ANN-Reservoir_model-code/README.Rmd at main · busemorose/ANN-Reservoir_model-code · GitHub

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---
title: 'Model code: Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions'
author: "Guillaume Cinkus, Andreas Wunsch, Naomi Mazzilli, Tanja Liesch, Zhao Chen, Nataša Ravbar, Joanna Doummar, Jaime Fernández-Ortega, Juan Antonio Barberá, Bartolomé Andreo, Nico Goldscheider and Hervé Jourde"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Description

This repository contains the following elements:

- Reservoir model code
    - KarstMod files for each studied system
    - R script for performing the snow routine
- ANN model code

# Workflow

## Snow routine

The snow routine is detailed in the appendix D of the manuscript. The routine is inspired from the work of Chen et al. (2018), which successfully simulated spring discharge of a mountainous karst system heavily influenced by snow accumulation and melt. The workflow is:

1. Get time series of (i) precipitation, (ii) temperature, and (iii) potential clear-sky solar radiation (if needed)
2. Define subcatchment (if needed) then calculate their areas and their relative proportion to the whole catchment
3. Apply the snow routine function for each subcatchment. We recommend to shift the temperature time series according to an appropriate temperature gradient scaling with altitude. The inputs for the snow routine function are:
    - temperature vector (T,1)
    - precipitation vector (T,1)
    - potential clear-sky solar radiation vector (T,1)
    - model parameters vector: temperature threshold, melt factor, refreezing factor, water holding capacity of snow and radiation coefficient (T,5)
4. Apply the relative proportion of each subcatchment to their corresponding P time series (output of the snow routine)
5. Sum up the P time series of each subcatchment

**If working without solar radiation, radiation coefficient parameter needs to be `0` and potential clear-sky solar radiation must be a vector of `0` of the same length as temperature and precipitation time series.**

## KarstMod

```{r echo=FALSE, out.width=70}
knitr::include_graphics("data/karstmod.png")
```

Information on the KarstMod platform (Mazzilli et al., 2019b) can be found in the section 3.2 of the manuscript. The main workflow is:

1. Prepare the input data
2. Open the appropriate KarstMod file (if needed)
3. Import the input data
4. Define warm-up/calibration/validation periods
5. Define Output directory
6. Run calibration

It is possible to modify the model parameters, the objective function, the number of iterations, the maximum time, and other options. The `Save` button allows to save the new modifications and to get a new KarstMod file.

# Resources

For more details about the KarstMod platform, please refer to the User manual provided below (Mazzilli and Bertin, 2019a).

For more details about the hydrological models, please refer to the section 3 of the manuscript.

Download KarstMod: https://sokarst.org/en/softwares-en/karstmod-en/

Download KarstMod User manual: https://hal.archives-ouvertes.fr/hal-01832693

## References

Chen, Z., Hartmann, A., Wagener, T., and Goldscheider, N.: Dynamics of water fluxes and storages in an Alpine karst catchment under current and potential future climate conditions, Hydrol. Earth Syst. Sci., 22, 3807–3823, https://doi.org/10.5194/hess-22-3807-2018, 2018.

Hock, R.: A distributed temperature-index ice- and snowmelt model including potential direct solar radiation, J. Glaciol., 45, 101–111, https://doi.org/10.3189/S0022143000003087, 1999.

Mazzilli, N. and Bertin, D.: KarstMod User Guide - version 2.2, hal-01832693, 103927, 2019a.

Mazzilli, N., Guinot, V., Jourde, H., Lecoq, N., Labat, D., Arfib, B., Baudement, C., Danquigny, C., Soglio, L. D., and Bertin, D.: KarstMod: A modelling platform for rainfall - discharge analysis and modelling dedicated to karst systems, Environ. Model. Softw., 122, 103927, https://doi.org/10.1016/j.envsoft.2017.03.015, 2019b.

Pianosi, F., Sarrazin, F., and Wagener, T.: A Matlab toolbox for Global Sensitivity Analysis, Environmental Modelling & Software, 70, 80–85, https://doi.org/10.1016/j.envsoft.2015.04.009, 2015.