the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling compound flood risk and risk reduction using a globally-applicable framework: A case study in the Sofala region
Anaïs Couasnon
Frederiek C. Sperna Weiland
Willem Ligtvoet
Arno Bouwman
Hessel C. Winsemius
Philip J. Ward
Abstract. In low-lying coastal areas floods occur from (combinations of) fluvial, pluvial, and coastal drivers. If these flood drivers are statistically dependent, their joint likelihood might be misrepresented if dependence is not accounted for. However, few studies have examined flood risk and risk reduction measures while accounting for so-called compound flooding. We present a globally-applicable framework for compound flood risk assessments using combined hydrodynamic, impact and statistical modeling and apply it to a case study in the Sofala province of Mozambique. The framework broadly consists of three steps. First, a large stochastic event set is derived from reanalysis data, taking into account co-occurrence and dependence between all flood drivers based on a vine copula structure. Then, both flood hazard and impact are simulated for different combinations of drivers at non-flood and flood conditions. Finally, the impact of each stochastic event is interpolated from the simulated events to derive a complete flood risk profile. Our case study results show that from all drivers, coastal flooding causes the largest risk in the region despite a more widespread fluvial and pluvial flood hazard. Events with return periods larger than 25 year are more damaging when considering the observed statistical dependence compared to independence, e.g.: 12 % for the 100-year return period. However, the total compound flood risk in terms of expected annual damage is only 0.55 % larger. This is explained by the fact that for frequent events, which contribute most to the risk, limited physical interaction between flood drivers is simulated. We also assess the effectiveness of three measures in terms of risk reduction. For our case, zoning based on the 2-year return period flood plain is as effective as levees with a 10-year return period protection level, while dry proofing up to 1 m does not reach the same effectiveness. As the framework is based on global datasets and is largely automated, it can easily be repeated for many other regions for first order assessments of compound flood risk.
Dirk Eilander et al.
Status: closed
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RC1: 'Comment on nhess-2022-248', Anonymous Referee #1, 07 Nov 2022
General comment:
The manuscript presents a global framework for assessing flood risk and risk reduction strategies for compound flooding. The framework was applied to Sofala province in Mozambique. The Authors showed that coastal flooding causes the greatest impact regardless of the other drivers.
The manuscript is well-written and with a clear structure. However, there are a few criticalities that need to be addressed.
The novelty of this work compared to the current literature and to previous works from the same Authors is difficult to grasp. In its current form, the manuscript reads as a case study which is not enough. The advise is to highlight how this work addresses the limitations of previous studies and goes beyond what has been already done.
The global applicability claimed by the Authors is not really proven since they validated and applied it to the same location (line 82). How does this model apply and perform in other locations?
The description of the probabilistic model needs improvements. First, the Authors should clarify the data used in the vine-copula models, whether they are annual maxima (annual maxima obtained independently in each time series) or whether such data are shifted relative to each other (see table 1). It seems like the annual maxima of 5 different variables occur within a window of +-10 days, which seems a bit unlikely. Moreover, the introduction of a rate of occurrence of annual maxima should be better explained in the relation to the copula model. Why is this necessary? The vine-copula is built to generate sets of dependent variables, including sets in which all of the variables are extremes. This point also relates to the distinction the Authors made between compound events and non-compound events. What does make an event compound? Is this related to the impacts? How can it be defined a priori then?
Point-by-point comments:
Line 36: Do the Authors refer to the joint likelihood or the joint probability? The two concepts are different.
Line 49: Specify what are the “four drivers”.
Lines 68-74: this paragraph is difficult to read. For example, what is an event set? How is a “model event set from univariate distribution” different from a “stochastic event set from a multivariate probabilistic model”? (see also general comment)
Lines 82: Please further elaborate on the reason why global models are useful in data-scarce regions
Line 89: Add some information on the boundary conditions. For example, are the boundary conditions generated independently? How are the normal and extreme boundaries selected and combined? Are all normal or are all extremes?
Section Discharge and Total water level. Clarify the link between annual maxima analysis and hydrograph generation. What are the raw data used for annual maxima analysis and how this annual maximum relates to the hydrograph?
Line 150: why did the Authors use annual maxima if the temporal resolution of the data is hourly?
Line 155: Relative timing between drivers: it seems like the annual maxima of each driver occur around the same time, is it the case? Or the correlation in table 1 is the correlation obtained between the annual max Buzi discharge and the corresponding driver around that period (even if not extreme)? Discuss whether the timing in Table 1 makes sense. Also, why the Authors selected river discharge at Buzi and not a precipitation event? Precipitation might drive high water in the river unless other processes are of relevance.
Line 171: Add some discussion on the rate at which different combinations of drivers co-occur. Should not this come from the vine-copula? Please, clarify this second step. (see also general comment)
Lines 179: Do you try all the possible vine copulas? It would be good to show somewhere what the vine-copula selected looks like and the associated bi-variate copulas.
