Flood damage assessment is crucial for evaluating flood management policies. In particular, properly assessing damage to agricultural assets is important because they are complex economic systems particularly exposed to floods. The modelling approaches used to assess flood damage are of several types and can be fed by damage data collected post-flood, from experiments or based on expert knowledge. The process-based models fed by expert knowledge are the subject of research and also widely used in an operational way. Although identified as potentially transferable, they are in reality often case-specific and difficult to reuse in time (updatability) and space (transferability). In this paper, we argue that process-based models, based on a rigorous modelling process, can be suitable for application in different contexts. We propose a methodological framework aimed at verifying the conditions necessary to develop these models in a spirit of capitalisation by relying on four axes which are (i) the explicitation of assumptions, (ii) the validation, (iii) the updatability, (iv) the transferability. The methodological framework is then applied to the model we have developed in France to produce national damage functions for the agricultural sector. We show in this paper that the proposed methodological framework facilitates an explicit description of the modelling assumptions and data used, which is necessary to consider for a reuse in time or for transfer to another geographical area. In this sense, this methodological framework constitutes a solid basis for considering the validation, transfer, comparison and capitalisation of data collected around models based on processes relying on expert knowledge. In conclusion, we identify research tracks to be implemented so as to pursue this improvement in a spirit of capitalisation and international cooperation.
Worldwide, flooding causes huge damage
Several classifications of the methods used to model flood damage can be found in the literature
For the agricultural sector, no data-driven models were found in the literature. In Germany, no model such as FLEMOps or FLEMOc exists for agriculture
Methodological framework for the development of process-based flood damage models.
In this article, we analyse and discuss the methodological aspects required to develop process-based damage assessment models in a spirit of capitalisation. We propose a framework for the development of damage assessment models based on expert knowledge and illustrate its use around the model floodam.agri that we have developed and used to produce flood damage functions for the agricultural sector in France. Two questions are addressed: (i) is the methodological framework we propose useful for developing flood damage assessment models in the spirit of capitalisation? (ii) What methodological efforts are needed to develop process-based models that are not only context specific in this capitalisation and cooperation perspective?
In Sect.
Based on a review of the literature as well as on our own modelling experience, we propose the methodological framework presented in Table
Flood damage is usually classified into four types: direct tangible (e.g. physical damage due to contact with water), indirect
tangible (e.g. loss of production and income), direct intangible (e.g. loss of life) and indirect intangible
Then, process-based models try to reflect physical or biophysical processes that occur in the considered system and which generate flood impacts. These processes are numerous, depend on the component of the system considered and may depend on different flood parameters
Finally, flood damage results in an interaction between the flood impacts and human behaviour
Although the research community has put a lot of effort into improving flood damage models,
Although some research exists on updating flood hazard models, for example by integrating climate change
Transferring the flood damage model is a challenging issue
In France, since 2011, it has been mandatory for local flood risk managers to conduct cost–benefit analysis (CBA) of their flood management projects to make them eligible for financial support from the State. Meanwhile, as support, the French ministry in charge of the environment proposed a methodology to fulfil CBA
The floodam.agri model includes generic parts and can produce damage functions at different scales, depending on the calibration. We illustrate in this paper the use of floodam.agri to produce damage functions at the national scale. This methodology has followed seven stages (Fig.
Development process of the national flood damage functions for agriculture.
First, the
Second, to inform the conceptual framework, 30
Geographic distribution of the experts interviewed.
A questionnaire was designed (Supplement 1) to conduct semi-structured interviews that lasted about 1 h. It was structured in two parts in order to collect information (i) on the impacts on farm components and (ii) on the consequences for farmers' practices. Prior to every interview, production cycles in terms of physiological stages and agricultural work calendar were established based on the literature, for the categories of crop corresponding to the expert interviewed. This information was presented and discussed with the experts too.
Third, floodam.agri
Production process of the national French flood damage functions with floodam.agri.
