IAFE   05512
INSTITUTO DE ASTRONOMIA Y FISICA DEL ESPACIO
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Modelled and observed surface soil moisture spatio-temporal dynamics in a land-atmosphere hotspot
Autor/es:
POLCHER JAN; KARSZENBAUM HAYDEE; SALVIA MERCEDEZ; PILES MARÍA; RUSCICA ROMINA; SÖRENSSON ANNA AMELIA
Lugar:
Oxford
Reunión:
Conferencia; 'Understanding the impact of land-atmosphere exchanges', iLEAPS 5th Science Conference; 2017
Institución organizadora:
iLEAPS
Resumen:
Knowledge of surface soil moisture (SSM) spatio-temporal dynamics is essential for many practicalapplications such as weather forecasting, 􀃓oods/drought monitoring and water resource management. SSMis particularly relevant over climate transition regions such as southeastern South America (SESA), arecognized land-atmosphere interaction hotspot by studies using climate models and more recently remotesensing products (RSPs).SESA has the largest population density of the continent and is the most productive region in terms ofagriculture, livestock and industry. It comprises the low and 􀃓at Pampas plains where the most severesubtropical storms of the globe are developed, making SESA an interesting region for studying SSM.A novel framework for studying SSM dynamics over the SESA hotspot is presented. SSM dry-downs duringnon-rainy days after precipitation events are studied on scales of the landscape. This is an essentialknowledge to better understand how the surface will respond to changes in the characteristics of rainfall.The dry-down is critical for soil moisture stress of plants, surface Bowen ratio, surface warming andatmospheric response.The ORCHIDEE land surface model and the recent version of SMOS RSP (v.620) were chosen since they areparticularly suited for this study. ORCHIDEE provides a high vertical resolution of the soil surface, making itsuitable for comparison with RSPs. SMOS uses L-band, which is suitable for densely vegetated areas likeSESA and the v.620 includes a new parameterization for forested areas that reduces uncertainties andimproves the data quality 􀃒ltering.ORCHIDEE and SMOS daily data are employed at 25 km for summers in 2010-2014. Results are analyzed interms of soil and land cover characteristics, sampling frequency, observational uncertainties and alsocompared to other metrics.