Wet-dry hydroclimatic pressure and thermal intensification in contrasting Peruvian basins
DOI:
https://doi.org/10.21142/SS-0702-2026-e174Keywords:
hydroclimatic trends, precipitation extremes, meteorological drought, temperature trends, gridded precipitation products, multi-product analysisAbstract
Peruvian basins span abrupt topographic and climatic gradients, yet sparse, heterogeneous observations hinder consistent basin-scale comparisons of hydroclimatic change. In this study, we developed a multi-product screening framework for nine contrasting basins over the period 1981-2025, using daily precipitation data from CHIRPS, PISCOp v3.0 and ERA5-Land, together with ERA5-Land minimum, mean and maximum temperature data. Four wet indicators (Rx1day, Rx5day, R95pTOT and SDII), four dry indicators (CDD, SPI-3 drought frequency and duration, and annual minimum SPI-12), and four thermal indicators were evaluated using Theil-Sen slopes and Hamed-Rao variance-corrected Mann-Kendall tests, with false-discovery-rate control. Basin-level precipitation intensification data was retained only when at least two products agreed, no product showed a criteria-supported opposite signal, and a minimum effect-size threshold was met. Classification sensitivity was tested across 36 analytical scenarios. The Mayo basin displayed the clearest wet-pressure intensification, with all four wet indicators classified as robust. Mantaro met the criteria for two wet indicators and was wet-dominant under the base cutoff, whereas Grande and Alto Apurímac each retained one robust wet indicator and remained inconclusive. No robust dry indicator was retained in any basin, and no dry-dominant or dual-pressure profile emerged. All basins exhibited ERA5-Land thermal intensification meeting the predefined criteria, with mean-temperature trends of 0.092-0.331°C per decade. Classification was found to be stable in most basins, whereas Mantaro remained strongly threshold-sensitive. The framework distinguishes coherent basin-scale change from product-dependent evidence and supports targeted monitoring and local validation, without implying risk, attribution or vulnerability.
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Copyright (c) 2026 Luis Velarde, Becky Berrocal, Jamil Ramirez

This work is licensed under a Creative Commons Attribution 4.0 International License.








