Study on the driven mechanism of hydrologic drought based on the lithology-combined structure of the Karst drainage basin in South China

Hydrologic drought, considered as a typical natural phenomenon in the background of global climate changes, is the continuation and development of meteorological and agricultural droughts, and is the ultimate and most thoroughly drought (Kang & Guo 1991). The drought cannot be simply attributed to the climate anomalies and precipitation reduction under the special and fragile Karst ecological environments, but to the water storage capacity of underlying surface media shown as the lithology, landform, soil, vegetation, etc. The watershed lithology is one of important medium factors, and their functions are mainly shown that (i) for the different types of watershed lithologies, there are different kinds of watershed landforms developed in Karst basins, which will lead to the different runoff characteristics on the surface and underground, and affect the watershed storage capacity (Liang 1995, 1997; Liang & Wang 1998); (ii) the different soil structures and vegetation types are developed due to different types of watershed lithologies with different anti-weathering abilities and weathering crust thicknesses formed in Karst basins, and they influence the infiltration and runoff of rainfall on the surface and underground, which results in the different watershed storage capacity; and (iii) for the different types of watershed lithologies, with the different mechanical properties and the different development intensities and scales of bedrock fissures, are developed the different water-stored spaces or drainage channels in Karst basins, which affects the watershed storage capacity. For example, Liang (1995) discussed the flood peak effects caused by the coupling differences between the different spatio-temporal scales of Karst drainage basins and the different spatial configurations of landform types. It found that there is a positive proportional relationship between peak values and watershed areas in the Non-Karst basins, and a special relationship in the middle and small Karst basins due to the double geomorphic system structures on the surface and underground. Meanwhile, the watershed lithology is one of the important factors affecting the low-flow runoff moduli in the middle and small Karst basins. For instance, the larger the proportion of watershed limestones, the smaller the low-flow runoff moduli, and the larger the proportion of watershed dolomites, the larger the low-flow runoff moduli (Liang & Wang 1998). Wang's research (Wang et al. 2002) showed that the impacts of different natural factors on the low-flow runoff in the dry seasons, such as the spatial scales of Karst basins, watershed lithologies, and landform types, are very significant. For instance, the larger the proportion of peak-cluster depressions, the smaller the low-flow runoff moduli, and the larger the proportion of the Fenglin Rongyuan (Basin), the larger the low-flow runoff moduli. Therefore, to make the identification and extraction of lithologic types in Karst drainage basins, and to analyze their spatial coupling structures in this paper, it is helpful to study the rule of watershed water storage, and to reveal the driven mechanism of watershed hydrologic droughts (Liang 1995, 1997; Liang & Wang 1998). For the previous studies on hydrological droughts, some scholars have carried out the quantitative expressions for the hydrologic drought characteristics by the theory of runs (Yevjevich 1967) and have studied the extreme hydrologic drought characteristics obeyed the normal, lognormal, and gamma distributions (Sen 1977, 1990, 1991; Guven 1983; Sharma 1998). The Regional Drought Area Index (RDAI) and the Drought Potential Index (DPI) are used to characterize regional hydrological droughts (Fleig et al. 2011), and the relationship between the drought duration and intensity was also further discussed by Kim & Valdés (2006) and Panu & Sharma (2009). The influences of river regulation and water diversion from other places on the process and level of hydrologic droughts are analyzed by Wen et al. (2011) employing the Standardized Runoff Index (SRI) and the Standardized Precipitation Index (SPI). The level, process, and recurrence period of hydrologic droughts are studied by the Palmer Drought Severity Index (PDSI), the Soil Moisture Model (SMM), and the Standardized Rainfall Index (SRI), as well as the Vegetation Health Index (VHI) (Nyabeze 2004; Mondal & Mujumdar 2015), respectively. The time-series analysis and random simulation of hydrologic drought severities have been carried out using an autoregression model (Abebe & Foerch 2008), and the probabilistic prediction of hydrologic droughts is analyzed by Hao et al. (2016) using a conditional probability based on the meta-Gaussian model. Seibert et al. (2017) made the seasonal forecast for hydrologic droughts using a statistical analysis method in the Limpopo Basin. In addition, Rudd et al. (2017) firstly characterized droughts over the last century by a national-scale gridded hydrologic model well-simulated low flow in many catchments across Great Britain. The historic drought periods (1891–2015) were better identified employing time series of monthly mean river flow and soil moisture based on the threshold level method. Meanwhile, the spatial–temporal distribution differences between the meteorological and hydrological droughts are explored by some scholars (Hisdal & Tallaksen 2003; Tallaksen et al. 2009; Yang et al. 2021) in terms of basin scales. The temporal and spatial evolution patterns of hydrological droughts are studied by employing different drought indexes in terms of the national or regional scales. For example, the hydrological drought severity in region of Thessaly, Greece with varying geomorphologic characteristics are explored by Vasiliades & Loukas (2009) using the Palmer four indices (PDSI, Weighted PDSI, PHDI, and the moisture anomaly Z-index) combined with the UTHBAL conceptual water balance model. Tabari et al. (2013) discussed the hydrological drought characteristics by the Streamflow Drought Index (SDI) in the northwest of Iran over the period 1975–2009. It was