Fellowship Project Title: Drydown Evaluation Simultaneous Terrestrial calibRation Employing SSM & LST
Fellowship project summary:
The rate at which land surface soils dry following rain events is an important feature of the climate system. Surface soil moisture (SSM) “drydowns”, i.e. the soil moisture temporal dynamics following a significant rainfall event, play a crucial role in determining the surface water budget, in particular by influencing the partitioning between runoff, drainage, and evaporation. They are also important when predicting the water availability for vegetation, and the occurrences of droughts and heat-waves. As such, improved understanding and characterisation of the drivers of SSM drydowns will give fundamental and combined insight into the coupling between the carbon, water, and energy cycles. Furthermore, the associated variations of land surface temperature (LST) during drydowns can be used to understand evapotranspiration rates and calibrate the surface-soil thermal properties.
DESTRESS will investigate processes governing SSM drydowns using SSM_cci and LST_cci satellite retrievals, and will further evaluate and improve the representation of SSM drydowns in land-surface models (LSMs) used to predict climate change. We will use SM_cci and LST_cci products in synergy within a Bayesian Data Assimilation (DA) framework to reduce the uncertainty associated with simulated drydowns, and in turn improve LSM skills and climate projections.
Research Fellow: Nina Raoult
Host Institution: Laboratoire des Sciences du Climat et de l’Environnement
Raoult N. et al (2021) Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations. Journal of Hydrometeorology. https://doi.org/10.1175/JHM-D-20-0115.1
N. Raoult, R. C. Ruscica, M. M. Salvia and A. A. Sörensson, "Soil Moisture Drydown Detection Is Hindered by Model-Based Rescaling," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 2505205, doi: 10.1109/LGRS.2022.3178685.
MacBean, N., Bacour, C., Raoult, N., Bastrikov, V., Koffi, E. N., Kuppel, S., Maignan, F., Ottlé, C., Peaucelle, M., Santaren, D., and Peylin, P.: Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies with the ORCHIDEE Terrestrial Biosphere Model. Global Biogeochemical Cycles, 36, e2021GB007177, doi: 10.1029/2021GB007177