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Identifying non-stationarity in the dependence structures of meteorological factors within and across seasons and exploring possible causes

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Abstract

Precipitation (P) and temperature (T) are key components of the hydrometeorological system, and their dependence structures exhibit significant dynamic changes, including non-stationary behavior, in response to environmental variations. These changes affect local hydrological processes and impact the predictability of the hydrometeorological system. However, the dynamics of dependence structures among meteorological factors during corresponding and adjacent seasons, as well as their underlying causes, have not been fully revealed. Therefore, this study comprehensively explored the dynamics of the precipitation-temperature dependence structure (PTDS) and temperature-temperature dependence structure (TTDS), and their possible causes. Firstly, non-stationary of PTDS was identified using a copula model. Then the main drivers of PTDS were determined by the random forest (RF) model and variable projection importance (VIP) criteria. These drivers include both conventional factors such as local meteorological factors (e.g., P, T, wind speed (WS), vapor pressure, relative humidity and sunshine duration (SD)) and teleconnection factors (e.g., Sunspots, the Arctic Oscillation, Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO)). Additionally, the normalized difference vegetation index (NDVI) was used to investigate the response of dependence structure to vegetation dynamics. Finally, the ridge regression model was applied to construct driver models for the dynamics of dependence structures. The Loess Plateau was selected as the study area because of its high ecological sensitivity and typical human afforestation activities. The results show that: (1) non-stationarity in the PTDS occurred in different seasons and at various stations; (2) the primary drivers of PTDS and TTDS dynamics are predominantly local meteorological factors; (3) there is a strong correlation between SD and ENSO, and the impacts of PDO on local meteorological factors (WS and T) play a crucial role in driving the PTDS dynamics; and (4) NDVI is the main driver, primarily influencing T and ultimately affecting the dynamics of PTDS and TTDS. These findings suggest that there are significant ecological impacts through radiative or non-radiative feedback mechanisms under warming scenarios. Overall, this study provides new insights into the drivers and mechanisms behind the dynamics of dependence structures among meteorological elements. It contributes to a deeper understanding of the changing local hydrometeorological processes.

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Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Adler RF, Gu G, Sapiano M, Wang J-J, Huffman GJ (2017) Global precipitation: means, variations and trends during the satellite era (1979–2014). Surv Geophys 38(4):679–699

    Article  Google Scholar 

  • Bhatti MI, Do HQ (2019) Recent development in copula and its applications to the energy, forestry and environmental sciences. Int J Hydrogen Energy 44(36):19453–19473

    Article  CAS  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Britannica T (2021) Editors of Encyclopaedia (April 29). glacier summary. Encycl Britannica. https://www.britannica.com/summary/glacier

  • Chan JCL, Zhou W (2005) PDO, ENSO and the early summer monsoon rainfall over south China. Geophys Res Lett 32(8):93–114

    Article  Google Scholar 

  • Crowhurst D, Dadson S, Peng J, Washington R (2021) Contrasting controls on Congo Basin evaporation at the two rainfall peaks. Clim Dyn 56:1609–1624

    Article  Google Scholar 

  • Dahal N, Shrestha U, Tuitui A, Ojha H (2018) Temporal changes in precipitation and temperature and their implications on the streamflow of Rosi River, Central Nepal. Climate 7(1)

  • Das J, Jha S, Goyal MK (2020) Non-stationary and copula-based approach to assess the drought characteristics encompassing climate indices over the Himalayan states in India. J Hydrol 580:124356

    Article  Google Scholar 

  • Dong H et al (2021) Copula-based non-stationarity detection of the precipitation-temperature dependency structure dynamics and possible driving mechanism. Atmos Res 249

  • Du Z, Zhao J, Pan H, Wu Z, Zhang H (2019) Responses of vegetation activity to the daytime and nighttime warming in Northwest China. Environ Monit Assess 191(12):721

    Article  Google Scholar 

  • Feng Q et al (2016a) Relationship between large scale atmospheric circulation, temperature and precipitation in the Extensive Hexi region, China, 1960–2011. Quatern Int 392:187–196

    Article  Google Scholar 

  • Feng S, Hao Z (2021) Quantitative contribution of ENSO to precipitation-temperature dependence and associated compound dry and hot events. Atmos Res 260:105695

    Article  Google Scholar 

  • Feng X et al (2016b) Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat Clim Chang 6(11):1019–1022

    Article  Google Scholar 

  • Ferré J (2009) 3.02-Regression diagnostics. In: Brown SD, Tauler R, Walczak B (eds) Comprehensive chemometrics. Elsevier, Oxford, pp 33–89

    Chapter  Google Scholar 

  • Forthofer RN, Lee ES, Hernandez M (2007) 13-Linear regression. In: Forthofer RN, Lee ES, Hernandez M (eds) Biostatistics, 2nd edn. Academic Press, San Diego, pp 349–386

