This repository contains the code for the paper "Exploring Nonlinear Responses of Lake Nutrients and Algal Blooms to Restoration Measures: A Three-Dimensional Flux Network Modelling Approach".

The parameter configurations of the EFDC model can be found in the SI.

All directories and coefficients should be assigned in the python script file meta.py.

Run the Python script water_quality_cal.py to calculate the TN, TP and Chla. Set the scenarios to compute values for certain scenarios. The default is to calculate all the scenarios.

Run the Python script flux_cal.py to calculate the nutrient storages and fluxes. Set the scenarios to compute values for certain scenarios. The default is to calculate all the scenarios.

Run the Python script flux_network_extract.py to build the nutrient networks based on nutrient storages and fluxes. Set the parameter frequency can control the network aggregation periods. Available values: "m" for monthly average and "d" for daily average.

Ecological network analysis (ENA) is a widely used method in the food web assessments. Here, it was used to provide systematic information on N and P cycling in lake ecosystems. Set the correct directory of the network files and then run the R script flow_analysis.R to get the values of indicators and visualization results.

Files to create the figures in the main text:

• water_quality_calibration_maintext.py: Fig. 2;
• flux_stack_maintext.py: Fig. 3;
• water_quality_scenario_maintext_si.py: Figs. 4a, 4b, 5a, and 5b;
• flux_network_maintext.py: Figs. 4c, 4d, 5c, and 5d;
• nonlinearity_maintext.py: Fig. 7.

Files to create the figures in the SI:

• water_quality_calibration_si.py: Fig. S1-S7;
• water_quality_calibration_area.py Fig. S8;
• water_quality_scenario_maintext_si.py: Figs. S9 and S10;
• flux_network_si.py: Figs. S11-S17.