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Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGE mean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications. [ABSTRACT FROM AUTHOR]

Selecting the best structure and parameterization of rainfall–runoff models is not straightforward and depends on a broad number of factors. In this study, the "Modello Idrologico Semi-Distribuito in continuo" (MISDc) was tested on 63 mountainous catchments in the western Po Valley (Italy) and the optimal model parameters were regionalized using different strategies. The model performance was evaluated through several indexes analysing hydrological regime, high-flow condition and flow–duration curve (FDC). In general, MISDc provides a good fit behaviour with a Kling-Gupta Efficiency index greater than 0.5 for 100% and 84% of cases for calibration and validation, respectively. Concerning the regionalization, spatial proximity approach is the most accurate solution obtaining satisfactory performance. Lastly, the predicted FDCs showed an excellent similarity with the observed ones. Results encourage to apply MISDc over the study area for flood forecasting and for assessing water resources availability thanks to the modest computational efforts and data requirements. [ABSTRACT FROM AUTHOR]

In a changing climate, assessing the effects that the variation of the expected rainfalls can cause to slope stability is of primary importance. Precipitations are expected to increase, and, in particular, there will be more events characterised by extreme rainfalls, which legitimates the possibility of an increase in landslide activity. A probabilistic physically based model, which takes into account the uncertainty in soil characterisation, has been applied to a study area in central Italy, forced with different scenarios of expected rainfalls. The results of the prediction are compared in terms of variation of percentage of unstable territory. It is observed that the projection of the expected rainfall produces a general increase of the number of potentially unstable zones. Although many uncertainties in the analyses of the climatic trends and in their related effects at the ground still exist, the presented approach shows that physically based methods can be used to support quantitative projections of the expected impacts. [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]

Rainfall thresholds represent the main tool for the Italian Civil Protection System for early warning of the threat of landslides. However, it is well-known that soil moisture conditions at the onset of a storm event also play a critical role in triggering slope failures, especially in the case of shallow landslides. This study attempts to define soil moisture (estimated by using a soil water balance model) and rainfall thresholds that can be employed for hydrogeological risk prevention by the Civil Protection Decentrate Functional Centre (CFD) located in the Umbria Region (central Italy). Two different analyses were carried out by determining rainfall and soil moisture conditions prior to widespread landslide events that occurred in the Umbria Region and that are reported in the AVI (Italian Vulnerable Areas) inventory for the period 1991-2001. Specifically, a 'local' analysis that considered the major landslide events of the AVI inventory and an 'areal' analysis subdividing the Umbria Region in ten sub-areas were carried out. Comparison with rainfall thresholds used by the Umbria Region CFD was also carried out to evaluate the reliability of the current procedures employed for landslide warning. The main result of the analysis is the quantification of the decreasing linear trend between the maximum cumulated rainfall values over 24, 36 and 48 h and the soil moisture conditions prior to landslide events. This trend provides a guideline to dynamically adjust the operational rainfall thresholds used for warning. Moreover, the areal analysis, which was aimed to test the operational use of the combined soil moisture-rainfall thresholds showed, particularly for low values of rainfall, the key role of soil moisture conditions for the triggering of landslides. On the basis of these results, the Umbria Region CFD is implementing a procedure aimed to the near real-time estimation of soil moisture conditions based on the soil water balance model developed ad hoc for the region. In fact, it was evident that a better assessment of the initial soil moisture conditions would support and improve the hydrogeological risk assessment. [ABSTRACT FROM AUTHOR]

Predicting the spatial and temporal occurrence of rainfall triggered landslides represents an important scientific and operational issue due to the high threat that they pose to human life and property. This study investigates the relationship between rainfall, soil moisture conditions and landslide movement by using recorded movements of a rock slope located in central Italy, the Torgiovannetto landslide. This landslide is a very large rock slide, threatening county and state roads. Data acquired by a network of extensometers and a meteorological station clearly indicate that the movements of the unstable wedge, first detected in 2003, are still proceeding and the alternate phases of quiescence and reactivation are associated with rainfall patterns. By using a multiple linear regression approach, the opening of the tension cracks (as recorded by the extensometers) as a function of rainfall and soil moisture conditions prior the occurrence of rainfall, are predicted for the period 2007-2009. Specifically, soil moisture indicators are obtained through the Soil Water Index, SWI, a product derived by the Advanced SCATterometer (ASCAT) on board the MetOp (Meteorological Operational) satellite and by an Antecedent Precipitation Index, API. Results indicate that the regression performance (in terms of correlation coefficient, r) significantly enhances if an indicator of the soil moisture conditions is included. Specifically, r is equal to 0.40 when only rainfall is used as a predictor variable and increases to r = 0.68 and r = 0.85 if the API and the SWI are used respectively. Therefore, the coarse spatial resolution (25 km) of satellite data notwithstanding, the ASCAT SWI is found to be very useful for the prediction of landslide movements on a local scale. These findings, although valid for a specific area, present new opportunities for the effective use of satellite-derived soil moisture estimates to improve landslide forecasting. [ABSTRACT FROM AUTHOR]

