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Climatic extremes are changing and decision-makers express a strong need for reliable information on future changes over the coming decades as a basis for adaption strategies. In the hydrological-hydraulic context, to estimate changes on floods, a modeling chain composed by general circulation models (GCMs), bias correction (BC) methods, and hydrological modeling is generally applied. It is well-known that each step of the modeling chain introduces uncertainties, resulting in a reduction of the reliability of future climate projections. The main goal of this study is the assessment of the accuracy and variability (i.e., model accuracy, climate intermodel variability, and natural variability) on climate projections related to the present period. By using six different GCMs and two BC methods, the 'climate intermodel variability' is evaluated. 'Natural variability' is estimated through random realizations of stochastic weather generators. By comparing observed and simulated extreme discharge values, obtained through a continuous rainfall-runoff model, 'model accuracy' is computed. The Tiber River basin in central Italy is used as a case study. Results show that in climate projections, model accuracy and climate intermodel variability components have to be clearly distinguished. For accuracy, the hydrological model is found to be the largest source of error; for variability, natural variability contributes for more than 75% to the total variability while GCM and BC have a much lower influence. Moreover, accuracy and variability components vary significantly, and not consistently, between catchments with different permeability characteristics. [ABSTRACT FROM AUTHOR]

A framework for a comprehensive synthetic rainfall-runoff database was developed to study catchment response to a variety of rainfall events. The framework supports effective flood risk assessment and management and implements simple approaches. It consists of three flexible components, a rainfall generator, a continuous rainfall-runoff model, and a database management system. The system was developed and tested at two gauged river sections along the upper Tiber River (central Italy). One of the main questions was to investigate how simple such approaches can be applied without impairing the quality of the results. The rainfall-runoff model was used to simulate runoff on the basis of a large number of rainfall events. The resulting rainfall-runoff database stores pre-simulated events classified on the basis of the rainfall amount, initial wetness conditions and initial discharge. The real-time operational forecasts follow an analogue method that does not need new model simulations. However, the forecasts are based on the simulation results available in the rainfall-runoff database (for the specific class to which the forecast belongs). Therefore, the database can be used as an effective tool to assess possible streamflow scenarios assuming different rainfall volumes for the following days. The application to the study site shows that magnitudes of real flood events were appropriately captured by the database. Further work should be dedicated to introduce a component for taking account of the actual temporal distribution of rainfall events into the stochastic rainfall generator and to the use of different rainfall-runoff models to enhance the usability of the proposed procedure. [ABSTRACT FROM AUTHOR]

Nowadays, the availability of soil moisture estimates from satellite sensors offers a great chance to improve real-time flood forecasting through data assimilation. In this paper, two real data and two synthetic experiments have been carried out to assess the effects of assimilating soil moisture estimates into a two-layer rainfall-runoff model. By using the ensemble Kalman filter, both the surface- and root-zone soil moisture (RZSM) products derived by the Advanced SCATterometer (ASCAT) have been assimilated and the model performance on flood estimation is analyzed. RZSM estimates are obtained through the application of an exponential filter. Hourly rainfall-runoff observations for the period 1994-2010 collected in the Niccone catchment (137 km2), Central Italy, are employed as case study. The ASCAT soil moisture products are found to be in good agreement with the modeled soil moisture data for both the surface layer (correlation coefficient (R) of 0.78) and the root zone (R = 0.94). In the real data experiment, the assimilation of the RZSM product has a significant impact on runoff simulation that provides a clear improvement in the discharge modeling performance. On the other hand, the assimilation of the surface soil moisture product has a small effect. The same findings are also confirmed by the synthetic twin experiments. Even though the obtained results are model dependent and site specific, the possibility to efficiently employ coarse resolution satellite soil moisture products for improving flood prediction is proven, mainly if RZSM data are assimilated into the hydrological model. [ABSTRACT FROM AUTHOR]

Derivation of travel time based on Saint-Venant equations may substantially improve rainfall-runoff routing, particularly in the Clark approach and GIS-based hydrological routing models. Kinematic wave (KW) and diffusive wave (DW) approximations are often applied in lieu of the dynamic wave (DYW) solution to study overland flow regime. In this study, for the first time, travel-time relationships are presented based on DW approximation. In terms of equilibrium time, the DW approximation improved results from 5% up to 15% over the KW approximation. After obtaining the semianalytical solution of the DW travel time, discharge hydrographs over a rectangular plane through the well-known time-area method were derived. Considering the DYW solution as the benchmark, it was found that the time-area approach by adopting the proposed DW wave travel time formulation, provided satisfactory results for 2.50F02<25 and F0>0.1. Using the travel time based on KW approach, the time-area method was found fairly accurate for K0F02>25 and F0>0.1. K0 and F0 are the well-known kinematic wave number and Froude number, respectively. Therefore, it is perceived that as K0F02 decreases, the difference between KW and DW approximations becomes greater. [ABSTRACT FROM AUTHOR]

