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Silvopastoral systems (SPSs) have been shown to improve ecosystem resilience and provide sustainable land management solutions in the Sahel. However, accurately estimating the contribution of Sahelian ecosystems to the overall greenhouse gas (GHG) balance is a challenge, in particular regarding the magnitude of carbon dioxide (CO 2) and nitrous oxide (N 2 O) emissions from soils. In this work, we spatialized and applied the process-based model Sahelian Transpiration Evaporation and Productivity – GENeral model of litter DEComposition – N 2 O (STEP–GENDEC-N 2 O) to investigate the magnitude and spatial and temporal patterns of herbaceous mass, as well as CO 2 and N 2 O emissions from soil (not net emissions) in Sahelian SPSs. Our results show that over the last decade (2012–2022), there was a heterogeneous spatial distribution of herbaceous mass production and of soil CO 2 and N 2 O emissions in Sahelian SPSs. Spatial variations in soil CO 2 emissions are primarily controlled by soil carbon content, temperature, herbaceous mass, and animal load, while soil nitrogen content, soil water content, and animal load are the main factors driving the spatial variations in N 2 O emissions from soil. The estimated CO 2 and N 2 O emissions from soil in Sahelian SPSs over the 2012–2022 period were equal to 58.79 ± 4.83 Tg CO 2 -C yr -1 (1 Tg = 10 12 g) and 21.59 ± 3.91 Gg N 2 O-N yr -1 (1 Gg = 10 9 g), respectively. These values are generally lower than estimates reported in the literature for tropical areas and croplands. Furthermore, our simulations indicated a significant annual rising trend of soil CO 2 and N 2 O emissions between 2012 and 2020 as herbaceous mass increased, making more C and N available for the nitrification, denitrification, and decomposition processes. By mapping soil CO 2 and N 2 O emissions, we provide crucial insights into the localization of emission hotspots in Sahelian SPSs, thereby offering valuable information that can be used to devise and implement effective strategies aimed at fostering carbon sequestration in the Sahel. [ABSTRACT FROM AUTHOR]

Silvopastoral systems (SPSs) have been shown to improve ecosystem resilience and provide sustainable land management solutions in the Sahel. However, accurately estimating the contribution of Sahelian ecosystems to the overall greenhouse gas (GHG) balance is a challenge, in particular the magnitude of carbon dioxide (CO2) and nitrous oxide (N2O) emissions from soils. In this work, we spatialized and applied the process-based model STEP-GENDEC-N2O to investigate the magnitude, spatial, and temporal patterns of herbaceous mass, as well as CO2 and N2O emissions from soil in Sahelian SPSs. Our results show that over the last decade (2012-2022), there was a heterogeneous spatial distribution of herbaceous mass production, as well as of soil CO2 and N2O emissions in Sahelian SPSs. Spatial variations in soil CO2 emissions are primarily controlled by soil carbon content, temperature, herbaceous mass, and animal load, while soil nitrogen content, soil water content, and animal load are the main factors driving the spatial variations in N2O emissions from soil. The estimated CO2 and N2O emissions from soil in Sahelian SPSs over the 2012-2022 period were equal to 58.79 ± 4.83 Tg CO2-C yr-1 (1 Tg = 1012 g) and 21.59 ± 3.91 Gg N2O-N yr-1 (1 Gg = 109 g), respectively. These values are generally lower than estimates reported in the literature for tropical areas and croplands. Furthermore, our simulations indicated a significant annual rising trend of soil CO2 and N2O emissions between 2012-2020 as herbaceous mass increases, making more C and N available for nitrification, denitrification and decomposition processes. By mapping soil CO2 and N2O emissions, we provide crucial insights into the localization of emission hotspots in Sahelian SPSs, thereby offering valuable information that can be used to devise and implement effective strategies aimed at fostering carbon sequestration in the Sahel. [ABSTRACT FROM AUTHOR]

