MIXMOD-E

Agronomic, hydrologic, meteorological and climatic predictions rely on our ability to accurately represent soil evaporation (E) process, which is the boundary condition for the soil and atmosphere. For such wide range of applications, E should be modeled over extensive areas at multiple scales. Since the 60s many E models have been developed. Mechanistic theoretically-based models have been very useful to understand and describe the physical processes regulating E including gravity drainage, capillary rise, vapor diffusion, and the interplay with the atmospheric evaporative limitation. However, their regionalization has been a notorious challenge because of the unavailability and high uncertainty of soil hydraulic properties over extended areas (~100 m - 100 km) and the lack of data at such scales. Simplified models have been generally used across different application scales but their regionalization has been based on empiricism or ad hoc relationships with soil hydraulic properties or texture. In fact, none of the existing E formulations has been validated over an extensive range of soils and soil-atmospheric conditions and no consensus exists on the best way to parameterize E. The fast development of local, regional and global monitoring networks (e.g. Ameriflux, GHGEurope) now permits the advent of improved models. A related problem is that E is not directly observable using remote sensing platforms although our capability to monitor E-related quantities such as soil moisture is growing rapidly with new remote sensing technologies such as SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) L-band missions. Several studies have shown the potential of combining shortwave-derived vegetation cover, thermal-derived surface temperature, and microwave-derived surface soil moisture to partition evapotranspiration (ETR) into E and plant transpiration (TR) and to indirectly retrieve E. However, no remote sensing method has come up yet because spaceborne temperature and soil moisture products are readily available at different spatial resolutions, and because there is no quasi-instantaneous E model that combines consistently these data. The objective of this proposal is therefore to fill this gap in the E representation by developing an original phenomenological (intermediate between theory and experiment) model 1) that is valid over a wide range of soils and soil-atmospheric conditions while based on the data available at the application scales (scale-aware model) and 2) that can be coupled to readily-available remotely sensed vegetation cover, surface temperature and surface soil moisture (remote sensing-aware model). The different steps will be: 1) to develop a texture-based E model using an extensive in situ data set and compare it with state-of-the-art models, 2) to investigate three (mechanistic, data-based, mathematical) approaches to quantitatively evaluate the impact of soil moisture profile and soil surface state on E, 3) to implement the new model in a thermal-based disaggregation scheme of SMOS/SMAP soil moisture and to validate the E retrieved at high (~100 m) resolution using in-situ measurements collected under various pedo-hydro-climatic conditions in Chile, France, Morocco and Spain, and 4) to implement the new model in the CNRM (ISBA/SURFEX) and ECMWF (H-TESSEL) land surface models and to validate large scale simulated E using the remotely sensed estimates (previously validated at high resolution using in-situ measurements) aggregated at the corresponding resolutions.