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Journal Article

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


Fernandez Palomino,  Carlos Antonio
Potsdam Institute for Climate Impact Research;


Hattermann,  Fred Fokko
Potsdam Institute for Climate Impact Research;


Krysanova,  Valentina
Potsdam Institute for Climate Impact Research;


Lobanova,  Anastasia
Potsdam Institute for Climate Impact Research;

Vega-Jácome,  Fiorella
External Organizations;

Lavadao,  Waldo
External Organizations;

Santini,  William
External Organizations;

Aybar,  Cesar
External Organizations;

Bronstert,  Axel
External Organizations;

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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.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_26335
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.