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<p>Drought indices based on probabilistic statistical distributions are widely used in drought assessment, and their calculation generally assumes stationarity in hydro-meteorological variables. However, the nonstationarity induced by climate change and human activities may largely challenge the traditional stationarity-based drought assessments. In this study, we systematically diagnose the nonstationary changes in precipitation, water deficit, runoff, and soil moisture across the Chinese mainland from 1961 to 2019. These hydro-meteorological variables are key inputs for the calculation of meteorological, hydrological, and agricultural drought indices. Additionally, we investigate the impact of nonstationarity in hydro-meteorological variables on drought assessments. We find that 44.7%, 33.8%, and 65.8% of annual water deficit, runoff, and soil moisture, respectively, present nonstationary changes over the whole Chinese mainland. Spatially, the nonstationarity exhibits a “higher in the north, lower in the south” pattern, and the proportions of the nonstationary grid or station increase as the considered temporal scales become longer. Our results also prove that the consideration of nonstationarity in the drought calculation can improve the fitting performance of hydro-meteorological time series, thus can give a more precise description of drought conditions than conventional stationary drought (SD) indices, especially for the agricultural drought indices. Additionally, there is a significant difference between the nonstationary drought (NSD) indices and SD in drought categories. A comparison with historical typical drought events reveals that NSD indices considering the nonstationary changes in hydro-meteorological variables, more accurately indentifies drought categories. More than 28% of months identified as mild-to-severe droughts by the SD indices are reclassified into lower drought categories by the NSD indices, which indicates that the nonstationarity in hydro-meteorological variables can largely influence the drought categories. These findings highlight the necessity of incorporating nonstationarity into drought assessment frameworks and provide a scientific basis for more reliable drought management and mitigation strategies.</p>

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The influence of nonstationarity in hydro-meteorological variables on different drought types across the Chinese mainland

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Xiaoyu Zhang,
  • Dunxian She,
  • Jun Xia

摘要

Drought indices based on probabilistic statistical distributions are widely used in drought assessment, and their calculation generally assumes stationarity in hydro-meteorological variables. However, the nonstationarity induced by climate change and human activities may largely challenge the traditional stationarity-based drought assessments. In this study, we systematically diagnose the nonstationary changes in precipitation, water deficit, runoff, and soil moisture across the Chinese mainland from 1961 to 2019. These hydro-meteorological variables are key inputs for the calculation of meteorological, hydrological, and agricultural drought indices. Additionally, we investigate the impact of nonstationarity in hydro-meteorological variables on drought assessments. We find that 44.7%, 33.8%, and 65.8% of annual water deficit, runoff, and soil moisture, respectively, present nonstationary changes over the whole Chinese mainland. Spatially, the nonstationarity exhibits a “higher in the north, lower in the south” pattern, and the proportions of the nonstationary grid or station increase as the considered temporal scales become longer. Our results also prove that the consideration of nonstationarity in the drought calculation can improve the fitting performance of hydro-meteorological time series, thus can give a more precise description of drought conditions than conventional stationary drought (SD) indices, especially for the agricultural drought indices. Additionally, there is a significant difference between the nonstationary drought (NSD) indices and SD in drought categories. A comparison with historical typical drought events reveals that NSD indices considering the nonstationary changes in hydro-meteorological variables, more accurately indentifies drought categories. More than 28% of months identified as mild-to-severe droughts by the SD indices are reclassified into lower drought categories by the NSD indices, which indicates that the nonstationarity in hydro-meteorological variables can largely influence the drought categories. These findings highlight the necessity of incorporating nonstationarity into drought assessment frameworks and provide a scientific basis for more reliable drought management and mitigation strategies.