Fourth, floodam.agri must be
Fifth, a
Sixth, based on focus group discussions, some
Seventh, the process resulted in
Ready-to-use national damage functions were produced for 15 of the 28 sorts of crop of the GPR typology (Supplement 2). These 15 sorts accounted for 89 % of agricultural areas located in flood-prone areas in metropolitan France in 2010, according to the GPR database. They indicate the estimated expected value of damage in euros by hectare, depending on the water depth, submersion duration, season of occurrence of the flood, and flow speed. The maximum expected damage is the lowest by hectare for sunflower crops (EUR 1611) and the highest for arboriculture and orchards (EUR 93 549) (Table
For illustrative purpose, Fig.
Example of the national flood damage function developed using floodam.agri for the category “arboriculture”.
The threshold effects in the relationship between the damage and the water depth correspond to the water depths at which new types of plant organs are reached by water (e.g. leaves, fruits).
In this section, the methodological framework (Table
The floodam.agri model is a conceptual model developed on the basis of the literature and previous works
Boundaries and components considered in floodam.agri.
Distribution of the physiological stages and crop management sequence of apple crop over the weeks of a year selected for the national functions.
Interviews were conducted on the vulnerability of farm buildings and their contents (equipment and stock) as well as cattle. However, these elements have not been integrated into floodam.agri to date. Furthermore, floodam.agri also does not consider damage induced at the farm scale, i.e. damage induced on farm activity due to direct damage on farm equipment, for example as evaluated in
Equations (
The added value is the difference between the outcome of the plot (
The methodological framework proposes to discuss this question following two sub-questions.
Are the biophysical processes that cause the damage taken into account in the model explicitly considered? Are the links between biophysical processes and flood parameters clearly defined?
For each component, Table
Biophysical processes considered or not in the national flood damage functions produced with floodam.agri.
The parameters used to characterise the floods are (i) the height, (ii) the duration of submersion, (iii) the velocity, and (iv) the season. Flood impacts on crops were described as a function of physiological stages instead of time of the year to maintain the adaptability of our model to different contexts. The relevance of the choice of these physiological stages to the sensitivity of the component to flooding was discussed with the experts. For apple, for example (Fig.
Table
Mortality of plant material for apple crop as a function of the physiological stages and biophysical processes involved (low velocity).
For perennial crops, on a plot, the crop borne by the destroyed plant material (
Flood impacts on yield variation for apple crop as a function of the physiological stages and biophysical processes involved.
The flood impacts on the soil component taken into account in floodam.agri are erosion and littering (Table
Equipment on the plots (i.e. irrigation systems, fences, greenhouses, and trellis) can be deteriorated or destroyed (Table
The assumptions made on the decision rules of farmers after the flood are linked to the damage endured and the physiological stage of the crops. They are detailed for each component below.
The behaviour of farmers in the standard situation is defined by the crop management sequence, which is the logical and orderly sequence of tasks that must be performed to achieve the set yield
List of additional or cancelled tasks taken into account in floodam.agri.
Direct (
Then, delayed damage (
Farmers' strategy for replantation as a function of mortality of plant material (
The possible strategies following the loss of yield are different depending on whether the crop is perennial or annual. Table
Strategies for the continuation of the crop management sequence and associated equation.
In floodam.agri, farmers of perennial crops have only two choices: continue (Eq. 11) or stop (Eq. 12) the crops. In all cases, the basic assumption is that of a continuity of the production of the current crop. That is to say that no radical change in the orientation of the farm's production is envisaged. Most of the time they decide to continue the crop management sequence also because leaving rotten fruit in the orchard or vineyard could lead to disease development. For example, for apple crops, the harvest is always carried out unless the total yield losses, i.e. combining yield losses alone and plant material losses, are (i) more than 95 % and the flooding took place before the maturity stage, (ii) more than 75 % and the flooding takes place at the maturity stage (a chemical treatment is then carried out). Moreover, if they continue, for the case of apple crops, there is no variation of intermediate consumptions because the treatments are already very regular in normal situations.
Regarding annual crops, farmers generally have to modify their usual crop management plan then the additional expenses in terms of treatments to avoid moisture-related diseases (Eq. 10). They can also decide to stop the crop (Eq. 12).
