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A reliable estimate of the areal reduction factor (ARF), useful for implementing the upscaling procedure of point rainfall information, is of crucial relevance in many hydrological applications aimed at hydraulic structures design. Despite the availability of different methodologies in the literature, the choice of the best formulation to be applied for ARF assessment in a specific region remains an open problem. In fact, the transposition of ARF formulations to areas different from those where they were developed, even with similar geographical features, could lead to incorrect ARF estimates and relevant errors in design rainfalls, especially for short durations. In this paper, a new deterministic fixed-area approach is proposed; it relies upon an averaging procedure of the ratios between areal and local annual maximum rainfalls. The proposed approach was applied to the study area of Umbria region in central Italy, where a parametric relation expressing ARF in a wide range of rainfall duration (5–2,880 min) and areas (up to about 6,100 km2) was derived. A comparison of the proposed approach with the most widely used empirical methodologies was also performed. The methodology can be adopted in any other region where a network characterized by a minimum length of rainfall time series of 7 years and a proper spatial density of stations is available. [ABSTRACT FROM AUTHOR]

Hydraulic complexity metrics referred to as M1 and M2 play an important role when it comes to the analysis of habitat metrics. In the present paper, the significance of these parameters is analysed by using laboratory data as well as field observations along the Tiber River in Italy. Based on the laboratory data, the estimated parameters allow us to characterise the high/low-velocity areas. Based on field observations, larger magnitudes of M1 are linked to the zones with large changes in cross-sectional flow velocity. Larger magnitudes of M2 are observed at the left bank of the channel for all flow conditions, suggesting locations with larger kinetic energy consumption for aquatic organisms. Overall, the findings of the present research would be of particular interest in quantifying biologically important flow patterns occurring at different spatial scales within different streams and flow conditions. [ABSTRACT FROM AUTHOR]

In many developing countries, due to economic constraints, a single station on a river reach is often equipped to record flow variables. On the other hand, hydrographs at the upstream sections may also be needed for especially assessing flooded areas. The upstream flow hydrograph prediction is called the reverse flood routing. There are some reverse flood routing pocedures requiring sophisticated methods together with substantial data requirements. This study proposes a new reverse flood routing procedure, based upon the simple kinematic wave (KW) equation, requiring only easily measurable downstream stage data. The KW equation is first averaged along a channel length at a fixed time, t, assuming that channel width is spatially constant, and then the spatially averaged equation is averaged in time, Δt. The temporally averaged terms are approximated as the arithmetical mean of the corresponding terms evaluated at time t and t + Δt. The Chezy roughness equation is employed for flow velocity, and the upstream flow stage hydrograph is assumed be described by a two parameter gamma distribution (Pearson Type III). The spatially averaged mean flow depth and lateral flow are related to the downstream flow stage. The resulting routing equation is thus obtained as a function of only downstream flow stage, meaning that the method mainly requires measurements of downstream flow stage data besides the mean values of channel length, channel width, roughness coefficient and bed slope. The optimal values of the parameters of reverse flood routing are obtained using the genetic algorithm. The calibration of the model is accomplished by using the measured downstream hydrographs. The validation is performed by comparing the model-generated upstream hydrographs against the measured upstream hydrographs. The proposed model is applied to generate upstream hydrographs at four different river reaches of Tiber River, located in central Italy. The length of river reaches varied from 20 to 65 km. Several upstream hydrographs at different stations on this river are generated using the developed method and compared with the observed hydrographs. The method predicts the time to peak with less than 5% error and peak rates with less than 10% error in the short river reaches of 20 km and 31 km. It also predicts the time to peak and peak rate in other two brances of 45 km and 65 km with less than 15% error. The method satisfactorily generates upstream hydrographs, with an overall mean absolute error (MAE) of 42 m3/s. [ABSTRACT FROM AUTHOR]

