This article proposes a hybrid reverberation model by integrating two conventional models, namely, the multichannel linear prediction (MCLP) model and the spatial coherence model. The late reverberation is divided into two components. One component is modeled using an MCLP model, and the other is modeled using the spatial coherence model. In contrast with the conventional models, the proposed hybrid model increases modeling capacity, especially in the case of long reverberation time. In order to optimally estimate model parameters, joint speech dereverberation and separation is taken into account. The hybrid reverberation model is then used in conjunction with the multichannel nonnegative matrix factorization (MNMF). The method called Hybrid-FastMNMF is proposed by treating the reverberation component modeled by the spatial coherence model as a noise source and estimating its parameters similarly to speech sources. Furthermore, prior knowledge of the spatial coherence matrix is employed to whiten the observations, resulting in another method called Hybrid-FastMNMF-W. Experimental findings demonstrate the proposed methods' superior performance in terms of joint speech dereverberation and separation, and they further justify the efficiency of the proposed hybrid reverberation model.