Climate-Risk Assessment Framework for Airports under Extreme Precipitation Events: Application to Selected Italian Case Studies

Round 1

Reviewer 1 Report

Review Comments

The Paper “Climate risk assessment framework for airports under extreme precipitation events: application to selected Italian case studies” is an interesting work to evaluate the risk related to extreme precipitation events on specific Italian airports experimental study. The expected climate variations are evaluated using an ensemble of high-resolution climate projections from the EURO-CORDEX initiative for the short (2021-2050), medium (2041-2070), and long-term future period (2071-2100), under RCP 2.6, RCP 4.5, and RCP 8.5 climate scenarios.  The results of different indicators showed excellent relationships. However, it would be better to use CMIP6 (as CMIP6 has been improved as compared to CMIP5 in many aspects).

Overall, I believe the paper has interesting potentials, though there are some issues that should be carefully addressed.

Comments
The introductory section is rather general and should be rewrite with additional elements focusing on the specific study. The review of literature on the specific subject (Climate risk assessment under extreme events) should be more thorough and the selection of approach should be further justified.

Although, the goals of the study are mentioned, the framework of their achievement is not clearly described. It is important to set clearly the objectives of the study and need more clarification that will provide the context of the overall approach, formulate the discussion base and help the reader to follow more easily the structure of the paper. 

In the introduction, there is need to address the questions of the research (which could be answered either through the literature review or through the results and discussion), such as: What advantages/disadvantages have of the selected models/techniques? What is the reason to choose CMIP5? Why not CMIP6?

Author Response

Revision of

" Climate risk assessment framework for airports under extreme precipitation events: application to selected Italian case studies"

 

Carmela De Vivo, Giuliana Barbato, Marta Ellena, Vincenzo Capozzi, Giorgio Budillon, Paola Mercogliano

 

RC = Reviewer comment

AR = Authors’ reply

 

 

Reviewer #1

 

RC: The Paper “Climate risk assessment framework for airports under extreme precipitation events: application to selected Italian case studies” is an interesting work to evaluate the risk related to extreme precipitation events on specific Italian airports experimental study. The expected climate variations are evaluated using an ensemble of high-resolution climate projections from the EURO-CORDEX initiative for the short (2021-2050), medium (2041-2070), and long-term future period (2071-2100), under RCP 2.6, RCP 4.5, and RCP 8.5 climate scenarios.  The results of different indicators showed excellent relationships. However, it would be better to use CMIP6 (as CMIP6 has been improved as compared to CMIP5 in many aspects).

Overall, I believe the paper has interesting potentials, though there are some issues that should be carefully addressed.

Comments
The introductory section is rather general and should be rewrite with additional elements focusing on the specific study. The review of literature on the specific subject (Climate risk assessment under extreme events) should be more thorough and the selection of approach should be further justified.

Although, the goals of the study are mentioned, the framework of their achievement is not clearly described. It is important to set clearly the objectives of the study and need more clarification that will provide the context of the overall approach, formulate the discussion base and help the reader to follow more easily the structure of the paper. 

In the introduction, there is need to address the questions of the research (which could be answered either through the literature review or through the results and discussion), such as: What advantages/disadvantages have of the selected models/techniques? What is the reason to choose CMIP5? Why not CMIP6?

 

AR: Dear reviewer, we are very grateful for your positive evaluation of our study, and we are glad to clarify some aspects of the paper and modify it accordingly to your suggestions that for sure will improve the quality and clearness of the work. The replies to your remarks are set out below. Note that the changes in the main text of the manuscript are highlighted in yellow.

