-
Being the largest freshwater lake in the North China Plain, Baiyangdian Lake plays an important role in water resource management, flood control initiatives and regulatory regimes in the basin. The BYD Basin belongs to a temperate continental monsoon climate with an annual precipitation of 510 mm and an average annual pan evaporation of 1746 mm. The annual mean daily air temperature is –2.6℃. Historically, nine rivers flowed into Baiyangdian Lake; however, in recent years climate change and anthropogenic activities (e.g., through water transfer initiatives and irrigation activities) have caused most of these rivers to dry up. Moreover, extreme climate events (e.g., rainstorms and drought) have led to changes in hydrological regimes (Wang et al., 2019). The BYD Basin is particularly sensitive to changes in hydrological regimes, which include 94 km2 of raised fields and greater than 3700 ditches that subdivide the basin into 140 small shallow lakes. To preserve the water ecology of Baiyangdian Lake, ecological water transfer projects have been implemented since the 1980s to maintain its integrity (Liu, 2014). Specifically, the planning outline of the Xiong’an New Area, which has jurisdiction over Baiyangdian Lake, included an ordinance for its ecological restoration. Inevitably, alterations in hydrological regimes will result in changes to aquatic ecosystems (Zhao et al., 2012; Wang et al., 2014; Li et al., 2016). This study used a daily precipitation time series that spanned from 1959 to 2016, constructed from data obtained from eight local meteorological stations (Fig. 1), to explore extreme climate events in the region. Corresponding daily streamflow data of four hydrological stations (
i.e., Daomaguan Station, Zhongtangmei Station, Fuping Station, Dongcicun Station) representing four sub-basins in the upstream that are less subject to human interference were used to investigate the hydrological response to an extreme drought event in the BYD Basin, China (Fig. 1). -
According to Yu and D’Odorico (2014), drought can be described as ‘inputs with the recognition that this meteorological definition may lead to a variety of responses depending on ecosystem hydrology’, which can be divided into meteorological, hydrological, agricultural and socioeconomic drought.
Drought can be quantified through a two steps process. Firstly, annual precipitation anomalies were calculated. In order to avoid individual wetter years that can be interspersed between long and pronounced dry periods, results are smoothed with a three year moving window. Secondly, the exact start and end months of dry period were determining based on the accumulated monthly precipitation anomalies. This method has been successfully used to quantify drought, and details can be found in Yang et al. (2017). The piecewise regression model used to detect potential turning points in the cumulative monthly anomaly series.
$$ y = \left\{ {\begin{array}{*{20}{c}} {{\beta _0} + {\beta _1} + \varepsilon\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; t \le \delta }\\ {{\beta _0} + {\beta _1}t + {\beta _2}\left({t - \delta } \right) + \varepsilon \;\;\;t > \delta } \end{array}} \right. $$ (1) where t is the month, and y is the accumulated monthly precipitation anomaly; b0, b1 and b2 are the regression coefficients; δ is the assumed turning point based on annual anomaly analysis. The range of the δ value was set to 12 months prior to and following the start and end year, which was determined through annual anomaly analysis. Linear least squares (LLS) regression was used to estimate the three regression coefficients, and a t-test was used to determine whether β2 equates to zero.
Drought duration, severity, and intensity were used to describe characteristics for a drought event. Drought duration was the time difference between the start and end months. Drought severity presented as the accumulative precipitation anomaly during the drought event. And drought intensity was defined as the ratio of drought severity over drought duration. Hydrological drought was calculated respective to streamflow and baseflow using the above procedures.