Two additional strategies are possible for annuals crops as a function of the period of occurrence of the flood and the loss of yield. It is possible to re-sow the same crop if the flood occurs early enough in the crop's development cycle (e.g. up to the emergence stage for winter and summer field crops). In this case, the damage is expressed in terms of yield loss due to the later seeding plus the additional seeding costs (Eq. 13). The possibility of planting another catch crop is also being considered. This is particularly the case when the flooding occurs too late on a winter cereal for the same crop to be re-sown. Grain farmers may then consider planting a spring or summer cereal. This alternation is part of the crop rotation that they practice on a multi-year basis. In this case, the damage is expressed in terms of the possible loss of product linked to the realisation of this new crop to which is added the cost of a new sowing (Eq. 14).
For the soil and equipment, the repair and replacement actions have been defined with experts as a function of flood impacts on the component. The damage to the soil component (
For the case of orchards, Table
Illustration of assumptions elaborated with experts for soil damage in the case of orchards for the national damage functions.
The damage to equipment (
In this section, the methodological framework (Table
As specified in Sect.
To date, no comparison of floodam.agri has been made with other models. To our knowledge, this has not been done for any flood damage assessment model for agriculture. Comparing floodam.agri with existing flood damage model for agriculture such as the flood damage functions developed by the FHRC in the United Kingdom or AGDAM in the United States would required a common case study. No such initiative has been done yet. We hope that the effort of explicitness made in this article contributes toward this direction. As a first step, Table
The national flood damage functions that were produced using floodam.agri were used by stakeholders (engineering firms and project developers) between 2014 and 2022 in more than 200 CBAs. This proves that floodam.agri has met the expectations of the stakeholders involved in the process, namely the Ministry of the Environment, the local authorities in charge of the project and the consulting firms that carry out the CBA.
Example of illustrations used during the focus group of experts for the case of apple crops.
Within the framework of the development of floodam.agri, we implemented a specific methodology allowing us to discuss and validate in groups during workshops the setting in the model of the information collected in individual interviews. This qualitative research method is the focus group. The aim of these workshops is multiple. They allow the coherence of the information collected in individual interviews to be verified and discussed collectively. Above all, they allow the results of the overall modelling chain (loss of plant material, yield, associated behaviours) to be presented to the experts who were interviewed separately on the different components of the model and to allow them to readjust their assumptions if necessary.
The following topics were discussed using illustrations (Fig. the biophysical processes considered for each component, the ranges of yield loss in function of flood parameter, the determination of impacts for each components in function of flood parameter, the farmers' strategies for crop continuation, the additional or cancelled tasks and as a consequence the variation in crop expenses, the replanting strategies, the list of recovery tasks and their estimated cost (hours of work, equipment).
Each assumption was discussed until all experts agreed to validate them. Following this work, the list of changes to be made was established (Supplement 3) and implemented.
In this section, the methodological framework (Table
To produce flood damage functions, floodam.agri requires (i) an estimate of usual yields, (ii) an estimate of selling prices, (iii) an estimate of intermediate consumptions and (iv) the physiological stages and crop management sequence. Table
Data sources.
GPR: Graphical Plot Register; AAS: Annual Agricultural Statistics database; SAD: Scales of Agricultural Disasters; ASB: Agricultural Situation Bulletin; IPPAC: Index of Producer Prices of Agricultural Commodities; LR data: technical and economic memento of the main agricultural productions in Languedoc–Roussillon and fact sheets on the Languedoc–Roussillon region.
Vintage and update frequency of database used to apply floodam.agri at the national scale in France.
The vintage used and the frequency of updates are specified in Table
Table
To sum up, Tables input data come from a single database which tracked over time (e.g. yields), input data come from different databases with different update frequencies (e.g. selling prices and intermediate consumptions), input data come from expert knowledge (e.g. physiological stages).
In this section, the methodological framework (Table
The possibility to adapt floodam.agri to different contexts was a requirement and has been anticipated in the modelling process. The different steps for adaptation from the simplest to the most demanding are identified according to the differences between the context in which floodam.agri was developed and the context in which it could be transferred. Methodological proposals are made for each of these steps (Fig.
Steps of adaptation to transfer floodam.agri.
The first possibility of adaptation concerns the compatibility between the flood damage functions produced with floodam.agri and existing hydraulic and hydrological models in terms of resolutions. As the resolution of flood parameters is higher in floodam.agri, it can easily generate flood damage functions with a higher resolution. For example, for the national application, it was proposed to simplify the season parameter and we defined four seasons (Appendix
To generate national damage functions, we had to adapt the damage function typology developed in level 3 (Appendix
This step encompasses two aspects. First, the adjustment of crop technical–economic data (yields or selling prices) requires ensuring that data listed in Sect.
