# Coping with Extreme Events: Effect of Different Reservoir Operation Strategies on Flood Inundation Maps

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## Abstract

**:**

## 1. Introduction

## 2. Case Study

#### 2.1. The Chiascio River Basin

^{2}. Calcareous and permeable rocks characterize the upper part of the basin; the middle part of the main geological formation is composed by flysch and the soil has low permeability, the downstream part is composed mainly by alluvial aquifers. The catchment has a complex orography that can significantly enhance the widespread frontal rainfalls causing major flood events.

- -
- Crest height: 338 m a.s.l.;
- -
- Crest wideness: 14 m;
- -
- Minimum level of the reservoir: 270 m a.s.l.;
- -
- Spillway elevation: 330 m a.s.l.;
- -
- Outlet gate: circular shape with an area of 35 m
^{2}; - -
- Inlet elevation: 280 m a.s.l.

^{2}and the reservoir volume is 221 Mm

^{3}.

#### 2.2. The Plan for the Prevention and Protection from Hydrogeological Risk

^{3}/s, the duration of the event is equal to 63 h, and the time to peak is around 26 h. Several dam regulation strategies are implemented to investigate the role of the dam in the mitigation of the flood wave.

#### 2.3. Hydrological Data

## 3. Methodology: The Cascade Model

#### 3.1. The Hydrological Model

_{e}, is:

_{a}is the initial abstraction, which can be expressed as a function of S. The SCS expressed I

_{a}= 0.2S on the basis of the results obtained for several experimental watersheds [22]. The potential maximum retention, S, is related to a dimensionless Curve Number (CN) defined as a function of land use, soil type and antecedent wetness conditions (AWC).

^{2}and η a parameter to be calibrated. Equation (2) with η = 1 was obtained considering 26 watersheds in Central Italy with areas ranging from 12 km

^{2}to 4147 km

^{2}. However, this result refers to the effective rainfall hyetographs determined by the extended form of the two-terms Philip’s infiltration equation and the use of the geomorphological unit hydrograph for the rainfall-runoff transformation. Therefore, η is considered here as a calibration parameter to take into account the differences due to the use of the SCS-CN method and the SCS unit hydrograph.

_{c}is the area of the gate, which has a circular shape, C

_{d}is the discharge coefficient, h is the total head.

_{s}is a variable coefficient of discharge, L is the effective length of the crest, H is the total head on the crest including velocity of approach head.

#### Hydrological Model Calibration

^{3}/s, Figure 3, that represents the maximum discharge that flows in the downstream river reach without significant flooding. The dam retained 21 Mm

^{3}while the total inflow volume was 36 Mm

^{3}.

_{p,sim}and Q

_{p,obs}are the simulated and observed discharge peak values, respectively.

_{sim,j}and Q

_{obs,j}are the simulated and observed discharge values, respectively, at the time jth for a total number of time steps N equal to 71, $\overline{Q}$ is the mean of observed discharge values. The Mean Absolute Error (MAE) is then estimated:

#### 3.2. The Hydraulic Model

#### 3.3. Flood Inundation Maps

## 4. Results

^{3}/s and is used as a forcer to the dam. The dam behavior is represented within the hydrological model, obtaining as output of the model the discharge values outflowing from the dam corresponding to different regulation strategies. These discharge values are used as input of the hydraulic model to generate the corresponding flood inundation maps. Then, a flood map envelope is estimated as indicated by Equation (8), Figure 6. The envelope allowed us to understand the effect of different operation rules on the floodplain downstream the dam. A historical flood event has been used because of its characteristics of magnitude and because it was of great interest to investigate how different management strategies would have coped with the incoming flood wave.

^{3}/s when the water elevation in the reservoir is 290 m a.s.l.), while there is a substantial reduction in the other two cases.