found that some of the streamflow volume series did not follow the normal distribution, and extreme droughts occurred most frequently in the last 12 years from 1997–1998 to 2008–2009. Leng et al. (2015) assessed the climate change impact in China on droughts from meteorological, agricultural, and hydrologic perspectives by the SPI, SSWI, and SRI, respectively. They thought that the meteorological, hydrological, and agricultural droughts will become more severe, prolonged, and frequent in 2020–2049 compared with 1971–2000. Vazifehkhah & Kahya (2018) analyzed the influences of winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) extreme phases on hydrological drought using a Standardized Streamflow Index (SSI) over Turkey and northern Iran. The droughts in various magnitudes during the positive extreme phases of NAO were found in Western and Eastern Turkey while fewer droughts were observed in Iran during the negative NAO and AO extreme phases. The hydrological droughts were more strongly affected by the negative NAO and AO extreme phases in a shorter period than by the positive NAO and AO phases in a longer period. Ding et al. (2021) studied the propagation relationship between meteorological and hydrological drought, and the most susceptible regions in China and global scale. There was a stronger relationship between the two types of drought in summer and autumn than in spring and winter. The most susceptible regions were tropical and subtropical Chinese southern zones in China and equatorial and warm temperate climate zones in global. The variable threshold level approach is employed to analyze the hydrological drought characteristics in Bundelkhand region of Central India during 1974–2009 (Swetalina & Thomas 2016) and to estimate the return period of hydrologic droughts in southeastern semi-arid region of Iran. It was found that the vegetation cover play important roles in the hydrologic drought length (Modarres & Sarhadi 2010). Drought duration, severity, and frequency are analyzed for steep topography in the central Vietnam and other parts of Southeast Asia at different timescales (Firoz et al. 2018). The more severe droughts are observed in groundwater dependent areas, where have longer durations rather than higher intensities (Rudd et al. 2017). The relationship between the El Niño-Southern Oscillation (ENSO) and hydrologic variability in the United States is discussed using Empirical Orthogonal Function (EOF)/Principal Component Analysis (PCA), and it is found the positive relationships in the Pacific Northwest, whereas negative relationships in southern California and the northern Great Plains (Ryu et al. 2010). Finally, to explore the feasibility of the SRI in Southwest China, Liu et al. (2016) simulated a long-term daily hydrological and meteorological data series by the Xinanjiang model and calculated the SRI. It proves that the onset, severity, and duration of extremely droughts are detected well by the SRI. For the studies on hydrological droughts in China, Feng & Wang (1997) and Feng & Jia (1997) mainly discussed the influence factors of runoff volume in the dry season, and the identification method of hydrological droughts based on the theory of runs, and analyzed the hydrologic drought severities by the time fractal dimensions. Feng (1993, 1994, 1995) studied the probability density and distribution functions of extreme hydrologic drought durations. The joint distribution of hydrologic drought characteristics is constructed employing the Copula Joint Distribution Function (Yan et al. 2007; Ma & Song 2010; Xu et al. 2010; Zhou et al. 2011). However, most of the researches are mainly to make the identification, characteristic analysis, and prediction for hydrologic droughts by different drought indices, respectively. For instance, Zhai et al. (2015) established a new hydrologic drought assessment index such as Standard Water Resources Index (SWRI) and developed a complete basic framework of hydrologic drought identification, assessment, and characteristic analysis combined with the distributed hydrologic model, the copula function, and the statistical test method. The Standardized Streamflow Drought Index (SSDI) is established based on the optimal distributions such as the logistic, normal, two-parameter lognormal, or Weibull probability distribution (Zhao & Zhao 2016), and it is validated in the applicability and rationality in the Fenhe River Basin. Combined with the percentages of runoff anomaly and precipitation anomaly, Wu et al. (2016) constructed the Regional Hydrological Drought Index (RHDI) and obtained the corresponding frequency of drought grade, and further determined the threshold value of the different drought levels for regional hydrological droughts. The Copula Model of two-variable joint distribution of hydrologic drought characteristics is built based on the test method of Cramer–von Mises Statistics associated with Rosenblatt transfer (Tu et al. 2016), and it is analyzed the hydrologic drought characteristics and water shortage responses in the Dongjiang River Basin under a changing environment. Ren et al. (2016) quantitatively separated the contribution of climate change and human activities on runoff reduction based on the Variable Infiltration Capacity Model (VICM) and analyzed the spatio-temporal evolution characteristics of hydrological droughts by the SRI. The Standard Precipitation Evapotranspiration Index (SPEI) and the SDI are used to analyze the evaluation characteristics of both meteorological and hydrologic droughts and to discuss the response of hydrologic droughts to meteorological droughts (Li et al. 2016). The SRI, SDI, and associated indicators with the trend, time lag cross-correlation are applied to analyze the spatial–temporal characteristics of meteorological and hydrologic droughts across the Yellow River Basin (YRB) during the periods 1961–2010 (He et al. 2015a). According to the correlation characteristics of seasonal runoff, the copula prediction model of hydrologic droughts based on the copula function, runoff distribution function, and SRI is constructed by Zhang et al. (2016), and the empirical analysis is carried out by the hydrologic station of the Aksu River West Bride.

