@article{moursi_youssef_poole_castro-bolinaga_chescheir_richardson_2023, title={Drainage water recycling reduced nitrogen, phosphorus, and sediment losses from a drained agricultural field in eastern North Carolina, USA}, volume={279}, ISSN={["1873-2283"]}, DOI={10.1016/j.agwat.2023.108179}, abstractNote={An experimental study was conducted to evaluate the effect of drainage water recycling (DWR) on reducing nitrogen (N), phosphorus (P), and sediment losses from agricultural fields to downstream surface water bodies. The two-year study (May 2019-April 2021) was conducted at an agricultural field in eastern North Carolina, U.S.A. A reservoir existed at the site was used to store subsurface drainage and surface runoff water during wet periods and provide supplemental irrigation during dry periods of the crop growing season. On average, the reservoir retained 14% of received inflow, with a higher flow reduction in the dry year (2019–2020; 29%) than the wet year (2020–2021; 8%). The hydraulic retention time (HRT) for the reservoir was 33.8 days for the dry year and 12.4 days for the wet year. The reservoir significantly reduced the loadings of N by 47%, P by 30% and sediment by 87%. Nitrogen load reduction was primarily driven by nitrate assimilation, the dominant form of N in the reservoir. Phosphorus load reduction was attributed to Orthophosphate assimilation as the reservoir released more particulate P than received. Reductions in both water flow and species concentration contributed to nutrient load reductions. Results suggested the removal efficiency of the reservoir would be highest during the summer and early fall months when the reservoir has a smaller water volume (due to irrigation), longer HRT, and warmer temperature. This study clearly demonstrated the potential of DWR for significantly reducing N, P, and sediment losses from agricultural land to receiving surface water. Further research is needed to investigate the physical, chemical, and biological processes that occur in the storage reservoir and affect the fate and transport of nutrients and sediment. The understanding of these processes will enable optimizing the treatment efficiency of DWR, which maximizes the system’s benefits and reduces construction cost.}, journal={AGRICULTURAL WATER MANAGEMENT}, author={Moursi, Hossam and Youssef, Mohamed A. and Poole, Chad A. and Castro-Bolinaga, Celso F. and Chescheir, George M. and Richardson, Robert J.}, year={2023}, month={Apr} }
 @article{moursi_youssef_chescheir_2022, title={Development and application of DRAINMOD model for simulating crop yield and water conservation benefits of drainage water recycling}, volume={266}, ISSN={["1873-2283"]}, DOI={10.1016/j.agwat.2022.107592}, abstractNote={Drainage water recycling (DWR) is an emerging practice that has the potential to increase crop yield and improve water quality. DWR involves capturing and storing subsurface drainage water and surface runoff in ponds or reservoirs, and using this water for supplemental irrigation during dry periods of the growing season. The main objective of this study was to enhance DRAINMOD model to simulate the hydrology and crop yield of DWR systems. The expanded model; named DRAINMOD-DWR, has a new module that conducts a water balance of the storage reservoir and simulates the interaction between the reservoir and the field, irrigated from and/or draining into the reservoir. The model predicts the long-term performance of DWR as affected by weather conditions, soil type, crop rotation, reservoir size, and irrigation and drainage management. Three performance metrics were defined based on model predictions to quantify irrigation, crop yield, and water capture benefits of DWR. To demonstrate the new features of the model, uncalibrated DRAINMOD-DWR was applied to a hypothetical DWR system with continuous corn using a 50-yr (1970–2019) weather record in Eastern North Carolina, U.S. Different reservoir sizes were simulated to demonstrate how the model can predict the effect of storage capacity on the system’s performance. The model predicted that a 3.0-m deep reservoir with a surface area of 4% of the field area would optimize corn yield for the simulated conditions. The model application clearly demonstrated the DRAINMOD-DWR model’s capability of optimizing the DWR system design to avoid under-sizing or over-sizing the storage reservoir, which reduces system’s performance and increases implementation cost. Research is needed to test DRAINMOD-DWR using field measured data, and to develop routines for simulating the fate and transport of nutrients and sediment in the storage reservoir, which would enable the model to predict the water quality benefits of DWR.}, journal={AGRICULTURAL WATER MANAGEMENT}, author={Moursi, Hossam and Youssef, Mohamed A. and Chescheir, George M.}, year={2022}, month={May} }
 @article{moursi_kim_kaluarachchi_2017, title={A probabilistic assessment of agricultural water scarcity in a semi-arid and snowmelt-dominated river basin under climate change}, volume={193}, ISSN={["1873-2283"]}, DOI={10.1016/j.agwat.2017.08.010}, abstractNote={Abstract Water resources planning and management is crucial and challenging in semi-arid regions to minimize water scarcity. Potential impacts due to climate change are a concern to water managers and stakeholders in semi-arid river basins with limited water availability. This study provides a probabilistic assessment of climate change impacts on water scarcity in the Sevier River Basin of Utah, which has a snowmelt-driven water supply and high agricultural water demands, using a decision-scaling framework. The methodology consists of a bottom-up approach that uses climate response functions, together with projections from 31 general circulation models (GCMs), to assess vulnerability to water scarcity for 2000–2099. Water scarcity is defined using an index comparing water availability to crop water demand predicted by the AquaCrop model from the Food and Agriculture Organization. Results showed that off-season precipitation is the most sensitive factor affecting water scarcity in the basin, followed by precipitation and temperature during the growing seasons. The GCM projections of temperature and precipitation suggest an increasing availability of water for agriculture in the basin. Still, a considerable risk probability of agricultural water shortage was found in years 2025 through 2049 with the emission scenario RCP4.5, suggesting the need for adaptation and mitigation strategies. The bottom-up decision scaling approach used here with a wide range of GCMs was practical to explore climate risk to agricultural water scarcity given the simplicity and minimal computational requirement.}, journal={AGRICULTURAL WATER MANAGEMENT}, author={Moursi, Hossam and Kim, Daeha and Kaluarachchi, Jagath J.}, year={2017}, month={Nov}, pages={142–152} }