## 1. Introduction

[2] Every year, human and economic losses are reported all around the world due to the presence of floods. Therefore, the scientific community actively is investing in improving the current flood forecasting systems. Conceptual rainfall-runoff models are an important component in operational flood forecasting systems. Generally, these models represent the study area by a number of water reservoirs through which different inflows and outflows (for example, infiltration, evapotranspiration, discharge) interact dynamically. Examples of such models are the Hydrologiska Byrans Vattenbalansavdelning (HBV) [*Lindström et al*., 1997] model and the Probability Distributed Model (PDM) [*Moore*, 2007] or variations derived from these models. From a technical point of view, the simplicity of conceptual models is an advantage that offers flexibility in the implementation. However, the identification of the model parameters that lead to realistic model predictions is a complex task. Moreover, the uncertainties in the forcings, model parameters, and simplifications in the model physics affect the overall performance of the conceptual model [*Kavetski et al*., 2006]. One way to reduce the predictive uncertainty of conceptual hydrologic models is the use of data assimilation to regularly update models using externally obtained data sets [*Vrugt et al*., 2006; *Moradkhani and Sorooshian*, 2008]. Nowadays, sequential data assimilation is also a key component in flood forecasting systems. The study carried out in this paper contributes to the ongoing research of improving sequential data assimilation methods.

[3] *Kalman* [1960] developed the discrete Kalman filter, which is a square-error estimator for linear systems. In his seminal paper, Kalman used the state-space representation in order to generalize the application to any kind of linear system. It was possible to extend the application of the filter to different systems and to develop nonlinear versions from the original Kalman filter, such as the extended Kalman filter [*Hoeben and Troch*, 2000], unscented Kalman filter [*Wan and Van Der Merwe*, 2000] and the ensemble Kalman filter (EnKF) [*Evensen*, 1994]. The EnKF is one of the most frequently used assimilation methods in hydrology [*Reichle et al*., 2002]. One limitation in the EnKF application is the underlying assumption of Gaussian forecast and observation errors. In order to tackle this limitation, nonparametric filters such as particle filters have been developed.

[4] In the particle filter methodology, the posterior of interest is described by the point mass approximation allowing for the representation of any kind of distribution. In other words, the assumption of Gaussian distributions, which is held in the application of the Kalman filter, is relaxed when using particle filters. This method has been used to assimilate discharge records into conceptual rainfall-runoff models [*Moradkhani et al*., 2005; *Weerts and El Serafy*, 2006] and to assimilate water stage records into hydraulic models [*Matgen et al*., 2010; *Giustarini et al*., 2011]. This method has also been used for the assimilation of soil moisture data [*Plaza et al*., 2012], for the estimation of model parameters [*Montzka et al*., 2011], and the estimation of root-zone soil moisture conditions [*Nagarajan et al*., 2010]. All these studies share a similar implementation of the particle filter, which is known as the generic particle filter or the standard particle filter (SPF). The SPF simplifies the computation of the importance weights allowing for a straightforward implementation. However, this simplification could affect the overall performance of the particle filter, mainly when the observation error is small. In *Weerts and El Serafy* [2006], the EnKF and the SPF are intercompared, leading to the conclusion that the EnKF is more robust with respect to forecast and observation errors. Other studies using the particle filter are discussed in *Leisenring and Moradkhani* [2011], *DeChant and Moradkhani* [2012], *Leisenring and Moradkhani* [2012], and *Liu et al*. [2012].

[5] Recently, the SPF has been applied in combination with the Bayesian model averaging approach in order to update the model weight at each assimilation time step [*Parrish et al*., 2012]. In the same context of model selection, particle Markov chain Monte Carlo (MCMC) methods [*Andrieu et al*., 2010] have been used [*Rings et al*., 2012; *Vrugt et al*., 2012] in more sophisticated implementations of the particle filter. *Moradkhani et al*. [2012] reported an increase of the effectiveness of the SPF by using MCMC moves in a joint state-parameter estimation study.

[6] The main goal of this study is to conduct an exploration of two possible options that can lead to an improvement in the operation of the particle filter when state estimation is performed in rainfall-runoff models. More specifically, a resample step based on MCMC methods is included in the SPF in order to improve the spread of particles. The second alternative consists of the enhancement of the importance sampling step in the Gaussian particle filter (GPF) [*Kotecha and Djuric*, 2003a] by considering a posterior estimate from an EnKF to generate the importance density function. The characteristics of the proposed techniques are studied in a synthetic experiment where artificial discharge records are assimilated into a conceptual rainfall-runoff model. The methodologies are assessed by the assimilation of in situ observed discharge data. A comparison is carried out between the proposed techniques, the EnKF, and the SPF.