Response of drought to climate extremes in a semi-arid inland river basin in China

doi:10.1007/s40333-024-0064-5

Journal of Arid Land

2024, Vol. 16

Issue (11): 1505-1521 DOI: 10.1007/s40333-024-0064-5

Research article

Response of drought to climate extremes in a semi-arid inland river basin in China

QU Zhicheng, YAO Shunyu, LIU Dongwei^*(

)

School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China

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Abstract

Against the backdrop of global warming, climate extremes and drought events have become more severe, especially in arid and semi-arid areas. This study forecasted the characteristics of climate extremes in the Xilin River Basin (a semi-arid inland river basin) of China for the period of 2021-2100 by employing a multi-model ensemble approach based on three climate Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, a linear regression, a wavelet analysis, and the correlation analysis were conducted to explore the response of climate extremes to the Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), as well as their respective trends during the historical period from 1970 to 2020 and during the future period from 2021 to 2070. The results indicated that extreme high temperatures and extreme precipitation will further intensify under the higher forcing scenarios (SSP5-8.5>SSP2-4.5>SSP1-2.6) in the future. The SPEI trends under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were estimated as -0.003/a, -0.004/a, and -0.008/a, respectively, indicating a drier future climate. During the historical period (1970-2020), the SPEI and SDI trends were -0.003/a and -0.016/a, respectively, with significant cycles of 15 and 22 a, and abrupt changes occurring in 1995 and 1996, respectively. The next abrupt change in the SPEI was projected to occur in the 2040s. The SPEI had a significant positive correlation with both summer days (SU) and heavy precipitation days (R10mm), while the SDI was only significantly positively correlated with R10mm. Additionally, the SPEI and SDI exhibited a strong and consistent positive correlation at a cycle of 4-6 a, indicating a robust interdependence between the two indices. These findings have important implications for policy makers, enabling them to improve water resource management of inland river basins in arid and semi-arid areas under future climate uncertainty.

Key words： climate extremes climate change Standardized Precipitation Evapotranspiration Index (SPEI) Streamflow Drought Index (SDI) wavelet analysis multi-model ensemble Xilin River Basin

Received: 11 May 2024 Published: 30 November 2024

Corresponding Authors: ^*LIU Dongwei (E-mail: liudw@imu.edu.cn)

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Cite this article:

QU Zhicheng, YAO Shunyu, LIU Dongwei. Response of drought to climate extremes in a semi-arid inland river basin in China. Journal of Arid Land, 2024, 16(11): 1505-1521.

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http://jal.xjegi.com/10.1007/s40333-024-0064-5 OR http://jal.xjegi.com/Y2024/V16/I11/1505

Fig. 1 Overview of the Xilin River Basin based on the digital elevation model (DEM) and the location of hydrological station (a), as well as the spatial distribution of meteorological stations (b)

Table 1 Details of the CMIP6 GCMs used in the study

Table 2 Definitions of the six climate extremes used in this study

Table 3 Drought severity classification based on the SPEI and SDI

Fig. 2 Taylor diagrams comparing the daily maximum temperature (a), daily minimum temperature (b), and daily precipitation (c) from five Global Climate Models (GCMs) and reliability ensemble averaging (REA) results with observation (OBS) in the Xilin River Basin during the period of 1970-2020. SD, standard deviation; CC, correlation coefficient; RMSE, root mean square error.

Fig. 3 Climate extremes in the model simulations of the Xilin River Basin from 1970 to 2020. (a), FD (frost days); (b), SU (summer days); (c), TXx (Max Tmax); (d), R10mm (heavy precipitation days); (e), CWD (consecutive wet days); (f), CDD (consecutive dry days). To enhance visual clarity, the figures show a three-year sliding average. The colored arrow indicates the average rate of change under each scenario. The shaded area indicates the range of the standard deviation of the REA results.

Fig. 4 Time-series characteristics of climate extreme changes based on a Morlet wavelet analysis: continuous wavelet power spectrum (a−f) and wavelet variance (g-i). (a and g), FD; (b and h), SU; (c and i), TXx; (d and j), R10mm; (e and k), CDD; (f and l), CWD.

Fig. 5 Variations of the Standardized Precipitation Evapotranspiration Index (SPEI) and sliding t-test results during the historical period from 1970 to 2020 (a) and during the future period from 2021 to 2070 under different climate scenarios (b-d) in the Xilin River Basin

Table 4 Frequency of meteorological drought in the Xilin River Basin during the historical period (1970-2020) and during the future period (2021-2070) under the three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5)

Fig. 6 Change characteristics of the SPEI time-series based on a Morlet wavelet analysis. (a), continuous wavelet power spectrum; (b), wavelet variance.

Fig. 7 Change characteristics of the Streamflow Drought Index (SDI) time-series from 1963 to 2015 based on a Morlet wavelet analysis. (a), trend analysis; (b), continuous wavelet power spectrum; (c), wavelet variance. The sidebar in Figure 7b indicates correlation values ranging from -2.0 to 2.0.

Fig. 8 Correlation heatmap showing the relationships among the climate extreme indices, SPEI, and SDI. *, P<0.05 level; **, P<0.01 level. Ellipses pointing toward the upper right indicate positive correlations, while those pointing toward the upper left indicate negative correlations. The flatter the ellipse and the darker its color, the stronger the correlation.

Fig. 9 A wavelet coherence analysis showing the relationships of climate extreme indices and the SPEI with the SDI during the period of 1970-2020. (a), relationship between FD and SDI; (b), relationship between SU and SDI; (c), relationship between TXx and SDI; (d), relationship between R10mm and SDI; (e), relationship between CDD and SDI; (f), relationship between CWD and SDI; (g), relationship between the SPEI and SDI. The color bar indicates the covariance strength (power), with yellow representing strong co-movements and blue indicating weak ones. The black contoured shaded area marks the 5% significance level, and the black thick circles within the cone of influence highlight segments where the correlation between the series is statistically significant at the 95% confidence level. Additionally, the phase relationship is denoted by the arrow directions, with the upward and downward arrows indicating that climate extreme indices lead and lag the SDI, respectively. The left and right arrows suggest the anti-phase and in-phase relationships between the series, respectively.


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