Preprints
https://doi.org/10.5194/egusphere-2025-1102
https://doi.org/10.5194/egusphere-2025-1102
15 May 2025
 | 15 May 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Sensitivity of a Sahelian groundwater-based agroforestry system to tree density and water availability using the land surface model ORCHIDEE (r7949)

Espoir Koudjo Gaglo, Emeline Chaste, Sebastiaan Luyssaert, Olivier Roupsard, Christophe Jourdan, Sidy Sow, Nadeige Vandewalle, Frédéric Do, Daouda Ngom, and Aude Valade

Abstract. The Sahel region is characterized by its semi-arid climate and open-canopy agroforestry systems which play an important role in global carbon dynamics by sequestering an estimated 0.4 Mg C ha-1 yr-1, contributing to a total potential sequestration of approximately 558 Tg C if the agroforestry systems reach their maximum extent. However, land surface models (LSM) used in global climate modeling struggle to represent carbon dynamics in these ecosystems due to the inadequate representation of deep-roots tapping groundwater during dry periods, key environmental control for many agroforestry systems such as the widespread parklands based on the phreatophytic species Faidherbia albida. This study explores the sensitivity of Faidherbia albida parklands to tree density and water availability (rainfall and soil water content in the capillary fringe of the groundwater table) using a new configuration of the ORCHIDEE LSM. To this aim, the ORCHIDEE LSM was modified to simulate the growth of Faidherbia albida by simulating its inverted phenology based on forced temporal series of soil water content of soil layers between 4 m and 5 m and water saturation below 5 m and by adjusting the photosynthesis and carbon allocation parameters for Faidherbia albida and associated crops. The model was evaluated against independent eddy covariance and meteorological data from the Niakhar agroforestry site in Senegal. Simulation outputs were analyzed in terms of leaf area index (LAI), gross primary productivity (GPP), latent heat (LE), sensible heat (H) and net radiation (Rn). The model simulated tree GPP of 4.08 ± 0.21 tC ha-1 yr-1 compared to observed GPP of 5.06 ± 0.49 tC ha-1 yr-1. For croplands, the model produced GPP of 7.97 ± 0.89 tC ha-1 yr-1 compared to observed values of 7.78 ± 1.75 tC ha-1 yr-1. Simulations revealed that tree density positively influenced annual carbon uptake but reduced crop harvest at highest tree densities, indicating a trade-off between carbon sequestration and crop yield. Sensitivity analyses showed that interannual variability in soil water content in the capillary fringe of the groundwater table and rainfall influenced differently crop, tree and ecosystem carbon and energy fluxes. Despite its strengths, the model exhibited limited responsiveness of tree productivity to soil water content variability in the capillary fringe of the groundwater table, highlighting the need for enhanced representation of water uptake by tree roots in the model. These findings emphasize the importance of accurately modeling both surface soil water and groundwater dynamics and phenology to predict the responses of semi-arid agroforestry systems to climate variability. This study enhances our understanding of carbon and energy flux partitioning in complex, water-stressed and groundwater dependent agroforestry systems.

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Espoir Koudjo Gaglo, Emeline Chaste, Sebastiaan Luyssaert, Olivier Roupsard, Christophe Jourdan, Sidy Sow, Nadeige Vandewalle, Frédéric Do, Daouda Ngom, and Aude Valade

Status: open (until 11 Jul 2025)

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Espoir Koudjo Gaglo, Emeline Chaste, Sebastiaan Luyssaert, Olivier Roupsard, Christophe Jourdan, Sidy Sow, Nadeige Vandewalle, Frédéric Do, Daouda Ngom, and Aude Valade
Espoir Koudjo Gaglo, Emeline Chaste, Sebastiaan Luyssaert, Olivier Roupsard, Christophe Jourdan, Sidy Sow, Nadeige Vandewalle, Frédéric Do, Daouda Ngom, and Aude Valade

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Short summary
Agroforestry in the Sahel help store carbon and support food production, but land surface models struggle to capture their dynamics. We adapted the ORCHIDEE model to simulate Faidherbia albida, a tree that taps deep groundwater. This work highlights the need to integrate deep water uptake in land surface models for groundwater-dependent ecosystems, as it could enhance predictions, helping to sustain agroforestry in a changing climate.
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