Hydrological modeling of the Peruvian-Ecuadorian Amazon basin using GPM-IMERG satellite-based precipitation dataset

• Published in: Hydrology and earth system sciences Vol. 21; no. 7; pp. 3543 - 3555
• Main Authors: Zubieta, Ricardo, Getirana, Augusto, Espinoza, Jhan Carlo, Waldo, Lavado-Casimiro, Aragon, Luis
• Format: Journal Article
• Language: English
• Published: Germany Copernicus GmbH 14.07.2017; Copernicus Publications
• Subjects: Atmospheric precipitations; Basins; Basins (Geology); Computer simulation; Data; Datasets; Environmental aspects; Estimates; Global precipitation; Humidity; Hydrologic data; Hydrologic models; Hydrologic studies; Hydrology; Mathematical models; Measurement; Observations; Precipitation; Precipitation (Meteorology); Precipitation data; Rain; Rainfall; Rainfall measurement; Regions; River basins; Runoff; Satellites; Sensors; Statistical analysis; Statistical methods; Stream flow; TRMM satellite; Tropical climate; Tropical rainfall; Tropical Rainfall Measuring Mission (TRMM); Amazon Basin; Ecuador; Peru; Precipitation datasets; TRMM; Hydrological modeling; GPM; Amazon/Andes
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-21-3543-2017

In the last two decades, rainfall estimates provided by the Tropical Rainfall Measurement Mission (TRMM) have proven applicable in hydrological studies. The Global Precipitation Measurement (GPM) mission, which provides the new generation of rainfall estimates, is now considered a global successor to TRMM. The usefulness of GPM data in hydrological applications, however, has not yet been evaluated over the Andean and Amazonian regions. This study uses GPM data provided by the Integrated Multi-satellite Retrievals (IMERG) (product/final run) as input to a distributed hydrological model for the Amazon Basin of Peru and Ecuador for a 16-month period (from March 2014 to June 2015) when all datasets are available. TRMM products (TMPA V7, TMPA RT datasets) and a gridded precipitation dataset processed from observed rainfall are used for comparison. The results indicate that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, compared to observed rainfall (by 11.1% and 15.7 %, respectively). In general, GPM-IMERG, TMPA V7 and TMPA RT correlate with observed rainfall, with a similar number of rain events correctly detected (~20%). Statistical analysis of modeled streamflows indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets in southern regions (Ucayali basin). GPM-IMERG, TMPA V7 and TMPA RT do not properly simulate streamflows in northern regions (Marañón and Napo basins), probably because of the lack of adequate rainfall estimates in northern Peru and the Ecuadorian Amazon.