 

Climate risk quantification is a key issue for critical infrastructure particularly for airports as they are most exposed to the effects of extreme events. Many European airports have already developed adaptation strategies to make them more resilient to the changing climate, particularly the effects of extreme precipitation. However, in Italy, climate risk analyses for airports have not yet been conducted, despite the Mediterranean area is considered a climate change hot spot. In this context, the main objective of our work is to quantify the climate risk related to extreme precipitation events for the selected airports using the methodology proposed by De Vivo et al. 2022. This is based on the definition of risk proposed by the IPCC in the Fifth and Sixth Assessment Reports (AR5 and AR6) with regard to the risks arising from climate change. According to IPCC, the climate risk is a combination of hazard (H), exposure (E), and vulnerability (V) factors. The vulnerability is then divided in  sensitivity and adaptive capacity (Oppenheimer et al. 2014; Carrão et al. 2016; Ellena et al. 2020; Shah et al. 2020). Each risk component was evaluated through a list of indicators (selected from an extensive literature review), appropriately chosen for the airport context. To achieve the final objective of the work, i.e., the calculation of the final risk, we used the following procedure:

• Assessment of hazard, exposure and vulnerability indicators;
• Normalization of hazard, exposure and vulnerability data;
• Calculation of the three synthetic indices (H, E, V);
• Estimation of climate risk levels (R)

The intensity and frequency hazard indicators were calculated using the UERRA dataset for the observed period and future climatic variations starting from the ensemble of EURO-CORDEX models. We did not consider CMIP6 models because they are only available on a global scale. In order to better characterize local-scale phenomena such as precipitation, we decided to use regional models (i.e., EURO-CORDEX), based on CMIP5, which are characterized by higher spatial resolution (12 km).

The exposure and vulnerability indicators were calculated starting from the information contained in the several official documents and websites. Once these indicators have been calculated, they were normalized using the min-max method. In this way, every parameter is identified by numerical value from 0 to 1, facilitating the comparability between them. We finally proceeded to calculate the climate hazard (H), exposure (E) and vulnerability (V) index through an aggregation that consider the equal weight of each indicator, using the linear aggregation method. This method allows to estimate the synthetic index of the risk component by combining the normalized values of the indicators, previously weighted by associating the relative weights. By applying a uniform weighting, the indicators are simply summed and divided by the number of indicators.

After the weighted arithmetic aggregation, the quantile method was applied (in ArcGIS environment) to obtain qualitative hazard, exposure, and vulnerability classes (very low, low, intermediate, high, very high). 

We performed the mentioned operations for each set of indicators: hazard, exposure, sensitivity, and adaptive capacity. The last step involved estimating climate risk index by aggregation of the risk components (given by the multiplication of hazard, exposure, and vulnerability index) and subsequently normalized it on a scale from 0 to 1. The quantile method was applied to obtain qualitative risk classes: very low, low, intermediate, high, very high. We added these concepts in the Introduction and Methodology sections (see lines 65-72; lines 80-84).

In the Introduction section, we better defined the objectives of the study and the various steps to achieve those objectives, highlighting the aspects of the approach used (see lines 65-73). In addition, to better contextualize our work and deepen the literature review, we decided to add a new paragraph named “State of the art”(Paragraph 2) in which we summarize the main works to the proposed topic of research (e.g., climate risk assessment under extreme events) (see lines 98-158).

Author Response File: Author Response.pdf

Reviewer 2 Report

Thank you for the opportunity of reviewing the interesting paper. It covers the important issue of assessing the risks of extreme precipitation for airports in Italy.

I have the following comments:

1.    Title is fine and clarifies the research contents.

2.    Abstract: Please delate “(UERRA)” and spell out “C3S”.

3.    Introduction is fine.

4.    Literature review: The paper should review recent studies of risk assessment of climate change for critical infrastructure. Without literature review, it is difficult to understand the novelty of this study and the gaps that the study addresses.

5.    Methods: “min-max method” and “quantile classification method” are all right. But more details should be explained. In particular, it is not clear how to weight each factor. This is crucial for understanding reliability and applicability of this method.

6.    L 214-215 “The various structures of an airport are interconnected, and the interruption of the services offered by a single component can create criticalities in other activities”. Please explain how this interconnectivity is assessed in the framework.

7.    L279-280 “About risk awareness, all airports have implemented strategies to mitigate climate change and reduce CO2 emissions. The Airport Carbon Accreditation Program is currently the only institutionally approved program that independently assesses and recognizes the efforts of airports to manage and reduce their CO2 emissions” Carbon accreditation program is not related with risk reduction of the airports. Please justify this is related to “risk awareness”.

