Flood-Prone Areas Assessment Using Linear Binary Classifiers based on Morphological Indices

1/18 ASCE-‐ICVRAM-‐ISUMA 2014 Ins'tute for Risk and Uncertainty, University of Liverpool, 13-‐16 July 2014

Salvatore Manfreda*, Caterina Samela, Aurelia Sole, and Mauro Fiorentino

Università degli Studi della Basilicata

* [email protected]

Flood-Prone Areas Assessment Using Linear Binary Classifiers based on

Morphological Indices


Flood Exposure at the Global Scale

Flooding is evident in more than 1/3 of the world’s land area, in which some 82% of the world’s population resides. Dilley et al. (2005)


Flood Monitoring

Rela'vely poor density of gauging sta'ons in some regions, such as South America, Asia and Africa.

Herold and Mouton (HESSD, 2011)


River basin morphology intrinsically contains an extraordinary amount of informa'on on flood-‐driven erosion and deposi'onal phenomena, cons'tu'ng a useful indicator of the flood exposure of a given area (e.g. Arnaud-‐FasseTa et al., 2009; Tucker et al., 2001; Tucker and Whipple, 2002)

Geomorphic Approaches

Flood Plain


Digital Elevation Models

ü The advent of new technologies to measure topographic surface elevation (e.g., GPS, SAR, SAR interferometry, and laser altimetry) has given a strong impulse to the development of geomorphic approaches for valley bottoms identification using Digital Elevation Models (DEMs).

•  Digital terrain model obtained through interferometric data gathered by the space shuTle campaign by NASA with a cell-‐size of 90m. (CGIAR-‐CSI: hTp://srtm.csi.cgiar.org/)

•  ASTER GDEM 30m available from June 2009 (h=p://asterweb.jpl.nasa.gov/gdem.asp )


Research Questions

i)  What are the most significant geomorphological features for the delineation of flood prone areas? ii)  Is it possible to define a simplified approach for the delineation of flood prone areas starting from DEMs? iii)  Is it possible to use such procedure to map the flood exposure over large scale?


Some Definitions

FALSE POSITIVE

FALSE NEGATIVE


Accuracy, sensitivity, specificity

sensitivity (rtp) = true positive fraction = 1 – false negative fraction = TP / (TP + FN)

specificity (rtn) = true negative fraction = 1 – false positive fraction = TN / (TN + FP)

accuracy = (TP + TN) / (TP + TN + FP + FN)


The linear binary classifiers: Single Features The linear binary classifiers identifies areas subject to the flooding hazard using five single morphologic features and : 1.  the contributing area, As [m2]; 2.  the surface curvature, ∇2H [-]; 3.  the local slope, S [-]; 4.  the distance of each cell from the nearest stream, D [m]; 5.  the relative elevation to the nearest stream, H [m].


The linear binary classifiers: Composite Indices 1.  The modified topographic index (Manfreda et al., 2011)

TIm= ln(Adn/ tan(β)), where Ad is the drained area per unit

contour length, tan(β) is the local gradient. 2.  The downslope index, DWi, (Hjerdt et al., 2004) calculates

how far (Ld ) a parcel of water has to travel along its flow path to lose a certain amount of potential energy (d).

3.  The index H/D: the ratio between the flow distance D and elevation difference H.

4.  The index ln(h(As)/H): the ratio between water depth h with the elevation difference H, where h is calculated using an hydraulic scaling relationship: h(As)≈As

n.


The linear binary classifiers: composite indices 5.  The index ln(h(At)/H), where h(At) is computed as a

function of the contributing area At in the section of the drainage network hydrologically connected to the point under exam.

6.  The index (h(At)-H)/tan(αd): describes the change between water depth h(At) and the elevation difference H divided by the downslope index.

7.  The index (h(At)-H)/D: this index aims to describe, in each point of the investigated basin, the change between water depth h(At) and the elevation difference H divided by the distance D.


Case Study: The Upper Tiber River

Alluvial Plain DEM Flood Map

Upper Tiber Basin 5000 km2

Chiascio River 727 Km2


Flood Map used for Calibration

ü The “Piano di Assetto Idrogeologico” or PAI developed by Tiber River Basin Authority (TRBA) contains flood hazard maps based on detailed standard hydrologic and hydraulic models (TRBA PAI, 2010).

