Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series

Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong‐Yu; Li, Lingqi

doi:10.5194/hess-2017-259

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…However, there is no such restriction in GAMLSS, allowing time series modeling for observations with any underlying distribution (Rigby & Stasinopoulos, ). Because of its ability to relate time series variables with external covariates, GAMLSS has been employed by many studies for nonstationary modeling and attribution (Jiang et al, ; López & Francés, ; Tan & Gan, ; Villarini, Smith, et al, ; Villarini et al, , ; Xiong et al, ; Zhang et al, , ). GAMLSS has also been used for stochastic simulation of climate variables and in weather generators (Chun et al, ; Serinaldi & Kilsby, , ; Tye et al, ; Verdin et al, ).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Stationarity Assumption for Meteorological Drought Risk Estimation at the Multidecadal Scale in Contiguous United States

Apurv

Cai

2019

Water Resources Research

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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.Plain Language Summary Traditionally, statistical approaches adopted by water resource managers for planning and design of water resource systems and infrastructure are based on the assumption of stationarity; that is, it is assumed that the probabilistic characteristics of the hydrological and meteorological processes do not change with time, and hence, the planning and designs for future can be based on the past observations. In this paper, we have evaluated the validity of the stationarity assumption for meteorological drought risk estimation at the multidecadal scale by comparing drought risk calculated under the assumptions of stationarity and nonstationarity, respectively, in different parts of the continental United States. We find statistically significant nonstationarity in meteorological droughts in the Northwest, upper Midwest, the Northeast, eastern Great Plains and in parts of Nevada, Utah, Arizona, and New Mexico in the Southwest United States, which results in high interdecadal variability of drought risk in these regions. This result demonstrates that the assumption of stationarity can lead to underestimation of drought risk in these regions, thereby exposing water resource systems to failure under severe droughts.

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Section: Methodsmentioning

confidence: 99%

Evaluation of the Stationarity Assumption for Meteorological Drought Risk Estimation at the Multidecadal Scale in Contiguous United States

Apurv

Cai

2019

Water Resources Research

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“…As the largest tributary of the Yellow River, the Weihe River is located on the southern edge of the Loess Plateau and the northern edge of the Qinling Mountains in central China (Chang et al, 2015; Ji and Duan, 2019; Xiong et al ., 2018). The river originates from Weiyuan and joins the Yellow River at Tongguan, with a total length of 818 km, and flowing through three provinces, that is, Gansu, Ningxia and Shaanxi.…”

Section: Introductionmentioning

confidence: 99%

“…The river originates from Weiyuan and joins the Yellow River at Tongguan, with a total length of 818 km, and flowing through three provinces, that is, Gansu, Ningxia and Shaanxi. The Weihe River covers a drainage area of 134,800 km 2 involving 33°42′–37°20′ N, 104°18′–110°37′ E (Xiong et al ., 2018). The mean natural runoff of 10.4 × 10 9 m 3 accounts for 17.3% of total annual runoff of the Yellow River (Yang et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the influence of East Asian monsoon, the climate characteristics are dry and cold in the winter and wet and hot in the summer over the Weihe River Basin. The annual precipitation over the basin varies from 400 to 1,000 mm (Xiong et al ., 2018), with a mean value of 540 mm as recorded for the period 1954–2009. More than 50% of the precipitation occurs in the summer rain season.…”

Section: Introductionmentioning

confidence: 99%

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Streamflow variability in the past four centuries for the largest tributary of the Yellow River and its teleconnection with large‐scale climate forcing