    Chapter  Google Scholar 

  • Gao X et al. (2020) Temperature dependence of extreme precipitation over mainland China. J Hydrol 583

  • Genuer R, Poggi J-M, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recogn Lett 31(14):2225–2236

    Article  Google Scholar 

  • Gombay E, Horváth L (1996) On the rate of approximations for maximum likelihood tests in change-point models. J Multivar Anal 56(1):120–152

    Article  Google Scholar 

  • Guo W et al (2023) Drought trigger thresholds for different levels of vegetation loss in China and their dynamics. Agric for Meteorol 331:109349

    Article  Google Scholar 

  • He P, Xu L, Liu Z, Jing Y, Zhu W (2021) Dynamics of NDVI and its influencing factors in the Chinese Loess Plateau during 2002–2018. Region Sustain 2(1):36–46

    Article  Google Scholar 

  • Hirpa FA et al (2019) Streamflow response to climate change in the Greater Horn of Africa. Clim Change 156(3):341–363

    Article  Google Scholar 

  • Hodgkins GA et al (2017) Climate-driven variability in the occurrence of major floods across North America and Europe. J Hydrol 552:704–717

    Article  Google Scholar 

  • Hoerl AE, Kennard RWEe (2000) Ridge regression: biased estimation for nonorthogonal problems. Technometr J Stats Physical Chem 42

  • Huang S, Huang Q, Zhang H, Chen Y, Leng G (2016) Spatio-temporal changes in precipitation, temperature and their possibly changing relationship: a case study in the Wei River Basin, China. Int J Climatol 36(3):1160–1169

    Article  Google Scholar 

  • Iizumi T et al (2017) Responses of crop yield growth to global temperature and socioeconomic changes. Sci Rep 7(1):7800

    Article  Google Scholar 

  • Jiao W et al (2021) Observed increasing water constraint on vegetation growth over the last three decades. Nat Commun 12(1):3777

    Article  CAS  Google Scholar 

  • Kim H, Jung H-Y (2020) Ridge fuzzy regression modelling for solving multicollinearity. Mathematics 8(9)

  • Kotsias G, Lolis CJ, Hatzianastassiou N, Levizzani V, Bartzokas A (2020) On the connection between large-scale atmospheric circulation and winter GPCP precipitation over the Mediterranean region for the period 1980–2017. Atmos Res 233:104714

    Article  Google Scholar 

  • Kwon M, Yeh S-W, Park Y-G, Lee Y-K (2013) Changes in the linear relationship of ENSO–PDO under the global warming. Int J Climatol 33(5):1121–1128

    Article  Google Scholar 

  • Lesk C et al (2021) Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields. Nat Food 2(9):683–691

    Article  Google Scholar 

  • Li J, Peng S, Li Z (2017) Detecting and attributing vegetation changes on China’s Loess Plateau. Agric for Meteorol 247:260–270

    Article  Google Scholar 

  • Li P et al (2022a) Various maize yield losses and their dynamics triggered by drought thresholds based on Copula-Bayesian conditional probabilities. Agric Water Manag 261:107391

    Article  Google Scholar 

  • Li Y et al (2022b) High-resolution propagation time from meteorological to agricultural drought at multiple levels and spatiotemporal scales. Agric Water Manag 262:107428

    Article  Google Scholar 

  • Liang L et al (2020) Responses of abrupt temperature changes/warming hiatuses to changes in their influencing factors: a case study of northern China. Meteorol Appl 27(4):e1937

    Article  Google Scholar 

  • Liu G, Zhao P, Chen J (2011) A 150-year reconstructed summer Asian-Pacific Oscillation index and its association with precipitation over eastern China. Theoret Appl Climatol 103(1):239–248

    Article  Google Scholar 

  • Liu J, Li S, Ouyang Z, Tam C, Chen X (2008) Ecological and socioeconomic effects of China’s policies for ecosystem services. PNAS 105(28):9477–9482

    Article  CAS  Google Scholar 

  • Mann PJ, et al. (2022) Degrading permafrost river catchments and their impact on Arctic Ocean nearshore processes. Ambio 51(2):439–455

    Article  Google Scholar 

  • Nawaz Z, Chen Y, Guo Y, Wang X, Nawaz N (2019) Temporal and spatial characteristics of precipitation and temperature in Punjab. Pak Water 11:1916

    Article  Google Scholar 

  • Ning T, Liu W, Lin W, Song X (2015) NDVI variation and its responses to climate change on the Northern Loess Plateau of China from 1998 to 2012. Adv Meteorol 2015:725427

    Article  Google Scholar 

  • Park J, Byrne R, Böhnel H (2017) The combined influence of Pacific decadal oscillation and Atlantic multidecadal oscillation on central Mexico since the early 1600s. Earth Planet Sci Lett 464:1–9

    Article  CAS  Google Scholar 

  • Peng J et al (2019) The impact of the Madden-Julian Oscillation on hydrological extremes. J Hydrol 571:142–149