However, the present studies on the hydrologic droughts in Karst basins, except for some relevant research contents of this team (He & Chen 2013; He et al. 2013, 2014, 2015b, 2018a, 2018b), have not seen more detailed studies reporting. Thus, the objectives of this study are (i) to discuss the types of Karst drainage basins based on the spatial coupling structures of different lithologies in Section 4.1; (ii) to comparatively analyze the hydrological drought characteristics in terms of different time scales and spatial coupling of lithologies in Section 4.2; and (iii) to systematically study the driven mechanism of the single lithology and the different lithologies coupled to hydrological droughts in Section 4.3. Therefore, it is beneficial to promote the development of Karst hydrographic geomorphology.

The South China (SC), taking Guizhou, Yunnan, and Guangxi as the center, is a typical distribution region of the Cone Karst, Sword Karst, and Tower Karst. This study was to select the Karst drainage basins controlled by 55 hydrological stations in the SC as the research area. It is enclosed by the eastern longitudes of 101°55′55″–110°55′45″ and northern latitudes of 22°42′57″–29°13′11″ with an area of 352,526 km² and an average elevation of 1,065.62 m. The research area is mainly included the most area of Guizhou Province (37.97%), southeastern area of Yunnan Province (25.36%), and northwestern and northern areas of Guangxi Province (36.67%) (Figure 1). It is located in the climatic zones of north subtropical humid climate and south subtropical semi-humid climate with the mean annual rainfall of 1,000–1,300 mm, and the average annual temperature of 16–23 °C. The study area is divided into two parts of the Yangtze and Pearl River Basins by the Wumeng–Miaoling Mountains. That means the Jinshajiang River System, the Upper Yangtze River Mainstream System, the Wujiang River System, and the Dongting Lake System of the Yangtze River Basin are located in the north of research area, whereas the Duliu River System (in Guizhou), the Yuanjiang River System (in Yunnan), and the Xijiang River System (in Guangxi) of the Pearl River Basin in the south of research area.

Hydrologic drought is usually shown by the reduction and the cutoff of the runoff volumes on the surface and underground rivers, and the decline of table levels in the lakes and reservoirs (Van Loon & Van Lanen 2012; Van Loon & Laaha 2015). Hydrologic data of the 55 hydrological stations (including 25 in Guizhou, 19 in Guangxi, and 11 in Yunnan; Table 1) in this study were obtained from the Hydrology Statistical Yearbook compiled by the Ministry of Water Resources of the People's Republic of China (Hydrologic Year Book of People's Republic of China¹) and calculated the monthly average runoff volume during the periods 1970–2013. The hydrological stations selected in this paper must have 44 years of continuous hydrological observation, and continuous defect data must be less than 3 months. All the data will be made quality analysis (reliability, homogeneity, and representativeness), and for the months of defect data are interpolated by the cubic spline function. The interpolations must keep the same change trend as the total values during the periods 1970–2013. In order to eliminate the influence of different basin areas, hydrologic data were standardized in this study.

Firstly, we all know that the geological lithological types and structures are basically unchanged during the periods 1970–2013, because the formation and evolution of geologic lithologies are a slow geological process. This paper made some preprocessing for TM images corresponding to the mean runoff of the minimum month in 2006 (Time: January to December 2006) and extracted the study area of remote sensing images controlled by 55 hydrological stations (He et al. 2012). Secondly, the research area was extracted from the Comprehensive Geological Map of 1:500,000 in Guizhou, Yunnan, and Guangxi Provinces carried out by the geometric correction and projection correction, which was fused with the TM images. Thirdly, the remote sensing information of watershed lithologies was automatically extracted using object-oriented classification technology based on the Comprehensive Geological Map of 1:500,000 in Guizhou, Yunnan, and Guangxi Provinces (Jiao & Liang 2002). Finally, to make the statistics for the lithologic types of areas and to calculate the area percentages (Table 1).

A positive SRI means humid, whereas a negative SRI means drought. Moreover, when SRI is negative, the larger the absolute value of the SRIs means the serious hydrological drought. According to SRI, hydrological droughts can be divided into five levels, namely the Non-Drought for the 1.0 ≤ SRI ≤ 3.0, the Mild Drought for the −0.99 ≤ SRI ≤ 0.99, the Moderate Drought for the −1.49 ≤ SRI ≤ −1.0, the Severe Drought for the −1.99 ≤ SRI ≤ −1.5, and the Extreme Drought for the −3.0 ≤ SRI ≤ −2.0 (Table 2) (Nalbantis & Tsakiris 2009; Wen et al. 2011; Jehanzaib et al. 2020).

