics of Ligustrum vicaryi was reliable and feasible. The model evaluation results match the actual ones. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Combined Effect of Different Amounts of Irrigation and Mulch Films on Physiological Indexes and Yield of Drip-Irrigated Maize (Zea mays L.)
Water 2019, 11(3), 472; https://doi.org/10.3390/w11030472
Received: 15 January 2019 / Revised: 14 February 2019 / Accepted: 28 February 2019 / Published: 6 March 2019
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Abstract
Exploring the effect of irrigation on biodegradable film-covered drip-irrigated maize is essential for sustainable agricultural development in arid areas. These regions, like Xinjiang in China, are home to suitable irrigation and biodegradable films. Through field trials, four levels of irrigation, and two biodegradable [...] Read more.
Exploring the effect of irrigation on biodegradable film-covered drip-irrigated maize is essential for sustainable agricultural development in arid areas. These regions, like Xinjiang in China, are home to suitable irrigation and biodegradable films. Through field trials, four levels of irrigation, and two biodegradable films and one common polyethylene film were set up to study the effects of different treatments on the physiology, growth indicators, and yield of maize. The results showed that the effects of irrigation and biodegradable films on the photosynthetic index and fluorescence index of maize reached a very significant level (p < 0.01). The effect of single factor irrigation and biodegradable films on the photosynthetic index and fluorescence index of maize reached a significant level (p < 0.05). The photosynthesis index, fluorescence index, plant height, LAI (leaf area index) and yield of W3M3 treatment had the highest value, when compared with other treatments. The W1M1 treatment had the lowest value. The photosynthesis index, fluorescence index, plant height, LAI, and yield of the W3M2 treatment were second only to W3M3. In addition, the output was only 40 kg ha−1 less than W3M3. W3M2 has the best treatment effect from a perspective of sustainable agricultural development and efficient water saving; the optimal irrigation amount was 5625 m3 ha−1, induction period was 60 d, and thickness was 0.01 mm. The results of this study are of guiding significance for the promotion of the use of biodegradable films, search for suitable irrigation, and development of low-carbon agriculture. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
Water 2018, 10(11), 1497; https://doi.org/10.3390/w10111497
Received: 8 October 2018 / Revised: 17 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
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Abstract
Irrigated agriculture requires high reliability from water delivery networks and high flows to satisfy demand at seasonal peak times. Aquatic vegetation in irrigation channels are a major impediment to this, constraining flow rates. This work investigates the use of remote sensing from unmanned [...] Read more.
Irrigated agriculture requires high reliability from water delivery networks and high flows to satisfy demand at seasonal peak times. Aquatic vegetation in irrigation channels are a major impediment to this, constraining flow rates. This work investigates the use of remote sensing from unmanned aerial vehicles (UAVs) and satellite platforms to monitor and classify vegetation, with a view to using this data to implement targeted weed control strategies and assessing the effectiveness of these control strategies. The images are processed in Google Earth Engine (GEE), including co-registration, atmospheric correction, band statistic calculation, clustering and classification. A combination of unsupervised and supervised classification methods is used to allow semi-automatic training of a new classifier for each new image, improving robustness and efficiency. The accuracy of classification algorithms with various band combinations and spatial resolutions is investigated. With three classes (water, land and weed), good accuracy (typical validation kappa >0.9) was achieved with classification and regression tree (CART) classifier; red, green, blue and near-infrared (RGBN) bands; and resolutions better than 1 m. A demonstration of using a time-series of UAV images over a number of irrigation channel stretches to monitor weed areas after application of mechanical and chemical control is given. The classification method is also applied to high-resolution satellite images, demonstrating scalability of developed techniques to detect weed areas across very large irrigation networks. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Optimized Water and Fertilizer Management of Mature Jujube in Xinjiang Arid Area Using Drip Irrigation
Water 2018, 10(10), 1467; https://doi.org/10.3390/w10101467
Received: 7 September 2018 / Revised: 12 October 2018 / Accepted: 15 October 2018 / Published: 17 October 2018
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Abstract
Studying water–fertilizer coupling effects in a drip irrigation system is critical for sustainable agricultural development in arid areas, such as that of Xinjiang in China, to find out the optimized water and fertilizer management. Therefore, a two-year field experiment was conducted to find [...] Read more.
