Alternate-furrow fertigation has shown potential to improve water and fertilizer application efficiency in irrigated areas. A combination of simulation and optimization approaches permits researchers to identify optimum design and management practices in furrow fertigation, resulting in optimum cost, irrigation performance, or environmental impact. The objective of this paper is to apply one-dimensional (1D) surface and two-dimensional (2D) subsurface simulation-optimization models to the minimization of nitrate losses in two types of alternate-furrow fertigation, as follows: (1) variable alternate-furrow irrigation, and (2) fixed alternate-furrow irrigation. For comparison purposes, optimizations are also reported for conventional furrow irrigation. The model uses numerical surface fertigation and soil-water models to simulate water flow and nitrate transport in the soil surface and subsurface, respectively. A genetic algorithm is used to solve the optimization problem. Four decision variables (i...

Simulation models have recently been used in many studies for simulation of water flow and solute transport in soil under different irrigation systems. The objective of this study was to compare the HYDRUS-1D and HYDRUS-2D simulation models to simulate water and nitrate transfer for three furrow irrigation technologies [conventional furrow irrigation (CFI), fixed alternate furrow irrigation (FFI), and variable alternate furrow irrigation (AFI)] under fertigation practice. Filed measured data were used to calibrate and validate the one-dimensional (1D) and two-dimensional (2D) HYDRUS models. An inverse solution technique was applied to optimize soil-hydraulic and solute transport parameters to calibrate the models. The results indicated that the HYDRUS-2D model provided better performance to predict soil water contents, nitrate concentrations, and deep percolation caused by the geometry of the infiltration domain in furrow irrigation. Standard errors for HYDRUS-1D ranged from 0.107 to 0.170 for soi...

To control pollution sources and prioritize the reduction strategies of pollution, estimating the pollution load and contributing to prevent entering the different pollution sources to the water resources is very important. To estimate the pollution load, detailed quantitative and qualitative information of pollutant is needed. However, ground-based measurement is very costly and time consuming. Instead, approaches based on remotely sensing techniques exhibits very high potential in determining the water quality parameters over an extensive area in a short time period. The aim of this study was to evaluate the possibility of applying remotely sensing data and hydrometric station data to estimate the pollution load entering Anzali Wetland. So that, the quality parameters including nitrate concentration, total dissolved solids, total suspended solids and orthophosphate in the entrance point of three important rivers leading to Anzali Wetland (Bahmbar, Siahdarvishan, and Pirbazar Rivers) was measured at the time of satellites overpassing over the period of November 2011 through August 2012. Pre-processing practices including radiometric and geometric corrections were performed on the Landsat images. Then multi-variable equations were derived for estimating the water quality parameters based on ground truth data and spectral reflections in the range of visible to middle infrared. After validating the accuracy of water quality parameters derived from Landsat satellite images (by statistical indices), the contamination loads of nitrate, total dissolved solids, total suspended solids and orthophosphate entering the wetland were estimated. To assay the contamination load of each parameter, the river discharges were multiplied by the concentrations of these parameters which derived from satellite images in the period of April 2012 through July 2013. The highest pollution load occurred during this period at the entrance of Siahdarvishan River into Anzali Wetland. Comparison of pollution load of nitrate, orthophosphate, total suspended solids and total dissolved solids derived from satellite images during the study period revealed that the Pirbazar and Bahmbar rivers discharged the most loads of pollution to Anzali Wetland respectively.

The Anzali wetland is one of the internationally known freshwater wetlands which provides habitats for many plants and migratory birds, and plays the role of a treatment plant for running water into the Caspian Sea. However, its lagoon is getting shallower due to the inflow of sediment and deposition of organic detritus, and its water quality is deteriorating due to the inflow of wastewater and solid waste from neighboring cities. Therefore, monitoring of water quality parameters (WQP) in Anzali Lagoon is deemed a priority for protection and prevention of its degradation. The current study investigates the environmental condition of the wetland between 2014 to 2021 using remote sensing technology. A multi-sensor framework was developed in google earth engine for spatio-temporal trend analysis of water quality parameters in Anzali lagoon. Water quality parameters incluing TSS, TDS, pH and nitrate were derived using Landsat satellite data based on the calibrated regression equations introduced for this wetland. Spatio-temporal variations of WQP was depicted to determine the critical points and evaluate the trend analysis. The results showed that the WQP fluctuate significantly over time. The spatial distributions indicated that the critical points of the lagoon are changing from the Western to the center and Eastern parts. The comparison of the WQP parameters with the environmental standards confirmed that the lagoon condition is in critical stage, especially in terms of TSS. Nitrate values showed that 50% of the lagoon area exceeds the environmental standards, which indicates the nutritional status. The investigated WQP has had a significant positive trend in most areas, so the lagoon is under high environmental risk and unfriendly human activities should be controlled.