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Di Baldassarre, G.; Martinez, F.; Kalantari, Z.; Viglione, A. Drought and flood in the Anthropocene: Feedback mechanisms in reservoir operation. Earth Syst. Dyn.
**2017**, 8, 225–233. [Google Scholar] [CrossRef] - White, G.F. Human Adjustments to Floods; Department of Geography Research Paper No. 29; Department of Geography: Chicago, IL, USA, 1945. [Google Scholar]
- Di Baldassarre, G.; Wanders, N.; AghaKouchak, A.; Kuil, L.; Rangecroft, S.; Veldkamp, T.I.E.; Garcia, M.; van Oel, P.R.; Breinl, K.; Van Loon, A.F. Water shortages worsened by reservoir effects. Nat. Sustain.
**2018**, 1, 617–622. [Google Scholar] [CrossRef] - Ullberg, S.B. Forgetting Flooding?: Post-disaster Livelihood and Embedded Remembrance in Suburban Santa Fe, Argentina. Nat. Cult.
**2018**, 13, 27–45. [Google Scholar] [CrossRef] - Scolobig, A.; Pellizzoni, L.; Bianchizza, C. Public Participation and Trade-Offs in Flood Risk Mitigation: Evidence from Two Case Studies in the Alps. Nat. Cult.
**2016**, 11, 93–118. [Google Scholar] [CrossRef] - Scolobig, A.; De Marchi, B.; Borga, M. The missing link between flood risk awareness and preparedness: Findings from case studies in an Alpine. Nat. Hazards
**2012**, 63, 499–520. [Google Scholar] [CrossRef] - Molinari, D.; Menoni, S.; Aronica, G.T.; Ballio, F.; Berni, N.; Pandolfo, C.; Stelluti, M.; Minucci, G. Ex post damage assessment: An Italian experience. Nat. Hazards Earth Syst. Sci.
**2014**, 14, 901–916. [Google Scholar] [CrossRef] - Dessai, S.; Sims, C. Public perception of drought and climate change in southeast england. Environ. Hazards
**2010**, 9, 340–357. [Google Scholar] [CrossRef] - Di Baldassarre, G.; Viglione, A.; Carr, G.; Kuil, L.; Salinas, J.L.; Blöschl, G. Socio-hydrology: Conceptualising human-flood interactions. Hydrol. Earth Syst. Sci.
**2013**, 17, 3295–3303. [Google Scholar] [CrossRef] - Briscoe, J. Water security: Why it matters and what to do about it. Innov. Technol. Gov. Glob.
**2009**, 4, 3–28. [Google Scholar] [CrossRef] - Ridolfi, E.; Manciola, P. Water Level Measurements from Drones: A Pilot Case Study at a Dam Site. Water
**2018**, 10, 297. [Google Scholar] [CrossRef] - Bizzi, S.; Pianosi, F.; Soncini-Sessa, R. Valuing hydrological alteration in multi-objective water resources management. J. Hydrol.
**2012**, 472–473, 277–286. [Google Scholar] [CrossRef] - Bruno, M.C.; Siviglia, A. Assessing impacts of dam operations-interdisciplinary approaches for sustainable regulated river management. River Res. Appl.
**2012**, 28, 675–677. [Google Scholar] [CrossRef] - Richter, B.D.; Thomas, G.A. Restoring environmental flows by modifying dam operations. Ecol. Soc.
**2007**, 12, 12. [Google Scholar] [CrossRef] - Biscarini, C.; Di Francesco, S.; Ridolfi, E.; Manciola, P. On the simulation of floods in a narrow bending valley: The malpasset dam break case study. Water (Switzerland)
**2016**, 8, 545. [Google Scholar] [CrossRef] - van den Honert, R.C.; McAneney, J. The 2011 Brisbane Floods: Causes, Impacts and Implications. Water
**2011**, 3, 1149–1173. [Google Scholar] [CrossRef] - Apel, H.; Aronica, G.T.; Kreibich, H.; Thieken, A.H. Flood risk analyses—How detailed do we need to be? Nat. Hazards
**2009**, 49, 79–98. [Google Scholar] [CrossRef] - Montanari, A.; Young, G.; Savenije, H.H.G.; Hughes, D.; Wagener, T.; Ren, L.L.; Koutsoyiannis, D.; Cudennec, C.; Toth, E.; Grimaldi, S.; et al. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrol. Sci. J.
**2013**, 58, 1256–1275. [Google Scholar] [CrossRef] - Ridolfi, E.; Montesarchio, V.; Rianna, M.; Sebastianelli, S.; Russo, F.; Napolitano, F. Evaluation of rainfall thresholds through entropy: Influence of bivariate distribution selection. Irrig. Drain.
**2013**, 62. [Google Scholar] [CrossRef] - Montesarchio, V.; Napolitano, F.; Rianna, M.; Ridolfi, E.; Russo, F.; Sebastianelli, S. Comparison of methodologies for flood rainfall thresholds estimation. Nat. Hazards
**2015**, 75, 909–934. [Google Scholar] [CrossRef] - HEC. Hydrologic Engineering Center, Hydrologic Modeling System (HEC-HMS): User’s Manual 4.2; U.S. Army Corps of Engineers: Davis, CA, USA, 2016. [Google Scholar]
- Ponce, V.M.; Hawkins, R.H. Runoff Curve Number: Has it reached maturity? J. Hydrol. Eng.
**1996**, 1, 11–19. [Google Scholar] [CrossRef] - Singh, V.P. Derivation of surface water lag time for converging overland flow. J. Am. Water Resour. Assoc.
**1975**, 11, 505–513. [Google Scholar] [CrossRef] - Melone, F.; Corradini, C.; Singh, V.P. Lag prediction in ungauged basins: An investigation through actual data of the upper Tiber River valley. Hydrol. Process.
**2002**, 16, 1085–1094. [Google Scholar] [CrossRef] - HEC. Hydrologic Engineering Center, Hydraulic Reference Manual; US Army Corps of Engineers: Davis, CA, USA, 2016. [Google Scholar]
- Di Baldassarre, G.; Castellarin, A.; Montanari, A.; Brath, A. Probability-weighted hazard maps for comparing different flood risk management strategies: A case study. Nat. Hazards
**2009**, 50, 479–496. [Google Scholar] [CrossRef] - Brandimarte, L.; Di Baldassarre, G. Uncertainty in design flood profiles derived by hydraulic modelling. Hydrol. Res.
**2012**, 43, 753–761. [Google Scholar] [CrossRef] - Ridolfi, E.; Alfonso, L.; Di Baldassarre, G.; Dottori, F.; Russo, F.; Napolitano, F. An entropy approach for the optimization of cross-section spacing for river modelling. Hydrol. Sci. J.
**2014**, 59, 126–137. [Google Scholar] [CrossRef] - Manciola, P.; Di Francesco, S.; Biscarini, C. Flood protection and risk management: The case of Tescio River basin. IAHS-AISH Publ.
**2009**, 327, 174–183. [Google Scholar] - Di Francesco, S.; Biscarini, C.; Manciola, P. Characterization of a Flood Event through a Sediment Analysis: The Tescio River Case Study. Water
**2016**, 8, 308. [Google Scholar] [CrossRef] - Horritt, M.S.; Bates, P.D. Evaluation of 1D and 2D numerical models for predicting river flood inundation. J. Hydrol.
**2002**, 268, 87–99. [Google Scholar] [CrossRef] - Ackerman, C.T.; Jensen, M.R.; Brunner, G.W. Geospatial capabilities of HEC-RAS for model development and mapping. In Proceedings of the 2nd Joint Federal Interagency Conference, Las Vegas, NV, USA, 27 June–1 July 2010; Hydrologic Engineering Center, Institute for Water Resources, U.S. Corps of Engineers: Davis, CA, USA, 2010. [Google Scholar]
- ABT Autorità di Bacino del Fiume Tevere. Piano Stralcio di Assetto Idrogeologico. Approvato con DPCM del 10 Novembre 2006. Available online: http://www.abtevere.it/ (accessed on 13 February 2019).
- Horritt, M.S. A methodology for the validation of uncertain flood inundation models. J. Hydrol.
