Comprehensive evaluation of hydrological drought and the effects of 1 large reservoir on drought resistance in the Hun River basin , NE 2 China 3

Evolution of drought under changing climate and the operation of large reservoir play an important role in drought 9 warning and control. Thus, the evolution characteristics of hydrological drought and the effects of large reservoir on drought 10 resistance are explored in the Hun river basin (HRB). Firstly, Standardized runoff Index (SRI) was adopted to evaluate the 11 evolution characteristics of hydrological drought. Meanwhile, based on drought duration and severity identified by the run 12 theory, the copula function with the highest goodness of fit was selected to calculate the return period of hydrological 13 drought. Furthermore, the propagation time from meteorological to hydrological drought were determined by calculating the 14 Pearson correlation coefficients between 1-month SRI and multi-time scale Standardized precipitation index (SPI). Finally, 15 based on the cumulative precipitation deficit thresholds for triggering hydrological drought, the impact of large reservoir on 16 drought resistance of the basin was revealed. The results show that: (1) hydrological drought showed a slight strengthening 17 trend in the eastern, while presented alternate characteristics of drought and flood in the western and center of the HRB from 18 1967 to 2019; (2) the western and center of the HRB were vulnerable districts to hydrological drought with longer drought 19 duration and higher severity; (3) the most severe drought with drought duration of 23 months, severity of 28.7, and 20 corresponding return periods that both exceed the thresholds of duration and severity and exceed the threshold of duration or 21 severity were 371 years and 89 years, respectively; (4) the propagation time from meteorological to hydrological drought of 22 the lower reaches of large reservoir has been significantly prolonged owing to the operation of large reservoir; and (5) the 23 operation of large reservoir strengthened the drought resistance in the lower reaches while lightly weaken in the upper 24 reaches of large reservoir. 25


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from the 1-month scale SRI sequence. Fig. 2 shows the process of drought recognition based on the threshold method, and 8 the specific identification process is as follows:

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(1) When the SRI value is less than SRI 0, it shows drought characteristics. From the time (t1) when SRI value is less than 10 or equal to SRI0 to the time (t2) when SRI value is greater than or equal to SRI0, it is preliminarily recognized that a drought 11 https://doi.org/10.5194/nhess-2021-218 Preprint. Discussion started: 21 July 2021 c Author(s) 2021. CC BY 4.0 License.
where C (u,v) represents the copula function combining two random variables u and v; and φ is convex function.

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The dependency structure between drought duration and severity were modeled by the commonly used binary copula 19 functions, including Gumbel, Clayton, Frank, t and Normal copula (Wang et al., 2020). Root mean square error (RMSE) and 20 AIC test were applied to select the highest goodness of fit best fitting good (GOF) copula function. Several joint probability 21 expressions corresponding to bivariate return periods were used to further explore the occurrence frequency of hydrological   where C(x (u), y (v)) represents the joint cumulative probability of X ≤ u and Y ≤ v; x (u) and y (v) denote the cumulative 18 probability of X ≤ u and Y ≤ v; x and y are the marginal cumulative distribution of two random variable X and Y.

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In this study, the hydrological drought characteristics (drought duration and severity) of each drought event are taken as 20 the target, respectively, and the corresponding cumulative precipitation deficit (CPD, mm) is identified as the condition. The 21 conditional probability of hydrological drought under different CPD conditions would be calculated. Based on the 22 recognition of hydrological drought event by the run theory, the CPD of each drought event is calculated during the PTMH.

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The CPD is defined as: where CPDn is the corresponding CPD for the nth drought; Pi denotes the precipitation during the period of i; Pm represents 26 the average precipitation of the mth month.

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According to the method of determining the marginal distribution described in Section 3.3, GAM, EXP, GEV, Logn and 28 WBL distributions were applied to fit the CPD. The commonly used bivariate theoretical copula functions, including Clayton,

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Frank, and Gumbel copula were considered for modeling the dependence structure between CPD and drought duration and with average SRI value of -0.54 and minimum of -3.33. Fig. 3   2 In order to further explore the temporal evolution characteristics of hydrological drought, the trend characteristic U value 3 of M-K trend test were calculated. Table 2 shows the trend characteristic value U at the seasonal and annual scales. It is clear 4 from Table 2

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Based on the run theory, three drought factors, namely drought frequency, duration and severity, were identified from the 17 1-month scale SRI sequence. Drought events which were detected sum up to 133 in 3 districts of HRB during 1967-2019.

