A meteo-hydrological modelling system for the reconstruction of river runoff: the case of the Ofanto river catchment

Abstract

Abstract. A meteo-hydrological modelling system has been designed for the reconstruction of long time series of rainfall and river runoff events. The modelling chain consists of the mesoscale meteorological model of the Weather Research and Forecasting (WRF), the land surface model NOAH-MP and the hydrology-hydraulics model WRF-Hydro. Two 3-month periods are reconstructed for winter 2011 and autumn 2013, containing heavy rainfall and river flooding events. Several sensitivity tests were performed along with an assessment of which tunable parameters, numerical choices and forcing data most impacted on the modelling performance.The calibration of the experiments highlighted that the infiltration and aquifer coefficients should be considered as seasonally dependent.The WRF precipitation was validated by a comparison with rain gauges in the Ofanto basin. The WRF model was demonstrated to be sensitive to the initialization time and a spin-up of about 1.5 days was needed before the start of the major rainfall events in order to improve the accuracy of the reconstruction. However, this was not sufficient and an optimal interpolation method was developed to correct the precipitation simulation. It is based on an objective analysis (OA) and a least square (LS) melding scheme, collectively named OA+LS. We demonstrated that the OA+LS method is a powerful tool to reduce the precipitation uncertainties and produce a lower error precipitation reconstruction that itself generates a better river discharge time series. The validation of the river streamflow showed promising statistical indices.The final set-up of our meteo-hydrological modelling system was able to realistically reconstruct the local rainfall and the Ofanto hydrograph.

Simulation of extreme rainfall and streamflow events in small Mediterranean watersheds with a one-way coupled atmospheric-hydrologic modelling system

“…where DT is the time step duration [day]; Ksat [m s -1 ] is the saturated hydraulic conductivity; REFDK is the reference (silty clay loam) saturated hydraulic conductivity (default 2E-06 m s -1 ); and REFKDT is the infiltration partitioning scaling coefficient, which needs to be calibrated to empirically correct KDT for natural variability. As was demonstrated by previous studies (e.g., Naabil et al, 2017;Verri et al, 2017;Givati et al, 2016;Senatore et al, 2015), the model is sensitive to REFKDT.…”

“…In the second case, WRF-Hydro enhanced hydrologic routines update the land surface states and fluxes in the LSM grid, which are then used by the atmospheric component of the model. As summarized by (Rummler et al, 2019), WRF-Hydro is mainly used in its uncoupled mode for model calibration and flood forecasting (e.g., Lahmers et al, 2019;Maidment, 2017;Silver et al, 2017;Verri et al, 2017;Givati et al, 2016;Yucel et al, 2015). Conversely, the fully-coupled mode is usually adopted to investigate land-atmosphere feedbacks (Arnault et al, 2016(Arnault et al, , 2019Rummler et al, 2019;Senatore et al, 2015;Wehbe et al, 2019;Zhang et al, 2019).…”

“…They demonstrated that their method leads to better streamflow predictions than trial and error calibration and is as good as expert knowledge parameterization. Verri et al (2017) calibrated an uncoupled WRF/WRF-Hydro modelling system over the Ofanto river basin, in southern Italy.…”

Impact of high-resolution sea surface temperature representation on the forecast of small Mediterranean catchments' hydrological responses to heavy precipitation

“…Among the numerous scores available in the literature (for a review see e.g. Wilks, 2006), for each zone Fig. 12 shows the results with respect to the frequency bias index (FBI), FBI = hits + false alarms hits + misses ,…”

Impact of improved Sea Surface Temperature representation on the forecast of small Mediterranean catchments hydrological response to heavy precipitation

“…atmosphere) and two-way (with feedback) manner. WRF-Hydro system dramatically evolved in last years (Salas et al, 2018;Lin et al, 2018: Lahmers et al, 2019, being operationally adopted into the NOAA National Water Model (NWM) across the continental U.S, besides being used for research applications (e.g., Yucel et al, 2015;Senatore et al, 2015;Arnault et al, 2016;Verri et al, 2017).…”