A novel regional irrigation water productivity model coupling irrigation- and drainage-driven soil hydrology and salinity dynamics and shallow groundwater movement in arid regions in China

Abstract

Abstract. The temporal and spatial distributions of regional irrigation water productivity (RIWP) are crucial for making decisions related to agriculture, especially in arid irrigated areas with complex cropping patterns. Thus, in this study, we developed a new RIWP model for an irrigated agricultural area with complex cropping patterns. The model couples the irrigation- and drainage-driven soil water and salinity dynamics and shallow groundwater movement in order to quantify the temporal and spatial distributions of the target hydrological and biophysical variables. We divided the study area into 1 km × 1 km hydrological response units (HRUs). In each HRU, we considered four land use types: sunflower fields, wheat fields, maize fields, and uncultivated lands (bare soil). We coupled the regional soil hydrological processes and groundwater flow by taking a weighted average of the water exchange between unsaturated soil and groundwater under different land use types. The RIWP model was calibrated and validated using 8 years of hydrological variables obtained from regional observation sites in a typical arid irrigation area in North China, the Hetao Irrigation District. The model simulated soil moisture and salinity reasonably well as well as groundwater table depths and salinity. However, overestimations of groundwater discharge were detected in both the calibration and validation due to the assumption of well-operated drainage ditch conditions; regional evapotranspiration (ET) was reasonably estimated, whereas ET in the uncultivated area was slightly underestimated in the RIWP model. A sensitivity analysis indicated that the soil evaporation coefficient and the specific yield were the key parameters for the RIWP simulation. The results showed that the RIWP decreased from maize to sunflower to wheat from 2006 to 2013. It was also found that the maximum RIWP was reached when the groundwater table depth was between 2 and 4 m, regardless of the irrigation water depth applied. This implies the importance of groundwater table control on the RIWP. Overall, our distributed RIWP model can effectively simulate the temporal and spatial distribution of the RIWP and provide critical water allocation suggestions for decision-makers.

Topographical and Hydrological Investigations for the Design of a Drainage Network the Fields of the College of Agriculture are a Model

“…The pattern of distribution of groundwater depths (cm) and the direction of its movement for selected months before drainag operations Figure 3 shows the pattern of the distribution of the depths of groundwater and the direction of its movement for the months of March, April, May, June, after drainage operations, as it is noted that the wells close to the vertical drainage are a depth of [5] m. Drainage. While it is noted from the wells far from the tapping basin (1,2,3,13,14,15) the ground water level is still high, meaning that the drainage operation has reduced the groundwater level by about 75% of the total wells , and agrees with the findings of [17] it is noted that the direction of groundwater movement clear from the shapes is heading towards the basin of the tap and the contour lines show the groundwater levels within the study area from the surface of the soil as it is noted that the lowest depth of ground water (120,120,120,120) and the highest depth Ground water (82, 82, 80, 76 for the months of March, April, May and June, respectively). These results are consistent with the findings of [6], who found that the ground water levels have been reduced by the effectiveness of drainage operations.…”

“…It was also noted that the amount of dissolved salts has a pattern somewhat similar to the electrical conductivity pattern and is directly proportional [7]. The results also showed a difference in the total dissolved salts values of the drainage waters selected for the summer and winter seasons, as the total dissolved salts values for the summer season were higher than the winter season, and this is due to the increase in temperatures The heat in the summer, which leads to an increase in evaporation rates and the concentration of dissolved salts, and the rain water in the winter season 9 contributes to feeding the groundwater and reducing the concentrations of dissolved salts in it, and this is consistent with [17].…”

“…As the decision was taken to establish 35 monitoring wells according to the ground network that was established and a depth of (0-120) cm. The ground water monitoring holes were distributed to the right and left of the drainage line to be established, so that the ground water monitoring wells became (1,2,3,4,5,6,12,13,14,15,16,17,24,26) located on the left side of the drainage path and the rest of the wells are located on the right side of the drainage line path3.…”

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

“…According to equations ( 1) and ( 4), D * is calculated in (5). It represents the actual sensor placement advantage value.…”

“…Moreover, the development of artificial intelligence, cloud computing, and big data has also greatly improved SMWSN efficiency [2][3][4]. However, due to the particularity of soil moisture sensors, the original evaluation mechanism for wireless sensor networks is not well applicable to SMWSNs, so it is urgent to propose a novel network efficiency evaluation mechanism [5].…”