State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

Abstract

Abstract. This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property).Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km 2 ), a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…In fact, due to the complex nature of hydrological processes, spatially and temporally distributed measurements are needed in the model updating procedures to ensure a proper flood prediction (Clark et al, 2008;Rakovec et al, 2012;Mazzoleni et al, 2015a). Hydrological observations, used to update the water model states, can include streamflow (Pauwels and De Lannoy, 2006;Weerts and El Serafy, 2006;Pauwels and De Lannoy, 2009), snow cover (Andreadis and Lettenmaier, 2006), soil moisture (Brocca et al, 2010(Brocca et al, , 2012 or water level observations coming from in situ sensors (Madsen and Skotner, 2005;Neal et al, 2007) and remote sensing (Giustarini et al, 2011).…”

“…They found that, for the considered case study, the hydrologic process representation for the upper part of the basin is the major source of uncertainty. In Xie and Zhang (2010), Rakovec et al (2012), Lee et al (2012) and Chen et al (2012) the distributed streamflow observations were assimilated in hydrological models with different structures. Overall, the authors found that assimilation of observations from inner points of the basin helps to further improve the hydrograph estimation.…”

“…In most the hydrological application the main assumption is that the updating frequency is fixed. For example, in Rakovec et al (2012) fixed the updating frequency as 6, 12 and 24h. However, in this chapter, the updating frequency it is not a priori decided but it depends to the random nature of dynamic and intermittent streamflow observations.…”

“…In fact, spatially and temporally distributed measurements are needed in the model updating procedures due to the complex nature of the hydrological processes to ensure a proper flood prediction (Clark et al, 2008;Rakovec et al, 2012;Mazzoleni et al, 2015a). For this reason, it is important to assess the influence of StPh sensor locations on the results of DA procedures since different locations affect the hydrological model performance.…”

“…Blöschl et al (2008) proposed streamflow assimilation in grid-based operational flood forecasting systems; however they did not analyse the effect of varying distributed streamflow observations within the river basin. Recently, various authors assessed the effect of interior discharge gauges on hydrological forecasts (Xie and Zhang, 2010;Lee et al, 2011a;Chen et al, 2012;Rakovec et al, 2012;Lee et al, 2012;Mendoza et al, 2012;Mazzoleni et al, 2015aMazzoleni et al, , 2016.…”