Hydrol. Earth Syst. Sci., 21, 3543–3555, 2017 https://doi.org/10.5194/hess-21-3543-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset Ricardo Zubieta1,2, Augusto Getirana3,4, Jhan Carlo Espinoza1,2, Waldo Lavado-Casimiro5,2, and Luis Aragon2 1Subdirección de Ciencias de la Atmósfera e Hidrósfera (SCAH), Instituto Geofísico del Perú (IGP), Lima, Peru 2Programa de Doctorado en Recursos Hídricos, Universidad Nacional Agraria La Molina, Peru 3Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA 4Earth System Science Interdisciplinary Center, College Park, MD, USA 5Servicio Nacional de Meteorología e Hidrología (SENAMHI), Lima, Peru Correspondence to: Ricardo Zubieta ([email protected]) Received: 12 December 2016 – Discussion started: 14 December 2016 Revised: 7 March 2017 – Accepted: 1 June 2017 – Published: 14 July 2017 Abstract. In the last two decades, rainfall estimates provided adequate rainfall estimates in northern Peru and the Ecuado- by the Tropical Rainfall Measurement Mission (TRMM) rian Amazon. 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 1 Introduction in hydrological applications, however, has not yet been eval- uated over the Andean and Amazonian regions. This study Satellite-based precipitation data have been widely used uses GPM data provided by the Integrated Multi-satellite for hydrometeorological applications, such as hydrological Retrievals (IMERG) (product/final run) as input to a dis- modeling, especially in data-sparse regions like the Ama- tributed hydrological model for the Amazon Basin of Peru zon River basin (Collischonn et al., 2008; Getirana et al., and Ecuador for a 16-month period (from March 2014 to 2011; Paiva et al., 2013; Zulkafli et al., 2014; Zubieta et al., June 2015) when all datasets are available. TRMM products 2015). Rainfall is extremely variable in both space and time, (TMPA V7 and TMPA RT datasets) and a gridded precipi- particularly over regions characterized by topographic con- tation dataset processed from observed rainfall are used for trast, such as the western Amazon Basin (Espinoza et al., comparison. The results indicate that precipitation data de- 2009; Lavado et al., 2012). In this region, the Andes Moun- rived from GPM-IMERG correspond more closely to TMPA tains contribute to high spatiotemporal variability of rainfall V7 than TMPA RT datasets, but both GPM-IMERG and (Laraque et al., 2007; Espinoza et al., 2015). To improve ap- TMPA V7 precipitation data tend to overestimate, compared proximation and reduce uncertainty, detailed monitoring is to observed rainfall (by 11.1 and 15.7 %, respectively). In needed using a high-density rain gauge network. Only a low- general, GPM-IMERG, TMPA V7 and TMPA RT corre- density rain gauge network is available in the Amazon Basin late with observed rainfall, with a similar number of rain (AB), however, which limits understanding of hydrological events correctly detected ( ∼ 20 %). Statistical analysis of processes and hydrological modeling over the region (Geti- modeled streamflows indicates that GPM-IMERG is as use- rana et al., 2011; Paiva et al., 2013). Satellite-based datasets, ful as TMPA V7 or TMPA RT datasets in southern regions uniformly distributed in both space and time, offer an alter- (Ucayali Basin). GPM-IMERG, TMPA V7 and TMPA RT native for modeling hydrological events. Their usefulness in do not properly simulate streamflows in northern regions Andean–Amazon basins and their applicability as input to (Marañón and Napo basins), probably because of the lack of hydrological models have been evaluated recently by com- paring modeled and observed datasets. Results indicate that these datasets could be used for operational applications in Published by Copernicus Publications on behalf of the European Geosciences Union. 3544 R. Zubieta et al.: Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin some Andean–Amazon regions (Zulkafli et al., 2014; Zu- IPH hydrological model (Collischonn et al., 2007), which bieta et al., 2015). However, hydrological modeling using was recently adapted to the ABPE (Zubieta et al., 2015). satellite-based precipitation data does not yield successful re- The ABPE extends from the tropical Andes to the Peru- sults in equatorial regions. This is mainly because of inade- vian Amazon, with elevations ranging up to 6300 m above quate satellite estimates, because streamflows resulting from sea level, a drainage area of 878 300 km2 and a mean dis- hydrological modeling using observed rainfall show accept- charge of ∼ 35 500 m3 s−1 at the Tabatinga station (Lavado able performance in the Napo River basin in the equatorial et al., 2012). The ABPE is located in the northwestern AB region (Zubieta et al., 2015). (Fig. 1a), and its area corresponds to 14 % of the AB. It con- Hydrological modeling and forecasting are still poorly de- sists mainly of basins such as the Ucayali Basin (southern veloped in