The use of accurate streamflow estimates is widely recognized in the hydrological field. However, due to the model’s structural error, they often yield suboptimal streamflow estimates. Past studies have shown that soil moisture assimilation improves the performance of the hydrological model which often results in enhanced model estimates. Due to this reason, it is widely studied in the hydrological field.  However, the efficiency of the assimilation largely relies on the correct placement of the observation into the model. Ingesting futile observations often results in the degradation of model performance. On the contrary, performing assimilation only at those time steps when the assimilating variable is sensitive to the model output may yield desirable output. Further, it will avoid the assimilation of spurious observations. In this view, this study proposes a new approach where sensitivity-based sequential assimilation is performed on a conceptual Two Parameter Model (TPM). To demonstrate this approach, ASCAT soil moisture observations are assimilated into TPM using Ensemble Kalman Filter (EnKF) sequential approach. At first, the temporal evolution of the soil moisture sensitivity with respect to streamflow is established. Later, at those time steps when the soil moisture is sensitive, EnKF assimilation is performed. For this purpose, a moderately sized catchment in the Krishna basin, India is selected as the study area. Model calibration and validation are performed between 2000 to 2006 and 2007 to 2011 respectively. Model run without assimilation is considered as open-loop simulation. Streamflow simulation after assimilation showed a significant improvement when compared against the open-loop simulation. KGE value increased from 0.70 to 0.79 and PBIAS value reduced from 18.31 to 1.80. The highlighting factor is that only 39% of the total observations were used during the assimilation process. The initial results are encouraging and looks that the proposed approach shall be highly useful at those locations where data availability for assimilation purpose is a serious concern.  

The need for and the use of different data assimilation techniques to improve the quality of streamflow forecast is now well established. In this paper, the goal is to demonstrate the power of a new class of methods known as the Forward Sensitivity Method (FSM) which is based on the temporal evolution of model sensitivities with respect to the control variables consisting of initial conditions and parameters. FSM operates in two phases: The first phase provides a simple algorithm for placing observations at or near where the square of forward sensitivities attains their maximum values. Using only this selected subset of observations in a weighted least squares method, the second phase then provides an estimate of the unknown elements of the control variables. In this paper, FSM based assimilation is applied to a simple class of two parameter model in a medium-sized agriculture dominant watershed lying in the Krishna River Basin, India. Four assimilation scenarios were tested to determine the effect of assimilating only sensitive observations as well as the impact of temporally evolving initial condition sensitivity. Sensitivity results showed that observations during the monsoon time alone are enough for assimilation purposes, which has helped in reducing the computational time greatly. Assimilation and forecast results also indicated that the scenarios which assimilated only sensitive observations are better in estimating daily streamflow. From the obtained results, it is concluded that FSM based assimilation has significant potential to improve the streamflow simulations, especially in places where data availability remains a major challenge.

The accuracy of streamflow forecasts is important for efficient monitoring and mitigation of flood events. Unfortunately, the uncertainty in the model control variable which includes model parameters, initial and boundary conditions, propagates through the model, resulting in the degradation of streamflow forecast. Various studies in the past have shown the potential of soil moisture assimilation in hydrological models resulting in the improved forecast. Further, the efficiency of assimilation is based on the number and the distribution of observations used. This study proposes a new approach called Forward sensitivity method (FSM), which operates in two phases. By running the model and forecast sensitivity dynamics forward in time, the first phase places the observations at or near where the square of the forecast sensitivity with respect to the control takes maximum values. Then using only this subset of observations, the second phase estimates the unknown elements of the control by solving a resulting weighted least squares problem. The power of this approach is demonstrated by assimilating ASCAT soil moisture observations into a conceptual Two Parameter Model in a medium sized watershed lying in the Krishna River Basin, India. The model run extends for four monsoon years from June 2007 to June 2011 and two assimilation scenarios were tested. The first scenario uses all the observations, whereas, the second uses only sensitive observations during assimilation and the results were then compared against open loop simulation (model run without assimilation). Sensitivity results indicate that observations during monsoon time alone are sufficient for assimilation purpose, which accounts for only 37.42 percent of total observations. Also, the estimation and forecast results show improved streamflow performance when using only sensitive observations. From the results, it is concluded that FSM based assimilation can help in reducing the computation time greatly. Further, this study will be critically helpful in the places where data availability remains a major problem.