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DHF was most frequently affected by drought, with a total of 57 drought events, followed by XJWP and SY with 39 and 37 19 drought events, respectively. The box chart of drought duration and severity was drawn, and the spatial distribution of 20 drought was discussed (Fig. 5). were more serious than in eastern (DHF) districts. Nevertheless, the eastern region of the HRB was more sensitive to 1 short-duration drought, which were dominated by two-month and three-month drought events.

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In this study, five common functions including Gamma, EXP, GEV, Logn, and WBL, were used to fit the sequence of 6 duration and severity of hydrological drought events in the three sub-basins in the HRB and AIC and K-S test were applied 7 to select the best-fit distribution, and the consequences were shown in Table 3. Table 3 illustrates that all of the optimal 8 distributions passed the K-S test of α = 0.01. The joint distribution of drought duration and severity was determined using the 9 copula functions in the HRB. Based on the RMSE and AIC, the GOF copula functions were selected in the HRB (Table 4) .

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Besides, according to the univariate empirical frequency of drought duration and severity, three typical drought scenarios 12 were selected to analyze the return periods. The scenarios corresponding to the empirical frequency of 0.50, 0.25 and 0.05 of 13 the univariate were defined as moderate, severe and extreme drought. characteristics of smaller return period with high drought duration and large severity in eastern of the HRB. It is foreseeable 1 that eastern districts will be more likely to suffer from more serious drought events. 2

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Based on the superiority of SPI that it can be calculated at multi-time scales, the PTMH were determined by calculating the 5 Poisson correlation coefficient between the monthly SRI and the multi-time SPI. The PTMH was indicated by the month 6 with the strongest correlation. The Poisson correlation coefficient and the PTMH of four hydrological stations in HRB were 7 shown in Fig. 7. It can be observed from Fig. 7 that the PTMH in HRB had obviously seasonal characteristics. The high 8 correlation coefficients were mainly concentrated in spring and summer, and the corresponding PTMH ranged from 3 to 7 9 months, while the correlation coefficients were lower in autumn and winter with PTMH ranged from 2 to 5 months at BKQ 10 and DHF. Nevertheless, high correlation coefficients were concentrated from late summer to early winter, and the 11 corresponding PTMH ranged from 3 to 14 months, while the correlation coefficients were lower from late winter to early 12 summer with PTMH ranged from 4 to 23 months at BKQ and DHF. It can be seen from Fig. 7 that the PTMH at SY and 13 XJWP were significantly higher than at BKQ and DHF, which is likely that the operation of DHF reservoir has markedly 14 effect the PTMH of the HRB. 2 The detailed effects of DHF reservoir operation on the PTMH were revealed by calculating the PTMH of different periods. 10 It is clear from Fig. 8 that, from the point of view of the F-series, the PTMH of SY (14.9 months) and XJWP (11.9 months) 11 station were obviously higher than the BKQ (4.1 months) station's, whilst the PTMH of DHF (4.3 months) station was 12 almost equal to BKQ station's, which indicated that the PTMH was significantly postponed by the operation of DHF 13 reservoir. It signified that the operation of DHF reservoir has observably enhanced the drought resistance of the HRB.
14 Moreover, the PTMH of SY station was higher than the XJWP station's, which implied that the improvement effect is  In this model, five common functions including Gamma, EXP, GEV, Logn, and WBL, were used to fit the sequence of 10 CPD of hydrological drought events in the three sub-basins in the HRB and AIC and K-S test were applied to select the 11 best-fit distribution, and the consequences were shown in Table 3. The commonly used bivariate theoretical copula functions, 12 including Clayton, Frank, and Gumbel copula were considered for modeling the dependence structure between CPD and 13 drought duration (D-CPD) and severity (S-CPD), respectively. Based on the RMSE and AIC, the GOF Copula functions 14 were selected and shown in Table 6.
15  Fig. 9 shows the conditional probabilities of occurrence of hydrological droughts with different levels under the condition 2 of various CPD in four stations. After the operation of DHF Reservoir, the CPD interval triggering different levels of 3 hydrological drought and the improvement of drought resistance (IDR) were shown in Table 7. It can be seen from Table 7 4 that, in general, CPD intervals which triggered moderate, severe and extreme hydrological droughts in BKQ were lower than 5 SY and XJWP, while higher than that of DHF, which signified that the operation of DHF reservoir has remarkably 6 strengthened the drought resistance in the lower reaches of DHF reservoir while weakened the drought resistance in the 7 upper reaches of DHF reservoir. Moreover, it was clear from  In this paper, SPI and SRI were adopted to characterize meteorological and hydrological drought respectively, and the