Use of a derived distribution approach for flood prediction in poorly gauged basins: A case study in Italy

A derived distribution approach is developed for flood prediction in poorly gauged basins. This couples information on the expected storm scaling, condensed into Depth Duration Frequency curves, with soil abstractions modeled using Soil Conservation Service Curve Number method and hydrological response through Nash’s Instantaneous Unit Hydrograph. A simplified framework is given to evaluate critical duration for flood design. Antecedent moisture condition distribution is included. The method is tested on 16 poorly gauged Mediterranean watersheds in Tyrrhenian Liguria, North Western Italy, belonging to a homogeneous hydrological regions. The derived flood distribution is compared to the regional one, currently adopted for flood design. The evaluation of Curve Number is critical for peak flood evaluation and needs to be carefully carried out. This can be done including local Annual Flood Series data in the estimation of the derived distribution, so gathering the greatest available information. However, Curve Number influence decreases for the highest return periods. When considerable return periods are required for flood design and few years of data are available, the derived distribution provides more accurate estimates than the approach based on single site distribution fitting. A strategy based on data availability for application of the approach is then given. The proposed methodology contributes to the ongoing discussion concerning PUB (Prediction in Ungauged Basins) decade of the IAHS association and can be used by researchers and practitioners for those sites where no flood data, or only a few, are available, provided precipitation data and land use information are at hand.     

One‐dimensional and two‐dimensional hydrodynamic modeling derived flow properties: impacts on aquatic habitat quality predictions

Studies of the effects of hydrodynamic model dimensionality on simulated flow properties and derived quantities such as aquatic habitat quality are limited. It is important to close this knowledge gap especially now that entire river networks can be mapped at the microhabitat scale due to the advent of point‐cloud techniques. This study compares flow properties, such as depth and velocity, and aquatic habitat quality predicted from pseudo‐2D and fully 2D hydrodynamic modeling. The models are supported by high‐resolution, point‐cloud derived bathymetries, from which close‐spaced cross‐sections were extracted for the 1D modeling, of three morphologically and hydraulically different river systems. These systems range from small low‐gradient meandering pool–riffle to large steep confined plane‐bed rivers. We test the effects of 1D and 2D models on predicted hydraulic variables at cross‐sections and over the full bathymetry to quantify the differences due to model dimensionality and those from interpolation. Results show that streambed features, whose size is smaller than cross‐sectional spacing, chiefly determine the different results of 1D and 2D modeling whereas flow discharge, stream size, morphological complexity and model grid sizes have secondary effects on flow properties and habitat quality for a given species and life stage predicted from 1D and 2D modeling. In general, the differences in hydraulic variables are larger in the bathymetric than in the cross‐sectional analysis, which suggests that some errors are introduced from interpolation of spatially disaggregated simulated variables with a 1D model, instead of model dimensionality 1D or 2D. Flow property differences are larger for velocity than for water surface elevation and depth. Differences in weighted usable area (WUA) derived from 1D and 2D modeling are relatively small for low‐gradient meandering pool–riffle systems, but the differences in the spatial distribution of microhabitats can be considerable although clusters of same habitat quality are spatially comparable. Copyright © 2014 John Wiley & Sons, Ltd.     

Development of regionalized joint probability approach to flood estimation: a case study for Eastern New South Wales,Australia

Estimation of design flood in ungauged catchments is a common problem in hydrology. Methods commonly adopted for this task are limited to peak flow estimation, e.g. index flood, rational and regression‐based methods. To estimate complete design hydrograph, rainfall–runoff modelling is preferred. The currently recommended method in Australia known as Design Event Approach (DEA) has some serious limitations since it ignores the probabilistic nature of principal model inputs (such as temporal patterns (TP) and initial loss) except for design rainfall depth. A more holistic approach such as Joint Probability Approach (JPA)/Monte Carlo Simulation Technique (MCST) can overcome some of the limitations associated with the DEA. Although JPA/MCST has been investigated by many researchers, it has been proved to be difficult to apply since its routine application needs readily available regional design data such as stochastic rainfall duration, TP and losses, which are largely unavailable for Australian states. This paper presents regionalization of the model inputs/parameters to the JPA/MCST for eastern New South Wales (NSW) in Australia. This uses data from 86 pluviograph stations and six catchments from NSW to regionalize the input distributions for application with the JPA/MCST. The independent testing to three test catchments shows that the regionalized JPA/MCST generally outperforms the at‐site DEA. The developed regionalized JPA/MCST can be applied at any arbitrary location in eastern NSW. The method and design data developed here although primarily applicable to eastern NSW can be adapted to other Australian states and countries. Copyright © 2013 John Wiley & Sons, Ltd.