Process-based flood damage modelling relying on expert knowledge: a methodological contribution applied to the agricultural sector
| Autor: | P. Brémond, A.-L. Agenais, F. Grelot, C. Richert |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Natural Hazards and Earth System Sciences, Vol 22, Pp 3385-3412 (2022) |
| Druh dokumentu: | article |
| ISSN: | 3385-2022 1561-8633 1684-9981 |
| DOI: | 10.5194/nhess-22-3385-2022 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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