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• Probabilistic threshold integrates antecedent soil moisture with rainfall threshold. • Bayes' theorem is applied to estimate the probability of landslide occurrences. • Soil moisture is estimated by the hydrological simulations. Various methods have been proposed to define the rainfall thresholds for the landslide prediction. Once the threshold is determined, it remains the same regardless of the antecedent soil moisture conditions. However, given the important role of the antecedent soil moisture in the initiation of landslides, it is considered if the rainfall threshold level varies according to the antecedent soil moisture conditions, the prediction performance will be improved. Therefore, in this study we propose a probabilistic threshold to integrate antecedent soil moisture conditions with rainfall thresholds. In order to take into account the conditions with landslides and without landslides, the Bayesian analysis is applied to estimate the landslide occurrence probability given the various combinations of two factors: the antecedent soil moisture and the severity of the recent rainfall event. These combinations are then divided into conditions that are likely to trigger landslides and those unlikely to trigger landslides by comparing their probabilities with a critical value. In this way, the probabilistic threshold is determined. Here the soil moisture is estimated using the distributed hydrological model, and the severity of the rainfall event is characterized by the cumulated event rainfall-rainfall duration (ED) thresholds with different exceedance probabilities. The proposed approach was applied to a sub-region of the Emilia-Romagna region in northern Italy. The results show that the probabilistic threshold has a better prediction performance than the ED rainfall threshold, especially in terms of reducing false alarms. This study provides an effective approach to improve the prediction capability of the ED rainfall threshold, benefiting its application in the landslide prediction. [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]

Summary: We test the ability of three box models () to describe soil moisture dynamics in a regularly monitored experimental site in northwestern Italy. The models include increasingly complex representations of leakage and evapotranspiration processes. We force the models with the local rainfall, and we determine model parameters with a least-square minimization procedure. We study separately summer and annual dynamics. When we use the models to reproduce annual dynamics, we include a simple representation of the annual cycle of evapotranspiration. We test the models both in simulation mode, estimating their parameters from the same data used to verify them, and in forecast mode, determining the model parameters from one period, and using them to forecast soil water dynamics in a different period. The results indicate that simple box models can estimate soil water dynamics at midlatitudes, using rainfall as the only meteorological driver. The models displaying the best performances include the representation of leakage and evapotranspiration as nonlinear functions of soil water content. [ABSTRACT FROM AUTHOR]

Summary: Soil moisture is widely recognized as a key parameter in environmental processes mainly for the role of rainfall partitioning into runoff and infiltration. Therefore, for storm rainfall–runoff modeling the estimation of the antecedent wetness conditions (AWC) is one of the most important aspect. In this context, this study investigates the potential of scatterometer on board of the ERS satellites for the assessment of wetness conditions in three Tiber sub-catchments (Central Italy), of which one includes an experimental area for soil moisture monitoring. The satellite soil moisture data are taken from the ERS/METOP soil moisture archive. First, the scatterometer-derived soil wetness index (SWI) data are compared with two on-site soil moisture data sets acquired by different methodologies on areas of different extension ranging from 0.01km2 to ∼60km2. Moreover, the reliability of SWI to estimate the AWC at a catchment scale is investigated considering the relationship between SWI and the soil potential maximum retention parameter, S, of the Soil Conservation Service-Curve Number (SCS-CN) method for abstraction. Several flood events occurred from 1992 to 2005 are selected for this purpose. Specifically, the performance of the SWI for S estimation is compared with two antecedent precipitation indices (API) and one base flow index (BFI). The S values obtained through the observed direct runoff volume and rainfall depth are used as benchmark. Results show the great reliability of the SWI for the estimation of wetness conditions both at the plot and catchment scale despite the complex orography of the investigated areas. As far as the comparison with on site soil moisture data set is concerned, the SWI is found quite reliable in representing the soil moisture at layer depth of 15cm, with a mean correlation coefficient equal to 0.81. The characteristic time length parameter variations, as expected, is depended on soil type, with values in accordance with previous studies. In terms of AWC assessment at catchment scale, based on selected flood events, the SWI is found highly correlated with the observed maximum potential retention of the SCS-CN method with a correlation coefficient R equal to −0.90. Besides, SWI in representing the AWC of the three investigated catchments, outperformed both API indices, poorly representative of AWC, and BFI. Finally, the classical SCS-CN method applied for direct runoff depth estimation, where S is assessed by SWI, provided good performance with a percentage error not exceeding ∼25% for 80% of investigated rainfall–runoff events. [Copyright &y& Elsevier]

Summary: The main objective of this paper is to evaluate a threshold-based flash flood warning method, by considering a wide range of climatic and physiographic conditions, and by focusing on ungauged basins. The method is derived from the flash flood guidance (FFG, hereafter) approach. The FFG is the depth of rain of a given duration, taken as uniform in space and time on a certain basin, necessary to cause minor flooding at the outlet of the considered basin. This rainfall depth, which is computed based on a hydrological model, is compared to either real-time-observed or forecasted rainfall of the same duration and on the same basin. If the nowcasted or forecasted rainfall depth is greater than the FFG, then flooding in the basin is considered likely. The study provides an assessment of this technique based on operational quality data from 11 mountainous basins (six nested included in five larger parent basins) located in north-eastern Italy and central France. The model used in this study is a semi-distributed conceptual rainfall–runoff model, following the structure of the PDM (probability distributed moisture) model. Two general questions are addressed: (1) How does the efficiency of the method evolve when the simulation parameters can not be calibrated but must be transposed from parent gauged basins to ungauged basins? (2) How sensitive are the results to the method used to estimate the initial soil moisture status? System performances are evaluated by means of categorical statistics, such as the critical success index (CSI). Results show that overall CSI is equal to 0.43 for the parent basins, where the hydrological model has been calibrated. CSI reduces to 0.28 for the interior basins, when model parameters are transposed from parent basins, and to 0.21, when both model parameters and soil moisture status is transposed from parent basins. Performance differences between FFG and use of time-constant soil moisture status are very high for the parent basins and decrease with decreasing the system accuracy. The percent difference amounts to 53% for the parent basins, to 25% for interior basins with parameter transposition, and to 19% for interior basins with parameter and soil moisture status transposition. These results improve our understanding of the applicability and reliability of this method at various scales and under various scenarios of data availability. [Copyright &y& Elsevier]