**2006**, 326, 153–165. [Google Scholar] [CrossRef] - Merz, B.; Thieken, A.H.; Gocht, M. Flood Risk Mapping At The Local Scale: Concepts and Challenges. In Flood Risk Management in Europe: Innovation in Policy and Practice; Begum, S., Stive, M.J.F., Hall, J.W., Eds.; Springer: Dordrecht, The Netherlands, 2007; pp. 231–251. [Google Scholar]
- Di Baldassarre, G.; Schumann, G.; Bates, P.D.; Freer, J.E.; Beven, K.J. Flood-plain mapping: A critical discussion of deterministic and probabilistic approaches. Hydrol. Sci. J.
**2010**, 55, 364–376. [Google Scholar] [CrossRef] - Alfonso, L.; Mukolwe, M.M.; Di Baldassarre, G. Probabilistic Flood Maps to support decision-making: Mapping the Value of Information. Water Resour. Res.
**2016**, 52, 1026–1043. [Google Scholar] [CrossRef] - Ridolfi, E.; Yan, K.; Alfonso, L.; Di Baldassarre, G.; Napolitano, F.; Russo, F.; Bates, P.D. An entropy method for floodplain monitoring network design. AIP Conf. Proc.
**2012**, 1479, 1780–1783. [Google Scholar] - Di Baldassarre, G.; Kreibich, H.; Vorogushyn, S.; Aerts, J.; Arnbjerg-Nielsen, K.; Barendrecht, M.; Bates, P.; Borga, M.; Botzen, W.; Bubeck, P.; et al. Hess Opinions: An interdisciplinary research agenda to explore the unintended consequences of structural flood protection. Hydrol. Earth Syst. Sci. Discuss.
**2018**, 22, 5629–5637. [Google Scholar] [CrossRef] - AghaKouchak, A.; Feldman, D.; Hoerling, M.; Huxman, T.; Lund, J. Water and climate: Recognize anthropogenic drought. Nature
**2015**, 524, 409–411. [Google Scholar] [CrossRef] [PubMed] - Van Loon, A.F.; Gleeson, T.; Clark, J.; Van Dijk, A.I.J.M.; Stahl, K.; Hannaford, J.; Di Baldassarre, G.; Teuling, A.J.; Tallaksen, L.M.; Uijlenhoet, R.; et al. Drought in the Anthropocene. Nat. Geosci.
**2016**, 9, 89–91. [Google Scholar] [CrossRef] - Ye, B.; Yang, D.; Kane, D.L. Changes in Lena River streamflow hydrology: Human impacts versus natural variations. Water Resour. Res.
**2003**, 39, 1200. [Google Scholar] [CrossRef] - Bohensky, E.L.; Leitch, A.M. Framing the flood: A media analysis of themes of resilience in the 2011 Brisbane flood. Reg. Environ. Chang.
**2014**, 14, 475–488. [Google Scholar] [CrossRef] - UNISDR. Terminology on Disaster Risk Reduction United Nations International Strategy for Disaster Reduction; UNISDR: Geneva, Switzerland, 2009. [Google Scholar]
- Dawson, R.J.; Ball, T.; Werritty, J.; Werritty, A.; Hall, J.W.; Roche, N. Assessing the effectiveness of non-structural flood management measures in the Thames Estuary under conditions of socio-economic and environmental change. Glob. Environ. Chang.
**2011**, 21, 628–646. [Google Scholar] [CrossRef] - Ciullo, A.; Viglione, A.; Castellarin, A.; Crisci, M.; Di Baldassarre, G. Socio-hydrological modelling of flood-risk dynamics: Comparing the resilience of green and technological systems. Hydrol. Sci. J.
**2017**, 62, 880–891. [Google Scholar] [CrossRef] - Slovic, P.; Weber, E.U. Perception of risk posed by extreme events. In Proceedings of the Risk Management Strategies in an Uncertain World, New York, NY, USA, 12–13 April 2002. [Google Scholar]
- Aven, T.; Renn, O. Risk management. In Risk Management and Governance; Springer: Berlin/Heidelberg, Germany, 2010; pp. 121–158. [Google Scholar]
- Wachinger, G.; Renn, O.; Begg, C.; Kuhlicke, C. The Risk Perception Paradox—Implications for Governance and Communication of Natural Hazards. Risk Anal.
**2013**, 33, 1049–1065. [Google Scholar] [CrossRef] - Sandman, P.M. Risk Communication: Facing Public Outrage. Manag. Commun. Q.
**1988**, 2, 235–238. [Google Scholar] [CrossRef]

