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Many severe precipitation events occurred in Europe during the last decade and caused casualties and damages to the historical heritage and natural environment. Protection of civilians and safeguard of the territory are the kay mandates of a civil Protection (CP) agency. This is achieved through activities that mitigate hydro-meteorological risks, such as flooding and droughts. European CP agencies, supported by meteorological and hydrological institutes, aim to assess risk scenarios, to monitor and supervise events and risk levels, providing early warning to National and local authorities.Hydro meteorological risk management consists of three phases: pre-event (forecast), event (early warning, and monitoring) and post-event (recovery and survey).Near real time accurate estimations of hydrological variables such as precipitation and soil moisture are invaluable to the CP agencies, enable them to issue early warnings and plan for disaster relief at the local level.Besides measurements of key hydrological variables by ground-based instruments, often affected by a limited spatial coverage, advanced satellite-based precipitation and soil moisture products developed within different international programs, are becoming available and accessible to users in near-real time. The assessment of the accuracy and reliability of such products is necessary in order to be able to optimally exploit them for hydro-meteorological applications.The comparison of satellite-derived rainfall and soil moisture estimates with respect to ground-based measurements is a challenging task, both because of the temporal and spatial variability of the fields and the inherently different measurement types. This activity is particularly complex in Europe due to the heterogeneity in the ground-based networks of the different countries, due to sampling strategy and processing methodologies. For the validation and assessment of satellite-derived soil moisture products, these issues are exacerbated by the limited availability of ground observation networks that can be used as benchmark. Moreover, the measuring stations cover only small areas over Europe. As an example, in Central Italy only 14 probes are distributed over an area of about 8000 km2.In this study, a comparison between satellite derived products and ground data is presented, according to the EUMETSAT H SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) Validation protocols. Since 2005, eleven European countries joined in the framework of the H SAF to perform the satellite products monitoring and ground verification in their own regions of interest. Moreover, recent severe rainfall events are selected in order to understand how the main satellite product characteristics, i.e. accuracy, spatial pattern and resolution, update frequency and latency, impact the efficiency of a hydro-meteorological early warning system at a local level in an operational framework. State-of-the-art satellite rainfall and soil moisture products, obtained through international space agencies and programmes (EUMETSAT H SAF, ESA, NASA, and JAXA), are used over different European countries as forcing input for an early warning system for meteorological/hydrological events. [ABSTRACT FROM AUTHOR]

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products. In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm ( Brocca et al., 2014 ) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R ASC , are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013. The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency ( NS ) less than 30% when using SM2R ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas. [ABSTRACT FROM AUTHOR]

State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered: H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2RASC, considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010-13. In the validation period 2012-13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2RASC is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42-RT, respectively. The integration of the products is found to improve the threat score for medium-high rainfall accumulations. Since SM2RASC, H05, and 3B42-RT datasets are provided in near-real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems. [ABSTRACT FROM AUTHOR]

In the framework of the EUMETSAT H SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) project several soil moisture products, with different timeliness (e.g. NRT, offline, data records), spatial resolution, format (e.g. time series, swath orbit geometry) or the representation of the water content in various soil layers (e.g. surface, root-zone), are generated on a regular basis and distributed to users. The products are: H16 Metop ASCAT-B SSM NRT 12.5 km sampling, H101 Metop ASCAT-A SSM NRT 12.5 km sampling, H102 Metop ASCAT-A SSM NRT 25 km sampling, H103 Metop ASCAT-B SSM NRT 25 km sampling, H113 Metop ASCAT DR2018 SSM time series 12.5 km sampling, and the products H14 SM DAS 2, H27 SM DAS 3 of Soil wetness index in the roots region by scatterometer assimilation in a Land Data Assimilation System.In the framework of the H SAF project these products are evaluated with a validation protocol defined by the project partners. The validation methodology is based on Triple Collocation (TC) performed between the test data set, Noah GLDAS v2.1 and the passive ESA-CCI v04.3 soil moisture product, whereas the Pearson Correlation Coefficient (R) was only computed between the test data set and Noah GLDAS v2.1. In our study we will show, for each H SAF soil moisture product, the standard quality benchmark Signal-to-Noise Ratio (SNR) and the Pearson correlation coefficient (R) achieving a consistent and global validation of the different soil moisture products. [ABSTRACT FROM AUTHOR]