Propagation dynamics from meteorological to groundwater drought and their possible influence factors

Journal of Hydrology 578 (2019) 124102

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Research papers

Propagation dynamics from meteorological to groundwater drought and their possible influence factors

T

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Zhiming Hana, Shengzhi Huanga, , Qiang Huanga, Guoyong Lengb, Hao Wangc, Qingjun Baia, Jing Zhaoa, Lan Maa, Lu Wanga, Meng Dua a

State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an 710048, China Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China c State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China b

A R T I C LE I N FO

A B S T R A C T

This manuscript was handled by C. Corradini, Editor-in-Chief

The propagation of meteorological drought in a complete water cycle is not limited to hydrological and agricultural droughts, but also involves groundwater drought. Moreover, the intensification of water cycle under the background of global warming may also affect the time of drought propagation. Therefore, studying the dynamic propagation and possible influence factors from meteorological to groundwater drought is helpful to monitor and assess the risk of groundwater drought. Here we use terrestrial water storage anomalies observations from the Gravity Recovery and Climate Experiment satellites and simulated soil moisture and runoff variations from the Global Land Data Assimilation System to show that the groundwater storage anomalies in the Pearl River Basin (PRB). The standardized precipitation index and drought severity index were used to characterize meteorological and groundwater drought, respectively. Results indicated that: (1) the propagation time of meteorological to groundwater drought in the PRB during 2002–2015 was 8 months, and that in spring and summer was shorter than that in autumn and winter; (2) the time of drought propagation has a significant deceasing trend (p < 0.01), indicating that the water cycle in the PRB was accelerating; (3) increasing soil moisture accelerates the response of groundwater to precipitation in the surplus period due to the stored-full runoff mechanism, whilst intensifying evapotranspiration rate and heat wave facilitate the drought propagation in the deficit period; (4) compared with Arctic Oscillation and El-Niño Southern Oscillation, Pacific Decadal Oscillation is the main driving force to accelerate drought propagation in the PRB.

Keywords: Groundwater drought Meteorological drought Drought propagation Dynamic

1. Introduction Compared to other kinds of natural hazards, the spatial extent of drought is extremely larger and its influencing time is commonly much longer. Thus, the damages caused by drought are expected to be highly larger than other natural hazards (Mishra and Singh, 2010; Huang et al., 2014a). Generally, droughts are classified into four categories, including “meteorological drought”, “hydrological drought” “agricultural drought” and “socio-economic drought” (Mishra and Singh, 2010; Guo et al., 2019a,b). Another form of drought called “groundwater drought”. When groundwater systems are affected by drought, first groundwater recharge and later groundwater levels and groundwater discharge decrease. Such droughts are called groundwater droughts (Van Lanen and Peters, 2000). Furthermore, meteorological and groundwater droughts are inter-related through interactions that

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happen within the water cycle (Van Loon and Laaha, 2015). However, the earth's climate system is experiencing significant climate change, which directly leads to changes in the hydrological cycle (Yoon et al., 2015; Han et al., 2019; Fang et al., 2019a,b). This means that the propagation of groundwater systems to drought and their performance under drought conditions becomes increasingly important (Calow et al., 1999). Drought are mostly caused by periods of lower than average precipitation and propagation through the hydrological system (Peters et al., 2006). When meteorological drought propagates to the groundwater system, groundwater drought will not be detected as early as hydrological drought and agricultural drought. Inversely, only when groundwater drought seriously affects human life, it can arouse extensive attention. More important, extraction of groundwater will significantly affect the energy exchange between soil moisture and land-

Corresponding author. E-mail address: [email protected] (S. Huang).

https://doi.org/10.1016/j.jhydrol.2019.124102 Received 11 June 2019; Received in revised form 9 August 2019; Accepted 2 September 2019 Available online 04 September 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.