Entropy theory applied to hydrometric measurements enables establishing a relationship between the maximum and the mean velocities of flow passing a river section. In many developing countries, river discharge is still estimated by the velocity‐area method following the procedure established based on the point‐velocities measured at 0.6D depth from the water surface of many verticals of flow section. This study explores the establishment of a relationship between the maximum point‐velocity estimated at 0.6D depth of the vertical located at or nearer to the deepest flow depth, and the maximum point‐velocity occurring somewhere along the same vertical which in turn can be linked to the sectional mean flow velocity based on the established entropy theory. The appropriateness of the proposed two‐steps based approach of discharge estimation is first verified on the Tiber River and the Po River of Italy. The study finds that the values of the maximum point‐velocity estimated using the proposed approach closely matches with the recorded maximum flow velocity values along the same flow‐depths. After this verification, the proposed method was applied for discharge estimation at two Indian river gauging stations, where only the point‐velocity measurements at 0.6D depth locations of the verticals are recorded. The study finds that the discharges estimated by both the proposed approach and the velocity‐area method closely match with each other with the estimated Nash‐Sutcliffe Efficiency (NSE) values greater than 0.99. Thus the proposed two‐steps approach involving the entropy concept based relationship for discharge estimation enables to replace the tedious and time‐consuming velocity‐area approach. Key Points: Proposes a new method to estimate discharge using u0.6D,max velocity measurement. Estimation of state equilibrium constant using the historical records of the velocity‐area methodIt is establishing a relationship between u0.6D,max and umaxThe potential of the approach is demonstrated through uncertainty analysis for the Indian Rivers [ABSTRACT FROM AUTHOR]

River discharge is an important variable to measure in order to predict droughts and flood occurrences. Once the cross-sectional geometry of the river is known, discharge can be inferred from water level and surface flow velocity measurements. Since river discharges are of particular interest during extreme weather events, when river sites cannot be safely accessed, noncontact sensing technologies are particularly appealing. To this purpose, this work proposes a prototype of a low-cost continuous wave (CW) Doppler radar sensor, which is able to monitor the surface flow velocity of rivers. The prototype is tested at two gauged sites in central Italy, along the Tiber River. The surface flow velocity distribution across the river is monitored by means of the analysis of received Doppler signal. The surface velocity statistics are then extracted using a novel algorithm that is optimized to run on a microprocessor platform with minimal computing power (ArduinoUNO). In particular, the radar measurements are used to initialize a 2-D entropy-based velocity model (EVM) that is able to estimate river discharges in any flow condition. Finally, the results concerning the observed discharge provided by the EVM prove to be comparable with those obtained with more expensive commercial solutions. The results are important since the described methodology can be extended to small-size Doppler radar sensors onboard unmanned aerial vehicles (UAVs), the latter providing a method for mapping surface velocity of rivers. [ABSTRACT FROM AUTHOR]

Stream gauge-based information is the foundation for many hydrological applications in a river basin including the aquatic-habitat conservation. A simple two-parameter model for routing streamflow depth (alternatively, stream–stage) hydrographs and estimating corresponding discharge hydrographs in river channels is proposed using the multilinear approach, based on Nash-type discrete-cascade model. The applicability of this model is investigated by extending its framework to the realm of compound cross-section trapezoidal channels for both in-bank and overbank flows by using 20 flood events of the Tiber River in the Umbria region of Central Italy, and subsequently comparing the simulated results with the corresponding simulations of the HEC-RAS (Hydrologic Engineering Center – River Analysis System) hydrodynamic model and observed flow depth hydrographs. The field application, comparative study, and uncertainty and sensitivity analysis of the results demonstrate that the proposed multilinear discrete Nash-cascade stage-hydrograph (MDNS) routing model has the potential for routing floods in real-world rivers and canal irrigation systems, especially in operational mode. [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]