    Article  Google Scholar 

  • Peng S-S et al (2014) Afforestation in China cools local land surface temperature. PNAS 111(8):2915–2919

    Article  CAS  Google Scholar 

  • Phipps S, Brown J (2010) Understanding ENSO dynamics through the exploration of past climates. In: IOP conference series earth and environmental science, 9

  • Sklar A (1959) Fonctions de repartition a n dimensions et leurs marges. Publications de l'Institut de statistique de l'Université de Paris 8

  • Soomro S-e-h. et al (2021) Precipitation changes and their relationships with vegetation responses during 1982–2015 in Kunhar River basin, Pakistan. Water Supply 21(7):3657–3671

    Article  Google Scholar 

  • Sun Q, Miao C, Duan Q Wang Y (2015) Temperature and precipitation changes over the Loess Plateau between 1961 and 2011, based on high-density gauge observations. Glob and Planet Change 132:1–10

    Article  Google Scholar 

  • Van Dijk AIJM, Keenan RJ (2007) Planted forests and water in perspective. For Ecol Manag 251(1):1–9

    Article  Google Scholar 

  • Wang J, Sun M, Gao X, Zhao X, Zhao Y (2021) Spatial and temporal characteristics of precipitation and potential influencing factors in the loess plateau before and after the implementation of the grain for green project. Water 13(2).

  • Wang S et al (2016) Reduced sediment transport in the Yellow River due to anthropogenic changes. Nat Geosci 9(1):38–41

    Article  CAS  Google Scholar 

  • Wang Y et al (2022) Evaluation of non-stationarity in summer precipitation and the response of vegetation over the typical steppe in Inner Mongolia. Clim Dyn 58(9):2227–2247

    Article  Google Scholar 

  • Wang Y, Shao MA, Zhu Y, Liu Z (2011) Impacts of land use and plant characteristics on dried soil layers in different climatic regions on the Loess Plateau of China. Agric for Meteorol 151(4):437–448

    Article  Google Scholar 

  • Wold S (1995) PLS for multivariate linear modeling. Chemometric methods in molecular design

  • Wold S, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemomet Intell Lab Syst 58(2):109–130

    Article  CAS  Google Scholar 

  • Wu D, Xie X, Tong J, Meng S, Wang Y (2020) Sensitivity of vegetation growth to precipitation in a typical afforestation area in the loess plateau: plant-water coupled modelling. Ecol Model 430:109128

    Article  Google Scholar 

  • Wu X, Mao J (2016) Interdecadal modulation of ENSO-related spring rainfall over South China by the Pacific Decadal Oscillation. Clim Dyn 47(9):3203–3220

    Article  Google Scholar 

  • Wu X, Mao J (2017) Interdecadal variability of early summer monsoon rainfall over South China in association with the Pacific Decadal Oscillation. Int J Climatol 37(2):706–721

    Article  Google Scholar 

  • Xiong LH, Jiang C, Xu CY, Yu KX, Guo SL (2015) A framework of change-point detection for multivariate hydrological series. Water Resour Res 51(10):8198–8217

    Article  Google Scholar 

  • Zeng Y, Yang X, Fang N, Shi Z (2020) Large-scale afforestation significantly increases permanent surface water in China’s vegetation restoration regions. Agric for Meteorol 290:108001

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Key R&D Program of China (Grant Number 2022YFC3202303), the Xinjiang Uygur Autonomous Region Key R&D Program (Grant number 2022B03024-4), the National Natural Science Foundation of China (Grant Number 52279026), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant Number XDA28060100).

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Authors and Affiliations

  1. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, 710048, China

    Haixia Dong, Shengzhi Huang, Qiang Huang & Ziyan Li

  2. China Institute of Water Resources and Hydropower Research, State Key Lab Simulat & Regulat Water Cycle River, Beijing, 100038, China

    Hao Wang

  3. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Guoyong Leng

  4. Power China Guiyang Engineering Corporation Limited, Guiyang, 550081, China

    Lin Li

  5. Department of Remote Sensing, Helmholtz Centre for Environmental Research–UFZ, Permoserstrasse 15, 04318, Leipzig, Germany

    Jian Peng

  6. Remote Sensing Centre for Earth System Research, Leipzig University, 04103, Leipzig, Germany

    Jian Peng

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  1. Haixia Dong
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Contributions

HD, SH and HW contributed to the study conception and design. Material preparation, data collection and analysis were performed by HD, GL, ZL and LL. The first draft of the manuscript was written by HD, SH and QH, all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shengzhi Huang.

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Dong, H., Huang, S., Wang, H. et al. Identifying non-stationarity in the dependence structures of meteorological factors within and across seasons and exploring possible causes. Stoch Environ Res Risk Assess 37, 4071–4089 (2023). https://doi.org/10.1007/s00477-023-02496-z

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