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(IPCC) has indicated that the frequency, intensity, and duration of some climate extreme events will increase by the end of this century (Oliver, 2013; Huang et al., 2014b). In particular, Brutsaert (2012) found that the Pacific Decadal Oscillation (PDO) and El-Niño Southern Oscillation (ENSO) affected groundwater storage in four desert regions of North America. Apurv et al. (2017) found in the latest study that the interannual variability of precipitation has a strong control over groundwater recharge and consumption due to climate change. Therefore, teleconnection factors directly regional precipitation and indirectly also affect groundwater, thus affecting the propagation time of meteorological to groundwater drought. However, how teleconnection factors affect drought propagation through influencing meteorological factors is unclear. Based on these, it is particularly important to analyze the impact of teleconnection factors on the drought propagation. It can not only reveal the main driving factors affecting the propagation time, but also predict and evaluate the occurrence of regional drought events in advance. The primary objectives of this study are: (1) to capture and identify groundwater drought; (2) to investigate the propagation time and dynamic change of meteorological to groundwater drought; (3) to explore the possible influencing and driving factors on the propagation dynamics.

atmosphere, leading to the depletion of surface and groundwater resources, and thus leading to secondary disasters (Zeng et al., 2016) (e.g. land subsidence, soil salinization, seawater intrusion and permanent loss of aquifer storage capacity) (Forkutsa et al., 2009; Park et al., 2012). Many studies involving various methods have analyzed groundwater drought and explored the relationship between the precipitation and groundwater level (Castle et al., 2014; Tallaksen et al., 2006; Bloomfield et al., 2015; Huang et al., 2016). However, drought is a multi-scalar phenomenon with the effects of precipitation deficits (meteorological drought) becoming evident in various systems (e.g. surface and groundwater hydrology, vegetation activity, and crop production) at various temporal scale (Lorenzo-Lacruz et al., 2017). Meanwhile, research on drought propagation focuses on meteorological to hydrological drought, and seldom on meteorological to groundwater drought. Furthermore, the propagated of meteorological drought in the water cycle is not limited to hydrological and agricultural droughts, which is seldom noticed by people. Hence, studying the dynamic propagation of meteorological to groundwater drought is not only a vital step in the field of hydrology, but also an important practical significance for establishing an effective monitoring and early warning system of meteorological drought based on groundwater drought. In addition, the holistic regional groundwater assessments would be valuable in promoting environmental restoration and for hydrologic research, but such assessments are difficult to generate on the basic of well surveys, which are typically unsystematic (Rodell et al., 2009). Satellite observations of time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite mission present a new and valuable tool to fill these gaps in data availability and water monitoring (Tapley et al., 2004). Terrestrial water storage (TWS) variations observed by GRACE include the combined contributions of groundwater, soil moisture, surface water, snow, ice and biomass (Rodell et al., 2005). Besides, groundwater storage variations can be isolated from GRACE data given auxiliary information on the other components of TWS, from either in situ observations or land-surface models (Rodell et al., 2007). Meanwhile, TWS estimates derived from the GRACE have been widely used to examine regional-scale droughts worldwide (Hughes et al., 2012; Elmore et al., 2010). In particular, Zhao et al. (2017) developed a new drought severity index (DSI) based solely on GRACE TWS-based estimates. Compared to previous TWSbased drought studies, the GRACE-DSI is calculated without model assimilation and considers spatial and temporal variability of local hydro climatology. However, TWS contains all water components on land. When meteorological propagates to hydrology, agriculture and groundwater drought, GRACE-DSI shows a superimposed drought signal. Based on this, to remove its effect and thus isolate the groundwater storage anomalies (GWSA), we estimated the storage variations of soil moisture and runoff through the Global Land Data Assimilation System (GLDAS). Then, the GWSA are calculated by GRACE-DSI, and the groundwater drought index GWSA-DSI is effectively obtained. In general, groundwater responds more slowly to meteorological conditions than the near-surface components of the terrestrial water cycle (Changnon et al., 1988). Its residence time (the ratio of quantity in storage to average rate of recharge or discharge) ranges from months in shallow aquifers to a million or more years in deep desert aquifers (Sturchio et al., 2004). However, the Standardized precipitation index (SPI) can monitor both short-term and long-term drought effects and is comparable among different locations due to its probabilistic nature. Importantly, the fundamental advantage of SPI is that it can be computed for a variety of time scales (Soleimani Motlagh et al., 2017). Hence, it can be used as an effective tool for the propagation of meteorological to groundwater drought. Previous studies have shown that climate change has led to more frequent and severe extreme natural events (Liu et al., 2016, 2018; Günter et al., 2019; Huang et al., 2019; Ren et al., 2019). The Fifth Assessment Report of the Intergovernmental Panel on Climate Change

