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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±1.90 kg ha−1 mm−1) and O (14.86±1.79 kg ha−1 mm−1) were not significant during the 2019 growing season while significant differences in IWP of NO (22.61±5.88 kg ha−1 mm−1) and O (24.68±4.55 kg ha−1 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±0.02 kg ha−1 mm−1 , O: 0.10±0.03 kg ha−1 mm−1) and 2021 growing season (NO: 0.10±0.04 kg ha−1 mm−1 , O: 0.09 ±0.02 kg ha−1 mm−1). [ABSTRACT FROM AUTHOR]

Experiments were performed to evaluate flow and head variations along perforated screens (10-30 mm in diameter) using sand tanks which were connected and a perforated screen extended through these tanks to form a model collector well lateral up to 2.6 m in length. Hydraulic heads and discharge along the lateral and production rates of the model collector well were measured as the water level in the well, the lateral length, and diameter, and the hydraulic conductivity of the filter sand were varied. A mathematical model was developed to predict the axial flow velocity distribution and the discharge intensity variation along the lateral using the head distribution. Results showed that the production rate increased as the lateral length and diameter and the drawdown at the well increased. However, the production rate increase was not linearly related to these factors. When larger-diameter laterals were used, the axial flow velocity in the laterals decreased. This caused the hydraulic heads along the lateral to become more flattened, resulting in a lateral of high efficiency in terms of water production. This condition is similar to the assumption of the uniform discharge intensity along the lateral that many researchers have used in the analysis of the horizontal wells. Under the conditions of this study, a critical axial flow velocity was determined to be 1 m/s. Hydraulic efficiency decreased drastically when the velocity exceeded 1 m/s. The roughness coefficient (the Manning's n value) of the lateral varied as a function of factors such as axial velocity and discharge intensity, and it ranged from 0.010 to 0.015. DOI: 10.1061/(ASCE)0733-9437(2008)134:4(493) CE Database subject headings: River beds: Filtration; Wells: Mathematical models; Discharge: Velocity; Water treatment.

Several areas in Hawaii have background concentrations of unexploded ordnance (UXO) as a consequence of past and current military training. The presence of UXO and the release of their chemical compounds can be of environmental concern. The complete and incomplete detonation of explosives can ignite vegetation fires that cause organics in the topsoil to burn, and therefore, impact the environmental fate of these compounds. The sorption and transport of two UXOs, HMX and RDX, were investigated in a surface soil that was previously exposed to a fire event. In addition, a control soil that had not been exposed to a fire event was used. The use of burned and unburned soil to simulate soil that has been directly impacted by the explosion of UXOs combined with the uniqueness of the soil properties in Hawaii represents the novelty of this study. The surface soil that had been previously exposed to a fire event showed greater adsorption than the control, which was possibly due to a reduction of macropores in the burned soil and the presence of fine charcoal particles. Regardless of the presence or absence of a fire event, HMX showed a greater sorption affinity than RDX. [ABSTRACT FROM AUTHOR]

One of the ways to reduce the amount of leachate, while landfills are being filled, is to reduce the infiltration of rain water. One way to reduce infiltration into landfills, is through the installation of a surface cap or a cover that is water resistant or has low hydraulic conductivity. The details of an experiment conducted on reduction of infiltration using surface cap, carried out at a place near the Marine Corps Base Hawaii, in Kaneohe, are presented.

Large-scale nitrate contamination of ground water is often observed in irrigated areas where the soils are generally permeable, and nitrate plumes have been reported to occur at shallow depths in unconfined aquifers. It has been hypothesized that these nitrate plumes can be captured by the pumping action of irrigation wells and recycled at the field site. This paper presents results from a field and modeling study investigating the distribution of nitrate in an unconfined sand aquifer in an irrigated system. Results from tracer tests under both natural and forced gradients indicate that irrigation pumping has minor but measurable effects on solute transport. Hydrological and solute transport modeling using various pumping schedules suggests that, in most cases, the influence of irrigation pumping on solute transport will be minimal, even on local scales.

A simulation of the transport of dibromochloropropane (DBCP) and ethylene dibromide (EDB) in the vadose zone and ground water was conducted in the Mililani area of central Oahu, Hawaii to revisit the ground water contamination predictions made by researchers at the University of Hawaii in 1988 for selected wells of the Honolulu Board of Water Supply. Examination of sampled DBCP results in these wells indicated a large discrepancy between the observed and predicted concentrations in 1988. Therefore, a postaudit study was initiated in the same area to assist in better defining projected concentrations of contaminants in well waters in the future. With the new data, different modeling approaches were undertaken for simulating flow and contaminant transport in both the vadose and saturated zones. The new simulations indicate travel times of 14–32 years for contaminants to reach the water table, depending on the location. The approximate recovery time of wells in the study area is expected to occur after the year 2016 for DBCP. The predicted concentration of EDB in the wells is less than the current detection limit. Variabilities in model predictions due to parameter uncertainties and model limitations are also considered. Although the predicted concentrations from the present simulations match sampled data better than the concentrations from earlier modeling efforts, still a number of uncertainties remain. [ABSTRACT FROM AUTHOR]

A neural network analysis of agrichemical occurrence in groundwater was conducted using data from a pilot study of 192 small-diameter drilled and driven wells and 115 dug and bored wells in Illinois, a regional reconnaissance network of 303 wells across 12 Midwestern states, and a study of 687 domestic wells across Iowa. Potential factors contributing to well contamination (e.g., depth to aquifer material, well depth, and distance to cropland) were investigated. These contributing factors were available in either numeric (actual or categorical) or descriptive (yes or no) format. A method was devised to use the numeric and descriptive values simultaneously. Training of the network was conducted using a standard backpropagation algorithm. Approximately 15% of the data was used for testing. Analysis indicated that training error was quite low for most data. Testing results indicated that it was possible to predict the contamination potential of a well with pesticides. However, predicting the actual level of contamination was more difficult. For pesticide occurrence in drilled and driven wells, the network predictions were good. The performance of the network was poorer for predicting nitrate occurrence in dug and bored wells. Although the data set for Iowa was large, the prediction ability of the trained network was poor, due to descriptive or categorical input parameters, compared with smaller data sets such as that for Illinois, which contained more numeric information. [ABSTRACT FROM AUTHOR]

Assessment of pesticide and nitrate contamination of rural private wells was conducted using artificial neural networks. Data from 192 drilled and driven wells and 115 large-diameter dug and bored wells, available from two earlier studies, were used for training and testing purposes. Four separate models, two for the two well types and one each for pesticide and nitrate, were developed. Parameters such as depth to aquifer material from land surface, well depth, and distance to cropland were used as input parameters, and the concentrations of nitrate or pesticides were the outputs. While the training efficiency of the network reached between 95 and 100% for these four models, the prediction accuracy for the four models ranged from a low of slightly above 50% for nitrate in dug and bored wells to a high of 90% for pesticides in drilled and driven wells. Sensitivity analyses were performed to examine the impact of network architecture, training and testing parameters, and the size and type of input parameters on model predictions. Multiple hidden layers with a large number of nodes did not appear to have a significant impact on model predictions in two of the four models. The relative importance of input parameters was tested by adding or removing certain key parameters to the model, and it was observed that the parameters had a different impact on drilled/driven versus dug/bored wells. [ABSTRACT FROM AUTHOR]