Armando Brath

Comparison of short-term rainfall prediction models for real-time flood forecasting

This study compares the accuracy of the short-term rainfall forecasts obtained with time-series analysis techniques, using past rainfall depths as the only input information. The techniques proposed here are linear stochastic auto-regressive movingaverage (ARMA) models, artificial neural networks (ANN) and the non-parametric nearest-neighbours method. The rainfall forecasts obtained using the considered methods are then routed through a lumped, conceptual, rainfall‐runoff model, thus implementing a coupled rainfall‐runoff forecasting procedure for a case study on the Apennines mountains, Italy. The study analyses and compares the relative advantages and limitations of each time-series analysis technique, used for issuing rainfall forecasts for lead-times varying from 1 to 6 h. The results also indicate how the considered time-series analysis techniques, and especially those based on the use of ANN, provide a significant improvement in the flood forecasting accuracy in comparison to the use of simple rainfall prediction approaches of heuristic type, which are often applied in hydrological practice. q 2000 Elsevier Science B.V. All rights reserved.

Assessing the effectiveness of hydrological similarity measures for flood frequency analysis

This paper evaluates the relative performance of four hydrological similarity measures that are used to form homogeneous pooling groups for regional frequency analysis. One pair of similarity measures is based on seasonality indexes that reflect the timing of extreme events. A further pair of measures considers a characterisation, at the basin scale, of the frequency distribution of rainfall extremes and the extent of the impervious portion of the catchment. The measures are applied to a case study encompassing a large area in Northern-Central Italy. The similarity measures are examined in the context of a pooling scheme that is designed to identify hierarchical, focused pooling groups. The performance of the similarity measures is quantified using a Monte Carlo experiment. The results demonstrate that similarity measures based on seasonality indexes are effective for estimating extreme flow quantiles for the study area. For ungauged catchments, a similarity measure incorporating both rainfall statistics and permeability information is most effective.

Analysis of the effects of levee heightening on flood propagation: example of the River Po, Italy

Abstract The effects of human activities on flood propagation, during the period 1878–2005, in a 190-km reach of the middle—lower portion of the River Po (Northern Italy) are investigated. A series of topographical, hydrological and inundation data were collected for the 1878 River Po geometry and the June 1879 flood event, characterised by an inundated area of 432 km2. The aim of the study is two-fold: (1) to show the applicability of flood inundation models in reconstructing historical inundation events, and (2) to assess the effects of human activities during the last century on flood propagation in the middle—lower portion of the River Po. Numerical simulations were performed by coupling a two-dimensional finite element code, TELEMAC-2D, with a one-dimensional finite difference code, HEC-RAS.

Estimating the index flood using indirect methods

Abstract Three indirect techniques for index flood estimation are analysed in order to evaluate their applicability and effectiveness. These indirect techniques, based on both statistical and conceptual approaches, are applied to a set of 33 hydrometric stations, located in a large area in northern-central Italy. The results show that the statistical model, due to its flexible structure, has a better descriptive ability than the physically-based models, which are rigidly structured as they conceptualize the rainfall-runoff transformation. However, the rigid structure of the conceptual approaches reduces their dependence on the specific information of the single stations and therefore increases their robustness. Finally, the results highlight that direct estimation techniques could be advisable for catchments with peculiar geomorphoclimatic properties; that is to say properties which differ substantively from those of the majority of the basins considered in the identification of the indirect models. This conclusion seems to hold even when a very limited amount of hydrometric information is available.

The effects of the spatial variability of soil infiltration capacity in distributed flood modelling.

Spatial variability is recognized to exert a remarkable influence on the catchment hydrological response over a wide range of scales. Accordingly, one of the main advantages of distributed rainfall-runoff models is their capability of accounting for its effects. The application of these models, however, is often limited by the large amount of data required. Particularly, the spatial characterization of both the vegetation cover and soil type is needed in order to describe properly the spatial distribution of the soil infiltration properties. Lack of these data, which are often quite difficult to obtain in practice, is one of the reasons which may induce hydrologists to prefer the application of lumped models, despite their lower capability of describing hydrological processes. A possible way out for preserving the distributed nature of the model is neglecting the spatial variability of the soil infiltration process by lumping the correspondent model parameters at the basin scale, and describing the transfer of runoff production at the basin outlet through a distributed approach. Lumping the infiltration parameters might be a reasonable assumption if one is interested in modelling those situations where the spatial variability of the infiltration process is expected to exert a marginal effect on the river discharge. Accordingly, the present analysis is aimed at verifying the effects of the spatial variability of the soil infiltration parameters on the flood flows modelled by a distributed model, with special reference to a case study of a river basin located in northern Italy. The main purpose of this work is to derive indications in order to possibly reduce the data requirements of distributed models, thus encouraging their application even in the case of limited data availability.