2. Study area and data 2.1. Study area The PRB (102°14′ – 115°53′E; 21°31′ –26°49′N) is located in south China with a drainage area of 4.537 × 105 km2 (Fig. 1). The Pearl River is the fourth longest river of China, featuring the second largest runoff and fourth largest basin area of China with three major tributary basins: Dongjiang, Beijiang, and Xijiang basins (Li et al., 2016). The climate is tropical and subtropical with a long-term annual average temperature of 14–22 °C and a long-term annual average precipitation of 1200–2200 mm, and most of the precipitation in this basin occurs during April to September. The natural vegetation forest coverage rate of the basin is about 28%, of which Yunnan coverage rate is 32.7%, Guizhou 30.0%, Guangxi 39.3%, Guangdong 43%. Additionally, the Xijiang is the largest tributary basin covering 77.8% of the area of the PRB; the Beijiang River Basin is a typical karst landform featuring a complex topography; while the Dongjiang River Basin characterizing hilly topography is a densely populated and economically advanced area (Liu et al., 2010). 2.2. GRACE data In this study, the average monthly gravity field (between January 2004 and December 2009) was taken as the baseline of the monthly gravity-field time series (from April 2002 to December 2015), i.e., a monthly gravity field anomaly was generated following the subtraction of the baseline from the monthly gravity field. To eliminate the influence of noise, a Gauss smoothing kernel (Swenson and Wahr, 2002) was introduced into the calculation of gravity-field anomalies, which was expressed as an equivalent water height by the following equation (Wahr et al., 1998):

Δh(θ,λ ) =

2aπρave 3ρwater

∞

n

∑∑ n=0 m=0

2n + 1 Wn [ΔCnm cos(mλ ) 1 + kn

+ ΔSnm sin(mλ )] Pnm (cos(θ))

(1)

where Δh is the equivalent water height; θ is the colatitude; λ is the longitude; a is the equatorial radius (6378 km); ρave is the mean density of the earth (5517 kg/m3); ρwater is the density of water (1000 kg/m3); n is the degree of decomposition; m is the order; kn is the loading love number of the nth degree; Wn denotes Gauss smoothing kernel related to the nth degree, calculated by the recurrence formula W0 = 1, 2

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Fig. 1. Location of the PRB in China.

a monthly Normalized Difference Vegetation Index (NDVI) product, which uses the international Maximum value compositing to eliminate the interference of clouds and atmosphere by using the daily data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform with spatial resolution of 500 m (Huang et al., 2017; Zhao et al., 2019). Through mask processing in the PRB, the monthly NDVI raster images from 2002 to 2015 were cut out. The annual data of groundwater resources (2002–2015) can be obtained from the Ministry of Water Resources of China.

