{"title":"Merging Satellite Products and Rain-Gauge Observations to Improve Hydrological Simulation: A Review","authors":"Haileyesus Belay, A. Melesse, Getachew Tegegne","doi":"10.3390/earth3040072","DOIUrl":null,"url":null,"abstract":"Improving the quality of atmospheric precipitation measurements is crucial in the view of minimizing the uncertainty in weather forecasting, climate change impact assessment, water resource assessment and management, and drought and flood prediction. Remote sensing technology has considerably improved the spatio-temporal assessment of precipitation. Despite the advancement in the remote sensing technology, there is a need to investigate the robust approach towards integrating ground-based-measured and satellite-product precipitation to better understand the hydrologic process of any basin. Several data-merging methods have been proposed; however, the application of merged precipitation products for hydrological simulation has rarely been investigated. Thus, in this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, the limitations of eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized Weighting, (4) Inverse Root-Mean-Square Error Weighting, (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, and (8) the Kriging Method, and their advances with respect to hydrological simulation are discussed. Finally, future research directions towards improving data merging approaches are recommended.","PeriodicalId":51020,"journal":{"name":"Earth Interactions","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Interactions","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/earth3040072","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
Abstract
Improving the quality of atmospheric precipitation measurements is crucial in the view of minimizing the uncertainty in weather forecasting, climate change impact assessment, water resource assessment and management, and drought and flood prediction. Remote sensing technology has considerably improved the spatio-temporal assessment of precipitation. Despite the advancement in the remote sensing technology, there is a need to investigate the robust approach towards integrating ground-based-measured and satellite-product precipitation to better understand the hydrologic process of any basin. Several data-merging methods have been proposed; however, the application of merged precipitation products for hydrological simulation has rarely been investigated. Thus, in this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, the limitations of eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized Weighting, (4) Inverse Root-Mean-Square Error Weighting, (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, and (8) the Kriging Method, and their advances with respect to hydrological simulation are discussed. Finally, future research directions towards improving data merging approaches are recommended.
期刊介绍:
Publishes research on the interactions among the atmosphere, hydrosphere, biosphere, cryosphere, and lithosphere, including, but not limited to, research on human impacts, such as land cover change, irrigation, dams/reservoirs, urbanization, pollution, and landslides. Earth Interactions is a joint publication of the American Meteorological Society, American Geophysical Union, and American Association of Geographers.