West African Sahelian and Sudanian ecosystems provide essential services to people and also play a significant role within the global carbon cycle. However, climate and land use are dynamically changing, and uncertainty remains with respect to how these changes will affect the potential of these regions to provide food and fodder resources or how they will affect the biosphere–atmosphere exchange of CO 2. In this study, we investigate the capacity of a process-based biogeochemical model, LandscapeDNDC, to simulate net ecosystem exchange (NEE) and aboveground biomass of typical managed and natural Sahelian and Sudanian savanna ecosystems. In order to improve the simulation of phenology, we introduced soil-water availability as a common driver of foliage development and productivity for all of these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations as well as leaf area index data from satellite images (MODIS, MODerate resolution Imaging Spectroradiometer). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed good performance regarding biomass of crops, grass, or trees, yielding correlation coefficients of 0.82, 0.94, and 0.77 and root-mean-square errors of 0.15, 0.22, and 0.12 kg m -2 , respectively. The simulations indicate aboveground carbon stocks of up to 0.17, 0.33, and 0.54 kg C ha -1 m -2 for agricultural, savanna grasslands, and savanna mixed tree–grassland sites, respectively. Carbon stocks and exchange rates were particularly correlated with the abundance of trees, and grass biomass and crop yields were higher under more humid climatic conditions. Our study shows the capability of LandscapeDNDC to accurately simulate carbon balances in natural and agricultural ecosystems in semiarid West Africa under a wide range of conditions; thus, the model could be used to assess the impact of land-use and climate change on the regional biomass productivity. [ABSTRACT FROM AUTHOR]

The mechanistic model GO+ describes the functioning and growth of managed forests based upon biophysical and biogeochemical processes. The biophysical and biogeochemical processes included are modelled using standard formulations of radiative transfer, convective heat exchange, evapotranspiration, photosynthesis, respiration, plant phenology, growth and mortality, biomass nutrient content, and soil carbon dynamics. The forest ecosystem is modelled as three layers, namely the tree overstorey, understorey and soil. The vegetation layers include stems, branches and foliage and are partitioned dynamically between sunlit and shaded fractions. The soil carbon submodel is an adaption of the Roth-C model to simulate the impact of forest operations. The model runs at an hourly time step. It represents a forest stand covering typically 1 ha and can be straightforwardly upscaled across gridded data at regional, country or continental levels. GO+ accounts for both the immediate and long-term impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes exhaustive and versatile descriptions of management operations (soil preparation, regeneration, vegetation control, selective thinning, clear-cutting, coppicing, etc.), thus permitting the effects of a wide variety of forest management strategies to be estimated: from close to nature to intensive. This paper examines the sensitivity of the model to its main parameters and estimates how errors in parameter values are propagated into the predicted values of its main output variables.The sensitivity analysis demonstrates an interaction between the sensitivity of variables, with the climate and soil hydraulic properties being dominant under dry conditions but the leaf biochemical properties being most influential with wet soil. The sensitivity profile of the model changes from short to long timescales due to the cumulative effects of the fluxes of carbon, energy and water on the stand growth and canopy structure. Apart from a few specific cases, the model simulations are close to the values of the observations of atmospheric exchanges, tree growth, and soil carbon and water stock changes monitored over Douglas fir, European beech and pine forests of different ages. We also illustrate the capacity of the GO+ model to simulate the provision of key ecosystem services, such as the long-term storage of carbon in biomass and soil under various management and climate scenarios. [ABSTRACT FROM AUTHOR]

Aerosol- and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle, as they alter light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented or evaluated in current land surface models. Here, we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model (trunk version, v5453) and use the revised model, ORCHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multilayer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that, compared with the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs). Our results also indicate that the larger GPP under cloudy conditions compared with sunny conditions is mainly driven by increased diffuse light in the morning and in the afternoon as well as by a decreased vapor pressure deficit (VPD) and decreased air temperature at midday. The observations show that the strongest positive effects of diffuse light on photosynthesis are found in the range from 5 to 20 ∘ C and at a VPD < 1 kPa. This effect is found to decrease when the VPD becomes too large or the temperature falls outside of the abovementioned range, which is likely due to the increasing stomatal resistance to leaf CO 2 uptake. ORCHIDEE_DF underestimates the diffuse light effect at low temperature in all PFTs and overestimates this effect at high temperature and at a high VPD in grasslands and croplands. The new model has the potential to better investigate the impact of large-scale aerosol changes and long-term changes in cloudiness on the terrestrial carbon budget, both in the historical period and in the context of future air quality policies and/or climate engineering. [ABSTRACT FROM AUTHOR]