In the context of application, some biophysical processes or particular behaviours of farmers in cases of flooding that have not been considered in floodam.agri may appear. In this case, it will be necessary to consolidate the modelling (sensitivity and decision rules) with local experts.
If a crop is to be added to the list of 53 existing crops in floodam.agri, two options should be considered. First, it is necessary to determine whether the crop can be assigned to a vulnerability category. If so, it is necessary to calibrate the physiological stages, crop management sequence, yield and price of the crop. If not, it will be necessary to create a new crop category and to add new sensitivity and decision rules functions. For this, data collection from agricultural experts will be necessary. Moreover, agro-economic data will have to be collected to calibrate the functions.
This is the most demanding level of adaptation because it requires repeating for each crop category all the biophysical processes and the impact on farmers' decisions. This type of transfer necessarily requires work with experts.
To date, some adjustments have been made to the resolutions (step 1) or to the local data (step 2) in the frame of the mandatory CBA of flood management projects. In
The proposed framework clarifies the components, interactions and decision entities that are or are not considered in the damage assessment model.
In economic systems, added value is produced on spatial entities (plots in the agricultural case) and depends on production factors (material, labour, input) and decision rules. In the case of agriculture, the added value increases on the plots and is then stored and transformed in other spatial entities on or off the farm.
From the modelling experience presented in this article around floodam.agri, the proposed framework concerning the explicitation of assumptions appears to us to be effective for two main reasons. Firstly, the explanation of the assumptions facilitated the collection of information from the experts. Indeed, we found that the logic we proposed to deconstruct the biophysical processes and the decisions made by farmers was consistent with the cognitive approach of damage assessment of the experts. In this sense, the application of the framework reduces the uncertainties surrounding the collection of expert knowledge. Secondly, the explicitness of the assumptions appears to be a necessary condition for the implementation of the other axes, namely validation, updatability and transferability. For example, it is essential to know which processes have been taken into account in determining yield losses. Studies carried out in the context of drainage may only take into account processes such as root asphyxiation, which will be predominant, but in the case of floods with significant velocity effects, it is essential to integrate also the processes of uprooting or laying down. This effort to clarify assumptions is also necessary for capitalisation.
The proposed framework allows for a clear improvement in the validation methodology with experts involved in the modelling process. However, we are aware of the need to consolidate this aspect. Two avenues are usually identified: first, the comparison of model results with each other; second, the comparison with claims data
The proposed methodological framework requires the specification of all the data used, their source and their vintage. This makes it possible to consider updating the models produced for a given context over time. This is the case for the damage functions produced thanks to floodam.agri. This effort allows us to consider the transfer by comparison of existing databases from one context to another. A difficulty persists for data that are not tracked over time, and in this case we recommend either updating the data on the basis of expert opinions or using a discount rate whose value must be specified.
Transferability needs to be anticipated right from the design stage. We are convinced that process-based models have generic parts that can be transposed and specified in other contexts. The methodological framework has proven useful to describe these aspects and their specification. In particular, we propose a reflection with experts on the basis of vegetative cycles rather than on a monthly basis as was done by
The proposed methodological framework also provides a basis for future improvements. In this sense, the explicitness of the assumptions (biophysical processes, decision rules) should not be fixed but should be fed. This suggests the possibility of pooling efforts at an international scale. The tracks of improvement which we consider be a priority concern taking into account (i) other biophysical processes, (ii) agricultural buildings, (iii) breeding systems and (iv) adaptations of the trajectories of farms to floods.
Some biophysical processes such as pollution, salinisation or degradation of soil quality remain scarcely studied and should be consolidated.
For agricultural buildings, a similar approach by breaking down the basic components of the farm building (structure, equipment, input) could be conducted using the model floodam.building
Regarding livestock systems, the work carried out by the FHRC is a solid base that should be consolidated by addressing the issue of delayed effects related to the loss of animals as has been integrated through the loss of plant material for crops.
