ALBERT

Description: Can differential land-subsidence significantly alter river flooding dynamics, and thus flood risk in flood prone areas? Many studies show how the lowering of the coastal areas is closely related to an increase in the flood-hazard due to more important tidal flooding and see level rise. The literature on the relationship between differential land-subsidence and possible alterations to riverine flood-hazard of inland areas is still sparse, although several geographical areas characterized by significant land-subsidence rates during the last 50 years experienced intensification in both inundation magnitude and frequency. We investigate the possible impact of a significant differential ground lowering on flood hazard over a 77 km2 area around the city of Ravenna, in Italy. The rate of land-subsidence in the study area, naturally in the order of a few mm year−1, dramatically increased up to 110 mm year−1 after World War II, primarily due to groundwater pumping and gas production platforms. The result was a cumulative drop that locally exceeds 1.5 m. Using a recent digital elevation model (res. 5 m) and literature data on land-subsidence, we constructed a ground elevation model over the study area in 1897 and we characterized either the current and the historical DEM with or without road embankments and land-reclamation channels in their current configuration. We then considered these four different topographic models and a two-dimensional hydrodynamic model to simulate and compare the inundation dynamics associated with a levee failure scenario along embankment system of the river Montone, which flows eastward in the southern portion of the study area. For each topographic model, we quantified the flood hazard in terms of maximum water depth (h) and we compared the actual effects on flood-hazard dynamics of differential land-subsidence relative to those associated with other man-made topographic alterations, which resulted to be much more significant.

Description: The growing concern about the possible effects of climate change on flood frequency regime is leading Authorities to review previously proposed reference procedures for design-flood estimation, such as national flood frequency models. Our study focuses on Triveneto, a broad geographical region in North-eastern Italy. A reference procedure for design flood estimation in Triveneto is available from the Italian NCR research project "VA.PI.", which considered Triveneto as a single homogeneous region and developed a regional model using annual maximum series (AMS) of peak discharges that were collected up to the 1980s by the former Italian Hydrometeorological Service. We consider a very detailed AMS database that we recently compiled for 76 catchments located in Triveneto. All 76 study catchments are characterized in terms of several geomorphologic and climatic descriptors. The objective of our study is threefold: (1) to inspect climatic and scale controls on flood frequency regime; (2) to verify the possible presence of changes in flood frequency regime by looking at changes in time of regional L-moments of annual maximum floods; (3) to develop an updated reference procedure for design flood estimation in Triveneto by using a focused-pooling approach (i.e. Region of Influence, RoI). Our study leads to the following conclusions: (1) climatic and scale controls on flood frequency regime in Triveneto are similar to the controls that were recently found in Europe; (2) a single year characterized by extreme floods can have a remarkable influence on regional flood frequency models and analyses for detecting possible changes in flood frequency regime; (3) no significant change was detected in the flood frequency regime, yet an update of the existing reference procedure for design flood estimation is highly recommended and we propose the RoI approach for properly representing climate and scale controls on flood frequency in Triveneto, which cannot be regarded as a single homogeneous region.

Description: We propose and investigate the reliability of simplified graphical tools, which we term Hypsometric Vulnerability Curves, HVCs, for assessing flood vulnerability and risk over large geographical areas and for defining sustainable flood-risk mitigation strategies. These curves rely on the use of inundation scenarios simulated by means of quasi-two-dimensional (quasi-2-D) hydrodynamic models that reproduce the hydraulic behaviour of the floodable area outside the main embankment system of the study river reach. We present an application of HVCs constructed on the basis of land use and census data collected during the last 50 years for assessing the recent dynamics of the flood vulnerability and risk over a large floodable area along a 350 km stretch of the River Po (Northern Italy). We also compared the proposed simplified approach with a traditional approach based on simulations performed with the fully-2-D hydrodynamic model TELEMAC-2-D, a widely employed and well-known 2-D finite-element scheme. By means of this comparison, we characterize the accuracy of the proposed simplified approach (i.e. quasi-2-D model and HVCs) for flood-risk assessment over large geographical areas and different historical land-use scenarios.

Description: Several hydrological analyses need to be founded on a reliable estimate of the design storm, which is the expected rainfall depth corresponding to a given duration and probability of occurrence, usually expressed in terms of return period. The annual series of precipitation maxima for storm duration ranging from 15 min to 1 day, observed at a dense network of raingauges sited in northern central Italy, are analyzed using an approach based on L-moments. The analysis investigates the statistical properties of rainfall extremes and detects significant relationships between these properties and the mean annual precipitation (MAP). On the basis of these relationships, we developed a regional model for estimating the rainfall depth for a given storm duration and recurrence interval in any location of the study region. The applicability of the regional model was assessed through Monte Carlo simulations. The uncertainty of the model for ungauged sites was quantified through an extensive cross-validation.

Description: We investigate the links between the drainage density of a river basin and selected flood statistics, namely, mean, standard deviation, coefficient of variation and coefficient of skewness of annual maximum series of peak flows. The investigation is carried out through a three-stage analysis. First, a numerical simulation is performed by using a spatially distributed hydrological model in order to highlight how flood statistics change with varying drainage density. Second, a conceptual hydrological model is used in order to analytically derive the dependence of flood statistics on drainage density. Third, real world data from 44 watersheds located in northern Italy were analysed. The three-level analysis seems to suggest that a critical value of the drainage density exists for which a minimum is attained in both the coefficient of variation and the absolute value of the skewness coefficient. Such minima in the flood statistics correspond to a minimum of the flood quantile for a given exceedance probability (i.e., recurrence interval). Therefore, the results of this study may provide useful indications for flood risk assessment in ungauged basins.

