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Two methods, namely the velocity-area method and the entropy method, for assessing with uncertainty discharge measurements at gauged river sites are analysed and compared; uncertainty is represented through the grey number technique. Two different approaches for the 'greyification' of both methods are presented. In the first approach, the uncertainty affecting each measurement used to estimate the discharge is characterized by means of a grey number: all the grey uncertainty components are then combined through grey mathematics. In the second approach, greyification is applied to the relationship expressing the total uncertainty on the discharge measurement provided by the EN ISO 748 guidelines. Results of the application of the proposed methods to measurement data pertaining to three different gauged sections of the Tiber River, in central Italy, show that the first greyification approach leads to a broader discharge uncertainty estimate with respect to the second. Furthermore, as the greyification approach and the flow area quantification are the same, the velocity-area and entropy methods provide nearly the same estimate of the uncertainty affecting the discharge measurements, i.e., the grey discharges provided by the two methods are very similar. This testifies in favour of the entropy method, which is simpler than the other from an operative viewpoint. [ABSTRACT FROM AUTHOR]

Soil moisture is a key state variable in hydrology, it controls the proportion of rainfall that infiltrates, runoff and evaporates from the land. For hydrological applications, soil moisture monitoring at catchment scale is required and, for that, microwave remote sensing sensors might be used. However, due to their coarse-spatial resolution, the skepticism on the suitability to retrieve the soil moisture at catchment scale takes still place. This work attempts to bring out if coarse resolution sensors for soil moisture monitoring can have some perspectives for hydrological applications. Two soil moisture products derived from the Advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) are used for this purpose. The analysis is addressed by investigating: (i) the reliability of product data in the estimation of the wetness conditions of a catchment antecedent to rainfall events, and (ii) the benefit on runoff prediction if data are assimilated into a rainfall-runoff model. Rainfall-runoff observations are taken from several catchments in Italy and Luxembourg for testing. Results reveal that ASCAT and AMSR-E soil moisture products can be conveniently used to improve runoff prediction thus opening new important challenges and opportunities for the use of this new sources of data in the operational hydrology.

River discharge is an important quantity of the hydrologic cycle because it is essential for both scientific and operational applications related to water resources management and flood risk prevention. Streamflow measurements are sparse and for few sites along natural channels and, hence, they are not able to detect adequately the complexity of variation in surface water systems. Therefore, in recent years, the possibility to obtain river discharge estimates through remote sensing monitoring has received a great interest. In this context, the capability of the MODerate resolution Imaging Spectroradiometer (MODIS) for river discharge estimation is investigated here. Thanks to a very short revisiting time interval and a moderate spatial resolution (up to 250 m), MODIS has a significant potential for mapping flooded area extent and flow dynamics. Specifically, for the estimation of river discharge, the ratio of the MODIS channel 2 reflectance values between two pixels located within and outside the river is used. Time series of daily discharge between 2006 and 2010 measured at two gauging stations located along the Upper Tiber River basin (central Italy) are employed to test the procedure. The agreement between MODIS-derived and in situ discharge time series is found to be fairly good with correlation coefficient values close to 0.8.