The confidence we can have in hydrological analysis is directly related to the availability and quality of observed hydrological data. Data quality has a pivotal role in improving the accuracy of hydraulic models and hence the confidence we have in model outputs and the decisions we make based on them. This article delves into some key points, shedding light on the advancements in accessing and utilising hydrological data for flood analysis.
Incorporating good-quality data is critical to enhance the reliability of flood analysis. The quality of hydrological data directly impacts the confidence we have in flood hydrology estimates, including determining the magnitude of an extreme flood, estimating hydrograph shapes and flood durations, all of which significantly impact on estimated flood extents and frequency.
Over the past 5-10 years, there has been a significant improvement in the accessibility and quality of hydrological data. We’ve come a long way since having to rely on methods such as receiving data on CD-ROMs or through annual publications. In the current era, data are readily available online, regularly updated, and easily accessible through tools like the Environment Agency's Hydrological Explorer.
The influx of new data can be leveraged for both hydrological estimation and hydraulic modelling. By using these data, analysts can refine flood hydrology estimates and enhance the performance of hydraulic models. This, in turn, leads to more accurate flood maps, better-informed design decisions for flood defences, and improved community flood alerts.
Tools like the Hydrological Explorer and Flood Modeller have revolutionised data access. With features like Hydrology+Â in Flood Modeller 7.0, practitioners can now access and interact with data from various gauging stations instantaneously, enabling them to make more informed decisions in real time.
Primary sources of hydrological data in the UK include the National River Flow Archive and the Environment Agency's Hydrological Explorer. The inclusion of citizen science data, though of lower quality, also carries potential to provide high-density information. For example, spatially focused, convective rainfall events often fall between the coarsely spaced national rain gauge network. The use of personal rain gauge data, while of lower quality, is often more finely spaced. With intense rainfall events predicted to become increasingly common with climate change, use of these data will become increasingly important.
The importance of local data in hydrological analysis can lead to a significant reduction in uncertainties and improve the accuracy of flood estimates, ultimately influencing flood extent and risk assessments. Inherent uncertainties will always remain in hydrological data and analysis. Acknowledging these uncertainties is crucial for conducting to provide a more comprehensive understanding of the potential variations in flood analysis outcomes. Something we’ve talked about before in our Residual Uncertainty Analysis webinar.
We also need to stress the significance of hydraulic model calibration and verification. Tools like Hydrology+ enable the seamless integration of observed data into hydraulic models, facilitating rapid calibration and validation processes.
Hydrological data in flood analysis is part of an evolving landscape. With improved data accessibility and advanced tools, practitioners can work more efficiently, make better-informed decisions, reduce uncertainties, and enhance the reliability of flood assessments. The ongoing advancements in data-driven approaches promise a future where flood analysis is not only more accurate but also more accessible to a broader range of stakeholders.
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