Posterior sampling methods are increasingly being used to describe parameter and model predictive uncertainty in hydrologic modelling. This paper proposes an alternative to random walk chains (such as DREAM-zs). We propose a sampler based on independence chains with an embedded feature of standardized importance weights based on Kernel density estimates. A Markov Chain Monte Carlo sampling algorithm is proposed with Metropolis-Hastings (M-H) updates using an independence sampler. The independence sampler ensures that candidate observations are drawn independently of the current state of a chain, thereby ensuring efficient exploration of the target distribution. The M-H acceptance-rejection criterion is used to sample across 3 chains, which ensures that the chains are well mixed. Kernel density estimation on last 600 samples in a chain is used to calculate standardized importance weights within the independence sampler to ensure fast convergence of sampled points to the target distribution. Its performance is contrasted with a state of the art algorithm, Differential Evolution Adaptive Metropolis (DREAM-zs), based on a toy 10 dimensional bi-modal Gaussian mixture distribution and HYMOD model based synthetic and real world case studies. The comparison of KISMCS and DREAM-zs is done based on their convergence to ‘true’ posterior parameter distributions in case of synthetics case studies and their convergence to a stationary distribution in case of real world hydrological modeling case studies.Water ManagementCivil Engineering and Geoscience