Merging Satellite Products and Rain-Gauge Observations to Improve Hydrological Simulation: A Review

Improving the quality of atmospheric precipitation measurements is crucial in view minimizing uncertainty weather forecasting, climate change impact assessment, water resource assessment and management, drought flood prediction. Remote sensing technology has considerably improved spatio-temporal precipitation. Despite advancement remote technology, there a need to investigate robust approach towards integrating ground-based-measured satellite-product better understand hydrologic process any basin. Several data-merging methods have been proposed; however, application merged products for hydrological simulation rarely investigated. Thus, this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, limitations eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized (4) Root-Mean-Square Error (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, (8) Kriging Method, advances respect Finally, future research directions improving data approaches recommended.