| 1. |
- Getachew Mengistu, Anteneh, et al.
(författare)
-
Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa
- 2021
-
Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4170 .- 1726-4189. ; 18:9, s. 2843-2857
-
Tidskriftsartikel (refereegranskat)abstract
- The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments.A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R>0.9; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.
|
|
| 2. |
- Harper, Anna B., et al.
(författare)
-
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements
- 2021
-
Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 14:6, s. 3269-3294
-
Tidskriftsartikel (refereegranskat)abstract
- Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the "soil14_psi" experiments), when the critical threshold value for inducing soil moisture stress was reduced ("soil14_p0"), and when plants were able to access soil moisture in deeper soil layers ("soil14_dr&z.ast;2"). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.
|
|