Aerosol and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle by changing light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented in current land surface models. Here we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE land surface model (trunk version, v5453) and use the revised model, ORCHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multi-layer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that compared to the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs). Our results also indicate that the larger GPP under cloudy conditions compared to sunny conditions is mainly driven by increased diffuse light in the morning and in the afternoon, and by decreased VPD and air temperature at midday. The observations show strongest positive effects of diffuse light on photosynthesis are found in the range 5-20 °C and VPD < 1 kPa. This effect is found to decrease when VPD becomes too large, or temperature falls outside that range likely because of increasing stomatal resistance to leaf CO2 uptake. ORCHIDEE_DF underestimates the diffuse light effect at low temperature in all PFTs and overestimates this effect at high temperature and high VPD in grasslands and croplands. The new model has the potential to better investigate the impact of large-scale aerosol changes on the terrestrial carbon budget, both in the historical period and in the context of future air quality policies and/or climate engineering. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) constitutes the largest terrestrial C stock, particularly in the Andosols of volcanic areas. Quantitative information on distribution of SOC stocks is needed to construct a baseline for studying temporal changes in SOC. The spatial variation of soil short-range-order minerals such as allophane usually explains the variability of topsoil SOC contents, but SOC data for deeper soil layers are needed. We found that within a 1 km2 Costa Rican basin covered by coffee agroforestry, SOC stocks in the upper 200 cm of soil were highly variable (24 to 72 kgCm-2). Topsoil SOC stocks were not correlated with SOC stocks present in deeper layers. Diffuse-reflectance mid-infrared (MIR) spectroscopy made possible the analysis of a large number of samples (69 soil profiles, i.e. 598 soil samples) for ammonium-oxalate and sodium-pyrophosphate-extractable forms of Al, Fe, and Si, as well as SOC content and bulk density. Using the MIR spectra, we identified two different soil materials, which were identified as allophanic and halloysitic soil material. Allophanic soil occurred on top of the halloysitic soil. The thickness of the allophanic soil material, rich in SRO minerals and related to a young andic A horizon, explained the variability of SOC. This study illustrates that knowledge of topography and pedogenesis is needed to understand and extrapolate the distribution of SOC stocks at landscape scales. [ABSTRACT FROM AUTHOR]

Indonesia is currently one of the regions with the highest transformation rate of land surface worldwide related to the expansion of oil palm plantations and other cash crops replacing forests on large scales. Land cover changes, which modify land surface properties, have a direct effect on the land surface temperature (LST), a key driver for many ecological functions. Despite the large historic land transformation in Indonesia toward oil palm and other cash crops and governmental plans for future expansion, this is the first study so far to quantify the impacts of land transformation on the LST in Indonesia. We analyze LST from the thermal band of a Landsat image and produce a highresolution surface temperature map (30 m) for the lowlands of the Jambi province in Sumatra (Indonesia), a region which suffered large land transformation towards oil palm and other cash crops over the past decades. The comparison of LST, albedo, normalized differenced vegetation index (NDVI) and evapotranspiration (ET) between seven different land cover types (forest, urban areas, clear-cut land, young and mature oil palm plantations, acacia and rubber plantations) shows that forests have lower surface temperatures than the other land cover types, indicating a local warming effect after forest conversion. LST differences were up to 10.1±2.6 °C (mean±SD) between forest and clear-cut land. The differences in surface temperatures are explained by an evaporative cooling effect, which offsets the albedo warming effect. Our analysis of the LST trend of the past 16 years based on MODIS data shows that the average daytime surface temperature in the Jambi province increased by 1.05 °C, which followed the trend of observed land cover changes and exceeded the effects of climate warming. This study provides evidence that the expansion of oil palm plantations and other cash crops leads to changes in biophysical variables, warming the land surface and thus enhancing the increase of the air temperature because of climate change. [ABSTRACT FROM AUTHOR]