-
The Chapman-Maxwell method (i.e., the CM filter) used to separate baseflow from streamflow is a new algorithm of the digital filtering method (Zhang et al., 2017). The CM filter was developed to eliminate uncertainties, and it regards baseflow as a constant after quickflow has ceased (Chapman and Maxwell, 1996; Chapman, 1999; Graszkiewicz et al., 2011). The CM filter algorithm is determined by the following equation (Zhang et al., 2017):
$$ {Q}_{b\left(i\right)}=\dfrac{\alpha }{2-\alpha }{Q}_{b\left(i-1\right)}+\dfrac{1-\alpha }{2-\alpha }{Q}_{i} $$ (2) where Q and Qb is total streamflow and baseflow (mm/d), respectively; i is the time step; and α is the recession constant (1/day). Considering the prevalence of this linear scenario within natural river systems, baseflow can be expressed as follows (Brutsaert, 2005):
$$ \frac{dQ}{dt}=\frac{1}{\kappa }Q $$ (3) where t is the length of time (d), and κ is the characteristic drainage time scale (d). The α can be inferred as follows:
$$ \alpha ={e}^{-\frac{1}{\kappa }} $$ (4) The default recession constant (α) was 0.925, which was defined by Nathan and McMahon (1990) using Germann’s six watershed characteristics (Caissie and El-Jabi, 2003; Liu et al., 2020). In this study, the recession analysis method proposed by Brutsaert and Nieber (1977) (denoted as the BN77 method) was employed to estimate parameter α of the catchment. The BN77 method derives α from the lower envelope of a logarithmic plot of the recession rate (dQ/dt) that is plotted against the corresponding drought flow data Q. The lower envelope is the locus of points of the slowest recession rate determined by maintaining roughly 5% of the data points below it. This study adopted the Automatic Baseflow Identification Technique (ABIT) method developed by Cheng et al. (2016), which is considered a more rapid and objective method (i.e., the BN77 method) to estimate α (Tallaksen, 1995; Vogel and Kroll, 1996).
-
Results showed a decreasing trend in precipitation with an average annual change of –1.81 mm/yr2. Moreover, a downward abrupt change was detected in 1971, and the average annual precipitation prior to and following this abrupt change was 588 and 509 mm/yr, respectively (Fig. 2).
Figure 2. Variation and temporal trends (a) and abrupt changes determined by the Mann-Kendall method (b) of the annual precipitation averaged from eight meteorological stations throughout 1959–2016 in Baiyangdian Basin and its surrounding area. The u represents statistics variables in the Mann-Kendall method, UFk and UBk is statistics variables calculated from normal and invert series to obtain timing of abrupt change according to the cross point. Details for Mann-Kendall method can be seen in Mann, 1945; Kendall, 1948; Liang et al., 2010
Extreme drought was assessed according to monthly precipitation anomalies (Fig. 3). The duration of extreme drought covered 178 months (roughly 15 yr), from August 1996 to May 2011, namely, when precipitation was below the average of the BYD basin and its surrounding area (Fig. 3).
Figure 3. Ascertainment of extreme meteorological drought in Baiyangdian Basin. a) annual precipitation anomaly and b) the exact start and end months of meteorological drought
At the basin scale, precipitation deficits were observed to have occurred as early as the 1960s or the 1970s at three meteorological stations located in the southern region of the study area (Table 1), which were earlier than that observed in the other stations. Furthermore, half of the meteorological stations (i.e., 4/8) detected two occurrences of extreme drought, while a precipitation deficit was detected at most meteorological stations around the 1990s (except for the Shijiazhuang meteorological station). Extreme drought ranged from 49 mon (Yuxian) to 301 mon (Wutaishan), with an average value of 129 mon.
Table 1. Extreme meteorological drought events detected in Baiyangdian Basin and its surrounding area
Meteorological station Huailai Yuxian Wutaishan Shijiazhunag Meteorological drought 1980-08–1994-05
(165 mon)
1998-07–2007-08
(107 mon)1998-07–2002-08
(49 mon)1990-04–2015-08
(304 mon)1965-04–1976-06
(134 mon)
1979-07–1998-04
(105 mon)Meteorological station Raoyang Langfang Beijing Baoding Meteorological drought 1977-07–1984-07
(84 mon)
1996-08–2004-08
(96 mon)1995-09–2007-09
(137 mon)1998-07–2010-07
(144 mon)1979-08–1988-04
(104 mon)
1997-04–2007-07
(123 mon) -
Accounting for deviations from the default value (0.925), the ABIT method was used to obtain the recession constant α for all four sub-basins (Table 2), which Zhang et al. (2017) validated using the baseflow index (BFI) (i.e., the ratio of baseflow to total streamflow) obtained by applying tracer-based methods. Parameter κ ranged from 48.8 d in the middle reaches of the Tanghe River catchment to 161.3 d in the upper reaches of the Tanghe River catchment. The value of α ranged from 0.9797 to 0.9938 with an average of 0.9886, which was noticeably higher than the default recession constant value (0.9250). The recession constant α calculated with the ABIT method was used to separate baseflow from streamflow.