Description: This study considers the overall uncertainty affecting river flow measurements and proposes a framework for analysing the uncertainty of rating-curves and its effects on the calibration of numerical hydraulic models. The uncertainty associated with rating-curves is often considered negligible relative to other approximations affecting hydraulic studies, even though recent studies point out that rating-curves uncertainty may be significant. This study refers to a ~240 km reach of River Po and simulates ten different historical flood events by means of a quasi-twodimensional (quasi-2-D) hydraulic model in order to generate 50 synthetic measurement campaigns (5 campaigns per event) at the gauged cross-section of interest (i.e. Cremona streamgauge). For each synthetic campaign, two different procedures for rating-curve estimation are applied after corrupting simulated discharges according to the indications reported in the literature on accuracy of discharge measurements, and the uncertainty associated with each procedure is then quantified. To investigate the propagation of rating-curve uncertainty on the calibration of Manning's roughness coefficients further model simulations are run downstream Cremona's cross-section. Results highlight the significant role of extrapolation errors and how rating-curve uncertainty may be responsible for estimating unrealistic roughness coefficients. Finally, the uncertainty of these coefficients is analysed and discussed relative to the variability of Manning's coefficient reported in the literature for large natural streams.

Description: This study analyses the intersite dependence of nested catchment structures by modelling cross-correlations for pairs of nested and unnested catchments separately. Probabilistic regional envelope curves are utilised to derive regional flood quantiles for 89 catchments located in Saxony, in the Southeast of Germany. The study area has a nested structure and the intersite correlation is much stronger for nested pairs of catchments than for unnested ones. Pooling groups of sites (regions) are constructed based on several candidate sets of catchment descriptors using the Region of Influence method. Probabilistic regional envelope curves are derived on the basis of flood flows observed within the pooling groups. Their estimated recurrence intervals are based on the number of effective sample years of data (i.e. equivalent number of uncorrelated data). The evaluation of the effective sample years of data requires the modelling of intersite dependence. We perform this globally, using a cross-correlation function for the whole study area as well as by using two different cross-correlation functions, one for nested pairs and another for unnested pairs. In the majority of the cases, these two modelling approaches yield significantly different estimates for the effective sample years of data, and therefore also for the recurrence intervals. The reduction of the recurrence interval when using two different cross-correlation functions is larger for larger pooling groups and for pooling groups with a higher fraction of nested catchments. A separation into nested and unnested pairs of catchments gives a more realistic representation of the characteristic river network structure and improves the estimation of regional information content. Hence, applying two different cross-correlation functions is recommended.

Description: In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

Description: Comprehensive flood risk assessment studies should quantify the global uncertainty in flood hazard estimation, for instance by mapping inundation extents together with their confidence intervals. This appears of particular importance in the case of flood hazard assessments along dike-protected reaches, where the possibility of occurrence of dike failures may considerably enhance the uncertainty. We present a methodology to derive probabilistic flood maps in dike-protected flood prone areas, where several sources of uncertainty are taken into account. In particular, this paper focuses on a 50 km reach of River Po (Italy) and three major sources of uncertainty in hydraulic modelling and flood mapping: uncertainties in the (i) upstream and (ii) downstream boundary conditions, and (iii) uncertainties in dike failures. Uncertainties in the definition of upstream boundary conditions (i.e. design-hydrographs) are assessed through a copula-based bivariate analysis of flood peaks and volumes. Uncertainties in the definition of downstream boundary conditions are characterised by uncertainty in the rating curve with confidence intervals which reflect discharge measurement and interpolation errors. The effects of uncertainties in boundary conditions and randomness of dike failures are assessed by means of the Inundation Hazard Assessment Model (IHAM), a recently proposed hybrid probabilistic-deterministic model that considers three different dike failure mechanisms: overtopping, piping and micro-instability due to seepage. The results of the study show that the IHAM-based analysis enables probabilistic flood hazard mapping and provides decision-makers with a fundamental piece of information for devising and implementing flood risk mitigation strategies in the presence of various sources of uncertainty.

Description: A promising approach to catchment classification makes use of unsupervised neural networks (Self Organising Maps, SOM's), which organise input data through non-linear techniques depending on the intrinsic similarity of the data themselves. Our study considers ∼300 Italian catchments scattered nationwide, for which several descriptors of the streamflow regime and geomorphoclimatic characteristics are available. We compare a reference classification, identified by using indices of the streamflow regime as input to SOM, with four alternative classifications, which were identified on the basis of catchment descriptors that can be derived for ungauged basins. One alternative classification adopts the available catchment descriptors as input to SOM, the remaining classifications are identified by applying SOM to sets of derived variables obtained by applying Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) to the available catchment descriptors. The comparison is performed relative to a PUB problem, that is for predicting several streamflow indices in ungauged basins. We perform an extensive cross-validation to quantify nationwide the accuracy of predictions of mean annual runoff, mean annual flood, and flood quantiles associated with given exceedance probabilities. Results of the study indicate that performing PCA and, in particular, CCA on the available set of catchment descriptors before applying SOM significantly improves the effectiveness of SOM classifications by reducing the uncertainty of hydrological predictions in ungauged sites.