Journal Article

FZJ-2018-03144

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

Naz, B. S. (Corresponding author)FZJ* ; Kurtz, W. ; Montzka, C.FZJ* ; Sharples, W.FZJ* ; Görgen, K.FZJ* ; Keune, J. ; Gao, H. ; Springer, A. ; Hendricks-Franssen, H.-J.FZJ* ; Kollet, S.FZJ*

2018
Soc. Katlenburg-Lindau

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Please use a persistent id in citations: http://hdl.handle.net/2128/19111  doi:10.5194/hess-2018-24

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 observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period from 2000 to 2006 with the Community Land Model, version 3.5 (CLM3.5) integrated with the Parallel Data Assimilation Framework (PDAF) at 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). Our results show that estimates of soil moisture have improved, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm3/mm3 in open-loop simulations to 0.004 mm3/mm3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)
2. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)
2. EoCoE - Energy oriented Centre of Excellence for computer applications (676629) (676629)
3. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)

Appears in the scientific report 2018

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Database coverage:
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