Scholars have unravelled the complexities and underlying uncertainties in coupled human and water systems in various fields and disciplines. These complexities, however, are not always reflected in the way in which the dynamics of human–water systems are modelled. One reason is the lack of social data time series, which may be provided by longitudinal surveys. Here, we show the value of collecting longitudinal survey data to enrich sociohydrological modelling of flood risk. To illustrate, we compare and contrast two different approaches (repeated cross-sectional and panel) for collecting longitudinal data and explore changes in flood risk awareness and preparedness in a municipality hit by a flash flood in 2018. We found that risk awareness has not changed significantly in the timeframe under study (1 year). Perceived preparedness increased only among those respondents who suffered low damage during the flood event. We also found gender differences across both approaches for most of the variables explored. Lastly, we argue that results that are consistent across the two approaches can be used for the parametrisation of sociohydrological models. We posit that there is a need to enhance the representation of socio-demographic heterogeneity in modelling human–water systems in order to better support risk management.

中文翻译：

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用于洪水风险建模中时间动态多样化的纵向调查数据

学者们已经解开了各个领域和学科中人类和水系统耦合的复杂性和潜在的不确定性。然而，这些复杂性并不总是反映在人类-水系统动力学的建模方式中。原因之一是缺乏社会数据时间序列，这可能由纵向调查提供。在这里，我们展示了收集纵向调查数据以丰富洪水风险的社会水文模型的价值。为了说明这一点，我们比较和对比了收集纵向数据的两种不同方法（重复横截面和面板），并探讨了 2018 年遭受山洪暴发的城市的洪水风险意识和准备情况的变化。我们发现风险意识没有改变在研究的时间范围内（1年）显着。仅在洪水事件期间遭受低损失的受访者中，感知到的准备程度有所提高。对于所探索的大多数变量，我们还发现两种方法的性别差异。最后，我们认为两种方法一致的结果可用于社会水文模型的参数化。我们认为有必要在人水系统建模中加强社会人口异质性的表征，以更好地支持风险管理。