Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale

Abstract

Abstract. This paper examines the potential of scatterometer data from ERS satellites for improving hydrological simulations in both gauged and ungauged catchments. We compare the soil moisture dynamics simulated by a semidistributed hydrologic model in 320 Austrian catchments with the soil moisture dynamics inferred from the satellite data. The most apparent differences occur in the Alpine areas. Assimilating the scatterometer data into the hydrologic model during the calibration phase improves the relationship between the two soil moisture estimates without any significant decrease in runoff model efficiency. For the case of ungauged catchments, assimilating scatterometer data does not improve the daily runoff simulations but does provide more consistent soil moisture estimates. If the main interest is in obtaining estimates of catchment soil moisture, reconciling the two sources of soil moisture information seems to be of value because of the different error structures.

On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

“…processed SWI over the Zambesi river, south-eastern Africa, by means of a logarithmic regression model for obtaining a comparison to hydrometric measurements. Parajka et al (2006) first compared SWI to soil moisture estimates obtained by hydrological modelling over 320 Austrian catchments, and performed a data assimilation exercise in order to evaluate the potential for improving hydrological predictions in ungauged catchments.…”

The significance of soil moisture in forecasting characteristics of flood events. A statistical analysis in two nested catchments

“…However, these indices do not meet the requirements of a distributed modelling approach (Graeff et al, 2009;Longobardi et al, 2003). In fact, the application of spatial soil moisture patterns are potentially valuable for calibrating and validating models (Parajka et al, 2006;Rinderer et al, 2012) and the inclusion of locally measured soil moisture data in a conceptual rainfall-runoff model greatly improves flood forecasting, especially during high flow conditions (Aubert et al, 2003;Bronstert et al, 2012). The high spatiotemporal variability of soil moisture monitoring for large areas is not an easy task and hinders the general application of soil moisture assimilation in rainfall-runoff models (Brocca et al, 2009;Bronstert et al, 2012;Chen et al, 2014).…”

Misrepresentation and amendment of soil moisture in conceptual hydrological modelling

“…A number of attempts have been made by various studies to assimilate soil moisture observations in conceptual hydrological models. For example, Aubert et al (2003) used a sequential assimilation procedure by introducing ground measured soil moisture data into a conceptual rainfall-runoff model and obtained improved flow prediction results ;Brocca et al (2010) revealed that adopting the Advanced SCATterometer (ASCAT) soil moisture index into a rainfall-runoff model could improve model's runoff prediction; contrarily Parajka et al (2006) showed that assimilating the European remote sensing satellite (ERS) derived soil moisture data into a conceptual hydrological model would not improve the runoff model efficiency; Matgen et al (2012) presented that coarse-resolution remotely sensed soil moisture data added little or no extra value for runoff prediction. It is clear the effect of soil moisture assimilation in flow modelling is mixed.…”