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, Luís

doi:10.5194/hess-2016-656

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…TRMM products have also been used as an input for hydrological models (e.g. Getirana et al, 2011 ;Paiva et al, 2011 ;Guimberteau et al, 2012), 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, 2015(Zubieta et al, , 2017. 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: 83%

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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Section: Introductionmentioning

confidence: 83%

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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“…With the advent of RS data sets and higher computational capacity, several models have been developed, improving the physical representation of hydrological processes, increasing the model spatial resolution, and moving from monthly to daily estimates (Beighley et al., 2009; Coe et al., 2008; Luo et al., 2017; Miguez‐Macho & Fan, 2012; Paiva, Buarque, et al., 2013). These models usually adopt the following RS‐based input data: precipitation with the TMPA product (Collischonn et al., 2008; Getirana et al., 2012; Zubieta et al., 2015), and more recently GPM‐IMERG (Zubieta et al., 2017) and MSWEP (Beck, Van Dijk, et al., 2017); landscape properties including terrain lengths and slopes, based on DEMs (most studies using SRTM DEM); and land use and vegetation maps (global maps as FAO, or regional ones as the Brazilian RadamBrasil soil maps). The most common validation data sets from RS are water level from satellite altimetry (Section 4.1), surface water extent (Section 4.2), and total water storage (Section 5).…”

Section: Integrative and Interdisciplinary Studiesmentioning

confidence: 99%

Amazon Hydrology From Space: Scientific Advances and Future Challenges

Fassoni‐Andrade

Fleischmann

Papa

et al. 2021

Reviews of Geophysics

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As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite‐based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin‐scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes‐Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology‐oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space‐time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure.

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“…Dalam mengestimasi curah hujan, satelit cuaca geostasioner memiliki kelebihan antara lain mampu menyediakan data estimasi curah hujan dalam skala regional (Derin dan Yilmaz, 2014;Ringard et al, 2015Ringard et al, , 2017 dan memiliki jangkauan wilayah sangat luas dengan resolusi ruang hingga 5 km, serta memiliki resolusi waktu yang sangat tinggi hingga 10 menit. Kelebihan tersebut juga dapat dimanfaatkan sebagai input model hidrologi yang bermasalah dengan ketidaktersediaan data pengamatan hujan (Collischonn et al, 2008;Zubieta et al, 2015Zubieta et al, , 2017. Selain itu, data estimasi hujan satelit dapat menyediakan gambaran distribusi hujan di wilayah yang tidak terjangkau pengamatan baik oleh radar maupun oleh penakar hujan seperti di tengah laut, di tengah hutan, atau di puncak gunung.…”

Section: Pendahuluanunclassified

Perbaikan Estimasi Curah Hujan Berbasis Data Satelit Dengan Memperhitungkan Faktor Pertumbuhan Awan

Mulsandi

Mamenun

Fitriano

et al. 2020

JSTMC

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Intisari Permasalahan utama dalam mengestimasi curah hujan menggunakan data satelit adalah kegagalan membedakan antara awan cumuliform dengan awan stratiform dimana dapat menyebabkan nilai estimasi hujan under/overestimate. Dalam penelitian ini teknik estimasi curah hujan berbasis satelit yang digunakan adalah modifikasi Convective Stratiform Technique (CSTm). CSTm memiliki kelemahan ketika harus menghitung sistem awan konveksi dengan inti konveksi yang sangat luas karena akan memiliki nilai slope parameter kecil, sehingga menghasilkan estimasi curah hujan yang underestimate. Dengan melibatkan perhitungan faktor pertumbuhan awan di algoritma CSTm permasalahan tersebut dapat diatasi. Penelitian ini menerapkan algoritma CSTm dan faktor pertumbuhan awan (CSTm+Growth Factor) untuk mengestimasi kejadian hujan lebat yang menyebabkan banjir di Jakarta pada tanggal 24 Januari 2016 yang digunakan juga sebagai studi kasus di proyek pengembangan model NWP di BMKG. Hasil penelitian menunjukan bahwa perlibatan faktor pertumbuhan awan sangat efektif memperbaiki kelemahan teknik CSTm, diperlihatkan dengan peningkatan nilai korelasi dari 0.6 menjadi 0.8 untuk wilayah Kemayoran dan -0.1 menjadi 0.83 untuk wilayah Cengkareng. Secara umum gabungan teknik CSTm dan faktor pertumbuhan awan dapat memperbaiki estimasi nilai intensitas dan fase hujan. Abstract The main problem in estimating rainfall using satellite data is a failure to distinguish between cumuliform and stratiform clouds, which can cause under/overestimate of rains. In this research, the Modified Convective Stratiform Technique (CSTm) has been used to estimate rainfall based on satellite data. The weakness of the CSTm technique is defined when calculating the convective cloud system within a widely convective point. Cloud convective will have a low value of parameter slope and produce an underestimate of rainfall. This issue can be resolved by calculating the cloud growth factor on CSTm. CSTm algorithm and cloud growth factor (CSTm+Growth Factor) has been applied to this research to estimate heavy rainfall for floods event in Jakarta area on January 24th, 2016. The result showed that the cloud growth factor is very effective in improving the weakness of rainfall estimation using the CSTm technique. Correlation between estimation and observation rainfall has increased from 0,6 to 0,8 on Kemayoran and from -0,1 to 0,83 on Cengkareng. The coupled method of CSTm and cloud growth factor significantly improve in estimating phase and intensity of rainfall.

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

Cited by 10 publications

References 20 publications

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

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

Amazon Hydrology From Space: Scientific Advances and Future Challenges

Perbaikan Estimasi Curah Hujan Berbasis Data Satelit Dengan Memperhitungkan Faktor Pertumbuhan Awan

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