Line 189: How is an event defined?
Line 240: The Authors made a distinction between exposure and vulnerability. However, their definitions are missing. How is exposure defined? How is vulnerability defined? How do they contribute to the impacts? It is not clear which variables have been used to quantify these two concepts.
Line 243: Why is a bias correction needed?
Line 266: Is this return period associated with the univariate case? Is 5-years realistic for the case study? It would be good to justify the choices made.
From lines 287 – flood drivers. It is a bit unclear how an event is defined and the time series used in the vine-copula models. Why would a compound event be the event in which one variable is extreme? When generating a set of dependent variables, any results in terms of water depths, in this specific case, can be classified as compound (see also general comment)
Flood Hazard: Please, specify how the 100-year fully dependent event is identified, i.e., how the value of each variable is quantified. Also, are the drivers 4 or 5?
Figure 9: The definition of the percentage of base risk is not fully clear. What is the component of the total risk? Is the total risk different per strategy?
Citation: https://doi.org/10.5194/nhess-2022-248-RC1 -
AC1: 'Reply on RC1', Dirk Eilander, 16 Feb 2023
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2022-248/nhess-2022-248-AC1-supplement.pdf
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AC1: 'Reply on RC1', Dirk Eilander, 16 Feb 2023
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RC2: 'Comment on nhess-2022-248', Anonymous Referee #2, 05 Dec 2022
This manuscript presents a case study of a modelling framework that could be applied globally to investigate the impact of compound flood risk, at least for initial investigation. The manuscript is for the most part very well written and has a very clear structure.
Despite this, there are a small number of comments that need to be addressed. These are listed in page order below but the more important ones are hightlighted by *.
Section 2.2.3 Rainfall. Lines 152-153: Design rainfall events with a 24- hour duarion were created. Why was this duration chosen? How is this related to catchment response for the chosen area?
Line 160: How was the plus/minus ten days determined?
Line 176 and throughout the manuscript. The authors use both Pair Copula Constructions and Vine Copula interchangeble throughout the manscript.
*Section 2 and in particular section 3 (3.2). The authors present the results and talk about inaccuracies (Line 243). However, these are never combined . In Section 3.2 there is a lack of quantifying the statements and relating to the relevant inaccuracies in the data. For example, Line 322-323, the authors states interactions decrease flood depth in the esturary but upsteam increases flood depth. By how much and how does this related to the overall errors in the datasets. This is needed to understand if these changes are significant relative the data errors. Again Line 326, the authors do not quantify the lower volume of costal water entering the river mouth and if this is a significant amount.
Section 3.3. (Line 345-348). The authors state that the damage caused by pluvial damage is mostly related to the infiltration capactiy. Can this is quantified and what are the other factors that influence this.
*Section 3.5 Limitations and way forwards (Line 390 - 395). The authors mentiond the accuracy of the input data should be considered. It would be nice to see this point discussed in more detail. This is similar to the point above.
*Line 436-439. This statement is sums up the entire manuscript excellently. However, it needs to be stated more strongly throughout the manuscript and include in the manuscript (more clearly) the weaknesses in the approach.
Citation: https://doi.org/10.5194/nhess-2022-248-RC2 -
AC2: 'Reply on RC2', Dirk Eilander, 16 Feb 2023
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2022-248/nhess-2022-248-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Dirk Eilander, 16 Feb 2023
Status: closed
-
RC1: 'Comment on nhess-2022-248', Anonymous Referee #1, 07 Nov 2022
General comment:
The manuscript presents a global framework for assessing flood risk and risk reduction strategies for compound flooding. The framework was applied to Sofala province in Mozambique. The Authors showed that coastal flooding causes the greatest impact regardless of the other drivers.
The manuscript is well-written and with a clear structure. However, there are a few criticalities that need to be addressed.
The novelty of this work compared to the current literature and to previous works from the same Authors is difficult to grasp. In its current form, the manuscript reads as a case study which is not enough. The advise is to highlight how this work addresses the limitations of previous studies and goes beyond what has been already done.
The global applicability claimed by the Authors is not really proven since they validated and applied it to the same location (line 82). How does this model apply and perform in other locations?
The description of the probabilistic model needs improvements. First, the Authors should clarify the data used in the vine-copula models, whether they are annual maxima (annual maxima obtained independently in each time series) or whether such data are shifted relative to each other (see table 1). It seems like the annual maxima of 5 different variables occur within a window of +-10 days, which seems a bit unlikely. Moreover, the introduction of a rate of occurrence of annual maxima should be better explained in the relation to the copula model. Why is this necessary? The vine-copula is built to generate sets of dependent variables, including sets in which all of the variables are extremes. This point also relates to the distinction the Authors made between compound events and non-compound events. What does make an event compound? Is this related to the impacts? How can it be defined a priori then?