ü The TRBA PAI was developed using high precision bathymetric surveys of the channel surveyed as cross sections with average spacing interval of 200-400 meters. This detailed fluvial morphology was used as main input of a 1D hydraulic models (HEC-RAS and FRESCURE). simulating the effect of the design hydrographs considering return periods of 50, 200, and 500 years.


Single Features


Exploring the potential of New Composite Indices

Modified Topographic Index Downslope Index H/D

ln(h(At)/H) ln(h(As)/H)

(h(At)-H)/D

(h(At)-H)/tan(αd)


Summary of the Results

(B)

(A)

Local Features τ rfp rtp rfp+(1-‐rtp) AUC As -‐0.999 0.011 0.104 0.908 0.547 D -‐0.977 0.224 0.775 0.449 0.848 ΔH 0.018 0.731 0.930 0.802 0.543 S -‐0.940 0.424 0.943 0.481 0.798 H -‐0.954 0.239 0.897 0.342 0.896 C o m p o s i t e Indices

τ rfp rtp rfp+(1-‐rtp) AUC

TIm -‐0.277 0.412 0.936 0.476 0.800 DWi -‐0.260 0.230 0.874 0.356 0.900 H/D -‐0.978 0.252 0.501 0.751 0.664 log(h(At)/H) -‐0.379 0.222 0.859 0.363 0.898 log(h(As)/H) -‐0.650 0.305 0.886 0.420 0.873 (h-‐H)/DWi -‐0.342 0.057 0.459 0.598 0.578 (h-‐H)/D 0.062 0.051 0.472 0.579 0.766


Results: Flood maps of the entire Upper Tiber River


Conclusion

ü The present study investigates the role of different morphological features and indices in the identification of flood-prone areas over the upper Tiber River basin.

ü The indices that perform better are: the difference in elevation between the point considered and the source of risk (H), the downslope index (DWi) and the index ln(h(At)/H).

ü The outcomes of the present study are particularly promising; especially considering the number of artificial modification that characterizes the Tiber River.

ü Finally, geomorphic approaches represent a useful tool for preliminary studies on flood prone areas or to extend flood mapping over large areas.


Related Publication Samela, C., S. Manfreda, F. De Paola, M. Giugni and M. Fiorentino, Dem-based approaches for the delineation of flood prone areas in an ungauged basin in Africa, Journal of Hydrologic Engineering (under review), 2014.

Manfreda, S., F. Nardi, C. Samela, S. Grimaldi, A.C. Taramasso, G. Roth, A. Sole, Investigation on the Use of Geomorphic Approaches for the Delineation of Flood Prone Areas, Journal of Hydrology, Volume 517, 19 September 2014, Pages 863–876, (DOI: 10.1016/j.jhydrol.2014.06.009), 2014.

Manfreda, S., Samela, C., Sole, A., and Fiorentino, M., Flood-Prone Areas Assessment Using Linear Binary Classifiers based on Morphological Indices. Vulnerability, Uncertainty, and Risk: pp. 2002-2011. (DOI: 10.1061/9780784413609.201), 2014.

Manfreda, S. and Sole, A. ”Closure to “Detection of Flood-Prone Areas Using Digital Elevation Models” by Salvatore Manfreda, Margherita Di Leo, and Aurelia Sole.” Journal of Hydrologic Engineering, 18(3), 362–365, 2013.

Manfreda, S., M. Di Leo, A. Sole, Detection of Flood Prone Areas using Digital Elevation Models, Journal of Hydrologic Engineering, Vol. 16, No. 10, September/October 2011, pp. 781-790 (DOI: 10.1061/(ASCE)HE.1943-5584.0000367), 2011.

Fiorentino, M., S. Manfreda, V. Iacobellis, Peak Runoff Contributing Area as Hydrological Signature of the Probability Distribution of Floods, Advances in Water Resources, 30(10), 2123-2144, 2007.

Manfreda, S., A. Sole, e M. Fiorentino, Valutazione del pericolo di allagamento sul territorio nazionale mediante un approccio di tipo geomorfologico, L'Acqua, n. 4, 43-54, 2007 (In Italian).

Manfreda, S., A. Sole, M. Fiorentino, Can the basin morphology alone provide an insight on floodplain delineation?, on Flood Recovery Innovation and Response, WITpress, 47-56, 2008.

Flood-Prone Areas Assessment Using Linear Binary Classifiers based on Morphological Indices

Engineering

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