This paper supposed that is the information matrix of lithological types in the research areas, and is the drought level matrix of hydrologic droughts. In which is the area percentage of the jth type of lithologies, the ith level of hydrologic droughts, and is the area percentage of the ith level of hydrologic droughts.

In a maximum likelihood classification, the maximum value of will be selected, if there is ⁠, for all ⁠. Namely, the hydrological drought is belonged to the category ⁠. It is called the probability vector for all probability values of the impact of each lithologic type index on hydrologic drought levels, which will be used to describe the uncertainty of lithological type indicators.

As shown in Table 1, the watershed lithologies, without a single type, are mainly mixed with two or more than types of lithologies in Karst drainage basins. Therefore, to make the classification for the watershed sample areas by the system clustering method based on the watershed lithology-combined characteristics, and to draw the cluster pedigree by the SPSS software (Figure 2).

It can be seen that there are certain similarities between different basins in the cluster pedigree chart. The watershed lithological types could be divided into five types of combining structures according to the marking distance equal to 20 (Wang et al. 2002).

Class I (c26, c42, c34, c5, c19, c46, c16, c37, c41, c33, c13, c21, c9, c29): The largest (65%) is the area percentage of the dolomites, mud dolomite with mudstone (T), followed by the medium thickness limestones with breccia dolomite and siliceous limestone (P) with the area percentage 19%, and the smallest (1%) is the area percentage of the limestones and dolomites in the upper part of the basins, dolomite in the lower part, and quartz sandstone in the bottom (ε). Therefore, the basin with the lithology-combined structures is called a Dolomite-type Karst Basin.

Class II (c6, c20, c3, c24, c2, c12, c45, c17, c15): The largest (66%) is the area percentage (66%) of medium thickness limestones with breccia dolomite and siliceous limestone (P), followed by the dolomite, mud dolomite with mudstone (T) with the area percentage 21%, and the smallest (3%) is the area percentage of limestones with mud limestone and shale (J), and carbon shale with sandstone (D). Therefore, the basin with the lithology-combined structures is called a Limestone-type Semi-Karst Basin.

Class III (c53, c54, c28, c43, c48, c8, c32, c11, c44, c18, c30, c55, c27, c35, c38): The largest (38%) is the area percentage of limestones with dolomitic limestone (C), followed by the dolomite, mud dolomite with mudstone (T) with the area percentage 29%, and the smallest (1%) is the area percentage of thick block sandstones with mudstone (K). Therefore, the basin with the lithology-combined structures is called a Limestone-type Karst Basin.

Class IV (c1, c4, c23, c25, c31, c47, c10): The largest (26%) is the area percentage of limestones and dolomites in the upper part of the basin, dolomite in the lower part, and quartz sandstone in the bottom (ε), followed by the thick limestones with dolomite (O) with the area percentage 16%, and the smallest (2%) is the area percentage of dolomites with silicon strip dolomite in the upper part of the basin, and mud dolomite in the bottom (Z). Therefore, the basin with the lithology-combined structures is called a Dolomite-type Semi-Karst Basin.

Class V (c14, c36, c40, c49, c52, c50, c51, c7, c39, c22): The largest (27.85%) is the area percentage of carbon shales with sandstone (D), followed by the sericite slates with siltstone in the upper part of the basin, and siltstones with sericite slate in the bottom (Pt) with the area percentage 15%, and the smallest (2%) is the area percentage of thick limestones with dolomite (O). Therefore, the basin with the lithology-combined structures is called a Non-Karst Basin.

Hydrological drought characteristics in South China at the different time scales (SRI_3, SRI_6, SRI_9, and SRI_12) (SC) were calculated based on the data of monthly mean runoff during the periods 1970–2013 by the SRI in this study (Figure 3).

In terms of the temporal evolution of hydrologic droughts, (i) the distribution areas of hydrological droughts in the Karst drainage basins of South China are larger, and drought intensities are shown a decreasing trend. Among them, the hydrological droughts at the SRI_3, SRI_6, and SRI_9 are relatively serious with the area percentages of 98.99, 99.63, and 98.46%, respectively, and relatively lighter at the SRI_12 with the area percentage of 77.43% (Figure 3(d)). It means that the hydrological drought areas in Karst basins are gradually decreasing with the increase of time scales. (ii) The distribution areas of moderate hydrological droughts at the SRI_3 and SRI_6 are the largest with the area percentages of 43.23 and 52.26% shown in Figure 3(a) and 3(b), respectively, followed by the mild hydrologic droughts with the area percentages of 27.74 and 20.12%, and the smallest the Non-Drought. It indicates that the runoff regulation functions of Karst drainage basins are weaker or nonsignificant with the time scales decreasing. While the distribution areas of mild hydrological droughts at the SRI_9 and SRI_12 are the largest (62.19 and 66.21%), followed by the moderate hydrological drought (27.32%) at the SRI_9, the Non-Drought (22.57%) at the SRI_12, and the smallest the Non-Drought at the SRI_9, the severe hydrologic droughts at the SRI_12 (Figure 3(c) and 3(d)). (iii) For the severe or above droughts, the largest are the drought areas at the SRI_3 and SRI_6 with the area percentages of 25.04 and 27.28%, followed by the SRI_9 (8.97%), and the smallest (6.57%) at the SRI_12 in Figure 3(d). This means that the drought areas of the severe or above hydrologic droughts are also shown a decreasing trend with the increase of time scales. It further demonstrates that the runoff regulation effects or water storage functions of Karst drainage basins are more significant with the time scales increasing.

