The review article "Review of Recent Developments in Hydrologic Forecast Merging Techniques" by Md Rasel Sheikh and Paulin Coulibaly provides a comprehensive overview of the advancements in hydrologic forecast merging (HFM) techniques since the early 1990s. The authors highlight the importance of accurate forecasting in sustainable water resource management, particularly in predicting extreme flow phases. They discuss the inherent uncertainties in hydrologic forecasting and the need for combining multiple models or ensembles to enhance forecast accuracy and reduce uncertainty. The review covers various HFM techniques, including simple averaging methods (SAM), weighted averaging methods (WAM), Granger Ramanathan methods (GRA, GRB, GRC), regression-based methods (MLR, PCR, PLSR), Bayesian methods (BMA), neural network methods (NNM), fuzzy theory-based methods, genetic algorithm methods (GAM), and quantile methods. Each technique is evaluated based on its performance, advantages, and limitations. Key findings indicate that while HFM techniques generally outperform individual models in terms of forecasting efficiency, their performance varies across different applications. Bayesian model averaging (BMA) is the most popular method due to its ability to reduce uncertainty and provide accurate and reliable forecasts. Regression techniques are also robust and efficient, performing well across various model-merging applications. The article also discusses the practical applications of HFM techniques in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. It highlights the importance of selecting appropriate merging techniques based on the specific requirements, data characteristics, and trade-offs between accuracy, interpretability, and computational complexity. Overall, the review aims to bridge gaps in existing literature by covering a broader spectrum of HFM techniques and providing insights into emerging trends and future research directions in hydrologic forecast merging.The review article "Review of Recent Developments in Hydrologic Forecast Merging Techniques" by Md Rasel Sheikh and Paulin Coulibaly provides a comprehensive overview of the advancements in hydrologic forecast merging (HFM) techniques since the early 1990s. The authors highlight the importance of accurate forecasting in sustainable water resource management, particularly in predicting extreme flow phases. They discuss the inherent uncertainties in hydrologic forecasting and the need for combining multiple models or ensembles to enhance forecast accuracy and reduce uncertainty. The review covers various HFM techniques, including simple averaging methods (SAM), weighted averaging methods (WAM), Granger Ramanathan methods (GRA, GRB, GRC), regression-based methods (MLR, PCR, PLSR), Bayesian methods (BMA), neural network methods (NNM), fuzzy theory-based methods, genetic algorithm methods (GAM), and quantile methods. Each technique is evaluated based on its performance, advantages, and limitations. Key findings indicate that while HFM techniques generally outperform individual models in terms of forecasting efficiency, their performance varies across different applications. Bayesian model averaging (BMA) is the most popular method due to its ability to reduce uncertainty and provide accurate and reliable forecasts. Regression techniques are also robust and efficient, performing well across various model-merging applications. The article also discusses the practical applications of HFM techniques in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. It highlights the importance of selecting appropriate merging techniques based on the specific requirements, data characteristics, and trade-offs between accuracy, interpretability, and computational complexity. Overall, the review aims to bridge gaps in existing literature by covering a broader spectrum of HFM techniques and providing insights into emerging trends and future research directions in hydrologic forecast merging.