**Figure 1.**Localization of the case study area in central Italy. Flood Risk Management Plan: hazard map (high, medium and low probability) for areas surrounding the two towns of Valfabbrica (

**a**) and Assisi (

**b**).

**Figure 2.**Localization of the rainfall event in the Umbria region, Italy (

**left panel**); cumulated rainfall depth registered during the event occurred on November 2013 (

**right panel**).

**Figure 4.**Hydraulic model built with HEC-RAS. The 77 river cross-sections, panel below and a schematized picture of a bridge, panel above.

**Figure 6.**Flood map envelope. The frequency of each cell to be flooded is presented in color ranging from white (i.e., 0) to dark blue (i.e., 1).

**Figure 7.**Discharge outflowing from the dam in absolute value (left axis) against the initial level in the reservoir for the three opening of the outlet gate (i.e., open, half open and closed). The outflow is compared with the maximum possible outflow and the value is reported in percentage (right axis).

**Figure 8.**Three maps of the flooded area generated considering the initial water level in the reservoir equals to its maximum value (330 m a.s.l.) and for the three different opening of the outlet gate: closed (

**panel a**), half open (

**panel b**), open (

**panel c**).

**Figure 9.**Overlap of the three flood inundation maps obtained with the three different openings of the culvert and setting the initial level in the reservoir equal to its maximum value (i.e., 330 m a.s.l.).

**Figure 10.**Three details of a flooded area for three different opening of the outlet gate (in the box on each panel) generated considering a water level in the reservoir equals to its maximum value.

**Table 1.**Rainfall depths registered at rain gauges for different time intervals during the flood event occurred on November 2013 in the Chiascio basin.

Raingauges | Rainfall Depth | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

Total | Max 30 min | Max 1 h | Max 3 h | Max 6 h | Max 12 h | Max 24 h | Max 36 h | Max 48 h | Max 72 h | |

(mm) | (mm) | (mm) | (mm) | (mm) | (mm) | (mm) | (mm) | (mm) | (mm) | |

Gualdo Tadino | 328.9 | 10.4 | 19.2 | 51 | 88.4 | 174.4 | 240.8 | 281.6 | 311.8 | 328.8 |

Monte Cucco | 262.9 | 10.1 | 19.9 | 44.6 | 73.2 | 124.8 | 184.1 | 217.8 | 248.4 | 262.8 |

Torre dell’Olmo | 161.1 | 7.6 | 14.2 | 33.2 | 50.6 | 96.6 | 130.6 | 153.4 | 157.8 | 160.8 |

Branca | 154.1 | 6.8 | 10.8 | 24.6 | 44.6 | 69.8 | 100 | 134.8 | 147.6 | 154 |

Nocera Umbra | 149.3 | 5.8 | 8.5 | 20.5 | 39.1 | 64.6 | 93.3 | 125.1 | 135.8 | 149.2 |

Casa Castalda | 134.4 | 8.4 | 12.9 | 24.7 | 35.3 | 60.6 | 99.9 | 122.9 | 129.1 | 134.3 |

Gubbio | 132.7 | 5.8 | 10.8 | 21.4 | 35.4 | 68 | 104 | 124.4 | 130.4 | 132.4 |

Azzano | 119.6 | 8.5 | 15.3 | 22.5 | 34.3 | 60.7 | 87.3 | 119.4 | 119.5 | 119.5 |

