Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1007/s00477-023-02496-z
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Titel (primär) Identifying non-stationarity in the dependence structures of meteorological factors within and across seasons and exploring possible causes
Autor Dong, H.; Huang, S.; Wang, H.; Huang, Q.; Leng, G.; Li, Z.; Li, L.; Peng, J. ORCID logo
Quelle Stochastic Environmental Research and Risk Assessment
Erscheinungsjahr 2023
Department RS
Band/Volume 37
Heft 10
Seite von 4071
Seite bis 4089
Sprache englisch
Topic T5 Future Landscapes
Keywords Non-stationarity identification; Loess plateau; Precipitation; Temperature; Drivers; Changing environment
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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Dong, H., Huang, S., Wang, H., Huang, Q., Leng, G., Li, Z., Li, L., Peng, J. (2023):
Identifying non-stationarity in the dependence structures of meteorological factors within and across seasons and exploring possible causes
Stoch. Environ. Res. Risk Assess. 37 (10), 4071 - 4089 10.1007/s00477-023-02496-z