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Author: search.filters.author.K. TakahashiLanguage: search.filters.language.English

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    A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices
    (Springer Science and Business Media Deutschland GmbH, 2024-05-01)
    The northwestern Peruvian Amazon (NWPA) basin (78.4–75.8° W, 7.9–5.4° S) is an important region for coffee and rice production in Peru. Currently, no prediction models are available for estimating rainfall in advance during the wet season (January–February–March, JFM). Hence, we developed multiple linear regression (MLR) models using predictors derived from sea surface temperature (SST) indices of the Pacific, Atlantic, and Indian Oceans, including central El Niño (C), eastern El Niño (E), tropical South Atlantic (tSATL), tropical North Atlantic (tNATL), extratropical North Atlantic (eNATL), and Indian Ocean basin-wide with E and C removed (IOBW*) indices. Additionally, we utilized large-scale convection indices, namely, the eastern Pacific intertropical convergence zone (ITCZe) and South American Monsoon System (SAMSi) indices, for the 1981–2018 period. Rainfall in the lowland NWPA exhibits a bimodal annual cycle, whereas rainfall in the highland NWPA exhibits a unimodal annual cycle. The MLR model can be used to accurately capture the interannual variability during the wet season in the highland NWPA by utilizing predictors derived from the C and SAMSi indices. In contrast, regarding rainfall in the lowland NWPA, the Pacific SST variability, SAMS and tropical North Atlantic index were relevant. For long lead times, the MLR model provided reliable forecasts of JFM rainfall anomalies in the highlands (R3, approximately 2700 m asl) as these regions are governed by Pacific variability. However, the MLR model exhibited limitations in accurately estimating the wettest JFM season in the highlands due to the absence of a predictor for the amplified effect of the Madden–Julian Oscillation on rainfall.
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    Impact of Extreme Droughts on the Water Balance in the Peruvian–Ecuadorian Amazon Basin (2003–2024)
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-11-01)
    This study assesses the impact of extreme droughts on the surface and atmospheric water balance of the Peruvian Amazon basin during 2003–2024. It extends previous work by incorporating multiple datasets for precipitation (CHIRPS, MSWEP, and ERA5) and evapotranspiration (ERA5, GLDAS, Amazon-Paca, and observations from the Quistococha flux tower) and comparing three drought indices: Maximum Cumulative Water Deficit (MCWD), Standardized Precipitation Evapotranspiration Index (SPEI), and self-calibrated Palmer Drought Severity Index (scPDSI). The study focuses on the Peruvian–Ecuadorian Amazon basin, particularly on the Amazon and Madre de Dios river basins, closing at Tamshiyacu and Amaru Mayu stations, respectively. The results confirm four extreme drought years (2004–2005, 2009–2010, 2022–2023, and 2023–2024) with major precipitation deficits in dry seasons and significant reductions in runoff and total water storage anomalies (TWSAs), physically manifesting as negative surface balances indicating net terrestrial water depletion and negative atmospheric balances reflecting reduced moisture convergence, with residuals signaling hydrological uncertainties. The study highlights significant imbalances in the water cycle during droughts and underscores the need to use multiple indicators and datasets to accurately assess hydrological responses under extreme climatic conditions in the Amazon basin.
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    Extreme Droughts in the Peruvian Amazon Region (2000–2024)
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-06-01)
    Droughts in the Amazon region are expected to increase in frequency and intensity, which would negatively affect the tropical forest, leading to a positive climate–forest feedback loop that could potentially result in the collapse of this ecosystem. In this study, extreme drought conditions were identified in the Peruvian Amazon region for the period 2000–2024 using the maximum cumulative water deficit (MCWD) index, which is related to the tropical forest water stress. The ERA5, CHIRPS, and MSWEP datasets were used to estimate precipitation, while ERA5 data were used for evapotranspiration. This study focuses on the specificities of droughts and the differences across study areas. Six study areas were specified, three of them located in the Loreto department (northern Peruvian Amazon), another centered in Moyobamba city (western Peruvian Amazon), another in Ucayali, in the central Peruvian Amazon, and the other in Madre de Dios (southern Peruvian Amazon). It was found that the drought events are more frequent and intense in the central and southern regions of the basin. Based on the combined effect of the regional severity of the drought and its spatial extent, estimated from averaging across study areas and precipitation datasets, we identified the hydrological years of 2023-24, 2022-23, 2009-10, and 2004-05 as extreme droughts and 2015-16 and 2006-07 as moderate droughts.
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