Real-time flood forecasting via combined use of conceptual and stochastic models

Abstract Stochastic modelling of the simulation errors resulting from the off-line application of conceptual rainfall-runoff models is often performed in the context of real-time flood forecasting, in order to improve the forecasting accuracy. Although widely applied in the operational practice, such approach has not been yet extensively investigated in the scientific literature. This analysis is aimed at evaluating the benefits in discharge forecast accuracy that can be gained by this kind of approach and to provide some insights into the identification and estimation procedures of the optimal stochastic model to be applied when updating the forecasts. Application of univariate linear ARIMA models, even in the fractionally differenced form, has been herein considered for a case study referred to the Sieve River basin, located in Central Italy. The results highlight the dependence of the benefits retrievable from the stochastic updating procedure on the lead time of the flood forecasting.

Sensitivity of the peak flows to the spatial variability of the soil infiltration capacity for different climatic scenarios

Abstract The use of distributed rainfall-runoff models in applied hydrology has been widely increased in the latter years. One of the main advantages that these model possess with respect to their lumped-parameters counterpart is the possibility to account for the spatial variability of the local runoff production. However, the application of these models is often limited by the large amount of data required, which are quite difficult to obtain. A possible way out for reducing the data requirements of distributed models is to neglect the spatial variability of the soil infiltration process by lumping the correspondent model parameters at the basin scale. This might be a reasonable assumption if one is interested in modelling those situations where the spatial variability of infiltration is expected to exert a marginal effect on the river discharge. Accordingly, the present study is aimed at verifying the effects of the spatial variability of the soil infiltration parameters on the flood flows modelled by a distributed model, for different climatic scenarios. The analysis is carried out referring to the case study of the Secchia River basin, located in northern Italy.

Analysis of the relationships between flood peaks and flood volumes based on crossing properties of river flow processes

A stochastic approach is developed for the determination of the reduction ratio between the annual maximum flow averaged over a consecutive D hour period and the annual maximum of peak instantaneous flow with the same frequency of occurrence. Initially, crossing properties of the integrated flow process are related to those of the instantaneous flows, using a Gaussian hypothesis. The scale of fluctuation of the river flow process is the key parameter which governs the theoretical reduction function. Application is made to numerous historical series from Italy. To facilitate the applicability of the model, a method of calibration based on currently available hydrometric information is proposed. Quantiles of flood volumes are obtained from the theoretical reduction function. It is verified that the reduction ratio is independent of the return period and appears to be insensitive to the underlying distributions of the component processes. The non-Gaussian case and scaling properties are discussed.

At-site and regional assessment of the possible presence of non-stationarity in extreme rainfall in northern Italy

Abstract The possible presence of non-stationarity in long rainfall records, along with the related consequences in the estimation of the frequency distribution of the extreme events, was recently pointed out in several scientific studies. However, it is well known that the detection of the presence of non-stationarity may be affected by relevant uncertainties, which are mainly originated by the limited length of the available data samples. The present paper describes an analysis aimed at detecting the possible presence of non-stationarity in some long rainfall records observed in northern-central Italy. Firstly a regional analysis is performed in order to assess the possible presence of non-stationarity at regional scale. Secondly synthetic rainfall series are analysed in order to assess how much the sample variability of a short stationary series might induce effects which could be attributed to non-stationarity.

Flow Duration Curve from Satellite: Potential of a Lifetime SWOT Mission

A flow duration curve (FDC) provides a comprehensive description of the hydrological regime of a catchment and its knowledge is fundamental for many water-related applications (e.g., water management and supply, human and irrigation purposes, etc.). However, relying on historical streamflow records, FDCs are constrained to gauged stations and, thus, typically available for a small portion of the world’s rivers. The upcoming Surface Water and Ocean Topography satellite (SWOT; in orbit from 2021) will monitor, worldwide, all rivers larger than 100 m in width (with a goal to observe rivers as small as 50 m) for a period of at least three years, representing a potential groundbreaking source of hydrological data, especially in remote areas. This study refers to the 130 km stretch of the Po River (Northern Italy) to investigate SWOT potential in providing discharge estimation for the construction of FDCs. In particular, this work considers the mission lifetime (three years) and the three satellite orbits (i.e., 211, 489, 560) that will monitor the Po River. The aim is to test the ability to observe the river hydrological regime, which is, for this test case, synthetically reproduced by means of a quasi-2D hydraulic model. We consider different river segmentation lengths for discharge estimation and we build the FDCs at four gauging stations placed along the study area referring to available satellite overpasses (nearly 52 revisits within the mission lifetime). Discharge assessment is performed using the Manning equation, under the assumption of a trapezoidal section, known bathymetry, and roughness coefficient. SWOT observables (i.e., water level, water extent, etc.) are estimated by corrupting the values simulated with the quasi-2D model according to the mission requirements. Remotely-sensed FDCs are compared with those obtained with extended (e.g., 20–70 years) gauge datasets. Results highlight the potential of the mission to provide a realistic reconstruction of the flow regimes at different locations. Higher errors are obtained at the FDC tails, where very low or high flows have lower likelihood of being observed, or might not occur during the mission lifetime period. Among the tested discretizations, 20 km stretches provided the best performances, with root mean absolute errors, on average, lower than 13.3%.