The linear entropic relation between mean flow velocity [formula] and maximum velocity umax is defined through a dimensionless entropy parameter M, which is found constant for gauged river sections. This entropic relation has been tested for many rivers and has been found to be fundamental to addressing velocity measurements during high floods when sampling can be carried out only in the upper portion of flow area where the maximum velocity umax occurs. It is therefore of considerable interest to investigate the possible dependence of M on hydraulic and geometric characteristics so that it can be determined for ungauged river sites. Thus, this study attempts to define the dependence of M on the geometric and hydraulic characteristics of river cross sections by coupling Manning's equation expressing u with the equation for umax obtained through a logarithmic velocity distribution, which takes into account the possibility that umax may occur below the water surface. Analysis shows that M does not depend on the energy or water surface slope Sf, thus justifying why its mean value at a gauged site is always nearly the same, whatever the flood condition. Moreover, the hydraulic and geometric characteristics that permit the determination of M mainly include Manning's roughness, hydraulic radius, and locations where umax occurs and the hypothetical zero velocity. Then, a formulation relating Manning's roughness to M is proposed. Velocity measurements carried out in the past 20 years at two gauged sections along the Tiber River, Italy are used to test the analysis. [ABSTRACT FROM AUTHOR]

A multilinear stage-hydrograph routing method based on time distribution scheme is analyzed. The framework of the multilinear method is based on the Muskingum-type routing method, which is used as the linear submodel. The proposed method can implicitly model the nonlinear dynamics of the flood wave propagation by varying the model parameters at each routing time step. The suitability of this flood routing method is verified using a set of laboratory experimental data and the field data of the Tiber River in central Italy. [ABSTRACT FROM AUTHOR]

The genetic algorithm (GA) technique is applied to obtain optimal parameter values of the standard rating curve model (RCM) for predicting, in real time, event-based flow discharge hydrographs at sites receiving significant lateral inflows. The standard RCM uses the information of discharge and effective cross-sectional flow area at an upstream station and effective cross-sectional flow area wave travel time later at a downstream station to predict the flow rate at this last site. The GA technique obtains the optimal parameter values of the model, here defined as the GA-RCM model, by minimizing the mean absolute error objective function. The GA-RCM model was tested to predict hydrographs at three different stations, located on the Upper Tiber River in central Italy. The wave travel times characterizing the three selected river branches are, on the average, 4, 8, and 12 h. For each river reach, seven events were employed, four for the model parameters’ calibration and three for model testing. The GA approach, employing 100 chromosomes in the initial gene pool, 75% crossover rate, 5% mutation rate, and 10,000 iterations, made the GA-RCM model successfully simulate the hydrographs observed at each downstream section closely capturing the trend, time to peak, and peak rates with, on the average, less than 5% error. The model performance was also tested against the standard RCM model, which uses, on the contrary to the GA-RCM model, different values for the model parameters and wave travel time for each event, thus, making the application of the standard RCM for real time discharge monitoring inhibited. The comparative results revealed that the RCM model improved its performance by using the GA technique in estimating parameters. The sensitivity analysis results revealed that at most two events would be sufficient for the GA-RCM model to obtain the optimal values of the model parameters. A lower peak hydrograph can also be employed in the calibration to predict a higher peak hydrograph. Similarly, a shorter travel time hydrograph can be used in GA to obtain optimal model parameters that can be used to simulate floods characterized by longer travel time. For its characteristics, the GA-RCM model is suitable for the monitoring of discharge in real time, at river sites where only water levels are observed. [ABSTRACT FROM AUTHOR]

