Abstract
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 m3m−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.
Highlights
Background & SummarySoil moisture (SM) is characterized by complex dynamics across a wide range of spatial and temporal scales[1] that can impact hydrological processes such as runoff, evaporation and transpiration from vegetation through changing soil moisture[2]
The 3 km ESSMRA was generated using three main steps: (1) the regional land surface model setup over Europe, (2) implementation of a data assimilation (DA) framework, and (3) validation of ESSMRA based on observations and other reanalysis products
The coupled land surface data assimilation system (CLM-Parallel Data Assimilation Framework (PDAF)) uses the Community Land Model (CLM) version3.5 (CLM3.5) which offers significant improvements in estimating the components of the terrestrial water cycle compared to earlier versions (i.e. CLM2.0 and 3.0)[24]
Summary
Soil moisture (SM) is characterized by complex dynamics across a wide range of spatial and temporal scales[1] that can impact hydrological processes such as runoff, evaporation and transpiration from vegetation through changing soil moisture[2]. Soil moisture reanalysis products are needed which can provide downscaled estimates of SM with complete spatiotemporal coverage by merging coarse-resolution SM observations with a high resolution LSM using data assimilation (DA) techniques[3,10,11,12,13] These products overcome the shortcomings of sparse spatial and temporal distributions in observations and provide a better estimate of SM than obtained only by modeling or by satellite observations alone. At the European regional scale, there have been few studies which provide soil moisture reanalysis through DA techniques by assimilating surface soil moisture information from satellite into land surface models[10,19,20,21,22,23] Though these global and regional reanalysis products are an attractive data source, they have a relatively coarse resolution (typically at 25–50 km grid spacing) and may not provide locally representative information of soil moisture which is important for regional hydrologic and agriculture applications. The relatively longer time scale and fine spatial resolution of this new European gridded ESSMRA dataset could provide a valuable data source for many hydrological applications over larger regimes and to regional and continental scale studies
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