ABSTRACT As the global population approaches 9 billion by 2050, challenges of food and water scarcity intensify. Hydroponics, an innovative and eco-friendly technology, has gained prominence in addressing these challenges. This study employs Life Cycle Assessment (LCA) to comprehensively evaluate the environmental and economic impacts of utilizing reclaimed water in a hydroponic system. Results from midpoint, endpoint, and normalized analyses reveal key contributors to the hydroponic system's environmental burden, including water, substrates, fertilizers, and energy sources. Significant impacts have been observed in marine and terrestrial ecotoxicity, as well as photochemical ozone formation. Reclaimed water consistently demonstrates lower environmental impacts compared to conventional water across various indicators, such as climate change (131 kg CO2 eq.), fine particulate matter formation (0.108 kg PM2.5 eq.), and freshwater consumption (0.291 cubic meters). The study emphasizes the potential of hydroponics with reclaimed water to offer sustainable and environmentally friendly agricultural practices. The detailed LCA results provide valuable insights for policymakers and stakeholders, promoting the adoption of hydroponics to address food and water scarcity challenges. From the findings, reclaimed water in hydroponics lowers the environmental impacts as compared to conventional water and PVC (Polyvinyl chloride) along with electricity is the major contributor in environmental burden.
Using a model as a management tool requires testing of the model against field measured data priorto its application for solving natural resource problems. This study was designed to calibrate and evaluate thesubsurface drainage component of the Soil Water Assessment Tool (SWAT) model for three managementsystems at a research site near Nashua, Iowa: continuous corn - chisel plow, corn-soybean - no-till andsoybean-corn - no-till. Each system was analyzed for two different research plots that varied in soil type andslope gradient. Calibration was performed with 1995 measured tile drain flows while validation was carried outusing measured tile drain flows for 1993-1994 and 1996-1997. In general, SWAT adequately tracked themeasured tile drain flows, except that the peak flows were consistently under-predicted. Differences of 2 to11% between the predicted and measured values and model efficiencies ranging between 0.47 to 0.67 weredetermined for the average annual simulated tile flows. The r2 values determined for the simulated monthly tiledrain flows ranged from 0.70 to 0.97 for the calibration period and 0.49 to 0.67 for the validation period. Theoverall evaluation of the SWAT model indicates that the model has the capability of predicting subsurface flowssatisfactory for different soil, slope, and weather conditions.
Abstract Precise prediction of streamflow ensures reliable planning and management of water resources. Physical-based prediction models are prone to significant uncertainties due to the complexity of processes involved as well as due to the uncertainties in model parameters and parameterizations. This study evaluates the performance of daily streamflow prediction in Astore a snow-fed mountainous region, by coupling physical-based semi-distributed hydrological Soil and Water Assessment Tool (SWAT) with data-driven (DD) Bidirectional Long Short-Term Memory (BiLSTM) model. Firstly SWAT and BiLSTM models are calibrated individually then coupled in three modes; SWAT-D-BiLSTM: flows obtained from SWAT with default parameters values used as one of the input in BiLSTM, SWAT-T-BiLSTM: flows obtained from SWAT with three most sensitive parameters values used as one of the input in BiLSTM and SWAT-A-BiLSTM: flows obtained from SWAT with all sensitive parameters values used as one of the input in BiLSTM. Input selection for DD model was carried out by cross correlation analysis of temperature, precipitation, and total rainfall with streamflow. The calibration, validation, and prediction of coupled models are carried out for periods 2007–2011, 2012–2015 and 2017–2019, respectively. Prediction performance is evaluated based on Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R 2 ), and Percentage Bias (PBIAS). Temperature showed greater correlation of 0.7 at 1-day lag as compared to precipitation and total rainfall with streamflow at daily time scale. The results showed that integrated model SWAT-A-BiLSTM outperformed SWAT-T-BiLSTM followed by SWAT-D-BiLSTM, BiLSTM and SWAT respectively. This study recommends coupling of hydrological models facing uncertainties with DD models.
A validation study has been performed using the Soil and Water Assessment Tool (SWAT) model with data collected for the Upper Maquoketa River Watershed (UMRW), which drains over 16,000 ha in northeast Iowa. This validation assessment builds on a previous study with nested modeling for the UMRW that required both the Agricultural Policy EXtender (APEX) model and SWAT. In the nested modeling approach, edge-of-field flows and pollutant load estimates were generated for manure application fields with APEX and were then subsequently routed to the watershed outlet in SWAT, along with flows and pollutant loadings estimated for the rest of the watershed routed to the watershed outlet. In the current study, the entire UMRW cropland area was simulated in SWAT, which required translating the APEX subareas into SWAT hydrologic response units (HRUs). Calibration and validation of the SWAT output was performed by comparing predicted flow and NO3-N loadings with corresponding in-stream measurements at the watershed outlet from 1999 to 2001. Annual stream flows measured at the watershed outlet were greatly under-predicted when precipitation data collected within the watershed during the 1999-2001 period were used to drive SWAT. Selection of alternative climate data resulted in greatly improved average annual stream predictions, and also relatively strong r2 values of 0.73 and 0.72 for the predicted average monthly flows and NO3-N loads, respectively. The impact of alternative precipitation data shows that as average annual precipitation increases 19%, the relative change in average annual streamflow is about 55%. In summary, the results of this study show that SWAT can replicate measured trends for this watershed and that climate inputs are very important for validating SWAT and other water quality models.
A validation study has been performed using the Soil and Water Assessment Tool (SWAT) model with data collected for the Upper Maquoketa River Watershed (UMRW), which drains over 16,000 ha in northeast Iowa. This validation assessment builds on a previous study with nested modeling for the UMRW that required both the Agricultural Policy EXtender (APEX) model and SWAT. In the nested modeling approach, edge-of-field flows and pollutant load estimates were generated for manure application fields with APEX and were then subsequently routed to the watershed outlet in SWAT, along with flows and pollutant loadings estimated for the rest of the watershed routed to the watershed outlet. In the current study, the entire UMRW cropland area was simulated in SWAT, which required translating the APEX subareas into SWAT hydrologic response units (HRUs). Calibration and validation of the SWAT output was performed by comparing predicted flow and NO3-N loadings with corresponding in-stream measurements at the watershed outlet from 1999 to 2001. Annual stream flows measured at the watershed outlet were greatly under-predicted when precipitation data collected within the watershed during the 1999-2001 period were used to drive SWAT. Selection of alternative climate data resulted in greatly improved average annual stream predictions, and also relatively strong r2 values of 0.73 and 0.72 for the predicted average monthly flows and NO3-N loads, respectively. The impact of alternative precipitation data shows that as average annual precipitation increases 19%, the relative change in average annual streamflow is about 55%. In summary, the results of this study show that SWAT can replicate measured trends for this watershed and that climate inputs are very important for validating SWAT and other water quality models.
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