Potential advantages of flow-area rating curves compared to classic stage-discharge-relations

River monitoring is a critical issue for hydrological modelling that strongly relies on the use of Flow Rating Curves (FRCs). In most of the cases, FRCs are approximated by least-squares fitting, whose performance may be influenced by measurements variability, which is often limited in high values. In this context, a new formulation has been recently introduced to exploit available knowledge on cross-sectional geometry for a more robust derivation of FRCs. This method combines the wetted-area/stage and the cross-sectionally averaged velocity/stage functions in the FRCs derivation limiting, at least partially, the uncertainty in the extrapolation of higher discharge values. The methodology is tested on four gauged cross-sections of the Tiber River basin, where a relatively high number of measurements are available. This dataset is used to test the reliability of the new approach with respect to the classic method in relatively stable river cross-sections. A jackknifing approach is used to understand the role played by the number of gaugings and range of observations on the applicability of the new formulation highlighting its advantages in data-scarce environments. In particular, we observed that the new approach becomes advantageous when the observations are limited both in terms of the range of observations or in terms of sample size (i.e., <10 samples).

Figure: Mean value of the FRCs and the confidence interval estimated using different sample sizes: a) sample size=5; b) sample size=10; c) sample size=15; d) sample size=30. Red diamonds represent all the available hydrometric measurements.

How to cite: Manfreda, S., A. Pizarro, T. Moramarco, L. Cimorelli, D. Pianese, S. Barbetta, Potential advantages of flow-area rating curves compared to classic stage-discharge-relationsJournal of Hydrology, Volume 585, 124752, 2020. [pdf]

The GEOframe-NewAge Modelling System Applied in a Data Scarce Environment

In this work, the semi-distributed hydrological modeling system GEOframe-NewAge was integrated with a web-based decision support system implemented for the Civil Protection Agency of the Basilicata region, Italy. The aim of this research was to forecast in near real-time the most important hydrological variables at 160 control points distributed over the entire region. The major challenge was to make the system operational in a data-scarce region characterized by a high hydraulic complexity, with several dams and infrastructures. In fact, only six streamflow gauges were available for the calibration of the model parameters. Reliable parameter sets were obtained by simulating the hydrological budget and then calibrating the rainfall-runoff parameters. After the extraction of the flow-rating curves, six sets of parameters were obtained considering the different streamflow components (i.e., the baseflow and surface runoff) and using a multi-site calibration approach. The results show a good agreement between the measured and modeled discharges, with a better agreement in the sections located upstream of the dams. Moreover, the results were validated using the inflows measured at the most important dams (Pertusillo, San Giuliano and Monte Cotugno). For rivers without monitoring points, parameters were assigned using a principle of hydrological similarity in terms of their geology, lithology, and climate.

Figure: Representation of the simplified embedded reservoir model.

How to Cite: Bancheri, M., R. Rigon and S. Manfreda, The GEOframe-NewAge modelling system applied in a data scarce environment, Water, 12, 86, 2019. [pdf]

On the derivation of flow rating-curves in data-scarce environments

River monitoring is a critical issue for hydrological modelling that relies strongly on the use of flow rating curves (FRCs). In most cases, these functions are derived by least-squares fitting which usually leads to good performance indices, even when based on a limited range of data that especially lack high flow observations. In this context, cross-section geometry is a controlling factor which is not fully exploited in classical approaches. In fact, river discharge is obtained as the product of two factors: 1) the area of the wetted cross-section and 2) the cross-sectionally averaged velocity. Both factors can be expressed as a function of the river stage, defining a viable alternative in the derivation of FRCs. This makes it possible to exploit information about cross-section geometry limiting, at least partially, the uncertainty in the extrapolation of discharge at higher flow values. Numerical analyses and field data confirm the reliability of the proposed procedure for the derivation of FRCs.

How to cite: Manfreda, S., On the derivation of flow rating-curves in data-scarce environmentsJournal of Hydrogy, 562, 151-154 (doi: 10.1016/j.jhydrol.2018.04.058) 2018.

