Ecological thresholds of Suaeda salsa to the environmental gradients of water table depth and soil salinity

Two field experiments were conducted at Sidi Salem region, Kafr El-Sheikh Governorate, Egypt, during two winter seasons 2018/2019 and 2019/2020 to study the impact of controlled drainage at 0.5, 0.75, 1.25 m and mole drain spacing 2 m on soil salinity, water-saving and sugar beet productivity. Results obtained that using controlled drainage saved irrigation water 24.56 and 11.35% in 1st season and 23.73 and 15.08% in 2nd season for 50, 75 cm depth of water table respectively, compared to 125 cm depth of water table. Application of mole drains seems to be more effective in decreasing soil salinity and sodicity especially, in the topsoil (0-60 cm) and narrow spacing between the plowed lines (2 m).Data showed that the water table level at 0.5 and 0.75 m treatments rose more rapidly and remained higher for longer time than the uncontrolled drainage treatment, the average water table depth was above specified depths between irrigation intervals from 3-7 days depending on the depth. There was a marked variation between the treatments that controlled drainage increased the yield at 0.50 m water table depth by 39 and 30% for both seasons, respectively. It can be concluded that the treatment of controlled drainage may give more profit than the uncontrolled one. At the same time, the contents of K+, Na+, alpha- amino N and alkalinity in root beet were insignificantly affected by controlled subsurface drainage in both seasons.

Download Full-text

Temporal and Local Heterogeneities of Water Table Depth under Different Agricultural Water Management Conditions

Water ◽

10.3390/w13162148 ◽

2021 ◽

Vol 13 (16) ◽

pp. 2148

Author(s):

Jonathan A. Lafond ◽

Silvio J. Gumiere ◽

Virginie Vanlandeghem ◽

Jacques Gallichand ◽

Alain N. Rousseau ◽

...

Keyword(s):

Water Management ◽

Water Table ◽

Cropping Systems ◽

Irrigation Management ◽

Sprinkler Irrigation ◽

Water Table Depth ◽

Management Systems ◽

Integrated Water Management ◽

Growing Seasons ◽

Good Drainage

Integrated water management has become a priority for cropping systems where subirrigation is possible. Compared to conventional sprinkler irrigation, the controlling water table can lead to a substantial increase in yield and water use efficiency with less pumping energy requirements. Knowing the spatiotemporal distribution of water table depth (WTD) and soil properties should help perform intelligent, integrated water management. Observation wells were installed in cranberry fields with different water management systems: Bottom, with good drainage and controlled WTD management; Surface, with good drainage and sprinkler irrigation management; Natural, without drainage, or with imperfectly drained and conventional sprinkler irrigation. During the 2017–2020 growing seasons, WTD was monitored on an hourly basis, while precipitation was measured at each site. Multi-frequential periodogram analysis revealed a dominant periodic component of 40 days each year in WTD fluctuations for the Bottom and Surface systems; for the Natural system, periodicity was heterogeneous and ranged from 2 to 6 weeks. Temporal cross correlations with precipitation show that for almost all the sites, there is a 3 to 9 h lag before WTD rises; one exception is a subirrigation site. These results indicate that automatic water table management based on continuously updated knowledge could contribute to integrated water management systems, by using precipitation-based models to predict WTD.

Download Full-text

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210066 ◽

2021 ◽

Vol 25 (2) ◽

pp. 227-234

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Water Table ◽

Environmental Sustainability ◽

Model Development ◽

Water Table Depth ◽

Water Levels ◽

Coefficient Of Determination ◽

Mean Square ◽

Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

Download Full-text