W1 = (1 + e − 2b)/(1 − e − 2b) − 1/ b , Wn + 1 = −(2n + 1)/ b·Wn + Wn − 1, b = In2/(1 − cos(r / a)) ; r is the filter radius; ΔCnm and ΔSnm are the gravity spherical harmonic coefficient and normalized Stokes coefficient residuals relative to the baseline, respectively; and Pnm (cos(θ)) is the nth degree and the mth-order fully normalized Legendre function. Monthly variations in land gravity fields are mainly caused by monthly TWS changes (Chen et al., 2009). Based on the GRACE data from The University of Texas Centre for Space Research (CSR), which can be freely downloaded from the GRACE Tellus website (http:// isdc.gfz-potsdam.de/grace-isdc/), and the spatial resolution of 1°. The data lost in the 165 months scale data analyzed in the article are 13 months. These months were filled in by averaging the values for each cell from the months either side of the missing data (Long et al., 2015).

2.5. Climate data Annual ENSO, PDO and AO data covering 1979 to 2015 were also applied in this study. The annual PDO data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (http://www.esrl.noaa.gov/psd/data/ correlation/amon.us.long.data). Regarding ENSO, we use Nino 3.4 Index derived from the NOAA Earth System Research Laboratory (http://www.esrl.noaa.gov/psd/data/correlation/nina34.data). For Arctic Oscillation (AO), its annual data were obtained from the NOAA National Climatic Data Center (http://www.ncdc.noaa.gov/teleconnections/ao.php).

2.3. GLDAS data GLDAS products include forcing data (e.g. precipitation, near-surface air temperature, downward shortwave and longwave radiation, specific humidity, wind speed and surface pressure), land surface states (e.g., soil moisture, surface runoff and subsurface runoff), and flux data (e.g. evapotranspiration and sensible heat flux). Two version of GLDAS data sets are available online, i.e., version 1 (GLDAS-1) and version 2 (GLDAS-2) (http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings), where GLDAS-1 data sets include 1.0° resolution data products from four land surface models (Noah, CLM, Mosaic and VIC), covering the period from 1979 to the present. Compared with the products of GLDAS-2, the data of soil moisture, temperature, atmospheric pressure and precipitation in GLDAS-1 have been proved to have well accuracy in many areas (Seyyedi et al., 2015; Ji et al., 2015). Moreover, to keep in line with the time scale of the GRACE- derived TWSA, this study used the GLDAS-1 products of 1979–2015, including precipitation, temperature, evapotranspiration, runoff and four layers of soil moisture (including 0–10 cm, 10–40 cm, 40–100 cm, 100–200 cm), with spatial and temporal resolutions of 1.0° and month respectively.

3. Methods 3.1. Standardized precipitation index The SPI (McKee et al., 1993) is a multivariate meteorological drought index based on probability distribution of precipitation. SPI values are dimensionless and are computed by fitting a Gamma distribution function to precipitation values during the time (month) period. The SPI can be adopted to indicate the precipitation conditions in a specific period in a long time series and shows the precipitation deficits corresponding to the period. Since the SPI can capture the drought features at various time scales (1, 3, 6, 9, 12, 24, 48 months), it has been broadly applied to investigate different aspects of droughts (McKee et al., 1993). Here, we have computed SPI for the time scales of 1–12-month using the precipitation data sets, GLDAS. The SPI can be calculated as follows:

2.4. Normalized difference vegetation index and annual groundwater data Remote sensing data set is provided by International Scientific & Technical Data Mirror Site, Computer Network Information Center, Chinese Academy of Sciences. (http://www.gscloud.cn). This product is 3

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SPI = S

t − (c2 t + c1 ) t + c0 , t= ((d3 t + d2 ) t + d1 ) t + 1

G (x ) =

1 β γ Γ(γ )0

∫0

x

ln

1 H (x )2

x γ − 1e−x / βdx , x > 0, Γ(γ ) =

frequency field. The theoretical distribution of the cross-wavelet power of the two times series with their background power spectra PkX and PkY (Torrence and Compo, 1998) is expressed as follows:

(2)

∫0

∞

x γ − 1e−x dx

D ⎛⎜ ⎝

(3)

where x denotes precipitation value; β and γ represent the scale parameter and shape parameter of the Γ function; S is positive and negative coefficients; c0 , c1, c2 , c3 and d1, d2 , d3 are calculated parameters. Their values are displayed as follows: c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1= 1.432788, d2 = 0.189269, d3 = 0.001308. G (x ) denotes the probability distribution of precipitation. When G (x ) > 0.5, then H (x ) = 1G (x ) and S = 1; otherwise, H (x ) =G (x ) and S = 1.

