This repository contains the climate input data, model output data, and code used in the paper titled "Sensitivity of a Sahelian groundwater-based agroforestry system to tree density and water availability using the land surface model ORCHIDEE (r7949)".Files Description:CO2_1700_2022_TRENDYv2023: Atmospheric CO2 concentration in ppm from 1700 to 2023.crujra_twdeg_v2.2.2_1901_1962: CRUJRA dataset used for model spin-up and the first transient simulation (1901-1962).corrected_crujra_twodeg_v2.2.2_1963_2020: Corrected CRUJRA dataset for the Faidherbia-Flux observatory site, used for the second transient simulation (1963-2020).in_situ_data_Faidherbia_Flux_2018_2023: In-situ climate dataset from the Faidherbia-Flux observatory (2018-2023) used for model calibration and evaluation.Rain_avg_2019-2021: Climate dataset used to analyze the effect of year-to-year rainfall fluctuations on gross primary productivity and energy fluxes.ORCHIDEE_Faidherbia_Flux: Contains the scripts, ORCHIDEE model output data, and the observations dataset. Note: Before use, replace the placeholder paths in the provided notebooks with your local file paths and ensure all necessary R and Python packages are installed in your environment.

This dataset accompanies the manuscript:Houphouet, A.D.L., Sangne, Y.C., Adou Yao, Y.C., Betbeder, J., Diarrassouba, A., & Hérault, B. (2024).Forest Structure Recovery Around West Africa’s Last Great Rainforest: Modeling Complex Dynamics in Taï National Park.It provides detailed plot-level forest structure and environmental data used to model forest recovery in and around Taï National Park, one of the largest remaining blocks of tropical rainforest in West Africa.📁 Included Datasets1. data_2ry.csv — Secondary Forest Plots104 plots sampled in regenerating forests after agricultural or mining use.Variables:Plot_ID: Unique plot identifierx, y: Longitude and latitude (decimal degrees, WGS84)year: Time since land abandonment (years)Gini: Structural homogeneity (unitless, inverse of tree size inequality)QMD: Quadratic Mean Diameter (cm)Lorey: Lorey’s Height (m, basal area-weighted tree height)AGB_Ha: Aboveground biomass (tons/ha)land_use: Previous land use (1 = Cocoa, 2 = Mining, 3 = Other)Animals: Number of mammal species observed via indirect signs (integer, 0–3+)water: Soil hydromorphy (1 = Hydromorphic, 0 = Well-drained)Maranthaceae: Presence of dense Marantaceae understory (0/1)AGB_ha_rmnt: Biomass of remnant trees (tons/ha)HumAct: Human disturbance score (0–3)pld_250: % forest cover within 250 m buffer (forest connectivity)2. data_OGF_und.csv — Undisturbed Old-Growth Forest7 plots in intact old-growth forest used as reference.Variables:Plot_ID, x, y, Gini, QMD, Lorey, Maranthaceae (same definitions as above)3. data_OGF_dist.csv — Disturbed Old-Growth Forest7 plots impacted by selective logging or extractive activities.Same variables as data_OGF_und.csv.4. data_OGF_sup.csv — Supplementary Old-Growth Forest (DBH ≥ 10 cm only)25 plots where only trees ≥10 cm DBH were inventoried (used to calibrate asymptotic reference values).Variables:Plot_ID, x, y, Gini, QMD, Lorey🗺️ Study Area and MethodsLocation: Taï National Park and surrounding landscape, Côte d’IvoirePeriod: September 2023 – July 2024Plot size: 625 m² (25 × 25 m)DBH threshold: ≥ 2.5 cm for most datasets; ≥10 cm for data_OGF_supBiomass calculation: Based on species-level allometries (Réjou-Méchain et al., 2017)📌 NotesVariable names align with those in the manuscript.Coordinates may be slightly imprecise to protect sensitive locations.Data are suitable for forest structure modeling and Bayesian recovery analysis.