Studying water–fertilizer coupling effects in a drip irrigation system is critical for sustainable agricultural development in arid areas, such as that of Xinjiang in China, to find out the optimized water and fertilizer management. Therefore, a two-year field experiment was conducted to find out how the combination of three levels of irrigation quotas and three levels of fertilizer amounts would affect the physiological and growth indexes of jujube, as well as ascertain the differences between drip irrigation and flood irrigation. The results showed that the interacted and coupled effects of irrigation and fertilization influenced most of the physiological indicators and growth indexes. On the other hand, the physiological and growth indexes were increased after transferring flood irrigation to drip irrigation, as the maximum chlorophyll content (CC) and photosynthetic nitrogen use efficiency (PNUE) values increased on average by 6.00%, and 11.39% in 2016, and 1.47% and 6.83% in 2017, respectively. Undoubtedly, inappropriate water and fertilizer management had negative impacts on jujube growth and yield. Based on the treatments and results in this paper, low fertilizer and moderate irrigation would be the best choice. The crop yield, irrigation water use efficiency (iWUE), and fertilizer partial productivity (PFP) increased by 6.77%, 29.48%, and 193.62% in 2016, and similar increments were also found in 2017 of 6.17%, 78.72%, and 133.06%, respectively. This indicated that fertilizer efficiency was promoted along with water use, and in turn, the water amounts were adjusted by the amount of fertilizer that was applied. Based on a comprehensive consideration of the physiological and growth indexes, a mathematical model was established, and the optimized irrigation and fertilizer amounts of jujube in northern Xinjiang area were found to be 815 mm and 400 kg ha−1 (with N–P2O5–K2O proportioned at 2–1–1.5). The results that were obtained in this paper would provide theoretical reference to the sustainable development of jujube plantation using drip irrigation in the arid areas. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
A Stochastic Optimization Model for Agricultural Irrigation Water Allocation Based on the Field Water Cycle
Water 2018, 10(8), 1031; https://doi.org/10.3390/w10081031
Received: 28 June 2018 / Revised: 29 July 2018 / Accepted: 31 July 2018 / Published: 3 August 2018
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Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
An Integrated Water-Saving and Quality-Guarantee Uncertain Programming Approach for the Optimal Irrigation Scheduling of Seed Maize in Arid Regions
Water 2018, 10(7), 908; https://doi.org/10.3390/w10070908
Received: 22 May 2018 / Revised: 30 June 2018 / Accepted: 2 July 2018 / Published: 9 July 2018
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Abstract
With population growth and water scarcity, efficient crop production has drawn attention worldwide. In the Hexi Corridor, the largest production base of maize seed in China, it is desired to develop efficient irrigation strategies for seed maize. Considering the double criteria of yield [...] Read more.
With population growth and water scarcity, efficient crop production has drawn attention worldwide. In the Hexi Corridor, the largest production base of maize seed in China, it is desired to develop efficient irrigation strategies for seed maize. Considering the double criteria of yield and seed quality, an integrated water-saving and quality-guarantee uncertain programming approach (IWQUP) was developed in this study to help with agricultural sustainable development. The IWQUP combined deficit irrigation theory, soil-water balance, and multiple uncertainties. The water-flowering model (WFM) and kernel weight prediction model with water production functions were used to reflect the relationship among water consumption, crop yield, and seed quality. Meanwhile, to deal with the widespread existence of uncertainties in nature and the decision-making process, interval programming and fuzzy programming were integrated within the framework of IWQUP, along with the use of the genetic algorithm and Monte Carlo simulation. The results showed that when the climatic condition is moist, decision-makers may use a low tolerance level in order to reduce the water waste, enhance the water use efficiency, and guarantee a relatively high seed quality. When the climate is harsh, a high tolerance level to water use constraints is recommended in order to guarantee yield. In addition, optimistic decision-makers could choose a relatively high tolerance level, but in moist years they should be careful in order to avoid water waste. The established model was compared with three other models to represent its practicability for offering decision-makers various references under different scenarios. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Probabilistic Analysis of Extreme Discharges and Precipitations with a Nonparametric Copula Model
Water 2018, 10(7), 823; https://doi.org/10.3390/w10070823
Received: 11 April 2018 / Revised: 14 June 2018 / Accepted: 18 June 2018 / Published: 22 June 2018
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Abstract
Urumqi River is an important river in the Xinjiang autonomous region, China, where floods or droughts are the major concerns of the local communities. This river’s discharge is mainly influenced by the natural factors such as precipitation and climates, rather than human activities. [...] Read more.
