Esta investigación evaluó los impactos del cambio climático en la oferta hídrica superficial en la subcuenca media y alta del río Piura, Perú, con el modelo hidrológico Soil and Water Assessment Tool (SWAT) a mediados del siglo XXI. El modelo SWAT fue calibrado y validado para un periodo de 23 años (1986 -2008) utilizando datos de clima diarios en seis ubicaciones, y caudales mensuales en una ubicación. Para las evaluaciones a futuro, se adoptaron los datos climáticos HADGEM2-ES y CSI-RO-Mk3-6-0, de los modelos climáticos globales (MCG), Coupled Model Intercomparison Project Phase 5 (CMIP5), en los escenarios RCP4.5 y RCP8.5. Los datos sesgados futuros (2025-2054) se corrigieron utilizando los datos del clima del período de referencia, y se redujeron de escala con el generador de clima MarkSim. La temperatura y precipitación en los escenarios de cambio climático proyectan un aumento promedio de + 2,9°C y 39,3%, respectivamente, respecto del periodo observado. La evapotranspiración futura mostro una tendencia general a disminuir, con un ligero aumento en el lado noroccidental de la cuenca. En particular, la tendencia promedio de la escorrentía mensual al 2050, en los cuatro escenarios, indica para los meses entre octubre a abril un aumento de + 71,8%, aprox. 55,9 m 3 /s; con el mayor incremento en noviembre. Por otro lado, entre los meses de mayo a setiembre, se tiene una disminución de -66,1%, aprox. 12 m 3 /s, con el mayor descenso en julio.

As precipitation is a fundamental component of the global hydrological cycle that governs water resource distribution, the understanding of its temporal and spatial behavior is of great interest, and exact estimates of it are crucial in multiple lines of research. Meteorological data provide input for hydroclimatic models and predictions, which generally lack complete series. Many studies have addressed techniques to fill gaps in precipitation series at annual and monthly scales, but few have provided results at a daily scale due to the complexity of orographic characteristics and in some cases the non-linearity of precipitation. The objective of this study was to assess different methods of filling gaps in daily precipitation data using regression model (RM) and machine learning (ML) techniques. RM included linear regression (LRM) and multiple regression (MRM) algorithms, while ML included multiple regression algorithms (ML-MRM), K-nearest neighbors (ML-KNN), gradient boosting trees (ML-GBT), and random forest (ML-RF). This study covered the Malas, Omas, and Cañete River (MOC) watersheds, which are located on the Pacific Slope of central Peru, and a nineteen-year period of records (2001–2019). To assess model performance, different statistical metrics were applied. The results showed that the optimized machine learning (OML) models presented the least variability in estimation errors and the best approximation of the actual data from the study zone. In addition, this investigation shows that ML interprets and analyzes non-linear relationships between rain gauges at a daily scale and can be used as an efficient method of filling gaps in daily precipitation series.

Flash floods, produced by heavy seasonal rainfall and characterized by high speeds and destructive power, are among the most devastating natural phenomena and are capable of causing great destruction in very little time. In the absence of hydrological data, morphometric characterization can provide important information on preventive measures against flash floods. A priority categorization of hydrographic units in the Cañete River basin was carried out using morphometric analysis together with a weighted sum analysis (WSA) based on a statistical correlation matrix. The delineation of the drainage network was performed based on Digital Elevation Model (DEM) data from the Shuttle Radar Topography Mission (SRTM). The Cañete River basin was subdivided into 11 sub-basins, and 15 morphometric parameters were selected. The priority category (very high, high, and moderate) of each sub-basin was assigned according to the value of the composite factor obtained through WSA. The results of this analysis showed that 26.08% of the total area is under a very high flash flood risk (sub-basins 3, 9, and 11), 38.46% is under a high flash flood risk (sub-basins 5, 7, 8, and 10), and 35.45% is under a moderate flash flood risk. This study concludes that flash floods predominate in sub-basin 3 and that downstream areas present characteristics of river flooding (sub-basins 9 and 11).