From the spatial distribution, (i) the hydrological drought intensity of the Karst drainage basins in South China is a gradually aggravating trend from the west to east parts and is showing a significant north–south stripe distribution at the SRI_3 and SRI_6 (Figure 3(a) and 3(b)). (ii) The mild, moderate, and severe or above hydrologic droughts are mainly distributed in the eastern, central, and western of research areas at the SRI_3 and SRI_6 with the area percentages of 28.75, 46.23 and 25.04%; and 20.51, 52.23 and 27.28%, respectively, and in the most part of the central, southwest, and southeast of research areas at the SRI_12 (Figure 3(c)). (iii) Whether at the SRI_3 and SRI_6, or at the SRI_9 and SRI_12, the severe or above hydrologic droughts are found in the Zhijin Basin of Guizhou Province, the Yiguba Basin of Yunnan Province, the Tianzhu and Du'an Basin of Guangxi Province, while the mild or below hydrologic droughts in the Panjiangqiao Basin of Guizhou Province and the Fengyu Basin of Guangxi Province.

As shown in Figure 4, (i) the moderate or below hydrological droughts (−1.5 < SRI) are observed in Classes I to V at the four types of time scales (SRI_3, SRI_6, SRI_9, and SRI_12). The severe or above hydrologic droughts (SRI < −1.5) are gradually decreased with the increase of time scales, especially the severe hydrologic droughts (−2.0 < SRI < −1.5) is shown a parallel distribution and rapid decline. (ii) For the SRI_3 (Figure 4(a)), it is presented a peak value for the mild and extreme hydrologic droughts in Classes I, IV, and V, and a low valley phenomenon for the extreme hydrologic droughts in Class II and the mild hydrologic droughts in Class III, as well as a trend of first increase and then decrease for the moderate hydrologic droughts (−1.5 < SRI < −1.0) in Classes I to V. (iii) In terms of the SRI_6 (Figure 4(b)), the peak value of mild hydrologic droughts is shown a north shifting phenomenon and that of extreme hydrologic droughts is shown a south shifting phenomenon. The distribution areas of moderate hydrologic drought (−1.5 < SRI < −1.0) in Classes I to V appear as a gradually decreasing trend and are obviously shown a peak value phenomenon in Class III. For the Classes I to V, the densities of moderate or below hydrological droughts are smaller with the density gradient changing slowly, and these of severe or above hydrologic droughts are larger with the density gradient changing rapidly. (iv) For the 9-month scale (SRI_9) (Figure 4(c)), the significant increasing trend and the obvious peak-valley alternating phenomena in Classes I to V are found for the distribution areas of mild hydrological droughts, the rapidly decreasing trend for these of severe or above hydrologic droughts, and the parallel distribution for these of moderate hydrological droughts. The mild hydrologic droughts are shown the two peaks and one valley in Class I, the single-peak distribution in Class V, and the peak flattening phenomenon in Class III, respectively. The severe or above hydrologic droughts are presented the peak value in Classes I, III, and IV, and the low valley in Classes II and V. The larger densities and density gradient changes rapidly are observed for the severe or above hydrological droughts in Classes I to V. (v) As shown in Figure 4(d) at the SRI_12, the distribution areas of mild hydrologic droughts in Classes I to V are accounted for a larger proportion, while those of severe or above hydrologic droughts in Class V has disappeared. There are still two peaks and one valley for the distribution regions of mild hydrologic droughts in Class I, the single-peak distribution for these of severe or above hydrologic droughts, and the low valley phenomenon in Class II. The gradient distributions obviously appeared for the hydrological droughts in Classes II to V, and the gradient changes of moderate or below hydrologic droughts are relatively slow, and these of severe or above hydrologic droughts are relatively steep.