The monitoring of soil moisture (SM) can be performed through remote sensing technologies. Among the operational microwave radiometers potentially able to provide information about SM and its variability in the space-time domain, in this work the capability of the AMSU (Advanced Microwave Sounding Unit) sensor has been investigated. In particular, SM information, achieved implementing two different AMSU-based indices, has been compared with both in situ measured and modeled observations for several sites spread out all over Italy. The results indicate that the AMSU sensor can be considered as a useful tool to provide quite reliable information about SM variability, mainly if its high temporal resolution is taken into account. [ABSTRACT FROM AUTHOR]

Analysis of forecasts obtained by a forecasting model called STAFOM, a Muskingum-type model for real-time application, shows that the model provides accurate forecast stage estimates for most of the selected case studies and flood events in the Upper-Middle Tiber River basin in central Italy. However, three main issues affected STAFOM: (1) its kinematic nature, (2) the lateral inflows representation, and (3) the occurrence of sudden fluctuations in water levels observed at the ends of the equipped river reach. Therefore, this simple stage forecasting model is hereby improved by incorporating a methodology relating local stage and remote discharge along river channels. This latter procedure, based on the rating curve model (RCM), is capable of reconstructing a discharge hydrograph at a river site where only the stage is monitored, while the discharge is recorded at another section located far away and for which a significant lateral inflow contribution is expected. Application of the new model, named STAFOM-RCM, to several flood events that occurred along four equipped river reaches of the Upper-Middle Tiber River basin, shows that it improves the stage forecast accuracy both in peak and stage hydrograph primarily for long river reaches, thus allowing consideration of a longer forecast lead time; and hence, avoiding the use of the old two-connecting river branch scheme that amplified the fluctuations in observed water levels. [ABSTRACT FROM AUTHOR]

• Irrigation detection is investigated through remote sensing soil moisture. • Diagnostic indices derived by the temporal stability theory are used. • High-resolution (100 m) maps of irrigated areas are produced through the k -means algorithm. • The spatial matching between the irrigation extent and the data resolution is crucial. Despite a detailed knowledge of the spatial-temporal dynamics of irrigation being necessary to optimize the agricultural production without exacerbating the pressure exercised on the water resource, such information is still often lacking worldwide. In this study, a double-scale analysis on the detectability of the irrigation occurrence over an area in central Italy through remote sensing soil moisture is proposed; the period of interest is a 3-year time span from 2017 to 2019. The detectability of district- or sub-district-scale irrigation signals through remotely sensed soil moisture data is investigated at two different spatial resolutions: 1 km and plot scale. Three soil moisture products sampled at 1 km resolution are evaluated: a DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled version of SMAP (Soil Moisture Active Passive) and two Sentinel-1-derived products, namely the 1 km version delivered by Copernicus and a plot-scale-born version developed by THEIA and aggregated at 1 km. The THEIA Sentinel-1 product aggregated at 100 m is used in the plot-scale analysis. Over the study area, the irrigation extent is determined by the fragmentation of the agricultural fields and the complex topography, making the adoption of plot-scale data necessary. Satisfactory results are obtained by comparing maps of irrigated areas at 100 m spatial resolution produced through the k -means clustering algorithm with ground-truth data, since the method fails only once out of seven in properly reproducing the irrigated or non-irrigated conditions occurred over four pilot agricultural fields. [Display omitted] [ABSTRACT FROM AUTHOR]

The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate. [ABSTRACT FROM AUTHOR]

The Mediterranean region has been identified as one of the main climate change hotspots: its sensitivity to global change is high and its evolution remains uncertain. The region experiences many interactions and feedbacks at the oceanic, atmospheric, and hydrological levels, while facing high anthropogenic activities. Analysing the water cycle over the Mediterranean region is of major importance to environmental and socio-economic aspects. The satellite monitoring of the Mediterranean water cycle represents one of the key challenges for the hydrological community.The presentation will show recent results on using satellite soil moisture and precipitation products for hydrometeorological prediction in the Mediterranean region, and particularly for the prediction of floods and landslides. Specifically, we will show the comparison of multiple satellite precipitation products for predicting flood in 100+ basins over the Mediterranean Basin by also using different hydrological models. Moreover, the assessment of satellite precipitation products for predicting the occurrence of landslides in Italy is carried out. Among the investigated satellite products, we have firstly considered state-of-the-art products such as TMPA (TRMM Multisatellite Precipitation Analysis), GPM (Global Precipitation Measurement), and H SAF (EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management). Secondly, we have tested the innovative products using the SM2RAIN algorithm for rainfall retrieval from multiple satellite soil moisture products including ASCAT (Advanced SCATterometer), SMOS (Soil Moisture and Ocean Salinity mission) and SMAP (Soil Moisture Active and Passive mission). [ABSTRACT FROM AUTHOR]

