Non-stationary modeling of seasonal precipitation series in Turkey: estimating the plausible range of seasonal extremes
Non-stationary modeling of seasonal precipitation series in Turkey: estimating the plausible range of seasonal extremes
复制标题
DOI：
10.1007/s00704-023-04807-4
复制
发表时间：
2023-12
影响因子：
3.4
通讯作者：
Fatih Tosunoğlu;Louise J. Slater;Katie Kowal;Xihui Gu;Jiabo Yin
中科院分区：
文献类型：
--
作者：
Fatih Tosunoğlu;Louise J. Slater;Katie Kowal;Xihui Gu;Jiabo Yin
人们日益认识到，在气候变化和气候变率的影响下，对气象时间序列的回归期进行估计时，采用平稳性假设并不总是合适的。在这里，我们评估了使用广义加性位置、尺度和形状模型（GAMLSS）模拟季节性降水系列的非平稳框架的能力，GAMLSS是一种在非平稳条件下模拟水文气象变量的成熟方法。考虑了土耳其各地95个站点1970-2021年的季节降水序列。四项广泛使用的同质性检验表明，季节降水序列总体上是可靠的，没有明显的人为影响或误差。拟合分布的参数被模拟为控制土耳其降水的大尺度振荡指数的函数，即北大西洋涛动（NAOI）、北海-里海模式（NCPI）、地中海涛动（MOI）和南方涛动（SOI）。在秋季、冬季、春季和夏季，有振荡指数的模型分别在85%、79%、76%和54%的站点上优于无协变量的模型。特别是，NCPI在冬季被视为一个重要的预测因子，而MOI在秋季和夏季很好地捕捉了全国各地的降水变化。在除夏季外的所有季节，NAOI都是另一个重要的预测因子，而SOI在某些地区则是重要的解释变量。利用非平稳模型，我们计算了不同回归期（即20年、50年和100年）的季节性降水估计，并考虑了每个站点最小和最大可能的极端降水情景。我们展示了如何使用从非平稳模型中获得的简单最小/最大值，可以帮助水资源管理者和政策制定者提供一个似是而非的极端值范围，而不是从传统的平稳方法中获得的单一确定性值。
It is increasingly recognized that the assumption of stationarity is not always appropriate for estimating return periods from meteorological time series under the effects of climate change and climate variability. Here, we assessed the capability of a non-stationary framework for modeling seasonal precipitation series using Generalized Additive Models for Location, Scale and Shape (GAMLSS), a well-established approach for modeling hydro-meteorological variables under non-stationary conditions. Seasonal precipitation series were considered from 95 stations covering the period of 1970–2021 across Turkey. Four widely used homogeneity tests showed that the seasonal precipitation series were generally reliable, with no obvious anthropogenic influences or errors. The parameters of the fitted distributions were modeled as a function of large-scale oscillation indices known to control precipitation across Turkey, namely the North Atlantic Oscillation (NAOI), North Sea-Caspian Pattern (NCPI), Mediterranean Oscillation (MOI), and Southern Oscillation (SOI). The model with oscillation indices performed better at 85%, 79%, 76%, and 54% of sites for autumn, winter, spring, and summer, respectively, than the model with no covariates. In particular, the NCPI was seen as a significant predictor during the winter, while the MOI captured precipitation variability well across the country during autumn and summer. The NAOI appeared as another important predictor during all seasons except summer, while the SOI appeared as a significant explanatory variable in certain regions. Using the non-stationary models, we then computed seasonal precipitation estimates for different return periods (i.e., 20, 50, and 100 years) and considered the minimum and maximum possible extreme precipitation scenarios at each site. We show how the use of simple minimum/maximum values derived from the non-stationary models can help provide water resource managers and policy makers with a plausible range of extreme values, rather than the single deterministic value obtained from the traditional stationary approach.