| Soil moisture is an important component of the hydrological cycle. In the
framework of modern flood warning systems, the knowledge of soil moisture is
crucial, due to the influence on the soil response in terms of
infiltration-runoff. Precipitation-runoff processes, in fact, are related to
catchment's hydrological conditions before the precipitation. Thus, an
estimation of these conditions is of significant importance to improve the
reliability of flood warning systems. Combining such information with other
weather-related satellite products (i.e. rain rate estimation) might
represent a useful exercise in order to improve our capability to handle
(and possibly mitigate or prevent) hydro-geological hazards.
Remote sensing, in the last few years, has supported several techniques for
soil moisture/wetness monitoring. Most of the satellite-based techniques use
microwave data, thanks to the all-weather and all-time capability of these
data, as well as to their high sensitivity to water content in the soil. On
the other hand, microwave data are unfortunately highly affected by the
presence of surface roughness or vegetation coverage within the
instantaneous satellite field of view (IFOV). Those problems, consequently,
strongly limit the efficiency and the reliability of traditional satellite
techniques.
Recently, using data coming from AMSU (Advanced Microwave Sounding Unit),
flying aboard NOAA (National Oceanic and Atmospheric Administration)
satellites, a new methodology for soil wetness estimation has been proposed.
The proposed index, called Soil Wetness Variation Index (SWVI), developed by
a multi-temporal analysis of AMSU records, seems able to reduce the problems
related to vegetation and/or roughness effects. Such an approach has been
tested, with promising results, on the analysis of some flooding events
which occurred in Europe in the past.
In this study, results achieved for the HYDROPTIMET test cases will be
analysed and discussed in detail. This analysis allows us to evaluate the
reliability and the efficiency of the proposed technique in identifying
different amounts of soil wetness variations in different observational
conditions. In particular, the proposed indicator was able to document the
actual effects of meteorological events, in terms of space-time evolution of
soil wetness changes, for all the analysed HYDROPTIMET test cases. Moreover,
in some circumstances, the SWVI was able to identify the presence of a sort
of "early" signal in terms of soil wetness variations, which may be
regarded as a timely indication of an anomalous value of soil water content.
This evidence suggests the opportunity to use such an index in the
pre-operational phases of the modern flood warning systems, in order to
improve their forecast capabilities and their reliability. |