Figure 5 shows that (i) the 11 kinds of watershed lithologies have produced some influences on hydrologic droughts, and these impacts are mainly concentrated in the mild, moderate, and severe hydrologic droughts at the SRI_3 and SRI_6 (Figure 5(a) and 5(b)), and in the moderate or below hydrologic droughts at the SRI_9 and SRI_12 (Figure 5(c) and 5(d)). (ii) For the 3-month scale (SRI_3) (Figure 5(a)), it mainly occurs the mild and moderate hydrologic droughts in the Pt to K distribution regions. Especially the occurrence probabilities are the largest (0.5–0.6) in the C, P, and T distribution regions, followed by in the Z, ε, O, J, and K distribution regions with the occurrence probabilities 0.35–0.45, while the smallest (0.2–0.3) in the Pt, S, and D distribution regions. (iii) For the 3-month scale (SRI_3) (Figure 5(a)), there are mainly the mild and moderate hydrologic droughts in the Pt to K distribution areas. Especially the occurring probability is the largest (0.5–0.6) in the C, P, and T distribution areas, followed by in the Z, ε, O, J, and K distribution areas with the occurring probabilities 0.35–0.45, while the smallest (0.2–0.3) in the Pt, S, and D distribution areas. The largest (0.75) is the occurring probabilities of severe and extreme hydrological droughts in the Pt, followed by in the S, D, and J distribution regions (0.15–0.35). (iv) It mainly occurs the moderate hydrologic droughts at the SRI_6 (Figure 5(b)). Especially the largest is the occurring probabilities in the C, P, T, and K distribution regions, followed by in the Z, ε, O, S, D, and J distribution regions with the occurring probability 0.35–0.4, the smallest (0.2) in the Pt distribution region. The largest probability (0.75) of severe or above hydrologic droughts is observed in the Pt distribution region, the smallest (0.1–0.2) in the rest of the lithologic distribution regions. The occurrence probability of the severe or above hydrologic droughts is the largest (0.75) in the Pt distribution area, while the smallest (0.1–0.2) in the rest of the lithologies. (v) For the 9-month and 12-month scales (SRI_9, SRI_12) shown in Figure 5(c) and 5(d), the mild hydrologic droughts are mainly presented in the Pt to K distribution regions. Among them, the occurrence probability of mild hydrologic droughts is the largest (0.55–0.8) in the C to K and Z distribution regions at the SRI_12, followed by in the Z to O distributions (0.3–0.6), while the smallest (0.2–0.3) in the S, D, and Pt distribution regions. For the C to K distribution regions, the mild to moderate hydrologic droughts are shown a slower change and smaller gradient at the SRI_9, a faster change and larger gradient at the SRI_12, and the severe and extreme hydrologic droughts have basically not happened. There are the slowest change and smallest gradient for the mild to moderate hydrologic droughts in the S and D distribution regions, and the severe and extreme hydrologic droughts have a certain occurrence probability (0.15). A slower change and smaller gradient are found for the mild to moderate hydrologic droughts in the Pt to O distribution regions, however the severe hydrological droughts have disappeared, and extreme hydrological droughts have a larger occurring probability (0.55).

It is known from the lithology-combined structures (Figure 6) that (i) the five kinds of structures have all produced the influences on the hydrological droughts. Among them, the impacts are mainly concentrated in the mild and moderate hydrologic droughts at the SRI_3 and SRI_6, the mild or below hydrologic droughts at the SRI_9 and SRI_12. This indicates that the response of Karst basins to atmospheric precipitation is shown a lag, and the lagged effects of different lithology-combined structures have great differences. It shows more and more significant with the increase of time scales. (ii) For the 3-month scale (SRI_3) shown in Figure 6(a), the mild and moderate hydrologic droughts are observed in Classes I to V, especially with the largest probability (0.6) in Class II, followed by in Classes I and IV with the occurrence probability 0.5. It hardly occurs the mild hydrologic droughts in Class III, but occurs the moderate hydrologic droughts with the occurring probability 0.4. The severe and extreme hydrologic droughts hardly happen in Classes I, II, and IV, and show the occurrence probabilities of 0.2–0.3 in Classes III and V. The mild to extreme hydrologic droughts are shown the larger gradient and faster change in Classes I and II, and the smaller gradient and slower change in Classes IV and V, as well as a certain fluctuation characteristics in Class III. (iii) For 6-month scale (SRI_6), it mainly occurs the mild hydrologic droughts in Classes I, II, and IV with the occurring probabilities of 0.5–0.6 (Figure 6(b)), and the moderate hydrologic drought in Classes III and V with the occurring probabilities of 0.35–0.4. Similar to the SRI_3, the severe and extreme hydrologic droughts do not basically happen at the SRI_6 in Classes I, II, and IV, and show a certain occurring probabilities of 0.05–0.2 in Classes III and IV. The occurrence probability of mild hydrologic droughts is gradually decreasing at the SRI_6 in Class V, and that of moderate hydrologic droughts is just the opposite. (iv) Compared with the SRI_3 and SRI_6, it mainly occurs the mild hydrologic drought at the SRI_9 and SRI_12 (Figure 6(c) and 6(d)). Especially the highest (0.45–0.9) is the occurring probability of mild hydrologic droughts in Class V, followed by in Classes I, II, and IV (0.6–0.75), and the smallest (0.4–0.45) in Class III. The mild to extreme hydrologic droughts in Classes I to V are presented the slower change and smaller gradient at the SRI_9, and the faster change and larger gradient at the SRI_12. It hardly occurs the severe or above hydrologic droughts at the SRI_9 and the moderate or above hydrologic droughts at the SRI_12. From the Classes I to IV, the mild hydrological droughts are shown the larger distribution areas with a higher probability at the SRI_9, and the smaller distribution areas with a lower probability at the SRI_12. Especially in Class V, the mild and moderate hydrologic droughts at the SRI_9 are mainly appeared the mild hydrologic droughts at the SRI_12.

