Extreme precipitation events in Arizona: how summer monsoon rainfall has become more intense, and winter precipitation is modulated by the Pacific Ocean

Department of Hydrology & Atmospheric Sciences
Weekly Colloquium
Thursday, April 25, 2019
4:00 pm in Harvill Building Room 101- Refreshments at 3:45

Eleonora Demaria
Southweset Watershed Research Center USDA-ARS and HAS Alumn


In semiarid Arizona, where virtually almost every drop of water is managed and accounted for, natural ecosystems and humans are constantly competing for limited water resources. Prospects of a warmer and drier future climate, along with human population growth, will add further demands on the already stressed water resources. This seminar will focus on temporal changes in extreme precipitation events during the winter and summer seasons in Arizona. Using solely ground observations, we investigate the contribution of Atmospheric Rivers (ARs) to the hydrologic response of the Salt and Verde River basins. We found that ARs contribute an average of 25%/29% of total seasonal precipitation for the basins. However, they contribute disproportionately to total heavy precipitation and account for 64%/72% of extreme total daily precipitation.  In contrast, summer rainfall is due to the North American Monsoon. Because summer rainfall is highly intermittent and localized, detecting temporal changes in rainfall intensities in response to climatic change using climate models or isolated rain gauges has led to contradictory results. Using sub-daily and daily observations from 59 rain gauges located in the densely-instrumented Walnut Gulch Watershed in southeastern Arizona we find an intensification of monsoon sub-daily rainfall intensities starting in the mid 1970s that has not been observed in previous studies or simulated with high-resolution climate models. Our results highlight the need for long-term, high spatiotemporal observations to detect environmental responses to a changing climate in highly-variable environments, and shows that analyses based on limited observations or gridded datasets fail to capture temporal changes potentially leading to erroneous conclusions.