Supplemental Material for: Multi-site evaluation of APEX for water quality: II. Regional parameterization

Model performance was assessed using Nash-Sutcliffe model efficiency (NSE), coefficient of determination (r2), and percent bias (PBIAS) as defined by Moriasi et al. (2007 and 2015). Threshold values indicating acceptable model performance based on these statistics are dependent on the spatial and temporal scales of the data, water quality constituents of interest, and the modeling objectives (Moriasi et al., 2015). Although some standard values have been suggested (Moriasi et al., 2007 and 2015), considerable variability exist in the published literature. For instance Ramanarayan et al. (1997) considered r2 \u3e0.5 and NSE \u3e0.40 as satisfactory for simulation of monthly surface water quality with the APEX model. Chung et al. (2002) defined r2 \u3e 0.5 and NSE \u3e 0.3 as satisfactory for monthly tile flow and NO3-N loss simulated with the Erosion Productivity Impact Calculator (EPIC) model. Wang et al. (2008) indicated r2 \u3e 0.5 and NSE \u3e 0.4 as acceptable for monthly runoff and nutrient concentrations using the APEX model. Moriasi et al. (2007) suggested NSE \u3e 0.5 with P-bias ±25% for streamflow, ±55% for sediment and ±70% for nitrogen and phosphorus for monthly values. They also indicated that NSE values can be relaxed for shorter time steps (daily events). Yin et al. (2009) reported NSE for event based runoff and sediment between 0.41-0.84 and r2 between 0.55 - 0.85. Mudgal et al. (2010) regarded r2 \u3e 0.5 and NSE \u3e 0.45 as threshold for satisfactory calibration and validation with event data

Supplemental Material for: Multi-site evaluation of APEX for water quality: II. Regional parameterization

Model performance was assessed using Nash-Sutcliffe model efficiency (NSE), coefficient of determination (r2), and percent bias (PBIAS) as defined by Moriasi et al. (2007 and 2015). Threshold values indicating acceptable model performance based on these statistics are dependent on the spatial and temporal scales of the data, water quality constituents of interest, and the modeling objectives (Moriasi et al., 2015). Although some standard values have been suggested (Moriasi et al., 2007 and 2015), considerable variability exist in the published literature. For instance Ramanarayan et al. (1997) considered r2 \u3e0.5 and NSE \u3e0.40 as satisfactory for simulation of monthly surface water quality with the APEX model. Chung et al. (2002) defined r2 \u3e 0.5 and NSE \u3e 0.3 as satisfactory for monthly tile flow and NO3-N loss simulated with the Erosion Productivity Impact Calculator (EPIC) model. Wang et al. (2008) indicated r2 \u3e 0.5 and NSE \u3e 0.4 as acceptable for monthly runoff and nutrient concentrations using the APEX model. Moriasi et al. (2007) suggested NSE \u3e 0.5 with P-bias ±25% for streamflow, ±55% for sediment and ±70% for nitrogen and phosphorus for monthly values. They also indicated that NSE values can be relaxed for shorter time steps (daily events). Yin et al. (2009) reported NSE for event based runoff and sediment between 0.41-0.84 and r2 between 0.55 - 0.85. Mudgal et al. (2010) regarded r2 \u3e 0.5 and NSE \u3e 0.45 as threshold for satisfactory calibration and validation with event data

Multisite Evaluation of APEX for Water Quality: I. Best Professional Judgment Parameterization

The Agricultural Policy Environmental eXtender (APEX) model is capable of estimating edge-of-field water, nutrient, and sediment transport and is used to assess the environmental impacts of management practices. The current practice is to fully calibrate the model for each site simulation, a task that requires resources and data not always available. The objective of this study was to compare model performance for flow, sediment, and phosphorus transport under two parameterization schemes: a best professional judgment (BPJ) parameterization based on readily available data and a fully calibrated parameterization based on site-specific soil, weather, event flow, and water quality data. The analysis was conducted using 12 datasets at four locations representing poorly drained soils and row-crop production under different tillage systems. Model performance was based on the Nash–Sutcliffe efficiency (NSE), the coefficient of determination (r2) and the regression slope between simulated and measured annualized loads across all site years. Although the BPJ model performance for flow was acceptable (NSE = 0.7) at the annual time step, calibration improved it (NSE = 0.9). Acceptable simulation of sediment and total phosphorus transport (NSE = 0.5 and 0.9, respectively) was obtained only after full calibration at each site. Given the unacceptable performance of the BPJ approach, uncalibrated use of APEX for planning or management purposes may be misleading. Model calibration with water quality data prior to using APEX for simulating sediment and total phosphorus loss is essential

Application of electrospun cellulose acetate nanofibre membrane based quasi-solid state electrolyte for dye sensitized solar cells

Dye Sensitized Solar Cells (DSSCs) are much more eco-friendly, low cost and easy to fabricate devices compared to silicon solar cells. In order to overcome their problems with safety and the long-term stability due to sealing problems, liquid electrolytes employed in these devices are replaced with quasi solid-state or gel electrolytes even though the overall efficiencies are somewhat lower. In this study DSSCs are fabricated and tested by replacing the conventional liquid electrolyte using a quasi–solid state electrolyte comprised of biodegradable electro spun cellulose acetate (CA) nanofibres. DSSCs fabricated with this quasi-solid state electrolytes showed an overall light to electricity conversion efficiency of 4.0% under the illumination of 100 mW cm-2 (AM 1.5). The corresponding values of short circuit current density (Jsc), open circuit voltage (Voc) and the fill factor (FF) of this device were 9.83mA cm-2, 699.1 mV and 0.58 respectively