Attributed to the soluble aqueous medium under the differential erosion or solution effects of soluble water, many sizes of spaces are formed in Karst drainage basins, which provide some places for atmospheric precipitation lag flow on the surface and underground, and makes the basins have a certain storage capacity. This paper justly attempts to explore the watershed storage capacities in terms of lithologic types and spatial coupling structures, and to reveal the mechanism of hydrological droughts. According to this research purpose, this paper firstly selected 55 samples in Karst distribution areas of South China, and collected monthly runoff data from 1970 to 2013, and extracted the basin lithology data. Secondly, the temporal and spatial evolution characteristics of hydrological droughts in Karst drainage basins of South China were analyzed using the SRI, and the mechanism of hydrological droughts were discussed employing the cluster statistical method and Bayesian formula of probability theory in terms of single lithology and spatial coupling of basin lithologies. Therefore, this study has realized the initial research assumption, and these research results have good applicability for Karst drought monitoring and prediction. However, this study mainly considered the Karst research areas, and the research results have certain limitations for the application in Non-Karst areas.

The double aqueous medium and dual water system structures on the surface and underground are developed in Karst drought basins compared with the normal basins, which results in the significant response of watershed runoff to atmospheric precipitation at different time scales (Wang et al. 2002; He et al. 2015a, 2015b). This study demonstrates that the hydrological droughts are relatively serious at the 3-, 6-, and 9-month scales, and a relatively lighter at the SRI_12. The occurrence level of hydrological droughts is mainly shown the extreme hydrological drought (6.28%) < severe hydrological drought (6.52%) < non-drought (7.15%) < moderate hydrological drought (32.8%) < mild hydrological drought (47.25%). It proves that Karst drain basins have a stronger water storage function, and their water storage capacities become more and more significant with the increase of time scales. Meanwhile, many types of lithologies, impure textures, and complex structures are observed in Karst drainage basins, where are developed the different sizes of water-stored spaces under the differential erosion or solution effects of soluble water, such as the solution gap, solution hole, and pipeline, as well as the underground cave and underground corridor. As a result, the response of different lithologic combination structures to atmospheric precipitation is significantly different (He & Chen 2013; He et al. 2013, 2014; López-Moreno et al. 2013). This study indicates that in terms of lithology combination structures, the hydrological drought is the most serious in Class V with the drought area 95.94%, followed by in Classes IV, III, and I with the drought areas of 94.41, 92.25, and 91.95%, respectively, and the relatively lighter (90.95%) in Class II. The severity of hydrological droughts is the Classes II and III (91.79%) < Classes I and IV (92.15%) < Class V (95.94%) in terms of the combination types of watershed lithologies, and the Classes I and III (90%) < Classes II and IV (92.2%) < Class V (95.94%) in terms of Karst lithologies. These conclusions are supported by He et al. (2015b).

The watershed lithologies are the material bases controlling the geomorphic development and soil formation. The different watershed lithologies are shown the different resistance abilities because their lithologic compositions, particle sizes, and composite structures have the larger differences, which promotes or inhibits the formation of water-stored spaces in Karst drainage basins. Therefore, the driving capacity of different watershed lithology types and spatial coupling structures to hydrological droughts have significant differences. This study shows that the driving probability of single lithology to hydrologic droughts is the Pt (0.15) < J, D and S (0.3) < T, O, and ε (0.4) < K (0.45) < P and Z (0.5) < C (0.6) in the distribution areas of mild hydrologic droughts, the Pt (0.15) < ε (0.2) < Z (0.25) < J, D, S, O, P, and C (0.3) < T (0.35) < K (0.4) in the distribution areas of moderate hydrologic droughts, the C (0) < Pt, P, and T (0.05) < Z, O, and K (0.1) < ε< D (0.15) < J and S (0.2) in the distribution areas of severe hydrologic droughts, and the C (0) < O, K, and ε (0.05) < P, T, and Z (0.1) < J and S (0.15) < D (0.2) < Pt (0.6) in the distribution areas of extreme hydrologic droughts. On the whole, the O, C, and T (0.15) < Pt, Z, ε, D, and P (0.2) < S, J, and K (0.25). This may be that the O, C, and T, dominated by the soluble calcite, are easy to be formed the different sizes of water-stored spaces under the differential dissolution or erosion effects of soluble water, which enhances the watershed storage capacity and inhibits the occurrence of hydrologic droughts in a certain extent. On the contrary, the S, J, and K, dominated by the insoluble quartz, feldspar, and kaolinite, are difficult to be formed the water-stored spaces, which weakens the watershed storage capacity and promotes the occurrence of hydrologic droughts. Thus, this study shows that the driving probability of hydrological droughts is the Class II (0.15) < Class III (0.19) < Class IV (0.2) < Class I (0.22) < Class V (0.25) in terms of lithology combination structures, the Classes II and III (0.17) < Classes I and IV (0.22) < Class V (0.25) in terms of the composite types of watershed lithologies, and the Classes I and III (0.18) < Classes II and IV (0.2) < Class V (0.25) in terms of Karst lithologies. It means that under the differential erosion or solution effects of soluble water, the water-stored spaces formed in Karst Basins is the most, followed by in the Semi-Karst Basins, the least in the Non-Karst Basins. This further proves that the watershed storage capacity is the Non-Karst Basin (V) < Semi-Karst Basin (II and IV) < Karst Basin (I and III).