Bao

Liu

et al. 2021

Intl Journal of Climatology

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Understanding long-term hydroclimate change and its evolution mechanism is beneficial to hydrological assessments and future planning in the Weihe River Basin, a region affected by the East Asian monsoon, as well as addressing increasing concerns about water resources due to social and economic development. The average June-July streamflow reconstruction was performed for the period from 1592 to 1988 for the Weihe River, based on the common growth responses relating tree-ring width, minimum density and hydrological variables, as inferred from the first principal component series of tree-ring width and density. The statistical results of calibration and verification confirmed that our model was credible and could explain 47.2% of the variance in the calibration period from 1937 to 1970. According to the criteria of one standard deviation less or greater than the mean value, 59 extremely low flow years and 62 high flow years were identified, accounting for 14.86% and 15.62%, respectively, of the past 397-year hydrological reconstruction. Severe dryness and wetness periods were in good agreement with other hydrological records in the basin, suggesting the coherence of hydroclimate changes at the basin scale, including extreme drought events from the 1920s to 1930s. The reconstructed streamflow had significant interannual and interdecadal periodic signals associated with spatial correlations with sea surface temperatures in the Pacific, Indian and Atlantic Oceans, indicating the relationships between hydrological variations in the Weihe River and the natural forcing related to the El Niño-Southern Oscillation, Pacific Decadal Oscillation, Indian Ocean Dipole pattern, North Atlantic Oscillation and Atlantic Multidecadal Oscillation (AMO). Our results provide new evidence for the potential of hydrological research using multiple tree-ring indicators, such as tree-ring width and minimum density, in the semiarid and semihumid areas of central China.

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“…Huang et al () investigated the propagation from meteorological to hydrological drought and found El Niño Southern Oscillation and Arctic Oscillation strongly impact the propagation time in the Wei River Basin. Xiong, Xiong, Chen, Xu, and Li () developed a nonstationary low‐flow distribution using eight indices derived from the climate and catchment conditions as candidate explanatory variables on basis of the generalized linear model. The above‐cited studies found that the temporal variability of low‐flow regimes can be explained by climate indices and catchment conditions to some degree.…”

Section: Introductionmentioning

confidence: 99%

Analysis of nonstationarity in low flow in the Loess Plateau of China

Gottschalk

Zhang³

et al. 2018

Hydrological Processes

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An approach for nonstationary low‐flow frequency analysis is developed and demonstrated on a dataset from the rivers on the Loess Plateau of China. Nonstationary low‐flow frequency analysis has drawn significant attention in recent years by establishing relationships between low‐flow series and explanatory variables series, but few studies have tested whether the time‐varying moments of low flow can be fully described by the time‐varying moments of the explanatory variables. In this research, the low‐flow distributions are analytically derived from the 2 basic explanatory variables—the recession duration and the recession coefficient—with the assumption that the recession duration and recession coefficient variables follow exponential and gamma distributions, respectively; the derived low‐flow distributions are applied to test whether the time‐varying moments of explanatory variables can explain the nonstationarities found in the low‐flow variable. The effects of ecosystem construction measures, that is, check dam, terrace, forest, and grassland, on the recession duration and recession coefficient are further discussed. Daily flow series from 11 hydrological stations from the Loess Plateau are used and processed with a moving average technique. Low‐flow data are extracted following the pit under threshold approach. Six of the 11 low‐flow series show significant nonstationarities at the 5% significance level, and the trend curves of the moments of low flow are in close agreement with the curves estimated from the derived distribution with time‐dependent moments of the recession duration and time‐constant moments of the recession coefficient. It is indicated that the nonstationarity in the low‐flow distribution results from the nonstationarity in the recession duration in all 6 cases, and the increase in the recession duration is resulted from large‐scale ecosystem constructions rather than climate change. The large‐scale ecosystem constructions are found to have more influence on the decrease in streamflow than on the increase in watershed storage, thus resulting in the reduction of low flow. A high return period for the initial fixed design value decreases dramatically with an increasing recession duration.

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Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series

Cited by 7 publications

References 7 publications

Evaluation of the Stationarity Assumption for Meteorological Drought Risk Estimation at the Multidecadal Scale in Contiguous United States

Evaluation of the Stationarity Assumption for Meteorological Drought Risk Estimation at the Multidecadal Scale in Contiguous United States

Streamflow variability in the past four centuries for the largest tributary of the Yellow River and its teleconnection with large‐scale climate forcing

Analysis of nonstationarity in low flow in the Loess Plateau of China

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