A millennial record of precipitation volatility along the Lower Missouri River, Central USA

The Missouri River Basin drains one-sixth of the surface area in the contiguous United States of America (USA) and is prone to both water deficit and surplus, leading to catastrophic and potentially compounding hydrologic hazards. Rising global temperatures amplify evaporation and moisture flux in the hydrologic cycle, resulting in volatile precipitation shifts between extremely dry and wet conditions (precipitation whiplash events). Observed and projected precipitation trends in the central interior of the USA, like many other mid-latitude world regions, indicate precipitation whiplash events are characteristic of modern climate change. Historically, managing river basins with precipitation extremes leading to drought or flood includes distinct and opposing strategies such that a measure for one hazard can negatively impact the effectiveness of measures for the other. There is an urgent need to understand the regional drivers and frequency of precipitation whiplash events to make climate-informed water management decisions and improve resilience in river basins dealing with droughts and floods in quick succession. This study leverages the long-term and detailed interannual perspective afforded by tree ring records to address the following objectives: 1) reconstruct a chronology of precipitation whiplash events in the Missouri River Basin during the last millennium, and 2) examine the climate drivers of pre-historic precipitation volatility. We synthesized a single standardized and detrended chronology for the lower Missouri River Basin from tree rings in the International Tree Ring Database (ITRB) that are well-calibrated with total precipitation from the Global Precipitation Climatology Center dataset. We present trend analysis findings to determine whether precipitation volatility in the Missouri River basin is increasing monotonically or has demonstrated long-term non-linear or periodic trends in response to climate change. Next, we used self-organizing maps (SOMs), a type of artificial neural network, to discern spatio-temporal precipitation patterns during precipitation whiplash events. Finally, we compare SOM in paired years to ocean and atmospheric variables from the Community Earth System Model–Last Millennium Ensemble (CESM-LME) to test the correlation between local precipitation volatility and large-scale ocean-atmospheric patterns. Results from this study include the first precipitation whiplash chronology in the central USA and provide timely insight into the drivers of non-linear precipitation extremes in an important socio-economic river basin.