Journal of Irrigation and Drainage Engineering

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.

This study aimed to clarify the effect of irrigation water amount and planting pattern on water use and yield of cotton in Northern Xinjiang. A field experiment was conducted during 2020–2021 comprising four irrigation water amounts (360, 450, 540, and 630 mm) and two planting patterns (same row spacing, R1; and wide-narrow row planting, R2). The results showed that the evapotranspiration ($ET$), soil evaporation ($Es$), and plant transpiration ($T$) of cotton under both planting patterns increased with the increasing irrigation amount. During the entire growth period, the annual average daily $ET$ and $T$ under R2 were higher than those under R1, but the annual average daily $Es$ under R2 were lower than those under R1. The daily $Es$ was highest between one and four days after irrigation. The $Es/ET$ under R1 was approximately 27.45%–31.82%, and that under R2 was approximately 24.52%–28.56% under the four irrigation treatments. The relationship between the $Es/ET$ and leaf area index was fitted based on an exponential equation ($R^2>0.85$). The yield increased with an increase in the irrigation amount, although there was no significant difference between the yields obtained with 540 mm and 630 mm irrigation amounts. The water use efficiency (WUE) increased first and then decreased with an increase in irrigation water amount, with the highest WUE observed under the 540 mm condition and the lowest under the 630 mm condition.

As a state with the most irrigated agricultural land in the United States, Nebraska relies on freshwater resources for its irrigation. In addition to these conventional water sources, using nonconventional alternatives for crop production can be important during water shortage times. In this research, effluent from a feedlot lagoon was used as an irrigation water source at a subsurface drip irrigation (SDI) system for corn (C) and sugar beet (SB) production in western Nebraska during the 2019 and 2021 growing seasons. The objective of this study was to evaluate the effect of air injection on C and SB yield when using feedlot runoff as an irrigation source in a SDI system. Results indicated that air injection treatment (O), compared to noninjection treatment (NO), increased corn yield by 5.50% in the 2019 growing season, yet differences were not significant. During the 2021 growing season, O significantly increased corn yield by 9.17% ($p=0.04$). Differences in irrigation water productivity (IWP) of NO ($14.19\pm 1.90\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$) and O ($14.86\pm 1.79\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$) were not significant during the 2019 growing season while significant differences in IWP of NO ($22.61\pm 5.88\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$) and O ($24.68\pm 4.55\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$) were observed during the 2021 growing season ($p=0.004$). In sugar beets, no significant difference was observed in crop yield or sucrose yield between O and NO during both growing seasons. Differences in IWP were not significant during the 2019 growing season (NO: $0.10\pm 0.02\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$, O: $0.10\pm 0.03\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$) and 2021 growing season (NO: $0.10\pm 0.04\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$, O: $0.09\pm 0.02\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{mm}}^{-1}$).

Entrapped air pockets reduce flood conveying capacity of a surcharged drainage tunnel, and could be released explosively as geysers from vertical dropshafts. Nevertheless, the impact of dropshaft inflow on air pocket expulsion has hitherto not been fully understood. In this work, experiments was performed on a simplified drainage system consisting of a dropshaft with inflow. An air pocket was introduced into the tunnel; its dynamics in the dropshaft was measured with a high speed camera and pressure transducers. A heuristic analysis on the kinematics of the air pocket showed that when the dropshaft inflow is greater than a critical flow dependent on the dropshaft diameter, the increased compression leads to stronger air pocket expulsion, as spewing and/or ejection of air-water mixture. Observed occurrence of the phenomena generally agrees with the prediction.