PISCO_HyM_GR2M: A model of monthly water balance in Peru (1981–2020)

• Autores: Llauca, Harold, Lavado Casimiro, Waldo Sven, Montesinos, Cristian, Santini, William, Rau Lavado, Pedro C.
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Universidad de Ingeniería y tecnología
• Repositorio: UTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utec.edu.pe:20.500.12815/502
• Enlace del recurso: https://hdl.handle.net/20.500.12815/502; https://doi.org/10.3390/w13081048
• Nivel de acceso: acceso abierto
• Materia: Efficiency; Runoff; Discharge rates; Fourier amplitudes; Hydrometric stations; Monthly water balance model; Spatial densities; Surface water balances; Precipitation (meteorology); Peru; https://purl.org/pe-repo/ocde/ford#1.05.01

Quantification of the surface water offer is crucial for its management. In Peru, the low spatial density of hydrometric stations makes this task challenging. This work aims to evaluate the hydrological performance of a monthly water balance model in Peru using precipitation and evapotranspiration data from the high-resolution meteorological PISCO dataset, which has been developed by the National Service of Meteorology and Hydrology of Peru (SENAMHI). A regional-ization approach based on Fourier Amplitude Sensitivity Testing (FAST) of the rainfall-runoff (RR) and runoff variability (RV) indices defined 14 calibration regions nationwide. Next, the GR2M model was used at a semi-distributed scale in 3594 sub-basins and river streams to simulate monthly discharges from January 1981 to March 2020. Model performance was evaluated using the Kling–Gupta efficiency (KGE), square root transferred Nash–Sutcliffe efficiency (NSE<inf>sqrt</inf> ), and water balance error (WBE) metrics. The results show a very well representation of monthly discharges for a large portion of Peruvian sub-basins (KGE ≥ 0.75, NSE<inf>sqrt</inf> ≥ 0.65, and −0.29 < WBE < 0.23). Finally, this study introduces a product of continuous monthly discharge rates in Peru, named PISCO_HyM_GR2M, to understand surface water balance in data-scarce sub-basins.