Nonstationary analysis of hydrological drought index in a coupled human-water system: Application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China

Shao, Shuting; Singh, Vijay P.; Ding, Hui-Sheng; Zhang, Jingru; Wu, Yanrui

doi:10.1016/j.jhydrol.2022.127692

Cited by 18 publications

(6 citation statements)

References 47 publications

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“…Therein, groundwater storage variables were obtained using the USGS RORA model [27,28]. Also, an inconsistent GAM-LSS was developed by identifying and establishing the interaction of runoff changes and their physical driving elements (e.g., groundwater storage variables, actual evapotranspiration, and underlying surface characteristics) as covariates [29]. This model studies how non-stationary fluctuations in yearly precipitation, groundwater storage factors, and actual evapotranspiration affect annual runoff.…”

Section: Methodsmentioning

confidence: 99%

“…A Generalized Additive Model in Location, Scale, and Shape (GAMLSS) is a semi parametric regression model that analyzes the frequencies of stationary and non-station ary runoff and other features [17][18][19][20][21][22]29,30].…”

Section: Gamlssmentioning

confidence: 99%

“…Taking into account the driving relationship between the various variables that have been constructed, this study uses the GAMLSS with physical influencing factors as covariates to conduct non-consistency analysis on the observed variable [29], and then the relationship expression of the observed variable's distribution parameters with the change in physical factors is obtained. The formula is as follows:…”

Section: Quantitative Statistical Simulation Of Observed Variable Cha...mentioning

confidence: 99%

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Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

Zhang,

Zhi,

Zhang

et al. 2024

Water

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The hydrological series in the Loess Plateau region has exhibited shifts in trend, mean, and/or variance as the environmental conditions have changed, indicating a departure from the assumption of stationarity. As the variations accumulate, the compound effects caused by the driving variables on runoff variations grow complex and interactive, posing a substantial risk to water security and the promotion of high-quality development in regions or river basins. This study focuses on the Tuwei River Basin in the Loess Plateau, which experiences significant changes in vegetation coverage and minimal human disturbance, and examines the cross-driving relationship between the runoff change and its driving variables (including hydrometeorological and environmental variables). A quantitative statistical analysis method based on the GAMLSS is then developed to estimate the interacting effects of changes in the driving variables and their contribution to runoff changes. Finally, various anticipated scenarios are used to simulate the changes in driving variables and runoff disturbances. The findings indicate the following: (1) The developed GU, LO, and NO distribution-based GAMLSSs provide a notable advantage in effectively capturing the variations in groundwater storage variables, actual evapotranspiration, and underlying surface parameters, as well as accurately estimating the impacts of other relevant variables. (2) The precipitation and groundwater storage variables showed predominantly positive contributions to the runoff change, but actual evapotranspiration had an adverse effect. The changes in underlying surface parameters, particularly since 2000, increase actual evapotranspiration, while decreasing groundwater storage, resulting in a progressive decrease in runoff as their contribution grows. (3) The scenario simulation results reveal that alterations to the underlying surface have a substantial influence on the evolution of runoff in the Tuwei River Basin. Additionally, there are cross-effects between the impact of various driving variables on runoff, potentially compounding the complexity of inconsistent changes in runoff sequences.

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Section: Methodsmentioning

confidence: 99%

Section: Gamlssmentioning

confidence: 99%

Section: Quantitative Statistical Simulation Of Observed Variable Cha...mentioning

confidence: 99%

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Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

Zhang,

Zhi,

Zhang

et al. 2024

Water

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“…These influences are manifest in static measures of drought like percentile mapping of drought classes via changes in drought characteristics such as return period (Kam et al, 2014), frequency (McCabe et al, 2004, and duration (Vicente-Serrano et al, 2021). Challenges to drought monitoring practices brought by nonstationary climate are also noted in recent studies in different places around the world, including the U.S. (Hoylman et al, 2022), Europe (Cammalleri et al, 2022), China (He et al, 2021;Shao et al, 2022), and India (Das et al, 2020).…”