Table 2. The time scale (κ) and recession constant (α) obtained from four sub-basins within Baiyangdian Basin using the Automatic Baseflow Identification Technique (ABIT) method
Hydrological
stationRiver Recession day (κ) / d Regression constant (α) Zhongtangmei Tanghe River 48.8 0.9797 Daomaguan Tanghe River 161.3 0.9938 Dongcicun Baigouyin River 97.1 0.9898 Fuping Shahe River 111.1 0.9910 To explore the impact of extreme meteorological drought (
i.e., extremely low precipitation), this study endeavored to detect the occurrence of hydrological drought in the BYD basin (Table 3). Hydrological drought was detected in both streamflow and baseflow in the BYD basin. It is noteworthy that except for the onset time (wherein the onset time of streamflow was earlier than that of baseflow), hydrological drought in streamflow and baseflow remained roughly consistent. Hydrological drought lagged behind meteorological drought; namely, the onset of the hydrological drought event was detected from October 1996 to June 1997, which lagged behind the onset of the meteorological drought event (start time: August 1996). Furthermore, hydrological drought recovery ( i.e., the end of hydrological drought) also lagged behind meteorological drought recovery with an average lag time of 55 mon. Additionally, the duration of hydrological drought exceeded that of meteorological drought, which was determined by means of precipitation deficits, ranging from 200 to 238 mon. Table 3. Detection of hydrological drought (both streamflow and baseflow) in Baiyangdian Basin
Hydrological stations River Catchment
area
/ km2Total streamflow drought Baseflow drought Start-end time Start-end lag time
/ monPeriod/mon Start-end time Start-end lag time / mon Period / mon Zhongtangmei Tanghe River 3480 1996-10–2013-05 2–24 200 1997-06–2015-12 7–67 225 Daomaguan Tanghe River 2770 1997-01–2014-08 5–39 212 1997-06–2015-12 9–54 236 Dongcicun Baigouyin River 2249 1997-03 (end) 7–67 238 1997-05 (end) 8–67 237 Fuping Shahe River 2210 1996-10–2016-06 2–61 237 1997-05–2016-06 5–61 234 -
The response of baseflow to drought varied at both seasonal and annual scales (Fig. 4). Compared to pre-drought periods, the precipitation deficit during the drought period significantly reduced baseflow both at both seasonal and annual scales in the four sub-basins. Therefore, baseflow exhibited decreasing trends at both seasonal and annual scales. This was especially the case for the similar patterns exhibited in baseflow at a seasonal scale. In other words, seasonal scale baseflow increased from spring (from March to May) to autumn (from September to November) before decreasing during winter (from December to February of the following year). It is interesting to note that we detected more rapid decreases during both the pre-drought and drought periods, which varied at a seasonal scale in the four sub-basins. During the pre–drought period, autumn yielded the highest decreasing rates with values of –1.60 × 106 (Zhongtangmei), −1.10 × 106 (Daomaguan), −4.89 × 106 (Dongcicun) and –1.10 × 106 (Fuping) × 106 m3/yr. During the drought period, the highest decreasing rates in the sub–basins occurred during different seasons, namely, −0.94 × 106 (summer), −0.70 × 106 (winter), −5.37 × 106 (autumn), and −2.21 × 106 m3/yr2 (autumn) for Zhongtangmei, Daomaguan, Dongcicun and Fuping, respectively. In all, baseflow rapidly decreased during drought periods, and mean annual baseflow decreased by 50.4%, 49.6%, 71.7% and 53.2% in the Zhongtangmei, Daomaguan, Dongcicun and Fuping sub–basins, respectively. Especially, compared to the other sub–basins, baseflow in the Dongcicun sub-basin exhibited a significant decreasing trend (P < 0.01) with a rate of −6.12 × 106 m3 at an annual scale. The BFI confirmed that the decrease in baseflow remained consistent with that of streamflow during both pre-drought and drought periods in the Dongcicun sub-basin. The prolong drought in the Dongcicun sub-basin significantly affected both streamflow and baseflow, which resulted in an extended hydrological drought period compared to the other three sub-basins.
