Development of an hourly hydrological model for Great Britain
Authors: zha qianyu , He Yi , Osborn Timothy 
Published: February 2023

Assessment of climate change impacts on flooding risks has been undertaken by using hydrological models calibrated at a daily time step and driven by daily outputs from the global or regional climate models. However, the daily scale model typically underestimates the magnitude of floods. A model run at a higher temporal resolution can be more capable of capturing flood peaks and hence more representative of the expected future flood magnitude (Beylich et al., 2021; Huang et al., 2019). This study aims to develop an hourly HBV hydrological model for Great Britain. The precipitation observations at an hourly time step for Great Britain [CEH-GEAR1hr] (Lewis et al., 2022) were used to calibrate the hourly HBV model. The model was also calibrated using daily observations from HadUK-Grid dataset (Hollis et al., 2019). The CAMELS-GB catchments in Great Britain (Coxon et al., 2020) were selected as the study area. Hourly time series of flow data were obtained from Environment Agency (EA) for England, Scottish Environment Protection Agency (SEPA) for Scotland and Natural Resources Wales (NRW) for Wales. Daily flow data are from National River Flow Archive (NRFA). The calibrating objective function for the HBV hydrological model at both daily and hourly time steps is Nash&#8211;Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), and also the modified Kling-Gupta efficiency (KGE), the ratio of the root-mean-square error to the standard deviation (RSR) &#160;and Pearson's correlation coefficient (r) were used to compare. For the daily model, more than 77% and 35% of the CAMELS-GB catchments achieve NSE values over 0.6 and 0.8, respectively. The hourly model performance is comparable with the daily model and the hourly model outperforms the daily model in capturing the peak flows.ReferencesBeylich, M., Haberlandt, U., Reinstorf, F., 2021. Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments. Hydrol. Res. 52, 821&#8211;833.&#160;Coxon, G., Addor, N., Bloomfield, J.P., Freer, J., Fry, M., Hannaford, J., Howden, N.J.K., Lane, R., Lewis, M., Robinson, E.L., Wagener, T., Woods, R., 2020. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth Syst. Sci. Data 12, 2459&#8211;2483.&#160;Hollis, D., McCarthy, M., Kendon, M., Legg, T., Simpson, I., 2019. HadUK&#8208;Grid&#8212;A new UK dataset of gridded climate observations. Geosci. Data J. 6, 151&#8211;159.&#160;Huang, Y., B&#225;rdossy, A., Zhang, K., 2019. Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data. Hydrol. Earth Syst. Sci. 23, 2647&#8211;2663.&#160;Lewis, E., Quinn, N., Blenkinsop, S., Fowler, H.J., Freer, J., Tanguy, M., Hitt, O., Coxon, G., Bates, P., Woods, R., Fry, M., Chevuturi, A., Swain, O., White, S.M., 2022. Gridded estimates of hourly areal rainfall for Great Britain 1990-2016 [CEH-GEAR1hr] v2.Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I &#8212; A discussion of principles. J. Hydrol. 10, 282&#8211;290.&#160;&#160;