Indonesia is currently one of the regions with the highest transformation rate of the land surface worldwide due to the expansion of oil palm plantations and other cash crops replacing forests on large scales. Land cover changes, which modify land surface properties, have a direct effect on the land surface temperature (LST), a key driver for many ecological functions. Despite the large historic land transformation in Indonesia toward oil palm and other cash crops and governmental plans for future expansion, this is the first study so far to quantify the impact of land transformation in Indonesia on LST. We analyse LST from the thermal band of a Landsat image and produce a high resolution surface temperature map (30 m) for the lowlands of the Jambi province on Sumatra (Indonesia), a region of large land transformation towards oil palm and other cash crops over the past decades. We compare LST, albedo, Normalized Differenced Vegetation Index (NDVI), and evapotranspiration (ET) of seven different land cover types (forest, urban areas, clear cut land, young and mature oil palm plantations, acacia and rubber plantations) and show that forests have lower surface temperatures than these land cover types indicating a local warming effect after forest conversion with LST differences up to 10.09 ± 2.6 °C (mean ± SD) between forest and clear cut land. The differences in surface temperatures are explained by an evaporative cooling effect offsetting an albedo warming effect. Our analysis of the LST trend of the past 16 years based on MODIS data shows that the average daytime surface temperature of the Jambi province increased by 1.05 °C, which followed the trend of observed land cover changes and exceed the effects of climate warming. Our study provides evidence that the expansion of oil palm plantations and other cash crops leads to changes in biophysical variables, warming the land surface and thus enhancing the increase in air temperature due to climate change. [ABSTRACT FROM AUTHOR]

Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (RSd / RSi) as a factor for converting ETi to ETd. This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict RSd from RSi followed by using the RSd / RSi ratio to convert ETi to ETd across different terrestrial ecosystems. Using RSi and RSd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. RS-based upscaled ETd produced a significant linear relation (R2 D0.65 to 0.69), low bias (-0.31 to -0.56 MJm-2 d-1; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJm-2 d-1; approx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANNdriven RS-based ETi upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ETd prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. The overall methodology appears to be promising and has substantial potential for upscaling ETi to ETd for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites. [ABSTRACT FROM AUTHOR]

Upscaling instantaneous evapotranspiration retrieved at any specific time-of-daytime (ETi) to daily evapotranspiration (ETd) is a key challenge in regional scale vegetation water use mapping using polar orbiting sensors. Various studies have unanimously cited the short wave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi on the terrestrial surfaces. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming short wave radiation (RSd/RSi) as a factor for converting ETi to ETd. The approach relies on the availability of RSd measurements that in many cases is hindered if not by cost but due to the environmental conditions such as cloudiness. This paper proposes an artificial neural network (ANN) machine learning algorithm first to predict RSd from RSi followed by using the RSd/RSi ratio to convert ETi to ETd across different terrestrial ecosystem. Using RSi and RSd observations from multiple subnetworks of FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (derived from simple mathematical computation), we developed ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon time-of-daytime, under varying sky conditions, and also at different geographic locations. Based on the measurements from 126 sites, we found RS-based upscaled ETd to produce a significant linear relation (R² = 0.65 to 0.69), low bias (−0.31 to −0.56 MJ m−2 d−1) (appx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m−2 d−1) (appx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN driven RS method and was found to produce lowest RMSE under cloudy conditions. The overall methodology appears to be promising and has substantial potential for upscaling ETi to ETd for field and regional scale evapotranspiration mapping studies using polar orbiting satellites. [ABSTRACT FROM AUTHOR]