Section: Agu Advancesmentioning

confidence: 99%

Emergent Trends Complicate the Interpretation of the United States Drought Monitor (USDM)

Li,

Smerdon,

Seager

et al. 2024

AGU Advances

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Effective drought management must be informed by an understanding of whether and how current drought monitoring and assessment practices represent underlying nonstationary climate conditions, either naturally occurring or forced by climate change. Here we investigate the emerging climatology and associated trends in drought classes defined by the United States Drought Monitor (USDM), a weekly product that, since 2000, has been used to inform drought management in the United States. The USDM classifies drought intensity based in part on threshold percentiles in key hydroclimate quantities. Here we assess how those USDM‐defined drought threshold percentiles have changed over the last 23 years, examining precipitation, runoff, soil moisture (SM), terrestrial water storage (TWS), vapor pressure deficit (VPD), and near‐surface air temperature. We also assess underlying trends in the frequency of drought classifications across the U.S. Our analysis suggests that the frequency of drought class occurrence is exceeding the threshold percentiles defined by the USDM in a number of regions in the United States, particularly in the American West, where the last 23 years have emerged as a prolonged dry period. These trends are also reflected in percentile‐based thresholds in precipitation, runoff, SM, TWS, VPD, and temperature. Our results emphasize that while the USDM appears to be accurately reflecting observed nonstationarity in the physical climate, such trends raise critical questions about whether and how drought diagnosis, classification, and monitoring should address long‐term intervals of wet and dry periods or trends.

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“…Pietro S. et al [16] used GAMLSS with precipitation as a covariate to analyze annual runoff data and found that it was more able to capture the variability of the observed data. Recently, some researchers have started to combine the GAMLSS model with other models, algorithms, or indexes, such as copula [15], the nonstationary SRI index [17], etc., to test the stationarity of the series or to perform the calculation of the nonstationary hydrological frequency. However, fewer attempts have been made to apply it to the practical work that hydrologists must perform.…”

Section: Introductionmentioning

confidence: 99%

Study of Nonstationary Flood Frequency Analysis in Songhua River Basin

Wang,

Liu,

Xing

et al. 2023

Water

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This study aimed to determine the influence of time and precipitation as covariates on the flood frequency distribution in the Songhua River tributaries under the nonstationarity assumption and to investigate the possibility of nonstationary models’ application in river management scope demarcation work. Nonstationary flood frequency analysis (NS-FFA) was conducted in three typical basins of the Songhua River (in Northeastern China) based on the generalized additive models for location, scale, and shape (GAMLSS), and stationary flood frequency analysis was used as a comparison. Under the stationarity assumption, the Pearson type Ⅲ (P-Ⅲ) distribution is the main theoretical distribution for the flood extremum at hydrological stations, followed by a lognormal (LN) distribution. Under the nonstationarity assumption, when time is considered a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 63.75%), followed by the Weibull distribution (with 18.75%). When precipitation is considered as a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 57.5%). We attempted to apply several FFA methods to calculate the design frequency in this study, referring to the work requirements for river management scope demarcation in three typical basins, and came to the following conclusions. From the simulation results of the p = 10% flood at the export stations of typical basins, it can be seen that time-covariate NS-FFA obtained the best simulation results. Two cases of the simulation under the stationarity assumption are positive, which will lead to a high design scale. The time-covariate GAMLSS in NS-FFA has the advantages of higher calculation accuracy and simpler processes. To better balance construction costs and disaster protection requirements, NS-FFA can be used to determine the design scale of water conservation projects; additionally, it can be used to demarcate the scope of river management. The accuracy of GAMLSS for FFA is also influenced by the complexity of the terrain, with basins with relatively simple terrain having higher calculation accuracy.

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Nonstationary analysis of hydrological drought index in a coupled human-water system: Application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China

Cited by 18 publications

References 47 publications

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

Emergent Trends Complicate the Interpretation of the United States Drought Monitor (USDM)

Study of Nonstationary Flood Frequency Analysis in Songhua River Basin

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