Urumqi River is an important river in the Xinjiang autonomous region, China, where floods or droughts are the major concerns of the local communities. This river’s discharge is mainly influenced by the natural factors such as precipitation and climates, rather than human activities. This paper quantifies the interdependent structure between Urumqi River’s discharge and precipitation using a nonparametric Copula method. It then analyzes the relationship between the extreme discharges of this river and extreme precipitation of the region. Comparison between simulation result and real data is conducted to verify the rationality of the model. Furthermore, the conditional probabilities of maximum and minimum discharge at different precipitation levels are also investigated using the Copula distribution functions. The results show a strong relationship between large discharge and heavy precipitation in this region. The upper dependence coefficient is nearly 0.6 and the probability of large discharge reaches 0.64 when the rainfall is greater than 159.56 mm. The relationship between small discharge and rainfall is insignificant. The lower dependence coefficient is zero, suggesting that the base flow and snowmelt from Tianshan likely contribute to Urumqi River’s discharge during the dry season. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Evaluation of Agricultural Water Pricing in an Irrigation District Based on a Bayesian Network
Water 2018, 10(6), 768; https://doi.org/10.3390/w10060768
Received: 19 April 2018 / Revised: 8 June 2018 / Accepted: 9 June 2018 / Published: 12 June 2018
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Abstract
In recent years, the large-scale development of land and water resources has led to a conflict between water supply and demand. Especially in arid regions, fragile ecosystems and continuous farmland expansion have threatened the ecological and social security of river basins. Therefore, it [...] Read more.
In recent years, the large-scale development of land and water resources has led to a conflict between water supply and demand. Especially in arid regions, fragile ecosystems and continuous farmland expansion have threatened the ecological and social security of river basins. Therefore, it is urgent to propose scientific and reasonable water resource management models to alleviate this conflict. Based on the principle of “the strictest water resource management measures” for river basin water resources, this study has taken Heshuo County, Xinjiang as the research object, using a full-cost method to determine agricultural water prices for the irrigation district at 0.35 RMB/m3 and 1.4 RMB/m3. With the participation of stakeholders and experts, current water rights trading and management systems were analyzed by a Bayesian network (BN) model. In addition, the impact of water-pricing policy on farmers’ planting behavior was also quantified. The results indicated that an increase in water prices can effectively reduce agricultural water consumption for irrigation, but it would also induce negative externalities involving groundwater (GW) preservation and farmers’ income. A water resource management model mainly directed by water-pricing policy, and supplemented by GW protection and agricultural subsidy policies, could effectively regulate farmers’ water-use behavior, guarantee farmers’ income, and protect GW. This study provides a successful management approach for coordinating the relationship between agricultural water resources and the ecological environment in an arid basin watershed and promoting the efficient use of agricultural water resources in irrigated areas. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
A Modified Water-Table Fluctuation Method to Characterize Regional Groundwater Discharge
Water 2018, 10(4), 503; https://doi.org/10.3390/w10040503
Received: 4 March 2018 / Revised: 5 April 2018 / Accepted: 9 April 2018 / Published: 19 April 2018
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Abstract
A modified Water-Table Fluctuation (WTF) method is developed to quantitatively characterize the regional groundwater discharge patterns in stressed aquifers caused by intensive agricultural pumping. Two new parameters are defined to express the secondary information in the observed data. One is infiltration efficiency and [...] Read more.
A modified Water-Table Fluctuation (WTF) method is developed to quantitatively characterize the regional groundwater discharge patterns in stressed aquifers caused by intensive agricultural pumping. Two new parameters are defined to express the secondary information in the observed data. One is infiltration efficiency and the other is discharge modulus (recurring head loss due to aquifer discharge). An optimization procedure is involved to estimate these parameters, based on continuous groundwater head measurements and precipitation records. Using the defined parameters and precipitation time series, water level changes are calculated for individual wells with fidelity. The estimated parameters are then used to further address the characterization of infiltration and to better quantify the discharge at the regional scale. The advantage of this method is that it considers recharge and discharge simultaneously, whereas the general WTF methods mostly focus on recharge. In the case study, the infiltration efficiency reveals that the infiltration is regionally controlled by the intrinsic characteristics of the aquifer, and locally distorted by engineered hydraulic structures that alter surface water-groundwater interactions. The seasonality of groundwater discharge is characterized by the monthly discharge modulus. These results from individual wells are clustered into groups that are consistent with the local land use pattern and cropping structures. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Effects of Drip Irrigation Models on Chemical Clogging under Saline Water Use in Hetao District, China
Water 2018, 10(3), 345; https://doi.org/10.3390/w10030345
Received: 26 January 2018 / Revised: 16 March 2018 / Accepted: 16 March 2018 / Published: 20 March 2018
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Abstract
Saline water is a major resource for agricultural irrigation in arid-semi arid regions, especially when it is combined with drip irrigation. However, highly saline water can easily cause clogging of the emitters in drip irrigation systems, adversely affecting crop growth. Hence, a 2a [...] Read more.
