| Website | www.jstagge.com |
| Pronouns | he/him |
New research led by Kay Sung explores 1,250 years of precipitation trends across U.S. watersheds.
Using tree rings, climate model output, & non-stationary SPI, we show how climate change is shaping seasonal rainfall.
New paper - how does moving window length affect the annual minima? Relevant for SPI/SPEI #drought
Abstract. Numerous drought indices originate from the Standardized Precipitation Index (SPI) and use a moving-average structure to quantify drought severity by measuring normalized anomalies in hydroclimate variables. This study examines the theoretical probability of annual minima based on such a process. To accomplish this, we derive a stochastic model and use it to simulate 10 ×106 years of daily or monthly SPI values in order to determine the distribution of annual exceedance probabilities. We believe this is the first explicit quantification of annual extreme exceedances from a moving-average process where the moving-average window is proportionally large (5 %–200 %) relative to the year, as is the case for many moving-window drought indices. The resulting distribution of annual minima follows a generalized normal distribution rather than the generalized extreme-value (GEV) distribution, as would be expected from extreme-value theory. From a more applied perspective, this study provides the expected annual return periods for the SPI or related drought indices with common accumulation periods (moving-window length), ranging from 1 to 24 months. We show that the annual return period differs depending on both the accumulation period and the temporal resolution (daily or monthly). The likelihood of exceeding an SPI threshold in a given year decreases as the accumulation period increases. This study provides clarification and a caution for the use of annual return period terminology (e.g. the 100-year drought) with the SPI and a further caution for comparing annual exceedances across indices with different accumulation periods or resolutions. The study also distinguishes between theoretical values, as calculated here, and real-world exceedance probabilities, where there may be climatological autocorrelation beyond that created by the moving average.
In #Ohio? Worried about this summer's #drought? Want to know how tree rings tell us about climate change? Want to know what models predict about Ohio under #climatechange? Want pizza?
Come out to the Columbus Science Pub Jan 9 at 7:30pm.
Stop by Monday morning #AGU to see/talk about advances in bringing #hydrology principles/models to proxy #reconstructions
https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1714583
Recent study looking at centuries of past and future #drought and pluvial change across North America
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL107400
Update to the #research #reproducibility policy at Journal of Water Resources Planning and Management