GFA tool – Geomorphic Flood Area tool

Plugin icon

GFA – tool is an open-source QGIS plug-in to realize a fast and cost-effective delineation of the floodplains in the contexts where the available data is scarce to carry out hydrological/hydraulic analyses.

The delineation of flood hazard and flood risk areas is a critical issue whose complete achievement regularly encounters several practical difficulties. In data-scarce environments (e.g. ungauged basins, large-scale analyses), useful information about flood hazard exposure can be obtained using geomorphic methods. In order to advance this field of research, we implemented in the QGIS environment an automated DEM-based procedure that exhibited high accuracy and reliability in identifying the flood-prone areas in several test sites located in Europe, United States and Africa. This tool, named Geomorphic Flood Area tool (GFA tool), enables a fast and cost-effective flood mapping by performing a linear binary classification based on the recently proposed Geomorphic Flood Index (GFI). The GFA tool provides a user-friendly strategy to map flood exposure over large areas. 

References:

Samela, C., R. Albano, A. Sole, S. Manfreda, An open source GIS software tool for cost effective delineation of flood prone areasComputers, Environment and Urban Systems, 70, 43-52 (doi: 10.1016/j.compenvurbsys.2018.01.013), 2018.  [pdf]

Samela, C., T.J. Troy, S. Manfreda, Geomorphic classifiers for flood-prone areas delineation for data-scarce environmentsAdvances in Water Resources,  102, 13-28, (doi: 10.1016/j.advwatres.2017.01.007), 2017. [pdf]

Manfreda, S., C. Samela, A. Gioia, G. Consoli, V. Iacobellis, L. Giuzio, A. Cantisani, A. Sole, Flood-Prone Areas Assessment Using Linear Binary Classifiers based on flood maps obtained from 1D and 2D hydraulic modelsNatural Hazards, 79 (2), 735-754, (doi: 10.1007/s11069-015-1869-5), 2015. [pdf]

Manfreda, S., F. Nardi, C. Samela, S. Grimaldi, A. C. Taramasso, G. Roth and A. Sole, Investigation on the Use of Geomorphic Approaches for the Delineation of Flood Prone AreasJournal of Hydrology, 517, 863-876, (doi: 10.1016/j.jhydrol.2014.06.009), 2014.

Github repository

QGIS Python Plugins Repository

MATLAB CODE

Geomorphic classifiers for flood-prone areas delineation for data-scarce environments

Knowing the location and the extent of the areas exposed to flood hazards is essential to any strategy for minimizing the risk. Unfortunately, in ungauged basins the use of traditional floodplain mapping techniques is prevented by the lack of the extensive data required. The present work aims to overcome this limitation by defining an alternative simplified procedure for a preliminary floodplain delineation based on the use of geomorphic classifiers. To validate the method in a data-rich environment, eleven flood-related morphological descriptors derived from remotely sensed elevation data have been used as linear binary classifiers over the Ohio River basin and its sub-catchments. Their performances have been measured at the change of the topography and the size of the calibration area, allowing to explore the transferability of the calibrated parameters, and to define the minimum extent of the calibration area. The best performing classifiers among those analysed have been applied and validated across the continental U.S. The results suggest that the classifier based on the Geomorphic Flood Index (GFI), is the most suitable to detect the flood-prone areas in data-scarce regions and for large-scale applications, providing good accuracies with low requirements in terms of data and computational costs. This index is defined as the logarithm of the ratio between the water depth in the element of the river network closest to the point under exam (estimated using a hydraulic scaling function based on contributing area) and the elevation difference between these two points.

How to cite: Caterina Samela, Tara J. Troy, Salvatore Manfreda, Geomorphic   classifiers   for   flood-prone   areas   delineation   for data-scarce   environmentsAdvances   in   Water   Resources (doi: 10.1016/ j.advwatres.2017.01.007) 2017. [pdf]