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Abstract The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters. Significance StatementWe typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns—a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought.
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Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends
There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail.
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- Award ID(s):
- 2002539
- PAR ID:
- 10517955
- Publisher / Repository:
- Copernicus Online Journals
- Date Published:
- Journal Name:
- Hydrology and Earth System Sciences
- Volume:
- 28
- Issue:
- 9
- ISSN:
- 1607-7938
- Page Range / eLocation ID:
- 2047 to 2063
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Analyzing gradual trends in meteorological drought has become increasingly important as anthropogenic climate change and natural climate variability interact to complicate measurement of drought severity. Complex seasonality and long-term trends pose a limitation in understanding spatial trends in nonstationary changes of meteorological drought in the United States. This study seeks to address this issue by simultaneously analyzing recurring seasonal patterns (stationary component) and long-term drought trends (nonstationary component), with a unique focus on nonlinear trends and common regional patterns. We analyzed 696 instrumental precipitation gauges with long historical records in the continental United States, using a novel spline-based model to disaggregate a 3-month meteorological drought index (SPI) into its seasonal and long-term components. The disaggregated components for each gauge were then clustered into subregions with similar seasonality and groupings with similar long-term trends using a two-step process. Our results identify clearly defined regions based on precipitation seasonality, while long-term trends are not spatially coherent with the seasonality. Instead, these findings support prior findings of an increasingly drier western United States and an increasingly wetter eastern United States over the last century, but with more nuanced spatial and temporal patterns. The new clustering analysis based on nonstationary meteorological drought trends can contribute to informing and adapting current water management strategies to long-term drought trends. Significance Statement This study considered 656 precipitation gauges across the continental United States to find regions with similar precipitation seasonality and then to group records with similar long-term climate trends. The study focused on 3-month average precipitation, a key indicator for drought monitoring. We identified eight regions across the United States with similar precipitation seasonality. From 1920 to the present, we found continuous drying trends throughout the western United States, continuously wetter trends in the northern plains, and an overall wetter trend interrupted by a midcentury dry period (1930–50) for much of the central Plains and Midwest. This study’s use of splines, or fitted curves, allowed these nonlinear patterns, which we believe better capture the nuances and intensification of climate change effects on precipitation.more » « less
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Global “hot spots” for land–atmosphere coupling have been identified through various modeling studies—both local and global in scope. One hot spot that is common to many of these analyses is the U.S. southern Great Plains (SGP). In this study, we perform a mesoscale analysis, enabled by the Oklahoma Mesonet, that bridges the spatial and temporal gaps between preceding local and global analyses of coupling. We focus primarily on east–west variations in seasonal coupling in the context of interannual variability over the period spanning 2000–15. Using North American Regional Reanalysis (NARR)-derived standardized anomalies of convective triggering potential (CTP) and the low-level humidity index (HI), we investigate changes in the covariance of soil moisture and the atmospheric low-level thermodynamic profile during seasonal hydrometeorological extremes. Daily CTP and HI z scores, dependent upon climatology at individual NARR grid points, were computed and compared to in situ soil moisture observations at the nearest mesonet station to provide nearly collocated annual composites over dry and wet soils. Extreme dry and wet year CTP and HI z-score distributions are shown to deviate significantly from climatology and therefore may constitute atmospheric precursors to extreme events. The most extreme rainfall years differ from climatology but also from one another, indicating variability in the strength of land–atmosphere coupling during these years. Overall, the covariance between soil moisture and CTP/HI is much greater during drought years, and coupling appears more consistent. For example, propagation of drought during 2011 occurred under antecedent CTP and HI conditions that were identified by this study as being conducive to positive dry feedbacks demonstrating potential utility of this framework in forecasting regional drought propagation.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/hess-28-2047-2024
