Abstract
Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.
Highlights
Soil moisture (SM) is a key variable of the hydrologic cycle, playing an important role in major processes related to infiltration and runoff generation, root water uptake and plant transpiration, and evaporation (Vereecken et al, 2016)
The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring
The difference between Community Land Model (CLM)-OL and CCISM were larger than the difference between CLM-data assimilation (DA) and Climate Change Initiative (CCI)-soil moisture (SM), which indicates that assimilation of CCI-derived soil moisture (CCI-SM) minimizes the overestimation of soil water content (SWC) in CLM-OL
Summary
Soil moisture (SM) is a key variable of the hydrologic cycle, playing an important role in major processes related to infiltration and runoff generation, root water uptake and plant transpiration, and evaporation (Vereecken et al, 2016). At continental spatial scales and inter-annual timescales, SM typically exhibits large variability (Brocca et al, 2010), depending on rainfall distribution, topography, soil physical properties, vegetation characteristics and human impacts such as irrigation. Monitoring this variability is a major challenge due to the scarcity of in situ SM observations networks. Recent advancements in satellite-based sensors offer great potential to monitor SM over large scales for continental water resources assessment, in areas where ground observation networks are sparse (Mohanty et al, 2013). Sparse data coverage in satellite observations limits their ability to provide spatially and temporally consistent time series of water balance estimates
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