Figure 4. Seasonal and annual baseflow in the four sub-basins during meteorological drought prior to the extreme meteorological drought event: a) Zhongtangmei, b) Daomaguan, c) Dongcicun and d) Fuping. The green box plots show the time prior to the extreme meteorological drought event (i.e., 1959–1995); the blue box plots show the time of meteorological drought (i.e., 1996–2011). The average baseflow volume, linear regression model slopes and the average baseflow index (BFI) values are shown at the top of the pillars. ** and * indicate significant correlation at 99% and 95% confidence levels, respectively
Baseflow Separation and Its Response to Meteorological Drought in a Temperate Water-limited Basin, North China
-
Abstract: Baseflow, a component of the total streamflow, plays a key role in maintaining aquatic habitats, particularly during extreme drought events. This study investigated baseflow response to a prolonged and extreme meteorological drought event in the Baiyangdian Basin (BYD basin), a temperate water-limited basin in North China. Applying a precipitation series, piecewise regression was used to determine this extreme meteorological drought event, while the Automatic Baseflow Identification Technique (ABIT) was used to estimate a recession parameter (α), which was used to isolate baseflow from total streamflow. Results showed that: 1) annual precipitation exhibited significant decreasing trends (P < 0.05) with an average change of –1.81 mm/yr2. The precipitation deficit revealed that the start and end date of the extreme meteorological drought event was from August 1996 to May 2011, respectively, persisting for a total of 178 months (roughly 15 yr); 2) hydrological drought (including streamflow and baseflow) lagged behind meteorological drought while predictably persisting longer than extreme meteorological drought (i.e., precipitation); and 3) baseflow decreased dramatically under meteorological drought at both seasonal and annual scales, resulting in significantly decreasing trends during drought periods. Findings from this study confirmed that hydrological events caused by extreme meteorological drought can alter the magnitude and duration of baseflow and total streamflow, which will have an inevitable influence on aquatic ecosystems.
-
Key words:
- baseflow /
- extreme drought /
- recession parameter /
- Baiyangdian Basin
-
Figure 2. Variation and temporal trends (a) and abrupt changes determined by the Mann-Kendall method (b) of the annual precipitation averaged from eight meteorological stations throughout 1959–2016 in Baiyangdian Basin and its surrounding area. The u represents statistics variables in the Mann-Kendall method, UFk and UBk is statistics variables calculated from normal and invert series to obtain timing of abrupt change according to the cross point. Details for Mann-Kendall method can be seen in Mann, 1945; Kendall, 1948; Liang et al., 2010
Figure 4. Seasonal and annual baseflow in the four sub-basins during meteorological drought prior to the extreme meteorological drought event: a) Zhongtangmei, b) Daomaguan, c) Dongcicun and d) Fuping. The green box plots show the time prior to the extreme meteorological drought event (i.e., 1959–1995); the blue box plots show the time of meteorological drought (i.e., 1996–2011). The average baseflow volume, linear regression model slopes and the average baseflow index (BFI) values are shown at the top of the pillars. ** and * indicate significant correlation at 99% and 95% confidence levels, respectively
Table 1. Extreme meteorological drought events detected in Baiyangdian Basin and its surrounding area
Meteorological station Huailai Yuxian Wutaishan Shijiazhunag Meteorological drought 1980-08–1994-05
(165 mon)
1998-07–2007-08
(107 mon)1998-07–2002-08
(49 mon)1990-04–2015-08
(304 mon)1965-04–1976-06
(134 mon)
1979-07–1998-04
