The accurate estimation of Muskingum model parameter is essential to give flood routing for flood control in water resources management. The Muskingum model continues to be a popular method for flood routing, and its parameter estimation is a global optimization problem with the main objective to find a set of optimal model parameter values that attains a best fit between observed and computed flow. Although some techniques have been employed to estimate the parameters for Muskingum model, an efficient method for parameter estimation in Muskingum model is still required to improve the computational precision. Therefore, in this paper, the differential evolution (DE) algorithm is studied for estimation of Muskingum model parameter. A case study with actual data from previous literature, the experimental results showed an excellent performance in its optimization result and performance analysis and demonstrates that DE is an alternative technique to estimate the parameters of the Muskingum model.

Chan‐hom and Linfa were binary typhoons that occurred in the western North Pacific in 2015. In this study, the impacts of FY‐3B satellite Microwave Humidity Sounder (MWHS) radiance observations on the analyses and forecasts of Linfa and Chan‐hom are assessed. The regional Weather Research and Forecasting model and its data assimilation (DA) systems, using three‐dimensional variational (3DVAR) and Hybrid (ensemble/3DVAR) methods are used. Assimilation of the FY‐3B MWHS data using the 3DVAR method slightly improves the descriptive wind and temperature fields. Positive impacts on the specific humidity forecasts, for levels higher than 850 hPa, are also obvious. 3DVAR adjusts the typhoons' initial positions and their dynamic structures favorably, yielding better tracks, intensities, and precipitation forecasts, compared to the experiment run without MWHS data (control). With the Hybrid method, the water vapor information from the MWHS data better improve the analyses through multivariable correlations with the flow‐dependent background error. The Hybrid method further improves the track, intensities, and precipitation forecasts. For Typhoon Linfa, with the coexistence of Typhoon Chan‐hom, the Hybrid method provides a more descriptive background error covariance matrix, than using 3DVAR. Experiments on multiple binary typhoon cases are also provided to further validate the robustness of the results on the FY‐3B satellite MWHS radiance data assimilation.