Z (p) PkX PkY < p⎟⎞ = v v ⎠

(6)

where Z v (p) is the confidence level corresponding to the probability p for a probability distribution function defined by the square root of the two χ 2 distributions (Grinsted et al., 2004). 4. Results 4.1. Identification and variation characteristics of groundwater drought in the PRB

3.2. GWSA-DSI To determine the anomaly in satellite-based GWSA, anomalies of other components of terrestrial water cycle i.e. soil moisture storage (SMSA), snow water equivalent storage (SWEA) and surface water reservoir storage (RESA) were removed from TWSA. Anomaly in any of the components was calculated by removing the mean value for the entire study period from the data value in the particular month. The study region is not subjected to snow accumulation activities. Hence, we have not considered snow water equivalents. Using a priori monitoring or model-based estimates of RESA and SMSA, the GWSA can be calculated as a residual from the disaggregation equation (Scanlon et al., 2015):

Fig. 2 shows that the groundwater drought mainly occurred in 2003–2006 and 2009–2010, which matched the two severe droughts (Wang et al., 2017) outbreaks in the PRB during that period. Among the drought from 2003 to 2006, the GWSA-DSI reached the lowest value −2.79 in July 2004, and its intensity and duration were particularly severe. During this period, most parts of southern China suffered the worst drought in 53 years. Drought has caused economic losses of more than 4 billion RMB, more than 7.2 million people have difficulties in drinking water (http://news.sina.com.cn/c/2004–11-03/ 06294122595s.shtml), which indicate that the GRACE-TWSA and the GWSA calculated by us have applicability in drought identification. In addition, the reliability of the GWSA based on GRACE had been verified in many areas (Henry et al., 2011; Bhanja and Mukherjee, 2016). To further verify the reliability of the results, we compared the annual groundwater resources in the PRB with the annual average GWSA-DSI based on its monthly series (Fig. 3), and found that the correlation reached 0.51 (p < 0.08). Although some short-term drought events occurred after 2009, the amplitude of GWSA-DSI increased significantly, which is consistent with the trend of groundwater resources data provided by the Ministry of Water Resources of China. Additionally, as a nature link between soil, atmosphere and water, vegetation plays an indicator role in the studies of global change. Vegetation can reflect the state of nature environment most intuitively and clearly (Sato and Tateishi, 2004). Soumendra et al. (2019) used data from more than 15, 000 groundwater observation wells in India to find that NDVI has a good correlation with groundwater level (r > 0.6) in natural vegetated areas. We carried out a 11-month moving average of NDVI and GWSA-DSI in the PRB (Fig. 4), and found that there was a signification positive correlation (r = 0.64, p < 0.01) between them. Moreover, during the two groundwater droughts, NDVI decreased dramatically, which further verifies the ability of GWSA-DSI to identify and capture groundwater drought.

(4)

GWSA = TWSA − SMSA − RESA

The DSI is a standardized drought severity index exclusively based on GRACE-derived TWS. We have utilized monthly estimates of CSR Mascon solutions (RL05M_v01) of land water storage from GRACE to compute GWSA-DSI following the algorithm provided in Zhao et al (2017).

