A global-scale evaluation of extreme event uncertainty in the <i>eartH2Observe</i> project

Abstract

Abstract. Knowledge of how uncertainty propagates through a hydrological land surface modelling sequence is of crucial importance in the identification and characterisation of system weaknesses in the prediction of droughts and floods at global scale. We evaluated the performance of five state-of-the-art global hydrological and land surface models in the context of modelling extreme conditions (drought and flood). Uncertainty was apportioned between the model used (model skill) and also the satellite-based precipitation products used to drive the simulations (forcing data variability) for extreme values of precipitation, surface runoff and evaporation. We found in general that model simulations acted to augment uncertainty rather than reduce it. In percentage terms, the increase in uncertainty was most often less than the magnitude of the input data uncertainty, but of comparable magnitude in many environments. Uncertainty in predictions of evapotranspiration lows (drought) in dry environments was especially high, indicating that these circumstances are a weak point in current modelling system approaches. We also found that high data and model uncertainty points for both ET lows and runoff lows were disproportionately concentrated in the equatorial and southern tropics. Our results are important for highlighting the relative robustness of satellite products in the context of land surface simulations of extreme events and identifying areas where improvements may be made in the consistency of simulation models.

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

“…Other evaluation approaches include triple collocation, which is a technique that estimates the variance of unknown errors of three independent variables without a reference or observed variable (e.g. Massari et al, 2017;Alemohammad et al, 2015;McColl et al, 2014;Roebeling et al, 2012). Compared to the ground-truthing approach, the hydrological evaluation approach has received limited attention (Camici et al, 2018;Poméon et al, 2017).…”

Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations

“…as found by Parker et al (2018) for the process of overbank inundation). The storage and conveyance of water in lakes, floodplains, groundwater and river channels, especially, is generally simulated only with relatively high uncertainty in the current generation of land surface models (LSMs) (Marthews et al, 2020;Marthews et al, 2019).…”

“…Most hydrological models are run uncoupled from the atmosphere and are therefore reliant on the availability of good precipitation and other atmospheric driving data. Uncertainties in the precipitation driving data may often be very significant and larger than the total uncertainty inherent within the model being run (Marthews et al, 2020). Previous studies have https://doi.org/10.5194/hess-2021-109 Preprint.…”

Suitability of 17 rainfall and temperature gridded datasets for largescale hydrological modelling in West Africa

“…Other evaluation approaches include triple collocation, which is a technique that estimates the variance of unknown errors of three independent variables without a reference or observed variable (e.g. Massari et al, 2017;Alemohammad et al, 2015;McColl et al, 2014;Roebeling et al, 2012). Compared to the ground-truthing approach, the hydrological evaluation approach has received limited attention (Camici et al, 2018;Poméon et al, 2017).…”

“…It is noteworthy that the goal of this study is not to estimate the intrinsic quality of the meteorological forcing (i.e. precipitation and temperature) but rather to understand the impact of the propagation of associated uncertainties on the 120 simulation of hydrological processes (Bhuiyan et al, 2019;Falck et al, 2015;Marthews et al, 2020).…”