Nowadays, in the scientific literature many rainfall-runoff (RR) models are available ranging from simpler ones, with a limited number of parameters, to highly complex ones, with many parameters. Therefore, the selection of the best structure and parameterisation for a model is not straightforward as it is dependent on a number of factors: climatic conditions, catchment characteristics, temporal and spatial resolution, model objectives, etc. In this study, the structure of a continuous semi-distributed RR model, named MISDc ('Modello Idrologico Semi-Distribuito in continuo') developed for flood simulation in the Upper Tiber River (central Italy) is presented. Most notably, the methodology employed to detect the more relevant processes involved in the modelling of high floods, and hence, to build the model structure and its parameters, is developed. For this purpose, an intense activity of monitoring soil moisture and runoff in experimental catchments was carried out allowing to derive a parsimonious and reliable continuous RR model operating at an hourly (or smaller) time scale. Specifically, in order to determine the catchment hydrological response, the important role of the antecedent wetness conditions is emphasized. The application of MISDc both for design flood estimation and for flood forecasting is reported here demonstrating its reliability and also its computational efficiency, another important factor in hydrological practice. As far as the flood forecasting applications are concerned, only the accuracy of the model in reproducing discharge hydrographs by assuming rainfall correctly known throughout the event is investigated indepth. In particular, the MISDc has been implemented in the framework of Civil Protection activities for the Upper Tiber River basin. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Estimation of antecedent wetness conditions is one of the most important aspects of storm rainfall-runoff modeling. This study investigated the use of observations of near-surface soil moisture carried out in a small experimental plot to estimate wetness conditions of five nested catchments, from 13 to 137 km2 in area, in Central Italy, including the plot itself. In particular, the relationship between the observed degree of saturation, θe, and the potential maximum retention parameter, S, of the soil conservation service-curve number (SCS-CN) method for abstraction was investigated using 15 rainfall-runoff events (ten for calibration and five for verification) that occurred in the period 2002–2005. Two antecedent precipitation indices (API) and one base flow index (BFI) were also considered for the estimation of wetness conditions. When interpreting S as the mean soil water deficit of the catchment, an inverse linear relationship with θe was found with the coefficient of determination decreasing with catchment area, but still significant for the largest catchment. On the contrary, the reliability of regression increased with catchment area when BFI was employed. Both API indices led to poor results for all investigated catchments. The accuracy of the modified SCS-CN method, i.e., incorporating θe for the estimation of S, coupled with a geomorphological unit hydrograph transfer function, was tested in simulating the catchment response. Assimilating the observed soil moisture in the rainfall-runoff model, both the runoff volume and the peak discharge were well predicted with average Nash–Sutcliffe efficiency greater than 90% in the verification phase. [ABSTRACT FROM AUTHOR]

This article discusses a hydrologic model developed for estimation of soil moisture before rainfall-runoff events in central Italy. According to the authors, the model is adapted for both infiltration at the short time-scale and evapotranspiration and a gravity driven approximation of drainage processes at longer time-scales. They found that runoff depth and peak discharge were dependent on pre-event soil moisture. The model showed improved results over the antecedent precipitation index for the antecedent wetness conditions tested.

The kinematic-wave and diffusive-wave approximations were investigated for unsteady overland flow resulting from spatially varying rainfall excess. Three types of boundary conditions were adopted: zero flow at the upstream end, and critical flow and zero depth-gradient at the downstream end. Errors were derived by comparing the dimensionless profiles of the flow depth over the plane with those computed from the dynamic-wave solution. It was found that the mean errors for both the approximations were independent of the type of rainfall excess distribution for KF0² > 5, where K is the kinematic-wave number and F0 is the Froude number. Therefore, the regions (KF0², F0) where the kinematic-wave and diffusive-wave solutions would be fairly accurate and for any distribution of spatially varying rainfall, were characterized. The kinematic-wave approximation was reasonably accurate, with a mean error of less than 5% and for the critical depth at the downstream end, for KF0² S 20 with F0 h 1; if the rainfall excess was concentrated in a portion of the plane, the field where the kinematic-wave solution was found accurate, it was more limited and characterized for KF02 > 35 with F0 h 1. The diffusive-wave solution was in good agreement with the dynamic-wave solution with a mean error of less than 5%, in the flow depth, for KF0² S 15 with F0 h 1; for rainfall excess concentrated in a portion of the plane, the accuracy of the diffusion wave solution was in a region more restricted and defined for KF0² S 30 with F0 h 1. For zero-depth gradient at the downstream end, the accuracy field of the kinematic-wave was found to be greater and characterized for KF0² > 10 with F0 h 1; for rainfall excess concentrated in a portion of the plane, the region was smaller and defined for KF0² > 15 with F0 h 1. The diffusive-wave solution was found accurate in the region defined for KF0² > 7•5, whereas for rainfall excess concentrated in a portion... [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]

Abstract: Soil moisture controls the partitioning of rainfall into runoff and infiltration and, consequently, the runoff generation. On the catchment scale its routine monitoring can be performed through remote sensing technologies. Within this framework, the purpose of this study is to investigate the potential of the Advanced Microwave Sounding Unit (AMSU), radiometer on board the NOAA (National Oceanic and Atmospheric Administration) satellites and operating since 1998, for the assessment of soil wetness conditions by comparing soil moisture data with both those measured in situ and provided by a continuous rainfall–runoff model applied to four catchments located in the Upper Tiber River (Central Italy). In particular, in order to perform a robust analysis an extensive and long-term period (nine years) of data was investigated. In detail, the Soil Wetness Variation Index, derived from the AMSU data modified in order to take account of the difference between the soil layer investigated by the satellite sensor and that used as a benchmark, was found to be correlated both with the in-situ and modeled soil moisture variations showing correlation coefficients in the range of 0.42–0.49 and 0.33–0.48, respectively. As far as the soil moisture temporal pattern is concerned, higher correlations were obtained (0.59–0.84 for the in-situ data and 0.82–0.87 for the modeled data set) partly due to the soil moisture seasonal pattern that enhances the correlation. Overall, the root mean square error was found to be less than 0.05m3/m3 for both the comparisons, thus assessing the potential of the AMSU sensor to quantitatively retrieve soil moisture temporal patterns. Moreover, the AMSU sensor can be considered as a useful tool to provide a reliable and frequently updated global soil moisture data set, considering its higher temporal resolution now available (about 4 passes per day) thanks to the presence of the sensor aboard different satellites. [Copyright &y& Elsevier]