Intercomparison of global reanalysis precipitation for flood risk modelling

Abstract

Abstract. Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the implications of four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in five basins in England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). The benchmark demonstrated better accuracy than reanalysis products when compared with observations of water depth and flood extent. All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40 % compared with 14 %–18 % for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4 %) and floodplain depth (28.6 %). Accuracy varied geographically, and no product performed best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data are available, but they should be used with caution due to the underestimation of impacts shown here. Until a more systematic international strategy for the collection of rainfall and flood impact data ensures more complete global coverage for validation, multiple reanalysis products should be used concurrently to capture the range of uncertainties.

HERA: a high-resolution pan-European hydrological reanalysis (1950–2020)

“…The efforts made in refining both spatial and temporal scale (high-resolution, bias corrected) also aim to make HERA suitable for the analysis of extremes. It is 470 notorious that large scale hydrological models forced by climate reanalysis often fail to reproduce extreme hydrological event characteristics, for example flood magnitudes tend to be typically underestimated (Brunner et al, 2021b;McClean et al, 2023). We analyse here how well HERA reproduces different daily flow quantiles (q05, median, q95) through the Person correlation coefficient (r) and the coefficient of determination (R 2 ) (Figure 8).…”

“…However, despite this general good agreement, there is a more pronounced deviation of simulated values from observations for lower flow values, expressed by a higher dispersion for Q05. These deviations can be attributed to errors or biases in our climate inputs (McClean et al, 2023), in the hydrological model 485 (Feyen and Dankers, 2009), but also to errors in flow measurements, especially for Q05 (Despax, 2016;Tomkins, 2014) and anthropogenic impacts on low and median flow regimes (Brunner, 2021) that are not accurately represented in the model (see Figure 7.c). The number of stations with large deviations in the reproduction of high flow statistics (Q95) is minor compared to Q05 and Q50.…”

“…Despite efforts in bias correcting and downscaling our climate inputs, it seems that on average, HERA slightly underestimates river discharges, with a more pronounced bias for high flows. These 595 biases can be related to an underestimation of precipitation in our climate inputs, in particular for extreme events (McClean et al, 2023;Mahto and Mishra, 2019).It has also been shown that uncertainties in precipitations can result in even higher uncertainties in runoff in (semi-)arid catchments (Beck et al, 2016;Sood and Smakhtin, 2015). Uncertainties inherent to model structures have long been overshadowed (Beck et al, 2017).…”

Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe

“…Reanalysis products provide soil moisture data over long time periods (Li et al, 2005;Baatz et al, 2021) and typically merge soil moisture observations and land surface model output by adopting data assimilation techniques, which often results in better soil moisture estimation than satellite products (Naz et al, 2020;Beck et al, 2021;Mahto and Mishra, 2019). At present, reanalysis products are employed in a wide range of fields such as hydrological model initialisation (Zheng et al, 2020), flood modelling (McClean et al, 2023;El Khalki et al, 2020;Zheng et al, 2023), drought monitoring (Chen et al, 2019;El Khalki et al, 2020) and climatology research (Miralles et al, 2014). Currently, many reanalysis products exist including ERA5-Land (Muñoz Sabater 2019;Muñoz-Sabater et al, 2021), CFSv2 (Saha et al, 2011(Saha et al, , 2014, MERRA2 (GMAO, 2015;Gelaro et al, 2017), JRA55 (JMA, 2013;Kobayashi et al, 2015), GLDAS-Noah (Rodell et al, 2004;Beaudoing and Rodell, 2020), CRA40 (Liu et al, 2017;Li et al, 2021), GLEAM (Miralles et al, 2011;Martens et al, 2017) datasets and SMAP Level 4 datasets (Reichle et al, 2019(Reichle et al, , 2017a (one should note that technically speaking, GLDAS-Noah and GLEAM datasets are global land model-based products; we termed them "reanalysis products" in this paper for consistency).…”

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

“…However, the task would be more challenging for the catchments suffering from scarce weather or hydrometric observatory stations [14]. To overcome this handicap in ungauged catchments, many studies adopted alternatives that use (i) nearby observatory records adjusted by spatial statistical techniques [15], (ii) remote sensing and satellite technology [16,17], and reanalysis products [18]. More recently, the use of global databases, such as Tropical Cyclone-related Precipitation Feature (TCPF), Data Observation Network for Earth (DataONE), the fifth generation ECMWF reanalysis for the global climate and weather known as ERA5, and Global Drought Monitoring (GDM) database have been recommended because of extensive spatiotemporal coverage and ease of access [18,19].…”