Point-by-point comments:
Line 36: Do the Authors refer to the joint likelihood or the joint probability? The two concepts are different.
Line 49: Specify what are the “four drivers”.
Lines 68-74: this paragraph is difficult to read. For example, what is an event set? How is a “model event set from univariate distribution” different from a “stochastic event set from a multivariate probabilistic model”? (see also general comment)
Lines 82: Please further elaborate on the reason why global models are useful in data-scarce regions
Line 89: Add some information on the boundary conditions. For example, are the boundary conditions generated independently? How are the normal and extreme boundaries selected and combined? Are all normal or are all extremes?
Section Discharge and Total water level. Clarify the link between annual maxima analysis and hydrograph generation. What are the raw data used for annual maxima analysis and how this annual maximum relates to the hydrograph?
Line 150: why did the Authors use annual maxima if the temporal resolution of the data is hourly?
Line 155: Relative timing between drivers: it seems like the annual maxima of each driver occur around the same time, is it the case? Or the correlation in table 1 is the correlation obtained between the annual max Buzi discharge and the corresponding driver around that period (even if not extreme)? Discuss whether the timing in Table 1 makes sense. Also, why the Authors selected river discharge at Buzi and not a precipitation event? Precipitation might drive high water in the river unless other processes are of relevance.
Line 171: Add some discussion on the rate at which different combinations of drivers co-occur. Should not this come from the vine-copula? Please, clarify this second step. (see also general comment)
Lines 179: Do you try all the possible vine copulas? It would be good to show somewhere what the vine-copula selected looks like and the associated bi-variate copulas.
Line 189: How is an event defined?
Line 240: The Authors made a distinction between exposure and vulnerability. However, their definitions are missing. How is exposure defined? How is vulnerability defined? How do they contribute to the impacts? It is not clear which variables have been used to quantify these two concepts.
Line 243: Why is a bias correction needed?
Line 266: Is this return period associated with the univariate case? Is 5-years realistic for the case study? It would be good to justify the choices made.
From lines 287 – flood drivers. It is a bit unclear how an event is defined and the time series used in the vine-copula models. Why would a compound event be the event in which one variable is extreme? When generating a set of dependent variables, any results in terms of water depths, in this specific case, can be classified as compound (see also general comment)
Flood Hazard: Please, specify how the 100-year fully dependent event is identified, i.e., how the value of each variable is quantified. Also, are the drivers 4 or 5?
Figure 9: The definition of the percentage of base risk is not fully clear. What is the component of the total risk? Is the total risk different per strategy?
Citation: https://doi.org/10.5194/nhess-2022-248-RC1 -
AC1: 'Reply on RC1', Dirk Eilander, 16 Feb 2023
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2022-248/nhess-2022-248-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Dirk Eilander, 16 Feb 2023
-
RC2: 'Comment on nhess-2022-248', Anonymous Referee #2, 05 Dec 2022
This manuscript presents a case study of a modelling framework that could be applied globally to investigate the impact of compound flood risk, at least for initial investigation. The manuscript is for the most part very well written and has a very clear structure.
Despite this, there are a small number of comments that need to be addressed. These are listed in page order below but the more important ones are hightlighted by *.
Section 2.2.3 Rainfall. Lines 152-153: Design rainfall events with a 24- hour duarion were created. Why was this duration chosen? How is this related to catchment response for the chosen area?
Line 160: How was the plus/minus ten days determined?
Line 176 and throughout the manuscript. The authors use both Pair Copula Constructions and Vine Copula interchangeble throughout the manscript.
*Section 2 and in particular section 3 (3.2). The authors present the results and talk about inaccuracies (Line 243). However, these are never combined . In Section 3.2 there is a lack of quantifying the statements and relating to the relevant inaccuracies in the data. For example, Line 322-323, the authors states interactions decrease flood depth in the esturary but upsteam increases flood depth. By how much and how does this related to the overall errors in the datasets. This is needed to understand if these changes are significant relative the data errors. Again Line 326, the authors do not quantify the lower volume of costal water entering the river mouth and if this is a significant amount.
Section 3.3. (Line 345-348). The authors state that the damage caused by pluvial damage is mostly related to the infiltration capactiy. Can this is quantified and what are the other factors that influence this.
*Section 3.5 Limitations and way forwards (Line 390 - 395). The authors mentiond the accuracy of the input data should be considered. It would be nice to see this point discussed in more detail. This is similar to the point above.
*Line 436-439. This statement is sums up the entire manuscript excellently. However, it needs to be stated more strongly throughout the manuscript and include in the manuscript (more clearly) the weaknesses in the approach.
Citation: https://doi.org/10.5194/nhess-2022-248-RC2 -
AC2: 'Reply on RC2', Dirk Eilander, 16 Feb 2023
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2022-248/nhess-2022-248-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Dirk Eilander, 16 Feb 2023
Dirk Eilander et al.
Dirk Eilander et al.
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