(105 mon)Meteorological station Raoyang Langfang Beijing Baoding Meteorological drought 1977-07–1984-07
(84 mon)
1996-08–2004-08
(96 mon)1995-09–2007-09
(137 mon)1998-07–2010-07
(144 mon)1979-08–1988-04
(104 mon)
1997-04–2007-07
(123 mon)Table 2. The time scale (κ) and recession constant (α) obtained from four sub-basins within Baiyangdian Basin using the Automatic Baseflow Identification Technique (ABIT) method
Hydrological
stationRiver Recession day (κ) / d Regression constant (α) Zhongtangmei Tanghe River 48.8 0.9797 Daomaguan Tanghe River 161.3 0.9938 Dongcicun Baigouyin River 97.1 0.9898 Fuping Shahe River 111.1 0.9910 Table 3. Detection of hydrological drought (both streamflow and baseflow) in Baiyangdian Basin
Hydrological stations River Catchment
area
/ km2Total streamflow drought Baseflow drought Start-end time Start-end lag time
/ monPeriod/mon Start-end time Start-end lag time / mon Period / mon Zhongtangmei Tanghe River 3480 1996-10–2013-05 2–24 200 1997-06–2015-12 7–67 225 Daomaguan Tanghe River 2770 1997-01–2014-08 5–39 212 1997-06–2015-12 9–54 236 Dongcicun Baigouyin River 2249 1997-03 (end) 7–67 238 1997-05 (end) 8–67 237 Fuping Shahe River 2210 1996-10–2016-06 2–61 237 1997-05–2016-06 5–61 234 -
[1] Aryal S K, Zhang Y, Chiew F, 2020. Enhanced low flow prediction for water and environmental management. Journal of Hydrology, 584: . doi: 10.1016/j.jhydrol.2020.124658 [2] Bao Z X, Zhang J Y, Wang G Q et al., 2012. Attribution for decreasing streamflow of the Haihe River basin, northern China: Climate variability or human activities? Journal of Hydrology, 460: 117–129. doi: 10.1016/j.jhydrol.2012.06.054 [3] Barker L J, Hannaford J, Chiverton A et al., 2016. From meteorological to hydrological drought using standard indicators. Hydrology and Earth System Sciences, 20(6): 2483–2505. doi: 10.5194/hess-20-2483-2016 [4] Beatty S J, Morgan D L, McAleer F J et al., 2010. Groundwater contribution to baseflow maintains habitat connectivity for Tandanus bostocki (Teleostei: Plotosidae) in a south-western Australian River. Ecology of Freshwater Fish, 19(4): 595–608. doi: 10.1111/j.1600–0633.2010.00440.x [5] Brutsaert W, 2005. Hydrology: An Introduction. Cambridge: Cambridge University Press, 618. [6] Brutsaert W, Nieber J L, 1977. Regionalized drought flow hydrographs from a mature glaciated plateau. Water Resource Research, 13(3): 637–643. doi: 10.1029/WR013i003p00637 [7] Caissie D, El-Jabi N, 2003. Instream flow assessment: From holistic approaches to habitat modelling. Canadian Water Resources Journal, 28(2): 173–183. doi: 10.4296/cwrj2802173 [8] Chapman T, Maxwell A, 1996. Baseflow separation-comparison of numerical methods with tracer experiments In: Proceedings of the 23rd Hydrology and Water Resources Symposium Preprints of Papers. Hobart: Institution of Engineers. 539–545. [9] Chapman T, 1999. A comparison of algorithms for stream flow recession and baseflow separation. Hydrological Processes, 13(5): 701–714. doi: 10.1002/(SICI)1099-1085(19990415)13:5 [10] Cheng L, Zhang L, Brutsaert W, 2016. Automated selection of pure base flows from regular daily streamflow data: objective algorithm. Journal of Hydrologic Engineering, 21(11): 06016008. doi: 10.1061/(ASCE)HE.1943-5584.0001427 [11] Du H, Xia J, Zeng S D et al., 2014. Variations and statistical probability characteristic analysis of extreme precipitation events under climate change in Haihe River Basin, China. Hydrological Processes, 28(3): 913–925. doi: 10.1002/hyp.9606 [12] Fan Y, Li H, Miguez-Macho G, 2013. Global patterns of groundwater table depth. Science, 339(6122): 940–943. doi: 10.1126/science.1229881 [13] Ficklin D L, Robeson S M, Knouft J H, 2016. Impacts of recent climate change on trends in baseflow and stormflow in United States watersheds. Geophysical Research Letters, 43(10): 5079–5088. doi: 10.1002/2016gl069121 [14] Fischer E, Knutti R, 2015. Anthropogenic contribution to global occurrence of heavy precipitation and high