Model Library

Capabilities

• SimplyP has fewer parameters than other models, making it quicker, cheaper, and more feasible for auto-calibration/uncertainty analysis and formal model comparison frameworks (Jackson-Blake et al., 2017);
• SimplyP retains enough complexity for research, policy, and land management scenario investigations (Jackson-Blake et al., 2017);
• SimplyP is simple but performs comparably to complex models, such as INCA-P, during calibration and testing in similar conditions (Jackson-Blake et al., 2017);
• SimplyP TDP peaks are less broad and more responsive to hydrological inputs compared to INCA-P, even when INCA-P’s soil water time constants were reduced (Jackson-Blake et al., 2017);
• SimplyP performs similarly for PP, SS, and TP, and may slightly outperform INCA-P in discharge and TDP modeling (Jackson-Blake et al., 2017).

Limitations

• The model uses manual calibration, which is time-consuming and may not yield better or more robust results with automatic calibration (Gao et al., 2024);
• Many processes included in INCA-P are omitted in SimplyP, including seasonal variability in soil water TDP concentrations and detailed sediment-related equations (Jackson-Blake et al., 2017);
• SimplyP simplifies key hydrological and sediment processes, assuming dynamic equilibrium in streams and reducing the complexity of sediment and P process representations (Jackson-Blake et al., 2017);
• Fertilizer, manure, and plant uptake fluxes are aggregated into a single gross annual P balance parameter, evenly applied or subtracted throughout the year, simplifying soil P processes (Jackson-Blake et al., 2017);
• Incidental P losses, potentially large P fluxes washed into watercourses when rainfall events coincide with fresh fertilizer and manure applications, are not yet included (Jackson-Blake et al., 2017);
• The Python version of the model is slow, but it has been recoded in C++ using the MOBIUS model building framework to improve performance (Jackson-Blake, 2019).

Inputs

Time series of daily precipitation, air temperature, Potential evapotranspiration (PET), LULC, Soil data, Discharge (optional), Water quality data (optional).

Outputs

Time series of daily fluxes and flow-weighted daily mean concentrations of TDP, PP and SS, and daily mean flow. The state of the internal stores may also be output (e.g., snow depth, volumes and flows from the two water stores, and P masses in the different stores).

References

Gao, L., Huang, X., Chen, Z., Zhuge, X., Tong, Y., Lu, X., & Lin, Y. (2024). Controlling phosphorus transport in Poyang Lake Basin under the constraints of climate change and crop yield increase. Water, 16(2), 295. https://doi.org/10.3390/w16020295

Jackson-Blake, L. A., Sample, J. E., Wade, A. J., Helliwell, R. C., & Skeffington, R. A. (2017). Are our dynamic water quality models too complex? A comparison of a new parsimonious phosphorus model, SimplyP, and INCA-P. Water Resources Research, 53(7), 5382–5399. https://doi.org/10.1002/2016WR020132

Objectives

This study aimed to: (1) identify the spatial distribution characteristics of anthropogenic source phosphorus input intensity in PLB; (2) identify the impact of climate change on the TP loading of typical rivers in the PLB; (3) identify the characteristics of phosphorus transport in PLB under the premise of climate change and crop yield increase.

This study developed a parsimonious phosphorus model, SimplyP, incorporating a rainfall-runoff model and a biogeochemical model able to simulate daily streamflow, suspended sediment, and particulate and dissolved phosphorus dynamics. in a small rural catchment in northeast Scot-land. The model’s complexity was then compared to one popular nutrient model, INCA-P.