GWSA - DSIi, j =

WnX (s ) WnY ∗ (s ) σX σY

¯ j GWSAi, j − GWSA σj

(5)

where i is year ranging from 2002 to 2015; j is month ranging from ¯ j and σj are the mean and standard January to December; and GWSA deviation of groundwater storage anomalies in month j , respectively. Besides, the GWSA-DSI is a dimensionless quantity used to detect drought events (Table 1). Detailed grading can be found in Zhao et al. (2017). 3.3. Cross-wavelet transformation The Cross-Wavelet Transformation (CWT) is able to present the correlation between two time series in both time and frequency domains, combined with the wavelet transformation and cross-spectrum analysis (Hudgins and Huang, 1996). The CWT is often used to explore the correlations between two annual hydrological and climatic time series. The CWT of two time series xn and yn can be expressed as W XY = W X W Y *, where * denotes their complex conjugation. The crosswavelet power is expressed as |W XY |. The complex argument arg (W xy ) is regarded as the local relative phase between xn and yn in time-

4.2. The propagation time from meteorological to groundwater drought and its seasonality The various time scale of SPI (1-month, 2-month, … , 24-month) were obtained by calculating the monthly precipitation data of the PRB from 1979 to 2015, we find that SPI-8 and the GWSA-DSI have a significant correlation (r = 0.56, p < 0.01) in the 2002–2015 series (Fig. 5). This is consistent with the response time of hydrological to meteorological drought in Nanpan River Basin (belonging to the PRB) is for 6 months (Zhang et al., 2014). Because when meteorological drought propagates to groundwater drought, it needs to pass through the soil layer and lags behind. Fig. 5 shows that the GWSA-DSI has captured multiple droughts in different degrees, of which the duration and intensity of three groundwater droughts in 2004, 2005–2006, 2009–2010 are the most abnormal. Meteorological drought always occurs before groundwater drought. The longer the SPI continues to decline, the more likely it will lead to more severe drought. However,

Table 1 Groundwater drought classifications based on GWSA-DSI. Drought ranges

Drought severity

GWSA-DSI value

No drought D1 D2 D3 D4

abnormally dry moderate drought severe drought extremely drought exceptionally drought

[−0.79, [−1.29, [−1.59, [−1.99, ≤−2.0

−0.50] −0.80] −1.30] −1.60]

4

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3

GWSA-DSI

1

-1

-3

-5

-7 2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Fig. 2. The GWSA-DSI changes in the PRB from 2002 to 2015 (the red line is the threshold line of GWSA-DSI monitoring drought less than −0.80). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4.3. Dynamic propagation from meteorological to groundwater drought

the number of groundwater droughts with low intensity and short duration increased obviously after 2009. Especially after 2011, there was no meteorological drought in the PRB, while the groundwater drought still occurred. It shows that the influence of groundwater drought is not only a single factor of precipitation (Bhuiyan, 2009). At the same time, it also shows that the propagation time from meteorology to groundwater drought tends to be faster. The correlation coefficients between the monthly GWSA-DSI and SPI series with various time scales in the PRB is illustrated in Fig. 6. It can be easily seen from Fig. 6 that the propagation time from meteorological to hydrological drought in the PRB has noticeably seasonal characteristics. Spring groundwater drought propagation time and SPI7 have the strongest correlation (r = 0.86, p < 0.01), summer and autumn have significant correlation with SPI-10 (r = 0.76, p < 0.01) and SPI-14 (r = 0.76, p < 0.01), respectively. Compared with other seasons, groundwater drought in December is significantly correlated with SPI-17 (r = 0.83, p < 0.01), indicating that the propagation time in spring and summer was shorter than that in autumn and winter (Table 2). This was consistent with the seasonal characteristics of meteorological propagation to hydrological drought (Huang et al., 2017a,b). It should be noted that the GWSA-DSI in December has a strong correlation (p < 0.01) with other scale SPI (Fig. 6). Precipitation in the PRB is mainly concentrated in summer and autumn, which results in meteorological drought and groundwater drought mostly occurring in winter.

