Calibration of Global Flood Models

Hirpa, Feyera A.; Lorini, Valerio; Dadson, Simon; Salamon, Peter

doi:10.1002/9781119427339.ch11

Cited by 4 publications

(3 citation statements)

References 56 publications

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“…The extent to which these characteristics (often not accounted for) affect uncertainties differs from location to location and involves complex interactions. These characteristics may not always be captured in the calibration and validation process and does not always include all streamflow observation stations (Hirpa et al., 2021; Wing et al., 2021). This means that model parameterization is not always sensitive to local characteristics.…”

Section: Discussionmentioning

confidence: 99%

The Role of Global Data Sets for Riverine Flood Risk Management at National Scales

Bernhofen

Cooper

Trigg

et al. 2022

Water Resources Research

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Flooding is a global problem that affects most regions in the world. In the last 20 years, over 1.65 Billion people were affected by flooding (UNDRR, 2020), while estimated losses as a result of flood events exceeded 1 Trillion US Dollars over the last 40 years (Munich RE, 2020). The impacts of flooding can be reduced through good flood risk management (FRM). For FRM measures to be most effective, they need to be implemented in areas of high flood risk, which are identified through flood risk assessments. These assessments follow the conceptual framework that flood risk is a product of hazard (the flooding), exposure (who or what is exposed to the flooding), and vulnerability (the susceptibility of the exposure to damages; UNISDR, 2015).

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Section: Discussionmentioning

confidence: 99%

The Role of Global Data Sets for Riverine Flood Risk Management at National Scales

Bernhofen

Cooper

Trigg

et al. 2022

Water Resources Research

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“…However, it is important to acknowledge that GRM simulations remain imperfect, exhibiting persisting biases linked to factors such as model parameterization and structural aspects (Bernhofen et al, 2018a;Hirpa et al, 2021;Zhou, Ma, et al, 2021). Biases in model forcing inputs also lead to consequent biases in river models (Hou et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Benchmark Framework for Global River Model (Version 1.0)

Zhou,

Yamazaki,

Revel

et al. 2024

Preprint

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Global River Models (GRMs), which simulate river flow and flood processes, have rapidly developed in recent decades. However, these advancements necessitate meaningful and standardized quality assessments and comparisons against a suitable set of observational variables using appropriate metrics, a requirement currently lacking within GRM communities. This study proposes the implementation of a benchmark system designed to facilitate the assessment of river models and enables comparisons against established benchmarks. The benchmark system incorporates satellite remote sensing data, including water surface elevation and inundation extent information, with necessary preprocessing. Consequently, this evaluation system encompasses a larger geographical area compared to traditional methods relying solely on in-situ river discharge measurements for GRMs. A set of evaluation and comparison metrics has been developed, including a quantile-based comparison metric that allows for a comprehensive analysis of multiple simulation outputs. The test application of this benchmark system to a global river model (CaMa-Flood), utilizing diverse runoff inputs, illustrates that the incorporation of bias-corrected runoff data leads to improved model performance across various observational variables and performance metrics. The current iteration of the benchmark system is suitable for global-scale assessments and can effectively evaluate the impact of model development as well as facilitate intercomparisons among different models. The source codes are accessiable from https://doi.org/10.5281/zenodo.10903211.

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“…This causes great uncertainties. During the last few decades, significant advances have been made in the creation of models and improvements in computation that allow data to be analysed in short periods (Hirpa et al, 2021;Luo et al, 2022).…”

Section: Motivationmentioning

confidence: 99%

Assessment of the effects of climate change on hydrological dam safety and urban floods

Soriano Martín

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Calibration of Global Flood Models

Cited by 4 publications

References 56 publications

The Role of Global Data Sets for Riverine Flood Risk Management at National Scales

The Role of Global Data Sets for Riverine Flood Risk Management at National Scales

Benchmark Framework for Global River Model (Version 1.0)

Assessment of the effects of climate change on hydrological dam safety and urban floods

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