To sum up, the watershed runoff and atmospheric precipitation do not happen at the same time and have a certain lag in time. Its lag time and intensity are greatly affected by the watershed storage capacity. The Karst drainage basin has a certain storage capacity attributed to the Karst lithology with the solubility under the differential dissolution or erosion effects of soluble water. This study proves that there are the most water-stored spaces or the strongest storage capacity in the Limestone Karst Basin (II and III), followed by in the Dolomite Karst Basin (I and IV), the least water-stored spaces or the weakest storage capacity in the Non-Karst Basin (V). Similarly, the most water-stored spaces or the strongest storage capacity are found in the Karst Basin (I and III), followed by in the Semi-Karst Basin (II and IV), the least water-stored spaces or the weakest storage capacity in the Non-Karst Basin (V). Therefore, this study provides a technical guidance for Karst drought monitoring and warning and a theoretical basis for drought relief, and effectively promotes the development of hydrogeology.

The watershed lithology is an important component of basin underlying surfaces. The development of the landform and river system and the formation of watershed-stored spaces controlled by the watershed lithologic types and structures will promote or inhibit the occurrence of hydrological droughts. As analyzed above, the driven mechanisms of Karst lithologies and their composite structures to hydrological droughts can be summarized as follows:

1. There is few single lithologic type in Karst drainage basins, and two or more than lithologic types mixed. The basins could be divided into five types according to watershed lithology-combined structures, namely the Dolomite Karst Basin (I), Limestone Semi-Karst Basin (II), Limestone Karst Basin (III), Dolomite Semi-Karst Basin (IV), and Non-Karst Basin (V), respectively.

2. The hydrological droughts of Karst drainage basins in South China are widely distributed, and the drought intensities are gradually decreasing with the time scales increasing. Among them, the hydrological droughts are relatively serious at the SRI_3, SRI_6, and SRI_9, and relatively lighter at the SRI_12. It indicates that the effects of Karst drainage basins on the runoff regulations are more and more significant with the increase of time scales, and hydrologic drought intensities are more and more lighter. Meanwhile, the hydrological droughts from the west to east parts, South China are gradually aggravating with the north–south stripe distribution that is the most significant at the SRI_3 and SRI_6.

3. The driven effects of Karst basins on hydrologic droughts are closely related to the solubility of lithology. Hence, the occurrence probability of hydrological droughts is the O, C, and T (0.15) < Pt, Z, ε, D, and P (0.2) < S, J, and K (0.25). The driven effects of different types of basins on hydrologic droughts have significant differences due to the different lithologic types and spatial coupling structures. Among them, it mainly occurs the mild and moderate hydrologic droughts at the SRI_3 and SRI_6, and the mild or below hydrologic droughts at the SRI_9 and SRI_12. The occurrence probability of hydrological droughts is the Limestone Semi-Karst Basin (II, 0.15) < Limestone Karst Basin (III, 0.19) < Dolomite Semi-Karst Basin (IV, 0.2) < Dolomite Karst Basin (I, 0.22) < Non-Karst Basin (V, 0.25) in terms of basin types, the Limestone Karst Basin (II, III, 0.17) < Dolomite Karst Basin (I, IV, 0.22) < Non-Karst Basin (V, 0.25) in terms of composite structures of basin lithologies, and the Karst Basin (I, III, 0.18) < Semi-Karst Basin (II, IV, 0.2) < Non-Karst Basin (V, 0.25) in terms of Karst lithologies, respectively.

This study was supported by the Natural Science Foundation of China (41471032; u1612441); the Natural and Scientific Research Fund of Guizhou Water Resources Department (KT201402); the Natural and Scientific Fund of Guizhou Science and Technology Agency (QKH J [2010] No. 2026, QKH J [2013] No. 2208); 2015 Doctor Scientific Research Startup Project of Guizhou Normal University.

All relevant data are included in the paper or its Supplementary Information.