the Andean and Amazonian regions. It is impor- ABPE), Marañón Basin (west of the ABPE) and Napo Basin tant to improve these tools, especially because of an intensi- (northern ABPE) (Fig. 1b). fication of extreme hydrological events in the Amazon Basin (Gloor et al., 2013), such as intense droughts in 2005 and 2010 (Marengo et al., 2008, 2011; Espinoza et al., 2011) and 2 Datasets used severe floods in 2009, 2012 and 2014 (Espinoza et al., 2012, GPM is an international US–Japanese Earth science mission 2013, 2014). Moreover, a high percentage of total annual pre- involving NASA and JAXA, respectively. The GPM mis- cipitation can fall in just a few days, causing soil erosion and sion improved and expanded on TRMM. GPM and TRMM landslides (Zubieta et al., 2017) provide precipitation data derived from different passive mi- In the last two decades, advances in satellite technol- crowave (PMW) sources used in IMERG and TMPA, re- ogy have improved rainfall estimation in much of the spectively (Huffman et al., 2015), including the Sounder for world (Huffman et al., 2007). The Tropical Rainfall Mea- Atmospheric Profiling of Humidity in the Intertropics by suring Mission (TRMM) Multi-satellite Precipitation Analy- Radiometry (SAPHIR), Advanced Technology Microwave sis (TMPA) precipitation dataset (Huffman et al., 2007) has Sounder (ATMS), Atmospheric Infrared Sounder (AIRS), been important for research and for many hydrological ap- Cross-Track Infrared Sounder (CRIS), and TRMM Com- plications in Amazon regions, and there is consensus among bined Instrument (TCI) algorithms (2B31). They also in- studies using TMPA in Amazon regions (Collischonn et al., clude TRMM Microwave Image (TMI, data ended on 8 2008; Getirana et al., 2011; Paiva et al., 2013; Zulkafli et April 2015), GPM Microwave Imager (GMI), Advanced Mi- al., 2014; Zubieta et al., 2015). The TRMM mission ended crowave Scanning Radiometer for Earth Observing Systems on 8 April 2015, however, after the spacecraft depleted its (AMSR-E), Special Sensor Microwave Imager/Sounder (SS- fuel reserves (https://pmm.nasa.gov/trmm/mission-end). De- MIS), Microwave Humidity Sounder (MHS), Special Sensor spite TRMM’s demise, this is not a substantive issue for some Microwave Imager (SSM/I), Advanced Microwave Sound- products, such as TMPA and TMPA-RT, which are expected ing Unit (AMSU), Operational Vertical Sounder (TOVS) and to run in parallel with the new Global Precipitation Measure- microwave-adjusted merged geo-infrared (IR). The precipi- ment (GPM) satellite until mid-2017 (Huffman et al., 2015). tation datasets used in this study are as follows: The GPM mission (Schwaller and Morris, 2011), launched in February 2014, comprises an international constellation a. GPM (product IMERG-V03D) data at several levels of satellites that provide rainfall estimations with signifi- of processing have been provided since March 2014 cant improvements in spatiotemporal resolution, compared (GPM-IMERG data are available at http://pmm.nasa. to TMPA products. This is true of GPM products such as In- gov/GPM, NASA, 2016). The input precipitation esti- tegrated Multi-satellite Retrievals (IMERG) estimations. Re- mates are computed using raw satellite measurements, cent studies highlight that the GPM-IMERG estimations can such as those from passive microwave sensors (TMI, adequately substitute for TMPA estimations both hydrolog- AMSR-E, SSM/I, SSMIS, AMSU, MHS, SAPHIR, ically and statistically, despite limited data availability (Liu, GMI, ATMS, TOVS, CRIS and AIRS), inter-calibrated 2016; Tang et al., 2016). to the GPM Combined Instrument (GCI, using GMI and The aim of this paper is to evaluate the use of rainfall es- Dual-frequency Precipitation Radar, DPR) and adjusted timates from GPM-IMERG for obtaining streamflows over with monthly surface precipitation gauge analysis data the Amazon Basin of Peru and Ecuador (ABPE) during a 16- (where available). All these datasets are used to obtain month period (from March 2014 to June 2015) for which all the best estimate of global precipitation maps. The tem- datasets are available. It provides a comparative analysis of poral resolution of IMERG-V03D is half-hourly, and it the GPM-IMERG, TMPA RT and TMPA V7 datasets and has a 0.1◦ by 0.1◦ spatial resolution. Unlike other satel- a ground-based precipitation dataset (PLU). PLU was de- lites, such as TRMM, GPM-IMERG can detect both veloped by spatial interpolation using the Peruvian National light and heavy rain and snowfall. Meteorology and Hydrology Service (SENAMHI) network. Each precipitation dataset was used as input for the MGB- b. TMPA 3B42 version 7 is obtained from the preprocess- ing of data provided by different satellite-based sensors Hydrol. Earth Syst. Sci., 21, 3543–3555, 2017 www.hydrol-earth-syst-sci.net/21/3543/2017/ R. Zubieta et al.: Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin 3545 Figure 1.