TY - JOUR
T1 - The suitability of remotely sensed soil moisture for improving operational flood forecasting
AU - Wanders, N.
AU - Karssenberg, D.
AU - de Roo, A.
AU - de Jong, S. M.
AU - Bierkens, M. F. P.
PY - 2013/11/1
Y1 - 2013/11/1
N2 - We evaluate the added value of assimilated remotely sensed soil moisture
for the European Flood Awareness System (EFAS) and its potential to
improve the prediction of the timing and height of the flood peak and
low flows. EFAS is an operational flood forecasting system for Europe
and uses a distributed hydrological model for flood predictions with
lead times up to 10 days. For this study, satellite-derived soil
moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system
for the Upper Danube basin and results are compared to assimilation of
discharge observations only. To assimilate soil moisture and discharge
data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on
the spatial (cross-) correlation of the errors in the satellite
products, is included to ensure optimal performance of the EnKF. For the
validation, additional discharge observations not used in the EnKF, are
used as an independent validation dataset. Our results show
that the accuracy of flood forecasts is increased when more discharge
observations are assimilated; the Mean Absolute Error (MAE) of the
ensemble mean is reduced by 65%. The additional inclusion of satellite
data results in a further increase of the performance: forecasts of base
flows are better and the uncertainty in the overall discharge is
reduced, shown by a 10% reduction in the MAE. In addition, floods are
predicted with a higher accuracy and the Continuous Ranked Probability
Score (CRPS) shows a performance increase of 5-10% on average, compared
to assimilation of discharge only. When soil moisture data is used, the
timing errors in the flood predictions are decreased especially for
shorter lead times and imminent floods can be forecasted with more
skill. The number of false flood alerts is reduced when more data is
assimilated into the system and the best performance is achieved with
the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both
upstream and downstream areas are used in combination with the soil
moisture data. These results show the potential of remotely sensed soil
moisture observations to improve near-real time flood forecasting in
large catchments.
AB - We evaluate the added value of assimilated remotely sensed soil moisture
for the European Flood Awareness System (EFAS) and its potential to
improve the prediction of the timing and height of the flood peak and
low flows. EFAS is an operational flood forecasting system for Europe
and uses a distributed hydrological model for flood predictions with
lead times up to 10 days. For this study, satellite-derived soil
moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system
for the Upper Danube basin and results are compared to assimilation of
discharge observations only. To assimilate soil moisture and discharge
data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on
the spatial (cross-) correlation of the errors in the satellite
products, is included to ensure optimal performance of the EnKF. For the
validation, additional discharge observations not used in the EnKF, are
used as an independent validation dataset. Our results show
that the accuracy of flood forecasts is increased when more discharge
observations are assimilated; the Mean Absolute Error (MAE) of the
ensemble mean is reduced by 65%. The additional inclusion of satellite
data results in a further increase of the performance: forecasts of base
flows are better and the uncertainty in the overall discharge is
reduced, shown by a 10% reduction in the MAE. In addition, floods are
predicted with a higher accuracy and the Continuous Ranked Probability
Score (CRPS) shows a performance increase of 5-10% on average, compared
to assimilation of discharge only. When soil moisture data is used, the
timing errors in the flood predictions are decreased especially for
shorter lead times and imminent floods can be forecasted with more
skill. The number of false flood alerts is reduced when more data is
assimilated into the system and the best performance is achieved with
the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both
upstream and downstream areas are used in combination with the soil
moisture data. These results show the potential of remotely sensed soil
moisture observations to improve near-real time flood forecasting in
large catchments.
U2 - 10.5194/hessd-10-13783-2013
DO - 10.5194/hessd-10-13783-2013
M3 - Article
SN - 1812-2116
VL - 10
SP - 13783
EP - 13816
JO - Hydrology and Earth System Sciences Discussions
JF - Hydrology and Earth System Sciences Discussions
IS - 11
ER -