The applicability of the kinematic wave (KW) approximation was investigated for unsteady flow in prismatic channels. Each of the 16 cases analyzed under steady flow conditions by Moramarco et al. (2008) was now perturbed such that synthetic discharge hydrographs varying around the steady value Q0 and characterized by different values of the wave period T and steady wave travel times, T0′, were obtained. Therefore, based on the kinematic wave number K and steady Froude number F0, a set of 224 experimental tests were carried out and the KW accuracy was investigated by routing synthetic discharge hydrographs. In this case, the dynamic wave solution DYW was also considered as a benchmark and adopting the critical-flow depth at the downstream end. The performance of KW was assessed using different accuracy measures: (a) errors in dimensionless maximum flow depth along the channel [variant_greek_epsilon]max; (b) explained variance NSQ on discharge hydrographs at the outlet section; and (c) errors in peak discharge [variant_greek_epsilon]Qp and water level [variant_greek_epsilon]hp at the downstream channel end, and errors in time to peak discharge [variant_greek_epsilon]tQp and water level [variant_greek_epsilon]thp always at the downstream end. Based on these KW accuracy measures, different scenarios for the KW applicability were found as a function of the quantities KF02 and T/T0′. It was found that the KW applicability depended on the accuracy measure of interest and different criteria were accordingly defined. Moreover, the comparison between the proposed KW applicability criteria and the existing ones shows that these latter tend to limit the KW application and, hence, they have to be used with caution. This insight was also inferred from an analysis of flood events that occurred along an equipped branch of the upper Tiber River in central Italy. Therefore, also based on the real case study, it was shown that there is not a single rule for the KW applicability but different criteria can be defined, taking care, however, that their application strictly depends on the selected accuracy measure. [ABSTRACT FROM AUTHOR]

Summary: This study developed a genetic algorithm model to predict flow rates at sites receiving significant lateral inflow. It predicts flow rate at a downstream station from flow stage measured at upstream and downstream stations. For this purpose, it constructed two different models: First is analogous to the rating curve model (RCM) of Moramarco et al. [Moramarco, M., Barbetta, S., Melone, F., Singh, V.P., 2005. Relating local stage and remote discharge with significant lateral inflow. J. Hydrologic Eng., ASCE, 10(1)] and the second is based on summation of contributions from upstream station and lateral inflows using kinematic wave approximation. The model was applied to predict flow rates at three different gauging stations located on Tiber River, Upper Tiber River Basin, Italy. The model used average wave travel time for each river reach and obtained average set of parameter values for all the events observed in the same river reach. The GA model was calibrated, for each river reach and for each formulation, by three events and tested against three other events. The results showed that the GA model produced satisfactory results and it was superior over the most recently developed rating curve method. This study further analyzed the case where only water surface elevation data were used in the input vector to predict flow rates. The results showed that using elevation data produces satisfactory results. This has an implication for predicting flow rates at ungauged river sites since the surface elevation data can be obtained without needing the detailed geometry of river section which could change significantly during a flood. [Copyright &y& Elsevier]

Two models, one linear and one non-linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non-linear model is based on a multilayer feed-forward back propagation (FFBP) artificial neural network (ANN) and uses flow-stage data measured at the upstream and downstream stations. ANN predicted the real-time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow-stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4-h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8-h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. [ABSTRACT FROM AUTHOR]

Streamflow measurements during high floods is a challenge for which the World Meteorological Organization fosters the development of innovative technologies for achieving an accurate estimation of the discharge. The use of non-contact sensors for monitoring surface flow velocities is of interest to turn these observed values into a cross-sectional mean flow velocity, and subsequently, into discharge if bathymetry is given. In this context, several techniques are available for the estimation of mean flow velocity, starting from observed surface velocities. Among them, the entropy-based methodology for river discharge assessment is often applied by leveraging the theoretical entropic principles of Shannon and Tsallis, both of which link the maximum flow velocity measured at a vertical of the flow area, named the y-axis, and the cross-sectional mean flow velocity at a river site. This study investigates the performance of the two different entropic approaches in estimating the mean flow velocity, starting from the maximum surface flow velocity sampled at the y-axis. A velocity dataset consisting of 70 events of measurements collected at two gauged stations with different geometric and hydraulic characteristics on the Po and Tiber Rivers in Italy was used for the analysis. The comparative evaluation of the velocity distribution observed at the y-axis of all 70 events of measurement was closely reproduced using both the Shannon and Tsallis entropy approaches. Accurate values in terms of the cross-sectional mean flow velocity and discharge were obtained with average errors not exceeding 10%, demonstrating that the Shannon and Tsallis entropy concepts were equally efficient for discharge estimation in any flow conditions. [ABSTRACT FROM AUTHOR]