temperature extremes. Nature Climate Change, 5(6): 560–564. doi: 10.1038/nclimate2617 [15] Gnann S J, Woods R A, Howden N J K, 2019. Is There a Baseflow Budyko Curve? Water Resources Research, 55(4): 2838–2855. doi: 10.1029/2018wr024464 [16] Graszkiewicz Z M, Murphy R E, Hill P I et al., 2011. Review of techniques for estimating the contribution of baseflow to flood hydrographs. In: Proceedings 34th IAHR Congress 2011—Balance and Uncertainty : Water in A Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering . Brisbane, 138. [17] Janssen E, Wuebbles D J, Kunkel K E et al., 2014. Observational- and model- based trends and projections of extreme precipitation over the contiguous United States. Earth’s Future, 2(2): 99–113. doi: 10.1002/2013ef000185 [18] Kendall M G, 1948. Rank correlation methods. Journal of the Institute of Actuaries, 75(1): 140–141. doi: 10.1017/S0020268100013019 [19] Knapp A K, Hoover D L, Wilcox K R et al., 2015. Characterizing differences in precipitation regimes of extreme wet and dry years: implications for climate change experiments. Global Change Biology, 21(7): 2624–2633. doi: 10.1111/gcb.12888 [20] Kobierska F, Jonas T, Kirchner J W et al., 2015. Linking baseflow separation and groundwater storage dynamics in an alpine basin (Dammagletscher, Switzerland). Hydrology and Earth System Sciences, 19(8): 3681–3693. doi: 10.5194/hess–19–3681–2015 [21] Li X, Cui B S, Yang Q C et al., 2016. Impacts of water level fluctuations on detritus accumulation in Lake Baiyangdian, China. Ecohydrology, 9(1): 52–67. doi: 10.1002/eco.1610 [22] Liang L, Li L, Liu Q, 2010. Temporal variation of reference evapotranspiration during 1961−2005 in the Taoer River basin of Northeast China. Agricultural and Forest Meteorology, 150(2): 298–306. doi: 10.1016/j.agrformet.2009.11.014 [23] Liu Qiang, 2014. Effects of groundwater level fluctuation on Phragmites australis evapotranspiration in the Baiyangdian Lake. Wetland Science, 12(5): 552–558. doi: 10.13248/j.cnki.wetlandsci.2014.05.003 [24] Liu Q, Ma X J, Yan S R et al., 2020. Lag in hydrologic recovery following extreme meteorological drought events: implications for ecological water requirements. Water, 12(3): 837. doi: 10.3390/w12030837 [25] Liu S H, Yan D H, Wang H et al., 2016. Standardized Water Budget Index and Validation in Drought Estimation of Haihe River Basin, North China. Advances in Meteorology, 9159532. doi: 10.1155/2016/9159532 [26] Lyne V, Hollick M, 1979. Stochastic time-variable rainfall-runoff modelling. Institute of Engineers Australia National Conference, 1979 Perth. 89–93. [27] Mann H B., 1945. Non-parametric test against trend. Econometrika, 13: 245–259. doi: 10.2307/1907187 [28] Mishra A K, Singh V P, 2010. A review of drought concepts. Journal of Hydrology, 391(1–2): 202–216. doi: 10.1016/j.jhydrol.2010.07.012 [29] Nathan R J, McMahon T A, 1990. Evaluation of automated techniques for base flow and recession analyses. Water Resources Research, 26(7): 1465–1473. doi: 10.1029/WR026i007p01465 [30] Prein A F, Rasmussen R M, Ikeda K et al., 2017. The future intensification of hourly precipitation extremes. Nature Climate Change, 7(1): 48–52. doi: 10.1038/nclimate3168 [31] Price K, 2011. Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: a review. Progress in Physical Geography: Earth and Environment, 35(4): 465–492. doi: 10.1177/0309133311402714 [32] Qin Y, Yang D W, Lei H M et al., 2015. Comparative analysis of drought based on precipitation and soil moisture indices in Haihe basin of North China during the period of 1960−2010. Journal of Hydrology, 526: 55–67. doi: 10.1016/j.jhydrol.2014.09.068 [33] Seager R, Naik N, Vecchi G A, 2010. Thermodynamic and dynamic mechanisms for large–scale changes in the hydrological cycle in response to global warming. Journal of Climate, 23(17): 4651–4668. doi: 10.1175/2010JCLI3655.1 [34] Smakhtin V U, 2001. Low flow hydrology: a review. Journal