Investigating Temporal and Spatial Precipitation Patterns in the Southern Mid-Atlantic United States

Abstract

The investigation of regional vulnerability to extreme hydroclimatic events (e.g., floods and hurricanes) is quite challenging due to its dependence on reliable precipitation estimates. Better understanding of past precipitation trends is crucial to examine changing precipitation extremes, optimize future water demands, stormwater infrastructure, extreme event measures, irrigation management, etc., especially if combined with future climate and population projections. The objective of the study is to investigate the spatial-temporal variability of average and extreme precipitation at a sub-regional scale, specifically in the Southern Mid-Atlantic United States, a region characterized by diverse topography and is among the fastest-growing areas in North America. Particularly, this work investigates past precipitation trends and patterns using the North American Land Data Assimilation System, Version 2 (NLDAS-2, 12 km/1 h resolution) reanalysis dataset during 1980–2018. Both parametric (linear regression) and non-parametric (e.g., Theil-Sen) robust statistical tools are employed in the study to analyze trend magnitudes, which are tested for statistical significance using the Mann-Kendall test. Standard precipitation indices from ETCCDI are also used to characterize trends in the relative contribution of extreme events to precipitation in the area. In the region an increasing trend (4.3 mm/year) is identified in annual average precipitation with ~34% of the domain showing a significant increase (at the 0.1 significance level) of +3 to +5 mm/year. Seasonal and sub-regional trends are also investigated, with the most pronounced increasing trends identified during summers along the Virginia and Maryland border. The study also finds a statistically significant positive trend (at a 0.05 significance level) in the annual maximum precipitation. Furthermore, the number of daily extremes (daily total precipitation higher than the 95th and 99th percentiles) also depicts statistically significant increases, indicating the increased frequency of extreme precipitation events. Investigations into the proportion of annual precipitation occurring on wet days and extremely wet days (95th and 99th percentile) also indicate a significant increase in their relative contribution. The findings of this study have the potential to improve local-scale decision-making in terms of river basin management, flood control, irrigation scheme scheduling, and stormwater infrastructure planning to address urban resilience to hydrometeorological hazards.

A Multivariate Scaling System Is Essential to Characterize the Tropical Cyclones' Risk

“…The rain‐gauged data have limitations due to their spatial coverage, and satellite‐based precipitation, available for 1979–2020, have relatively coarse spatial or temporal resolution and are subject to the inherent systematic biases and uncertainties in their retrieval algorithms (Shi & Song, 2015; Wang et al., 2019). Spatial homogeneity and temporally consistent precipitation estimate of reanalysis precipitation products make them a valuable source for historical analyses (Dollan et al., 2022). Considering the relatively fine temporal and spatial resolution of NLDAS‐2, in addition to the data availability, we used this data set to extract the precipitation during the events.…”

Seasonal variability of future extreme precipitation and associated trends across the Contiguous U.S.

“…On a regional scale, National Climate Assessment (NCA) reports an increasing trend of the heaviest precipitation (top 1%) in the Midwest and the Northeast (Melillo et al, 2014). A broad range of studies investigated the change in observed (Alexander et al, 2006;Dollan et al, 2022) and projected extremes (Sillmann et al, 2013;Ménégoz et al, 2020) at global and regional scales (Westra et al, 2013;Diaconescu et al, 2016) using a set of climate extremes defined by the Expert Team on Climate Change Detection and Indices (ETCCDI; Zhang et al, 2011). Detecting trends despite uncertainties originating from a variety of factors such as greenhouse emission scenarios and model differences (i.e., physics, parameterization, initialization) add to the difficulty of climate change assessment studies (Razavi et al, 2016).…”