8.    Verification of the index proposed is needed. Otherwise, the applicability of the index proposed cannot be confirmed. “Intense precipitation events occurred in the past have caused several damages to such infrastructures with flooding of parking areas, runways and aprons with consequent interruption of air traffic and inconvenience for passengers” (L 402-404). Further examination of the index's relationship with past disasters is needed to verify the index.

Author Response

AR: Dear reviewer, thank you for your relevant and constructive comments, which will help us improving the manuscript. The replies to your remarks are set out below. Note that the changes in the main text of the manuscript are highlighted in green.

 

2. We modified the paper according to your recommendations (see page 1, lines 21-22).

 

4. Thanks to the reviewer comments, we add a new section namely State of the art (paragraph 2) in which the most significant results of the studied already available in the literature are reported to highlight the importance of the topic and of the proposed methodology (see lines 98-158).

 

5. The first step of the proposed methodology was to calculate the hazard, exposure, and vulnerability indicators (sensitivity and adaptive capacity). The intensity and frequency of hazard indicators were calculated using the UERRA dataset for the observed period and the ensemble of EURO-CORDEX models for future climatic variations. The exposure and the vulnerability indicators were calculated starting from the information contained in the several official documents and websites. Once these indicators have been calculated, they were normalized using the min-max method (Equation 1).

 

                                                                                                                         (1)           

 

where x_i represents the individual data point to be transformed, x_max  corresponds to the highest value and x_min to the lowest value for each indicator. In this way, every parameter X_i is identified by a numerical value from 0 to 1, where the highest value corresponds to the highest contribution to any factors, considered separately from the others. This procedure allows transforming all the indicators in a range from 0 to 1, facilitating the comparability between them.

Regarding the adaptive capacity indicators, which in this study are not metric but nominal variables, were normalized using five classes to which a specific numerical value is assigned, according to the scheme reported in Master Adapt 2018:

• Class 1: optimal (numeric score: 0.1)
• Class 2: rather positive (numeric score: 0.3)
• Class 3: neutral (numeric score:0.5)
• Class 4: rather negative (numeric score:0.7)
• Class 5: critical (numeric score: 0.9)

The absence and presence of specific adaptation measures were assessed with the “critical class” score equal to 0.9 and the “optimal class” score equal to 0.1, respectively. As regard the mitigation initiatives, the highest score (0.1) was attributed to the Fiumicino and Ciampino airports, as they achieved the highest level of carbon accreditation (Level 4); in airports with Level 3+ (Malpensa, Linate, Bergamo, Napoli) the score corresponds to the "rather positive" class equal to 0.3, while those with the Level 2 (Palermo) and Level 1 (Cagliari) have a score of 0.5 and 0.7 respectively. In the case of Catania airport, which has not reached any level of accreditation, the score assigned is equal to 0.9. With respect to guideline for adaptation to climate change, since no airport has a specific adaptation plan, none of them achieved the maximum score. A score of 0.3 was assigned if there is a regional plan or strategy for adaptation to climate change or local initiatives (urban level), 0.5 if it is being developed.

About adaptive capacity indicators, lower values ​​should indicate positive conditions for vulnerability, while higher values refer to negative conditions (the greater the ability to minor adaptation is vulnerability). Unlike the other cases, here lower values reflect high adaptive capacity and vice versa.

In this case, the range of values ​​of the indicator must be inverted to have them all in the same normalization ranking (Ellena et al. 2020). To achieve this, the indicator value is subtracted from 1 to determine the final standardized value. After that, the min–max normalization formula was applied.

We finally proceeded to calculate the climate hazard index (H), exposure index (E), vulnerability index (V) through an aggregation that consider the equal weight of each indicator (w = 1), using the linear aggregation method (Equation 2):

 

                                                                  (2)

                                                                                                                                                              

Equation (2) allows estimation of the synthetic indicator of the I_C risk component by combining the normalized values of the indicators x_i,1 previously weighted by associating the relative weights w_i. By applying a uniform weighting (w =1), the indicators are simply summed and divided by the number of indicators.