of Hydrology, 240(3-4): 147–186. doi: 10.1016/S0022–1694(00)00340–1 [35] Sun F B, Roderick M L, Farquhar G D, 2012. Changes in the variability of global land precipitation. Geophysical Research Letters, 39(19): L19402. doi: 10.1029/2012gl053369 [36] Sutanto S J, Van Lanen H A J, 2020. Hydrological drought characteristics based on groundwater and runoff across Europe. Proceedings of the International Association of Hydrological Sciences, 383: 281–290. doi: 10.5194/piahs-383-281-2020 [37] Tallaksen L M, 1995. A review of baseflow recession analysis. Journal of Hydrology, 165(1−4): 349–370. doi: 10.1016/0022–1694(94)02540-r [38] Van Loon A F, Laaha G, 2015. Hydrological drought severity explained by climate and catchment characteristics. Journal of Hydrology, 526: 3–14. doi: 10.1016/j.jhydrol.2014.10.059 [39] Vogel R M, Kroll C N, 1996. Estimation of baseflow recession constants. Water Resources Management, 10(4): 303–320. doi: 10.1007/bf00508898 [40] Wang F, Wang X, Zhao Y et al., 2014. Long-term periodic structure and seasonal trend decomposition of water level in Lake Baiyangdian, Northern China. International Journal of Environmental Science and Technology, 11(2): 327–338. doi: 10.1007/s13762-013-0362-5 [41] Wang Wenke, Wang Yanlin, Duan Lei et al., 2006. Groundwater environment evaluation and renew ability measure in the Guanzhong basin. Zhengzhou, China: Huanghe Water Resource Publishing Press. (in Chinese) [42] Wang Y, Zhang X, Tang Q et al., 2019. Assessing flood risk in Baiyangdian Lake area in a changing climate using an integrated hydrological-hydrodynamic modelling. Hydrological Sciences Journal, 64(16): 2006–2014. doi: 10.1080/02626667.2019.1657577 [43] Wu J W, Miao C Y, Duan Q Y et al., 2019. Dynamics and attributions of baseflow in the semiarid Loess Plateau. Journal of Geophysical Research: Atmospheres, 124(7): 3684–3701. doi: 10.1029/2018JD029775 [44] Xu Ronghan, Wang Xiaogang, Zheng Wei, 2016. Research progresses in baseflow separation methods. Bulletin of Soil and Water Conservation, 36(5): 352–359. doi: (in Chinese) [45] Yang Y, Jia X X, Wendroth O et al., 2019. Noise assisted multivariate empirical mode decomposition of saturated hydraulic conductivity along a south-north transect across the Loess Plateau of China. Soil Science Society of America Journal, 83(2): 311–323. doi: 10.2136/sssaj2018.11.0438 [46] Yang Y, Yang Z F, Yin X A et al., 2018. A framework for assessing flow regime alterations resulting from the effects of climate change and human disturbance. Hydrological Sciences Journal, 63(3): 441–456. doi: 10.1080/02626667.2018.1430897 [47] Yang Y T, McVicar T R, Donohue R J et al., 2017. Lags in hydrologic recovery following an extreme drought: Assessing the roles of climate and catchment characteristics. Water Resources Research, 53(6): 4821–4837. doi: 10.1002/2017wr020683 [48] Yu K L, D’Odorico P, 2014. Climate, vegetation, and soil controls on hydraulic redistribution in shallow tree roots. Advances in Water Resources, 66: 70–80. doi: 10.1016/j.advwatres.2014.02.003 [49] Zhang J L, Zhang Y Q, Song J X et al., 2017. Evaluating relative merits of four baseflow separation methods in Eastern Australia. Journal of Hydrology, 549: 252–263. doi: 10.1016/j.jhydrol.2017.04.004 [50] Zhang Q, Sun P, Singh V P et al., 2012. Spatial-temporal precipitation changes (1956−2000) and their implications for agriculture in China. Global and Planetary Change, 82-83: 86–95. doi: 10.1016/j.gloplacha.2011.12.001 [51] Zhang S L, Yang D W, Yang Y T et al., 2018. Excessive Afforestation and Soil Drying on China’s Loess Plateau. Journal of Geophysical Research: Biogeosciences, 123(3): 923–935. doi: 10.1002/2017JG004038 [52] Zhao Y, Yang Z, Xia X H et al., 2012. A shallow lake remediation regime with Phragmites australis: incorporating nutrient removal and water evapotranspiration. Water Research, 46(17): 5635–5644. doi: 10.1016/j.watres.2012.07.053