Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

Abstract

Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events

“…We find this in this study too, with increasing forecast skill in the downwind direction of the operational radars with south-westerlies as the main wind direction. Hence, whereas the application of nowcasting in flood forecasting is likely to be beneficial (e.g., Berenguer et al, 2005;Moreno et al, 2013;Pierce et al, 2005;Poletti et al, 2019;Vivoni et al, 2006Vivoni et al, , 2007, the catchment properties will influence the eventual skill.…”

Improving real-time operational streamflow simulations using discharge data to update state variables of a distributed hydrological model

“…Continuum is the hydrological model operational in the forecasting chain and used for the experiment. It is described in Silvestro et al (2013Silvestro et al ( , 2015, and has been used for many research studies (e.g., Laiolo et al 2015;Cenci et al 2016;Corral et al 2019;Poletti et al 2019). The model's codes are available on github https://github.com/c-hydro/hmc-dev and an overview and the documentation of the continuum model and its ancillary tools can be found at https://hmc.readthedocs.io/en/latest/.…”

Overview towards improved understanding of the mechanisms leading to heavy precipitation in the Western Mediterranean: lessons learned from HyMeX

“…Caldas-Alvarez and Khodayar (2020) highlighted the positive impact exerted by moisture corrections on precipitating convection and the chain of processes leading to it across scales. Furthermore, the implementation of nudging methodologies to exploit non-conventional observations, such as rainfall estimates from remote sensing, provided positive results in applications to both nowcasting and short-term meteo-hydrological forecasting (Davolio et al, 2017;Poletti et al, 2019).…”