Variational assimilation of streamflow data in distributed flood forecasting

Abstract

Data assimilation has the potential to improve flood forecasting. However, research efforts are still needed for an effective development of assimilation schemes suitable for operational usage, especially in case of distributed hydrologic models. This work presents a new assimilation system of streamflow data from multiple locations in a distributed hydrologic model. The system adopts a mixed variational‐Monte Carlo approach, and is here tested with the hydrologic model MOBIDIC, that is part of the operational flood forecasting chain for Arno river in central Italy. The main objective of the work is to evaluate the actual gain that the system can lead to flood predictions in a real‐time operational usage. Accordingly, a specifically designed assessment strategy is employed. It is based on several hindcast experiments that include both high flow and false alarm events in the period 2009–2014 in Arno river basin. Results show that the assimilation system can significantly increase the accuracy of flow predictions in respect to open loop simulations in both cases. Specific performances depend on location and event, but in the majority of cases the error on predicted peak flow is reduced of more than 50% with a lead time of around 10 h. The analysis reveals also that the structure of the hydrologic model, the coherence between observations at various sites, and the initial watershed saturation level, considerably affect the obtainable performances. Conditions that may lead to a worsening of open loop predictions are identified and discussed.

Comment on “Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?” by Mazzoleni et al. (2017)

“…Ercolani and Castelli, 2017); moreover, the reliability of data from traditional sensors outperforms that of CSD. Hence, from a practical point of view, CSD have limited usefulness at locations already equipped with traditional sensors.…”

“…Several studies indicated that the information content in a rainfall-runoff record is sufficient to support models of only very limited complexity (Jakeman and Hornberger, 1993;Refsgaard, 1997). This im-plies that distributed, or semi-distributed, hydrological models are seldom calibrated; rather, they are commonly overparametrized, since calibration rarely involves their internal states (Sebben et al, 2012;Viero et al, 2014).…”

Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil

“…Advanced methods of data assimilation are an option for an optimal combination of information from models that are inherently imperfect, with also uncertain observations, thus obtaining estimates that are physically consistent with the reduction and quantification of the uncertainties (MCLAUGHLIN, 1995;EL SERAFY, 2006;GUPTA, 2007;CLARK et al, 2008;REICHLE, 2008;LIU et al, 2012;RIDLER et al, 2014;ERCOLANI;CASTELLI, 2017). Initially these methods were more popular in earth sciences for meteorological forecasting, characterization of atmospheric and oceanic conditions; however, in recent years they have been adapted for hydrological and hydraulic applications (LIU et al, 2012).…”

“…Data assimilation methods with applications of discharge data into distributed hydrological models are more abundant (WEERTS; EL SERAFY, 2006;CLARK et al, 2008;RAKOVEC et al, 2012;CHEN et al, 2012;DECHANT;CHEN et al, 2013;DUMEDAH;COULIBALY, 2013;ZHANG et al, 2014;MORADKHANI et al, 2005a;MORADKHANI et al, 2005b;SEO et al, 2009;ZHANG, 2010;PAUWELS;DE LANNOY, 2009;ERCOLANI;CASTELLI, 2017). However, recent advances in remote sensing encouraged the use of observations such as radar altimetry to estimate river levels (PAIVA et al, 2013b;ANDREADIS et al, 2007;BIANCAMARIA et al, 2011), microwave radiation to estimate soil humidity (NAGARAJAN et al, 2011;YAN;MORADKHANI, 2016), remote observation to infer the properties of snow as an extension of large scale coverage (ANDREADIS; LETTENMAIER, 2006), among others.…”

“…Chen et al (2012) demonstrated that the data assimilation methods in semi-distributed models provided a better representation of the maximum streamflow peaks compared to the lumped models. Various methods of data assimilation in distributed hydrological models were applied in different basins worldwide, as in Andreadis and Lettenmaier (2006), Clark et al, (2008), Feyen (2009), Mendoza (2010), Noh et al (2011), Chen et al, (2012, Paiva et al (2013b), Xie and Zhang (2010), Abaza et al (2014), Ercolani and Castelli (2017), Xu et al (2017), among others. Nevertheless, applications of data assimilation in South America basins are still insufficient.…”