Process-based evaluation of flood events across global water models

Global hydrological models are valuable tools to predict flood hazard across data-scarce regions and future climate scenarios. Their ability to create spatially coherent projections means their results are broadly used for scientific analysis and policy planning. However, the complexity of the models, coupled with the high volume of data they generate, poses significant challenges in evaluating the process representation contained within the models. Existing analysis show, how a model transfers input into output varies strongly between global water models in a long-term analysis. Yet, flood event prediction needs to take place at daily or higher temporal resolution. Are global hydrological models able to accurately represent flood generation? And do they accurately combine different flood-generating processes, such as extreme rainfall, snowmelt, or wet antecedent conditions, into extreme flows?

In this analysis, we compare simulations from five global hydrological models. The models are part of the global water sector within the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). In ISIMIP, all models are run with the same forcing data, on a daily resolution from 1901 to 2019. We extract and compare runoff time series across the 67400 land cells. For each cell, a threshold-based flood event extraction allows calculation of flood duration, magnitude, number of extreme events, etc. Additionally, we use the extracted events to compare model inputs, such as precipitation, or model fluxes, such as snowmelt, that contribute to high-flow generation.

Five models (CWatM, H08, LPJmL, ORCHIDEE, WaterGAP2), with four input variables and fluxes (precipitation, runoff, soil moisture, and snowmelt) at daily resolution over 67400 land cells results in 58 billion data points to analyse. Extracting this process-based statistical information from the model data reduces the dimensionality and scope of the high-resolution data to a form where comparison between models is possible. How do high flow statistics compare between models? Does the same extreme rainfall result in extreme flow across all models? What role does snowmelt and soil moisture play in runoff generation between models? These questions support an evaluation of flood events within global models through process-based model intercomparison.