Finally, an important challenge remains to take into account the adaptive capacities of farmers in the long term. Collecting data from agricultural experts who have witnessed flooding on a large number of farms allows us to model a standard behaviour. However, we are aware that this average view does not reflect the diversity of individual vulnerability situations at the farm level. Thus, at the individual scale, decisions, especially those concerning long-term issues such as replanting, will depend on individual parameters such as investment dynamics, the age of the farm manager, the farm's trajectory etc. While it would be possible to assess the economic relevance of certain measures in terms of damage avoided using floodam.agri (e.g. assessment of the damage avoided by establishing a grassland in place of a vineyard), the determinants of these adaptation decisions are much more complex at the level of individuals and in particular farms. Understanding the internal and external determinants of adaptation implementation would require a different approach and investigation at the individual level
Process-based flood damage assessment models relying on expert knowledge are widely researched and used operationally. However, it is often observed that this work cannot be capitalised on because the models are too attached to their development context. In this paper, we argue that process-based models, based on a rigorous modelling process, can be suitable for application in different contexts. We show that following a rigorous modelling process can contribute to their capitalisation and transferability. We propose a framework that improves the development of process-based flood damage models by meeting the properties of assumption explicitness, validation, updatability and transferability. We show that respecting these properties could help structure a common modelling effort at the international level.
By applying the proposed methodological framework to floodam.agri, we show that it is possible to describe explicitly the modelling assumptions. Given the complexity of the phenomena (biophysical and decisional processes) and the diversity of the data sources, we argue that the methodological framework is useful for structuring and anticipating from the beginning of the development process a spirit of capitalisation in time and space. This rigorous work is a necessary condition to consider the possibility of improvement in the long term and of cooperation around the development at an international scale. The framework proposed here thus opens up prospects for cooperation in improving and transferring existing models, particularly agricultural models. In terms of research, this work of methodological improvement must be carried out in parallel with the improvement of observation and data collection on the impacts of floods in terms of monetary damage but also with improvement of the understanding of biophysical damage processes, repair decisions and adaptation in the long term.
Different typologies had to be used in the development of floodam.agri. To work with the experts on the sensitivity of the crops, we used the families (level 1), categories (level 2) and subcategories (level 3) described in Table
Level 1 corresponds to five crop families. It brings together 24 categories of crops usually grouped in agronomy. However, this level is not fine enough to define homogeneous damage processes. The crop category (level 2) is the level where damage process is homogeneous. The crop sub-category (level 3) represents a total of 53 crops that can be related to the second level. For instance, winter wheat, barley, and rye are three types of crops that belong to the winter wheat category and to the grain and oleaginous crops family.
Then, we produced the ready-to-use national damage functions by adjusting the typology to be compatible with the Graphic Plot Register (GPR level, Table
Families and categories of crops considered in floodam.agri.
Categories of crops in the RPG database, area in flood-prone areas, and maximum damage estimated with floodam.agri.
The areas in flood-prone regions were estimated using the approximate potential flood extent (EAIP), which was estimated for the whole country within the frame of the first national flood risk assessment between 2011 and 2017. The maximum values of damage are calculated taking into account all possible combinations of flood parameters. The categories in bold are linked to a damage function produced with floodam.agri.
The resolution of the model is given in Table
Ranges and resolution of the flood parameters used in floodam.agri.
Categories of flood duration for the French flood damage functions.
Categories of time of occurrence of the flood for the French flood damage functions.
We present in Table
Illustration of the use of the methodological framework to describe and compare three process-based models.
floodam.agri has been implemented in R language is available at
The supplement related to this article is available online at:
FG and PB developed the conceptual damage model. ALA, PB and FG collected expert knowledge. ALA collected secondary data. FG and ALA implemented floodam.agri in R language. ALA and CR wrote a detailed description of the damage model in English and a first draft of the paper. PB proposed the methodological framework detailed in the paper. PB wrote the first complete version that was reviewed by all authors. All authors contributed significantly to the figures.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work benefited from the support of the French Ministry of Environment under the funding agreement no. 2200752351. The authors thank the two referees for their reviews that contributed to improving the quality of the paper. We are grateful to Katrin Erdlenbruch for her contribution to the first discussions on farm vulnerability to flooding.
This research has been supported by the French Ministry of Environment (grant no. 2200752351).
This paper was edited by Heidi Kreibich and reviewed by two anonymous referees.