La Bolsella | 115.5 | 8.4 | 10.6 | 20 | 31 | 50 | 88.4 | 106.8 | 110.2 | 115.4 |

Pianello | 107.7 | 6.6 | 10.2 | 24.8 | 34.2 | 50.4 | 88 | 103 | 106 | 107.6 |

Armenzano | 104.7 | 8.2 | 12.4 | 27.2 | 33.6 | 45.2 | 73.2 | 96.6 | 99.4 | 104.6 |

Carestello Meteo | 96.1 | 5 | 9 | 20.2 | 33 | 43.2 | 77.6 | 90.8 | 94.6 | 95.4 |

Spoleto | 96.1 | 8 | 14.8 | 18.6 | 27.2 | 42.8 | 65.6 | 95.2 | 95.2 | 96 |

Bastia Umbra | 91.1 | 6 | 10.4 | 19 | 28.4 | 45.7 | 77.7 | 90.7 | 90.7 | 91 |

Cannara | 88.5 | 5.6 | 9.1 | 17.6 | 29.7 | 44.8 | 73.1 | 88.4 | 88.4 | 88.4 |

Casanuova | 83.7 | 6.2 | 9.8 | 22.6 | 32.6 | 38.8 | 64 | 80.6 | 82.8 | 83.6 |

Foligno | 79.5 | 8.7 | 11.4 | 18.5 | 25.2 | 36.1 | 65.4 | 79.3 | 79.4 | 79.4 |

La Bruna | 71.5 | 25.2 | 25.8 | 26.2 | 33 | 45.2 | 71.4 | 71.4 | 71.4 | 71.4 |

Bevagna | 65.5 | 10.4 | 12.2 | 18.4 | 26.8 | 33.4 | 54 | 65.4 | 65.4 | 65.4 |

**Table 2.**Values of the estimation metrics used to calibrate the hydrological model: error between the peak values of the observed and simulated discharge (ε

_{peak}), the time to peak (t

_{to peak}), the Nash Sutcliff Efficiency index (NSE) and the Mean Absolute Error (MAE) at the two gauging stations of Pianello and Ponte Rosciano.

Gauge Station | ε_{peak} | t_{to peak} | NSE | MAE |
---|---|---|---|---|

(%) | (h) | - | (m^{3}/s) | |

Pianello | −3 | −7 | 0.77 | 18.90 |

Ponte Rosciano | 2 | 0 | 0.94 | 19.80 |

**Table 3.**Characteristics of the flood area downstream of Pianello, the initial water level in the reservoir is equal to 330 m a.s.l.

Outlet Gate | |||
---|---|---|---|

Closed | Half Open | Open | |

Flooded Aea (km^{2}) | 7.37 | 6.82 | 7.93 |

h_{mean} (m) | 1.58 | 1.46 | 1.52 |

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**MDPI and ACS Style**

Ridolfi, E.; Di Francesco, S.; Pandolfo, C.; Berni, N.; Biscarini, C.; Manciola, P.
Coping with Extreme Events: Effect of Different Reservoir Operation Strategies on Flood Inundation Maps. *Water* **2019**, *11*, 982.
https://doi.org/10.3390/w11050982

**AMA Style**

Ridolfi E, Di Francesco S, Pandolfo C, Berni N, Biscarini C, Manciola P.
Coping with Extreme Events: Effect of Different Reservoir Operation Strategies on Flood Inundation Maps. *Water*. 2019; 11(5):982.
https://doi.org/10.3390/w11050982

**Chicago/Turabian Style**

Ridolfi, Elena, Silvia Di Francesco, Claudia Pandolfo, Nicola Berni, Chiara Biscarini, and Piergiorgio Manciola.
2019. "Coping with Extreme Events: Effect of Different Reservoir Operation Strategies on Flood Inundation Maps" *Water* 11, no. 5: 982.
https://doi.org/10.3390/w11050982