Water depths and velocities predicted inside urban areas during severe storms are traditionally the final result of a chain of hydrologic and hydraulic models. The use of a single model embedding all the components of the rainfall–runoff transformation, including the flux concentration in the river network, can reduce the subjectivity and, as a consequence, the final uncertainty of the computed water depths and velocities. In the model construction, a crucial issue is the management of the topographic data. The information given by a Digital Elevation Model (DEM) available on a regular grid, as well as all the other elevation data provided by single points or contour lines, allow the creation of a Triangulated Irregular Network (TIN) based unstructured digital terrain model, which provides the spatial discretization for both the hydraulic and the hydrologic models. The procedure is split into four steps: (1) correction of the elevation z* measured in the nodes of a preliminary network connecting the edges with all the DEM cell centers; (2) the selection of a suitable hydrographic network where at least one edge of each node has a strictly descending elevation, (3) the generation of the computational mesh, whose edges include all the edges of the hydrographic network and also other lines following internal boundaries provided by roads or other infrastructures, and (4) the estimation of the elevation of the nodes of the computational mesh. A suitable rainfall–runoff transformation model is finally applied to each cell of the identified computational mesh. The proposed methodology is applied to the Sovara stream basin, in central Italy, for two flood events—one is used for parameter calibration and the other one for validation purpose. The comparison between the simulated and the observed flooded areas for the validation flood event shows a good reconstruction of the urban flooding. [ABSTRACT FROM AUTHOR]

A novel procedure for estimation of the vulnerability to seepage inducing piping processes in earthen levees affected by animal burrows is presented. The proposed methodology combines an available procedure of seepage vulnerability assessment for undamaged levees with the result of a finite element analysis software, which is used for identifying the seepage path and hydraulics head profile of both damaged and undamaged levees. The main steps of the procedure for estimating the impact of burrows in increasing the vulnerability of levees are presented. Twenty‐one levees along the Tanaro River (north‐western Italy) are used as a case study, and the results show that the critical conditions for the onset of inner erosion are achieved for shorter flood durations in damaged levees. If burrows occur, the probability of inner erosion (seepage probability) increases resulting in a potential increase of forming longer tunnels. This approach is a first attempt to quantify the seepage probability of extended levee systems affected by burrows and is applied for simplified geometrical and two‐dimensional representation of the cavities. This procedure can be applied by the hydraulic authorities to set the priorities in levees maintenance. Future research would focus on the analysis of more realistic burrows conditions. [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: The Muskingum–Cunge model (MC, ) can be used for real-time forecasting of discharges at the downstream end of a river reach for a predetermined lead time Δt ∗, which is closely connected to the two routing parameters k and x (). Similarly, the Rating Curve Method (RCM, ) can be used for real-time forecasting of discharges at the downstream end of a river reach for an assigned lead time TL , equal to the mean travel time of the flood events in the river reach. In this article two procedures are presented, the first based on use of the MC model alone and the second on a cascade combination of the MC and RCM models. These two procedures enable real-time forecasting of the discharges at one end of a river reach –with or without taking account of lateral inflows – for variable lead times ranging between 1h and the mean travel time of flood events in that reach, assuming that only the length and the mean slope and width of that reach are known. Each procedure is moreover associated with a suitable method for estimating the confidence band for the forecast which takes into account the heteroscedastic nature of forecasting errors. These two procedures were applied to two consecutive reaches of the Tiber river (Italy), characterised, respectively, by a strong and limited presence of lateral inflows, and to the reach obtained as a sum of the two. The results show the reliability of the two procedures: the first one produces good results for all forecast lead times considered, the second only when the lead times are close to the mean travel time and in such a case, especially when the mean travel time is long (long reach), it provides a forecast of better quality than the other procedure. [ABSTRACT FROM AUTHOR]