Once the sensitivity and the adaptive capacity are estimated, the process reaches the key point for estimating vulnerability, through the aggregation of the two partial indices. Here, the aggregate Vulnerability Index formula implies a simple average between sensitivity and adaptive capacity indices.

After the weighted arithmetic aggregation, the quantile method was applied (in ArcGIS) in order to obtain qualitative hazard, exposure and vulnerability classes (very low, low, intermediate, high, very high). 

We added these concepts in Section 3.2 “Conceptual approach for quantifying climate risk for the selected airports” (see lines 208-216; 221-237; 243-251).

 

6. In the proposed framework, we did not consider the so-called cascading effects for lack of models that satisfactorily capture these aspects for highly interconnected infrastructures (Forzieri et al. 2018).

However, when selecting the hazard indicators, we took into account specific thresholds describing meteorological conditions in which both physical damage to infrastructure and a disruption of airport operations might occur (Åstebøl et al. 2004; Keokhumcheng et al. 2012; Coffel and Horton 2015; Coffel et al. 2018; Forzieri et al. 2018; Monioudi et al. al. 2018; Borsky and Unterberger 2019).

 

7. We decided to also consider mitigation actions as risk awareness as they are related to climate change management. An airport committed to managing its emissions and with a high level of carbon accreditation has a greater awareness of the risks associated with climate change.

In this specific case, when we transformed the nominal indicators (concerning the adaptive capacity) into metric indicators, we assigned a different score for the various airports. The highest score (0.1) was attributed to the Fiumicino and Ciampino airports, as they achieved the highest level of carbon accreditation (Level 4); in airports with Level 3+ (Malpensa, Linate, Bergamo, Naples) the score corresponds to the "rather positive" class equal to 0.3, while those with the Level 2 (Palermo) and Level 1 (Cagliari) respectively 0.5 and 0.7. In the case of Catania airport, which has not reached any level of accreditation, the score assigned is equal to 0.9 (see lines 226-237).

 

8. In this study, the framework proposed by De Vivo et al. 2022 was used, which is based on the definition of risk of Sixth Assessment Report of IPCC where the risk is a function of hazard, exposure, and vulnerability factors. The proposed risk assessment associated with climate change is based on several steps involving the identification and selection of some indicators to be used as a proxy to describe a phenomenon and/or specific characteristics of a system or an area, to identify and evaluate the main factors and assets of the system most affected by climate change. More specifically, hazard indicators were identified from a detailed literature review, taking into account specific thresholds describing atmospherical conditions in which both physical damage to infrastructure and a disruption of airport operations could occur (Åstebøl et al. 2004; Keokhumcheng et al. 2012; Forzieri et al. 2018; Monioudi et al. al. 2018 ; Borsky and Unterberger 2019). The indicator-based approach is widely used in the scientific literature (Ellena et al. 2023; Ricciardi et al., 2023; Bucchignani et al. 2021; Kumar et al., 2021; Ellena et al., 2020; Francini et al. 2020; ICAO 2019; Forzieri et al., 2018) and all the relevant recent official documents adopted by the Italian government for risk assessment (MIMS Carraro 2022; PNACC 2022; ISPRA 2021).

The use of this approach was verified as follows. We selected, as examples, two specific events -November 22, 2022 and September 30, 2022 that resulted in closures and flooding at Ciampino and Palermo airports, respectively. The maps below show that the one the climate indicators adopted to assess climate hazard in the current work, exceeding 99th percentile of daily precipitation, occurred in both cases (Figure 1 and 2).  The period 1981-2010 has been considered for the calculation of the threshold (percentile). The indicator was calculated by using the ERA5 re-analysis (Hersbach et al., 2020) provided by ECMWF (European Center Medium Weather Forecast). ERA5 covers the entire Globe on regular latitude-longitude grids at 0.25° x 0.25° resolution. Hourly data on many atmospheric parameters together with estimates of uncertainty are available on Climate Data Store of Copernicus Climate Change Service [C3S].

 

 

 

 

 

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report