The suitability of remotely sensed soil moisture for improving operational flood forecasting

Abstract

Abstract. 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 (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer -Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set.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 35 %. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows 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 observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve nearreal time flood forecasting in large catchments.

Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance

“…Adaptive localization is not only easily implemented in the ETKF, it also automatically ensures that the cross-process correlation is localized differently than the intra-process correlation, making it particularly suitable for data assimilation in coupled surfacesubsurface models. Others have encountered the problem with cross-process correlation, notably Zupanski (2013), Li et al (2013) and Wanders et al (2014), although no definitive solution to the problem has been presented. Adaptive localization, such as the method applied in this study, may be one possible solution.…”

Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin

“…Some studies have already shown that this rainfall product does not perform well everywhere (Ward et al, 2011;Thiemig et al, 2012).…”

“…More recently, Wanders et al (2014) and Lievens et al (2015) assimilated the Soil Moisture Ocean Salinity (SMOS) soil moisture product into hydrological models. The first study assessed the impact of the joint assimilation of remotely sensed soil moisture (ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer -Earth observing system) and SMOS (Soil Moisture and Ocean Salinity)) on the flood predictions over the upper Danube basin using the distributed hydrological LIS-FLOOD model for operational services.…”

“…With the availability of more than 35 years of soil moisture at the global scale derived from a series of satellites (SMMR, SSM/I, TRMM-TMI, AMSR-E, ASCAT, Windsat; Liu et al, 2011Liu et al, , 2012Wagner et al, 2012), the soil moisture CCI (Climate Change Initiative by ESA; Hollmann et al, 2013) has been assimilated in many models for hydrological purposes such as streamflow simulation (Pauwels et al, 2001(Pauwels et al, , 2002, flood events prediction via runoff simulation (Brocca et al, 2010(Brocca et al, , 2012, drought prediction (Kumar et al, 2014) and root zone soil moisture simulations (Draper et al, 2012;Renzullo et al, 2014;Parrens et al, 2014) for a better prediction of agricultural yields (Chakrabarti et al, 2014). Han et al (2012) voluntarily degraded the precipitation input and showed that soil moisture, water table depth (WTD) and evapotranspiration simulations could be improved by assimilating surface soil moisture.…”

The Raam regional soil moisture monitoring network in the Netherlands

“…Regional water management can benefit from timely and reliable information about soil moisture content: it can improve quantifications of flood risks by its effect on rainfall estimations and streamflow predictions (Beck et al, 2009;Massari et al, 2014;Wanders et al, 2014) and negative anomalies to current plant water demands are an indicator of (the onset of) droughts (Carrão et al, 2016;Wilhite and Glantz, 1985). The agricultural sector depends on sufficient root zone soil water availability for crop growth, while excess of soil water leads to severe losses (Feddes et al, 1978).…”