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  A Novel High-Resolution Gridded Precipitation Dataset for Peruvian and Ecuadorian Watersheds: Development and Hydrological Evaluation

Fernandez Palomino, C. A., Hattermann, F. F., Krysanova, V., Lobanova, A., Vega-Jácome, F., Lavadao, W., Santini, W., Aybar, C., Bronstert, A. (2022): A Novel High-Resolution Gridded Precipitation Dataset for Peruvian and Ecuadorian Watersheds: Development and Hydrological Evaluation. - Journal of Hydrometeorology, 23, 3, 309-336.
https://doi.org/10.1175/JHM-D-20-0285.1

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Fernandez Palomino, Carlos Antonio1, Autor              
Hattermann, Fred Fokko1, Autor              
Krysanova, Valentina1, Autor              
Lobanova, Anastasia1, Autor              
Vega-Jácome, Fiorella2, Autor
Lavadao, Waldo2, Autor
Santini, William2, Autor
Aybar, Cesar2, Autor
Bronstert, Axel2, Autor
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (for ‘Rain for Peru and Ecuador’), at 0.1° spatial resolution for the period 1981-2015 covering Peru and Ecuador. It is based on the application of a) the random forest method to merge multi-source precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and b) observed and modeled streamflow data to firstly detect biases and secondly further adjust gridded precipitation by inversely applying the simulated results of the eco-hydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Paċific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low-, high- and peak-flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.

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Sprache(n): eng - Englisch
 Datum: 2021-12-012021-12-132022-03
 Publikationsstatus: Final veröffentlicht
 Seiten: 28
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: MDB-ID: No data to archive
PIKDOMAIN: RD2 - Climate Resilience
Organisational keyword: RD2 - Climate Resilience
Working Group: Hydroclimatic Risks
Research topic keyword: Atmosphere
Research topic keyword: Extremes
Regional keyword: South America
Model / method: Machine Learning
OATYPE: Green Open Access
DOI: 10.1175/JHM-D-20-0285.1
 Art des Abschluß: -

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Titel: Journal of Hydrometeorology
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 23 (3) Artikelnummer: - Start- / Endseite: 309 - 336 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/131112
Publisher: American Meteorological Society (AMS)