TY - JOUR
T1 - Evaluation of the Stationarity Assumption for Meteorological Drought Risk Estimation at the Multidecadal Scale in Contiguous United States
AU - Apurv, Tushar
AU - Cai, Ximing
N1 - Funding Information:
We are grateful for the financial support for this study by the Institute for Sustainability, Energy and Environment (iSEE), University of Illinois Urbana–Champaign. The study uses the Climate Research Unit monthly rainfall data set (CRU TS4.02) that has a 0.5° spatial resolution and covers the period 1901 to 2017. GAMLSS modeling was performed using the GAMLSS package (Stasinopoulos & Rigby, 2007) in the software R. The monthly time series of AMO was obtained from the National Oceanic and Atmospheric Administration (NOAA) website (https://www.esrl.noaa.gov/psd/data/ timeseries/AMO), the monthly time series of PDO was also obtained from the NOAA website (https://www.ncdc. noaa.gov/teleconnections/pdo), and the NH average temperature data were taken from the CRU data set (www.cru. uea.ac.uk/cru/data/temperature).
Funding Information:
We are grateful for the financial support for this study by the Institute for Sustainability, Energy and Environment (iSEE), University of Illinois Urbana?Champaign. The study uses the Climate Research Unit monthly rainfall data set (CRU TS4.02) that has a 0.5? spatial resolution and covers the period 1901 to 2017. GAMLSS modeling was performed using the GAMLSS package (Stasinopoulos & Rigby,) in the software R. The monthly time series of AMO was obtained from the National Oceanic and Atmospheric Administration (NOAA) website (https://www.esrl.noaa.gov/psd/data/timeseries/AMO), the monthly time series of PDO was also obtained from the NOAA website (https://www.ncdc.noaa.gov/teleconnections/pdo), and the NH average temperature data were taken from the CRU data set (www.cru.uea.ac.uk/cru/data/temperature).
Publisher Copyright:
©2019. American Geophysical Union. All Rights Reserved.
PY - 2019/6
Y1 - 2019/6
N2 - In this study, we analyze the nonstationarity in meteorological droughts at the multidecadal scale in different parts of the contiguous United States during 1901–2017. We develop metrics to compare the drought risk calculated under the assumptions of stationarity and nonstationarity and identify their spatial and temporal patterns. By analyzing the variability of drought risk in the past and exploring its ongoing patterns, we evaluate in which regions of the contiguous United States the assumption of stationarity can be safely used for drought risk planning and management. We find statistically significant interdecadal changes in the probability distribution functions of drought severity in parts of the Northwest, upper Midwest, the Northeast, eastern parts of Great Plains and in parts of Arizona, New Mexico, Utah, and Nevada in the Southwest. In these regions, the nonstationary risk has been significantly higher than the stationary estimate of risk in the past, which shows that the assumption of stationarity can lead to the underestimation of drought risk in these regions. The multidecadal drought risk shows low variability in California, parts of northern and western Great Plains, Ohio Valley, and in the Southeast, since the statistical properties of droughts have not changed significantly in these regions during 1901–2017. However, the meteorological drought risk has increased in California and the Southeast in the recent decades due to the influence of global warming and hence the assumption of stationarity for risk estimation may lead to underestimation of drought risk in future in these regions if this effect of global warming persists.
AB - In this study, we analyze the nonstationarity in meteorological droughts at the multidecadal scale in different parts of the contiguous United States during 1901–2017. We develop metrics to compare the drought risk calculated under the assumptions of stationarity and nonstationarity and identify their spatial and temporal patterns. By analyzing the variability of drought risk in the past and exploring its ongoing patterns, we evaluate in which regions of the contiguous United States the assumption of stationarity can be safely used for drought risk planning and management. We find statistically significant interdecadal changes in the probability distribution functions of drought severity in parts of the Northwest, upper Midwest, the Northeast, eastern parts of Great Plains and in parts of Arizona, New Mexico, Utah, and Nevada in the Southwest. In these regions, the nonstationary risk has been significantly higher than the stationary estimate of risk in the past, which shows that the assumption of stationarity can lead to the underestimation of drought risk in these regions. The multidecadal drought risk shows low variability in California, parts of northern and western Great Plains, Ohio Valley, and in the Southeast, since the statistical properties of droughts have not changed significantly in these regions during 1901–2017. However, the meteorological drought risk has increased in California and the Southeast in the recent decades due to the influence of global warming and hence the assumption of stationarity for risk estimation may lead to underestimation of drought risk in future in these regions if this effect of global warming persists.
KW - drought risk
KW - meteorological droughts
KW - multidecadal variability
KW - stationarity
UR - http://www.scopus.com/inward/record.url?scp=85068105211&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068105211&partnerID=8YFLogxK
U2 - 10.1029/2018WR024047
DO - 10.1029/2018WR024047
M3 - Article
AN - SCOPUS:85068105211
SN - 0043-1397
VL - 55
SP - 5074
EP - 5101
JO - Water Resources Research
JF - Water Resources Research
IS - 6
ER -