# Modeling pesticides fate in a river network using the aquatic biogeochemistry model pyNuts-Riverstrahler

Adrien Renaud, Vincent Thieu, Helene Blanchoud, Marie Silvestre

► To cite this version:

Adrien Renaud, Vincent Thieu, Helene Blanchoud, Marie Silvestre. Modeling pesticides fate in a river network using the aquatic biogeochemistry model pyNuts-Riverstrahler. 3rd OZCAR-TERENO Conference, Sep 2025, Paris, France. hal-05316259

HAL Id: hal-05316259

https://hal.science/hal-05316259v1

Submitted on 15 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Modeling pesticides fate in a river network using the aquatic biogeochemistry model pyNuts-Riverstrahler

Adrien Renaud(1), Vincent Thieu(1), Hélène Blanchoud(1), Marie Silvestre(1)

<figure><img src="image_2.png" /></figure>

adrien.renaud@sorbonne-universite.fr

## CONTEXT

<figure><img src="image_3.png" /></figure>

[1] T. Puech, C. Schott, C. Mignolet. Characterising the diversity and spatial differentiation of crop managements at a regional scale. European Journal of Agronomy, 2020, 120, pp.126112. (10.1016/j.eja.2020.126112).

[2] Gallois, N., Modélisation actualisée de l'état de pollution nitrique d'origine agricole des eaux souterraines du territoire Seine-Normandie sur la période 1970-2022. Dans Gallois, N. et Thieu, V. (eds.), Impact des pratiques agricoles sur la qualité de l'eau, PIREN-Seine, Rapport de synthèse phase VIII, Volume 2, 2024, pp. 5-35

This work is part of a project aimed at providing an integrated multi-scale approach to the life cycle of pesticides, from the agricultural plots to macro-regional territory.

It relies on an existing model chain, initially dedicated to nutrients life-cycle, and composed of:

1. an agronomic model (Stics).

2. a distributed physics-based hydrogeological model (CaWaQS).

3. a river biogeochemical model pyNuts-Riverstrahler.

**Poster goal:** to present the developments made on the pyNuts-Riverstrahler model with a first validation exercise on the Seine catchment.

## METHOD

### THE MODEL

<figure><img src="image_4.png" /></figure>

### PROCESSES IMPLEMENTED

Sorption/desorption on Particulate Organic Matter using linear model ($K_{oc}$):

$$C_w = \frac{1}{1 + K_{oc} * POM_{tot}} C_{tot}$$

$$C_s = \frac{K_{oc} * POM_{tot}}{1 + K_{oc} * POM_{tot}} C_{tot}$$

Biodegradation as a first order reaction in water column (w) and benthic layer (s):

No restriction on parent to metabolites relationships (multiples and in cascade) ...

$$C_{(w,s)}(t) = C_{(w,s)_0} e^{-\frac{\ln 2}{DT_{50(w,s)}} t} \\ m_{m(w,s)} = \text{degfrac} * m_{p(w,s)} * \frac{M_m}{M_p}$$

<figure><img src="image_5.png" /></figure>

... or on active substances

## CASE STUDY

### STUDY AREA

#### Seine basin

76 000 km² drainage area
27 500 km of river network

#### Modeling period

2016-2021

### WORKING ASSUMPTIONS

1. Only pyNuts-Riverstrahler modeling (no implementation on other models yet).

2. Atrazine and its metabolite the DEA
   → forbidden since 2001
   → hypothesis: groundwater as the only remaining transfer pathway

### INPUTS

1. Water fluxes from CaWaQS model of the Seine basin at a daily timestep.

2. Atrazine and DEA inputs estimated as the average interannual values in groundwater bodies based on the national water quality analysis database (Hub'eau API).

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

### RESULTS

<figure><img src="image_8.png" /></figure>

1. Seasonal variations due to water flow seasonality.

2. Over-estimated concentrations
   → bad assumption ?

→ missed processes ?

### 3. Pesticides properties

<table><thead><tr><td>Pesticide</td><td>K<sub>oc</sub> (L.kgC<sup>-1</sup>)</td><td>DT50<sub>w</sub> (d)</td><td>DT50<sub>s</sub> (d)</td><td>Metabolite</td><td>degFrac</td></tr></thead><tbody><tr><td>Atrazine</td><td>100</td><td>-</td><td>80</td><td>DEA</td><td>0.2</td></tr><tr><td>DEA</td><td>110</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

## PERSPECTIVES

1. Add the possibility to use non-linear models for sorption processes.

2. Add photolysis and hydrolysis as degradation processes (more relevant in rivers).

3. Improve code to monitor processes to calculate detailed mass balance.

4. Study the effect of pesticides on microorganisms modeled by the RIVE model.

5. Complete the model chain from crops to the river outlet.

<footnote>(1) Sorbonne Université, Université PSL, EPHE, CNRS, Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols, METIS, F-75005 Paris, France</footnote>