| Autor: |
Naz BS; Jülich Research Center GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG 3), Jülich, 52425, Germany. b.naz@fz-juelich.de.; Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, 52425, Germany. b.naz@fz-juelich.de., Kollet S; Jülich Research Center GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG 3), Jülich, 52425, Germany.; Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, 52425, Germany., Franssen HH; Jülich Research Center GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG 3), Jülich, 52425, Germany.; Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, 52425, Germany., Montzka C; Jülich Research Center GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG 3), Jülich, 52425, Germany.; Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, 52425, Germany., Kurtz W; Leibniz Supercomputing Centre, Boltzmannstr. 1, 85748, Garching, Germany. |
| Abstrakt: |
High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000-2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 in-situ soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m 3 m -3 and correlation values above 0.5 over 70% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales. |