This dataset accompanies the manuscript:Houphouet, A.D.L., Sangne, Y.C., Adou Yao, Y.C., Betbeder, J., Diarrassouba, A., & Hérault, B. (2024).Forest Structure Recovery Around West Africa’s Last Great Rainforest: Modeling Complex Dynamics in Taï National Park.It provides detailed plot-level forest structure and environmental data used to model forest recovery in and around Taï National Park, one of the largest remaining blocks of tropical rainforest in West Africa.📁 Included Datasets1. data_2ry.csv — Secondary Forest Plots104 plots sampled in regenerating forests after agricultural or mining use.Variables:Plot_ID: Unique plot identifierx, y: Longitude and latitude (decimal degrees, WGS84)year: Time since land abandonment (years)Gini: Structural homogeneity (unitless, inverse of tree size inequality)QMD: Quadratic Mean Diameter (cm)Lorey: Lorey’s Height (m, basal area-weighted tree height)AGB_Ha: Aboveground biomass (tons/ha)land_use: Previous land use (1 = Cocoa, 2 = Mining, 3 = Other)Animals: Number of mammal species observed via indirect signs (integer, 0–3+)water: Soil hydromorphy (1 = Hydromorphic, 0 = Well-drained)Maranthaceae: Presence of dense Marantaceae understory (0/1)AGB_ha_rmnt: Biomass of remnant trees (tons/ha)HumAct: Human disturbance score (0–3)pld_250: % forest cover within 250 m buffer (forest connectivity)2. data_OGF_und.csv — Undisturbed Old-Growth Forest7 plots in intact old-growth forest used as reference.Variables:Plot_ID, x, y, Gini, QMD, Lorey, Maranthaceae (same definitions as above)3. data_OGF_dist.csv — Disturbed Old-Growth Forest7 plots impacted by selective logging or extractive activities.Same variables as data_OGF_und.csv.4. data_OGF_sup.csv — Supplementary Old-Growth Forest (DBH ≥ 10 cm only)25 plots where only trees ≥10 cm DBH were inventoried (used to calibrate asymptotic reference values).Variables:Plot_ID, x, y, Gini, QMD, Lorey🗺️ Study Area and MethodsLocation: Taï National Park and surrounding landscape, Côte d’IvoirePeriod: September 2023 – July 2024Plot size: 625 m² (25 × 25 m)DBH threshold: ≥ 2.5 cm for most datasets; ≥10 cm for data_OGF_supBiomass calculation: Based on species-level allometries (Réjou-Méchain et al., 2017)📌 NotesVariable names align with those in the manuscript.Coordinates may be slightly imprecise to protect sensitive locations.Data are suitable for forest structure modeling and Bayesian recovery analysis.

This dataset contains the field data used for statistical analysis for allometric modelling of wood energy and above-ground biomass production of Faidherbia albida in northern Cameroon. It contains six files named as follows: - README : contain  the metadata on the data with thier descirption contained in the files;- Ecology (contains morphometric data on trees before pruning, their GPS coordinates, and the village in which the tree is located)- 2023_twigs (contains individual weights of piles of very small wood, twigs and branches grouped by tree)- 2023_small_wood (contains individual weights of bundles of small wood less than 15 cm in circumference at the thick end)- 2023_large_wood (contains individual weights of branch ends and large pieces of wood, sometimes grouped into bundles and grouped by pruned tree)- 2023_biomass_unpruned (contains individual measurements of each section of the trunk and branch starts, divided theoretically and to be used in volume calculations)- Individuals_values: contains a series of measurements taken on a variety of Faidherbia albida trees in the study villages. These morphometric measurements of Faidherbia albida individuals were used as a common basis for comparing predictive trends between our best equation and those drawn from the literature.

This dataset contains the field data used for statistical analysis for allometric modelling of wood energy and above-ground biomass production of Faidherbia albida in northern Cameroon. It contains six files named as follows: - README : contain  the metadata on the data with thier descirption contained in the files;- Ecology (contains morphometric data on trees before pruning, their GPS coordinates, and the village in which the tree is located)- 2023_twigs (contains individual weights of piles of very small wood, twigs and branches grouped by tree)- 2023_small_wood (contains individual weights of bundles of small wood less than 15 cm in circumference at the thick end)- 2023_large_wood (contains individual weights of branch ends and large pieces of wood, sometimes grouped into bundles and grouped by pruned tree)- 2023_biomass_unpruned (contains individual measurements of each section of the trunk and branch starts, divided theoretically and to be used in volume calculations)- Individuals_values: contains a series of measurements taken on a variety of Faidherbia albida trees in the study villages. These morphometric measurements of Faidherbia albida individuals were used as a common basis for comparing predictive trends between our best equation and those drawn from the literature.

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This repository contains the climate input data, model output data, and code used in the paper titled "Sensitivity of a Sahelian groundwater-based agroforestry system to tree density and water availability using the land surface model ORCHIDEE (r7949)".Files Description:CO2_1700_2022_TRENDYv2023: Atmospheric CO2 concentration in ppm from 1700 to 2023.crujra_twdeg_v2.2.2_1901_1962: CRUJRA dataset used for model spin-up and the first transient simulation (1901-1962).corrected_crujra_twodeg_v2.2.2_1963_2020: Corrected CRUJRA dataset for the Faidherbia-Flux observatory site, used for the second transient simulation (1963-2020).in_situ_data_Faidherbia_Flux_2018_2023: In-situ climate dataset from the Faidherbia-Flux observatory (2018-2023) used for model calibration and evaluation.Rain_avg_2019-2021: Climate dataset used to analyze the effect of year-to-year rainfall fluctuations on gross primary productivity and energy fluxes.ORCHIDEE_Faidherbia_Flux: Contains the scripts, ORCHIDEE model output data, and the observations dataset. Note: Before use, replace the placeholder paths in the provided notebooks with your local file paths and ensure all necessary R and Python packages are installed in your environment.

Source code and data. Replace the path in notebooks with your own path and install all the R and Python packages.

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