Saline water is a major resource for agricultural irrigation in arid-semi arid regions, especially when it is combined with drip irrigation. However, highly saline water can easily cause clogging of the emitters in drip irrigation systems, adversely affecting crop growth. Hence, a 2a processing tomatoes drip irrigation study was conducted in Hetao irrigation district. The chemical clogging of the emitters was analyzed using four drip irrigation models: RI1 (all fresh water irrigation), RI2 (saline water use in the flowering stage, fresh water in the fruiting stage), RI3 (fresh water use in the flowering stage, saline water in the fruiting stage), and RI4 (all saline water irrigation). The results revealed that the discharge ratio variation (Dra) and the Christiansen uniformity coefficient (CU) of RI4 decreased to 74.0% and 70.9%, respectively, which is considered as a clogged condition with poor irrigation uniformity. When compared to the all saline water irrigation model, the Dra and CU of fresh-saline alternating irrigation models (RI2 and RI3) were higher by 12.16% and 18.05%, respectively. Additionally, the dry weight (DW) of emitters fouling was less than that of RI4 by 16.30%. The Dra and CU showed linear relationships (R2 > 0.79) for the different irrigation models. However, as the Dra declined, the more adverse influence on maintaining the high CU was found in RI4. Using irrigation models with alternating fresh-saline water were recommended to control chemical clogging in drip irrigation systems. Calcium carbonate (CaCO3) was the dominant scale formed, which caused the emitters to clog when processing tomatoes were grown using a drip irrigation system with saline water. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessArticle
Identification of Hydrological Drought in Eastern China Using a Time-Dependent Drought Index
Water 2018, 10(3), 315; https://doi.org/10.3390/w10030315
Received: 31 January 2018 / Revised: 12 March 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
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Abstract
Long records (1960–2013) of monthly streamflow observations from 8 hydrological stations in the East Asian monsoon region are modeled using a nonstationarity framework by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). Modeling analyses are used to characterize nonstationarity [...] Read more.
Long records (1960–2013) of monthly streamflow observations from 8 hydrological stations in the East Asian monsoon region are modeled using a nonstationarity framework by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). Modeling analyses are used to characterize nonstationarity of monthly streamflow series in different geographic regions and to select optimal distribution among five two-parameter distributions (Gamma, Lognormal, Gumbel, Weibull and Logistic). Based on the optimal nonstationarity distribution, a time-dependent Standardized Streamflow Index (denoted SSIvar) that takes account of the possible nonstationarity in streamflow series is constructed and then employed to identify drought characteristics at different time scales (at a 3-month scale and a 12-month scale) in the eight selected catchments during 1960–2013 for comparison. Results of GAMLSS models indicate that they are able to represent the magnitude and spread in the monthly streamflow series with distribution parameters that are a linear function of time. For 8 hydrological stations in different geographic regions, a noticeable difference is observed between the historical drought assessment of Standardized Streamflow Index (SSI) and SSIvar, indicating that the nonstationarity could not be ignored in the hydrological drought analyses, especially for stations with change point and significant change trends. The constructed SSIvar is, to some extent, found to be more reliable and suitable for regional drought monitoring than traditional SSI in a changing environment, thereby providing a feasible alternative for drought forecasting and water resource management at different time scales. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Open AccessTechnical Note
Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm
Water 2018, 10(6), 815; https://doi.org/10.3390/w10060815
Received: 17 May 2018 / Revised: 15 June 2018 / Accepted: 18 June 2018 / Published: 20 June 2018
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Abstract
This paper employs an optimization algorithm called the salp swarm algorithm (SSA) for the parameter estimation of the soil water retention curve model. The SSA simulates the behavior of searching for food of the salp swarm and manages to find the optimal solutions [...] Read more.
This paper employs an optimization algorithm called the salp swarm algorithm (SSA) for the parameter estimation of the soil water retention curve model. The SSA simulates the behavior of searching for food of the salp swarm and manages to find the optimal solutions for optimization problems. In this paper, parameter estimation of the van Genuchten model based on nine soil samples, covering eight soil textures, is conducted. The optimization problem that minimizes the difference between the measured and the estimated water content is formulated, and the SSA is applied to solve this problem. To validate the competitive advantage of the SSA, the experimental results are compared with Particle Swarm Optimization algorithm, the Differential Evolution algorithm and the RETC program, which indicates that SSA performs better than the three methods. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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