Soil moisture is a key environmental variable, important to e.g., farmers, meteorologists, and disaster management units. We fuse surface soil moisture (SSM) estimates from spatio-temporally complementary radar sensors through temporal filtering of their joint signal and obtain a kilometre-scale, daily soil water content product named SCATSAR-SWI. With 25 km Metop ASCAT SSM and 1 km Sentinel-1 SSM serving as input, the SCATSAR-SWI is globally applicable and achieves daily full coverage over operated areas. We employ a near-real-time-capable SCATSAR-SWI algorithm on a fused 3 year ASCAT-Sentinel-1-SSM data cube over Italy, obtaining a consistent set of model parameters, unperturbed by coverage discontinuities. An evaluation of a therefrom generated SCATSAR-SWI dataset, involving a 1 km Soil Water Balance Model (SWBM) over Umbria, yields comprehensively high agreement with the reference data (median R = 0.61 vs. in situ; 0.71 vs. model; 0.83 vs. ASCAT SSM). While the Sentinel-1 signal is attenuated to some extent, the ASCAT's signal dynamics are fully transferred to the SCATSAR-SWI and benefit from the Sentinel-1 parametrisation. Using the SM2RAIN approach, the SCATSAR-SWI shows excellent capability to reproduce 5 day-accumulated rainfall over Italy, with R = 0.89 against observed rainfall. The SCATSAR-SWI is currently in preparation towards operational product dissemination in the Copernicus Global Land Service (CGLS). [ABSTRACT FROM AUTHOR]

• A Bayesian approach has been used for merging multiple satellite rainfall products. • We created a superior product that can be efficiently run in near-real time. • Soil moisture can provide useful information for improving satellite rainfall. This work investigates the potential of using the Bayesian-based Model Conditional Processor (MCP) for complementing satellite precipitation products with a rainfall dataset derived from satellite soil moisture observations. MCP – which is a Bayesian Inversion approach – was originally developed for predictive uncertainty estimates of water level and discharge to support real-time flood forecasting. It is applied here for the first time to precipitation to provide its probability distribution conditional on multiple satellite precipitation estimates derived from TRMM Multi-Satellite Precipitation Analysis real-time product v.7.0 (3B42RT) and the soil moisture-based rainfall product SM2RAIN-CCI. In MCP, 3B42RT and SM2RAIN-CCI represent a priori information (predictors) about the "true" precipitation (predictand) and are used to provide its real-time a posteriori probabilistic estimate by means of the Bayes theorem. MCP is tested across Italy during a 6-year period (2010–2015) at daily/0.25 deg temporal/spatial scale. Results demonstrate that the proposed methodology provides rainfall estimates that are superior to both 3B42RT (as well as its successor IMERG-early run) and SM2RAIN-CCI in terms of both median bias, random errors and categorical scores. The study confirms that satellite soil moisture-derived rainfall can provide valuable information for improving state-of-the-art satellite precipitation products, thus making them more attractive for water resource management and large scale flood forecasting applications. [ABSTRACT FROM AUTHOR]

• Available rainfall data are characterized by different time resolution, "ta" • A database involving metadata from many geographic areas is presented. • The "ta" history of rainfall data in a variety of rain gauges is reconstructed. • The registration methods of the rainfall data changed over the years. • Currently about 50% of rain gauge stations provide data with any "ta" Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), t a. We present an historical analysis of the time-evolution of t a based on a large database of rain gauge networks operative in many study areas. Globally, t a data were collected for 25,423 rain gauge stations across 32 geographic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early recordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with t a of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means t a = 1 min. However, a significant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations. [ABSTRACT FROM AUTHOR]

In this study, durum wheat kernels harvested in three climatically different Italian cultivation areas (Emilia Romagna, Umbria and Sardinia) in 2015, were analyzed with a combination of different isolation methods to determine their fungal communities, with a focus on Fusarium head blight (FHB) complex composition, and to detect fungal secondary metabolites in the grains. The genus Alternaria was the main component of durum wheat mycobiota in all investigated regions, with the Central Italian cultivation area showing the highest incidence of this fungal genus and of its secondary metabolites. Fusarium was the second most prevalent genus of the fungal community in all cultivation environments, even if regional differences in species composition were detected. In particular, Northern areas showed the highest Fusarium incidence, followed by Central and then Southern cultivation areas. Focusing on the FHB complex, a predominance of Fusarium poae , in particular in Northern and Central cultivation areas, was found. Fusarium graminearum secondary metabolites. These results show that durum wheat cultivated in Northern Italy may be subject to a higher FHB infection risk and to Fusarium incidence, durum wheat harvested in the Northern cultivation area showed the highest presence of Fusarium secondary metabolites. These results show that durum wheat cultivated in Northern Italy may be subject to a higher FHB infection risk and to Fusarium mycotoxins accumulation., Competing Interests: The authors declare no conflict of interest.