Model Library

Capabilities

• User-friendly with simpler input requirements than models like SWAT, SWMM, and HSPF;
• It includes algorithms for septic loads and point source discharge data;
• GWLF-E: modernized, more accessible, and enhanced version of the original GWLF;
• GWLF allows various input datasets;
• GWLF uses lumped parameter approaches for groundwater and flow-related processes, simplifying the modeling of complex dynamics like groundwater flow and nutrient transport​.

 Limitations

• GWLF is not an event model, predictions are monthly or yearly averages or totals (Borah et al., 2019);
• The model requires distributed inputs for pollutant simulation but lacks spatial structure and flow channel routing (Yuan et al., 2020);
• GWLF uses lumped parameters and a linear reservoir model, ignoring spatial variability in transport processes in groundwater modeling (Yuan et al, 2020);
• GWLF-E lacks soil P dynamics simulation, relying on specified empirical P concentrations (Neumann et al., 2021);
• Channel routing is not considered with GWLF-E (Neumann et al., 2021);
• In GWLF-E, instream processes are limited and poorly modeled, focusing mainly on the transformation between dissolved and particulate phosphorus during low-flow conditions​ (Neumann et al., 2021).

Inputs

Topography data, LULC data, Soil data, Precipitation (Daily time step), Temperature (Daily time step), Runoff sources and transport, and Chemical parameters on a daily time step.

Outputs

Monthly flow, soil erosion, and sediment yield, monthly TN and TP loads in flow, monthly groundwater discharge, annual erosion by land use, and yearly TN and TP loads for each land use type.

References

Wang, H., Lu, K., Zhao, Y., et al. (2020). Multi-model ensemble simulated non-point source pollution based on Bayesian model averaging method and model uncertainty analysis. Environmental Science and Pollution Research, 27, 44482–44493. https://doi.org/10.1007/s11356-020-10336-8

Qi, Z., Kang, G., Wu, X., Sun, Y., & Wang, Y. (2020). Multi-objective optimization for selecting and siting the cost-effective BMPs by coupling revised GWLF model and NSGAII algorithm. Water, 12, 235. https://doi.org/10.3390/w12010235

Objectives

The objective of this study was to present the use of multi-model ensemble applied to streamflow, total nitrogen (TN), and total phosphorus (TP) simulation and quantify the uncertainty resulting from model structure.

The objectives of the study are: (1) to add the nutrient channel routing algorithms into the RGWLF then calibrating and verifying the parameters of the model; (2) to determine the specific point source and non-point source management measures based on the established model; and (3) to couple the RGWLF model and NSGAII optimization algorithm based on a parameter sensitivity analysis to identify the optimal spatial allocation of BMPs for dissolved nitrogen.