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

Zubieta, Ricardo; Getirana, Augusto; Espinoza, Jhan Carlo; Lavado‐Casimiro, Waldo; Aragón, Luis E.

doi:10.5194/hess-21-3543-2017

Cited by 76 publications

(44 citation statements)

References 28 publications

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“…Our study found that IMERG-F V05 and 3B42V7 have acceptable hydrological capabilities in the Chindwin River basin, with IMERG-F being slightly better than 3B42V7. Similar findings were reported in the Ganjiang River basin [45], Beijiang River basin [24], upper Mekong River basin [36], Mishui basin [25], Yellow River source region of China [44], and Amazon basin of Peru and Ecuador [48]. Yuan et al [47] employed the first hydrological evaluation research in the Chindwin River basin and found that IMERG-F V03 performs comparatively worse than 3B42V7 in daily streamflow simulations.…”

Section: Evaluation Of Hydrological Performance Of the Gpm-era Sppssupporting

confidence: 66%

“…techniques. However, most relevant studies [24,25,36,45,47,48,50] focused on assessing the hydrological utility of IMERG at daily or monthly time scales, while evaluations at sub-daily temporal scales have seldom been reported [44,46,49]. Furthermore, studies on the hydrological performance of all the "Early", "Late", and "Final" runs of IMERG are rare [24,25,50].…”

Section: Introductionmentioning

confidence: 99%

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Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

et al. 2019

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Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM-and GPM-era satellite-based precipitation products (SPPs) in both near-and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) "Early", "Late", and "Final" run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash-Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.

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Section: Evaluation Of Hydrological Performance Of the Gpm-era Sppssupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

et al. 2019

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“…(). TRMM products have also been used as an input for hydrological models (e.g.Getirana et al, ; Paiva et al, ; Guimberteau et al, ), the results of which confirm the capability of this data source to reproduce the hydrological cycle in the southern part of the Peruvian Amazon (Zubieta et al, , ). However, additional studies concerning the validation of satellite‐based rainfall estimations in Amazonia are necessary, especially at the intra‐seasonal timescale, which has not yet been documented.…”

Section: Introductionmentioning

confidence: 99%

Intra‐seasonal rainfall variability in the Amazon basin related to large‐scale circulation patterns: a focus on western Amazon–Andes transition region

Paccini

Espinoza

Ronchail

et al. 2017

Intl Journal of Climatology

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International audienceThis study aims to relate the intra-seasonal rainfall variability over the Amazon basin to atmospheric circulation patterns (CPs), with particular attention to extreme rainfall events in the Amazon–Andes region. The CPs summarize the intra-seasonal variability of atmospheric circulation and are defined using daily low-level winds from the ERA-Interim (1.5° × 1.5°) reanalysis for the 1979–2014 period. Furthermore, observational data of precipitation and high-resolution TRMM 3B42 (∼25 km), 2A25 PR (∼5 km) and CHIRPS (∼5 km) data products are related to the CPs throughout the Amazon basin. Nine CPs are determined using a hybrid method that combines a neural network technique (self-organizing maps, SOM) and hierarchical ascendant classification. The CPs are characterized by a specific cycle with alternative transitions and a duration of 14 days on average. This configuration initially results in northerly winds to southerly winds towards the northern or eastern Amazon basin. The related rainfall suggests that it is driven mainly by CP dynamics. In addition, we demonstrate a good agreement amongst the four rainfall data sets: observed precipitation, TRMM 3B42, TRMM 2A25 PR and CHIRPS. Furthermore, special attention is given to the Amazon–Andes transition region. Over this region, two particular CPs (CP4 and CP5) are identified as the key contributors of maximum and minimum daily rainfall, respectively. Thus, during the dry season, 40.8% (11.4%) of the CP5 (CP4) days demonstrate rainfall of less than 1 mm day⁻¹, while during the wet season, 6.2% (14.6%) of the CP5 (CP4) days show rainfall amounts higher than the seasonal 90th percentile (10.4 mm day⁻¹). This study provides additional information concerning the intra-seasonal circulation variability in Amazonia and demonstrates the value of using remote sensing precipitation data in this region as a tool for forecast in areas lacking observable information

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“…To appropriate use of satellite precipitation data sets, it needed understanding the nature of their estimated errors. The satellite precipitation products (TMPA, CMORPH among others) estimate appropriated the rainfall over some Amazonian and Andean regions (Buarque et al, 2011;Zulkafli et al, 2014;Zubieta et al, 2015;Paccini et al, 2017;Zubieta et al, 2017b). Nonetheless, the error obtained is probably associated with the evolution of the precipitation and the accumulated errors from the sub-daily estimation provided by each component of the satellite (Turk et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…For the AB, most assessments of satellite-based precipitation estimates are usually described in the literature as follows: (a) the estimated precipitation rates are compared with in situ observations (Buarque et al, 2011;Condom et al, 2011;Scheel et al, 2011;Ward et al, 2011;Zulkafli et al, 2014). (b) The estimated rainfall is used as input to hydrological modelling experiments, and simulated streamflows are evaluated against observations (Collischonn et al, 2007;Getirana et al, 2010;Behrangi et al, 2011;Paiva et al, 2011;Guimberteau et al, 2012;Zulkafli et al, 2014;Zubieta et al, 2015;Wongchuig-Correa et al, 2017;Zubieta et al, 2017b). (c) The estimated precipitation variability is compared to in situ observations about atmospheric circulation patterns at synoptic and intraseasonal timescale (Paccini et al, 2017;Mayta et al, 2018).…”

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confidence: 99%

Assessing precipitation concentration in the Amazon basin from different satellite‐based data sets

Zubieta

Saavedra

Espinoza

et al. 2019

Intl Journal of Climatology

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Daily precipitation concentration in the Amazon basin (AB) is characterized using concentration index (CI), which is computed from HYBAM Observed Precipitation (HOP) data set, for 1980–2009 period. The ability of four satellite precipitation data sets (TMPA V7, TMPA RT, CMORPH and PERSIANN) to estimate CI is evaluated for 2001–2009 period. Our findings provide new information about the spatial irregularity of daily rainfall distribution over the AB. In addition, the spatial distribution of CI values is not completely explained by rainfall seasonality, which highlights the influence of different weather systems over the AB. The results of rainfall concentration indicate that the distribution of daily rainfall is more regular over northwest (northern Peru) and central Andes. Conversely, Roraima region and a large area of Bolivian Amazon register the highest irregularity in the daily rainfall. Bolivian Amazon also represents regions where the large percentage of total rainfall arises from extreme events (>90th percentile). Heavy rainfall episodes over Roraima region are induced by humidity influx come from Caribbean region, while heavy rainfall events over Bolivian Amazon and Andes region are induced by the northwards propagation of cold and dry air along both sides of Andes Mountains, but only propagate in all tropospheric levels for the Andes. The results also show that PERSIANN and TMPA7 data sets better estimates the daily precipitation concentration for whole AB, but with a relative error 8%. CI estimated from satellites does not agree well with HOP over the Andes and northern Peruvian Amazon. On the other hand, the temporal variability of CI can partly be detected using CMORPH and TMPAV7 data sets over the Peruvian Andes, and central and southern Brazil. Errors in CI estimating might be related to inaccurate estimation of daily rainfall. Finally, we conclude that satellite‐based precipitation data sets are useful for analysing rainfall concentration in some regions of AB.

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

Cited by 76 publications

References 28 publications

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

Intra‐seasonal rainfall variability in the Amazon basin related to large‐scale circulation patterns: a focus on western Amazon–Andes transition region

Assessing precipitation concentration in the Amazon basin from different satellite‐based data sets

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