In fields such as hydrology, meteorology, and civil engineering, the study of extreme precipitation events is useful to prevent rainfall related disasters. A widely-used practice to address such a problem is by using statistical inferences about precipitation intensity, duration and frequency (IDF). Despite of its great usefulness, the selection of the adequate data and methodology to characterize precipitation’s IDF in the urban area of high-altitude Andean cities remains an open issue for practitioners and decision makers. In this sense, the present paper develops an approach to schematically build the IDF curves for a sub-basin of the study case Andean city, Quito–Ecuador. The here-used data holds information from 12 meteorological stations. Then, the IDF curves are obtained by using both a parametrization followed by a Gamma distribution and a 3-parameter cumulative distribution function, also called mnp. Finally, the curve-fitting process is estimated numerically by adjusting the Sherman equation. Results (average $R^2=0.9$) demonstrated that the framework is well-suited for the high-altitude regime. As a noticeable outcome, a novel spatial interpolation-based analysis is introduced, which enabled the identification of extreme rainfall events according to its duration.

Quito, the capital of Ecuador, is an Andean city experiencing two water challenges: urban flooding driven by extreme precipitation events and water scarcity in the dry season. Climate change is expected to increase the probability of the occurrence of flash floods, sewer overflows, and landslides because of more intense precipitation. It might moreover reduce the river discharge in the dry season due to an increase in temperature and evapotranspiration. The previous studies in the region have used a limited number of climate models and have not focused on short-duration events, presenting biased impacts of climate change. To address this knowledge gap, this research employs an ensemble of 19 state-of-the-art CMIP6 general circulation models (GCMs) to analyze climate change impacts on intensity- duration-frequency (IDF) curves, urban flooding, and river discharge in Quito under four plausible future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Daily precipitation and temperature simulations are spatially downscaled with the delta change and quantile perturbation approaches. Temporal downscaling is then applied to obtain sub-daily precipitation time series and statistics in the form of IDF curves. The uncertainty contribution of the extreme value analysis is considered by including five statistical distributions for IDF estimations. Furthermore, composite design storms are built based on historical hyetographs recorded in the city and then applied to a calibrated hydraulic model (SWMM) for a part of Quito´s combined sewer system. Climate change impacts on the urban area are expressed as changes in the IDF curves and the flood volume. To analyze climate change impacts on river discharge, three conceptual hydrological models (NAM, GR4J, and VHM) are calibrated in one of the water-supplying catchments of the city, the San Pedro River. Here, the impacts are expressed as changes in the peak, mean, and low river discharges. The uncertainty contribution of the different components (climate models, emissions scenarios, hydrological models, and extreme value distributions) is quantified by a variance decomposition method. The findings suggest an increase in the intensity of short-duration extreme precipitation events by 10-30% for the near future (2021-2050) and by 20-50% for the far future (2070- 2099). As a result of this intensification, the flood volume in the sewer network of Quito magnifies at critical points. Moreover, in the San Pedro River, the peak discharges are projected to increase by 5-20% and 10-50% in the near and far future, respectively. In contrast, the low discharges in the dry season are projected to decrease up to 13-30% as fewer wet days are expected. The uncertainty analysis reveals that climate models dominate the total projection uncertainty, although the contribution of hydrological models and extreme value distributions is not negligible. The results of this research contribute to the planning of climate change adaptation strategies and actions to reduce future risks.