To further analyze the dynamic changes of the time from meteorology to groundwater drought propagation in the PRB, we slipped GWSA-DSI on the scale of 3 years and 5 years, and analyzed the correlation with SPI series with various time scales (Fig. 7). During the period of extreme groundwater drought from 2003 to 2006 and from 2009 to 2010, the propagation time from meteorological to groundwater drought corresponding to the three-year sliding window was mainly concentrated in 7–14 months (correlation was between 0.47 and 0.70, p < 0.01), while the propagation time corresponding to the fiveyear sliding window was mainly concentrated in 8–13 months (correlation was between 0.39 and 0.56, p < 0.01). In other periods, the drought propagation time of the two windows mainly concentrated in 7–10 months (correlation was between 0.40 and 0.54, p < 0.01). Therefore, the change of drought propagation time corresponding to different sliding windows indicates that the time of meteorological to groundwater drought is dynamic. More importantly, the propagation time of both sliding downwards shows a decreasing trend (Fig. 7), which further illustrates that the propagation time of meteorological to groundwater drought is accelerating, which means that the time of monitoring and preventing drought is shortening, which should be paid more attention to. 4.4. The possible influencing factors of accelerating drought propagation in the PRB To reveal the possible factors affecting the intensification of drought

Groundwater resources (km3)

140 120 100 80

r =0.51, p< 0.08

60 40 20 0 -1.5

-1

-0.5

0 GWSA-DSI

0.5

1

Fig. 3. The correlation between GWSA-DSI and groundwater resources in the PRB. 5

1.5

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2

0.8

1.5

0.7

NDVI

0.5 0.6

0 NDVI

-0.5

GWSA-DSI

1

GWSA-DSI 0.5

-1

NDVI trend

-1.5

GWSA-DSI trend 0.4 2002

-2 2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Fig. 4. The changes of NDVI and GWSA-DSI in PRB from 2002 to 2015 (after 11 months of sliding window processing.

drought situation (Teuling et al., 2013; Mo and Lettenmaier, 2015). At the same time, the NDVI of the PRB is relatively high, and when precipitation cannot provide enough water, vegetation will preferentially consume soil moisture, resulting in further loss of soil moisture. However, the buffering effect of soil moisture in the process of drought propagation slows down the propagation time (relative to the surplus period), but the result is more serious groundwater drought.

propagation in the PRB. Fig. 8 shows the relationship between the propagation time and meteorological and underlying surface factors under the sliding window for three years. It is found that the evapotranspiration (ET) rate (r = 0.79, p < 0.01) and precipitation (r = −0.76, p < 0.01) in meteorological factors and soil moisture (r = −0.48, p = 0.12) in underlying surface factors are the most important factors affecting the intensification of drought propagation. Although soil moisture has not passed the 99% significance test, as a medium of communication between precipitation and soil moisture, the influence of meteorological factors on groundwater resources is realized by controlling the process of soil moisture seepage (Green et al., 2011). Therefore, soil moisture should not be neglected when considering the propagation from meteorological to groundwater drought. However, in the process of accelerating the propagation of meteorology to groundwater drought in the PRB, the drought year (2003–2005 and 2009–2010) has a long propagation time (Fig. 7). To consider the role of ET rate, precipitation and soil moisture in the process of propagation, we divide it into dry and wet cases. In surplus period (precipitation and soil moisture increase, ET rate decrease), soil moisture is in a relatively wet state. The mechanism of stored-full runoff in the study area will accelerate the rate of precipitation infiltration to recharge groundwater, thus speeding up the response time of groundwater to precipitation. As a result, the propagation time is accelerated. In deficit period (precipitation and soil moisture decrease, ET rate increase), when meteorological propagates to groundwater drought, it will first cause the continuous loss of soil moisture, and the increase of ET rate will magnify the soil moisture anomaly and aggravate the

5. Discussion 5.1. The effect of human factors on groundwater drought In many parts of the world groundwater has long been exploited as a resource for public water supply and irrigation. Furthermore, “Free-forall” groundwater policies have resulted in water being pumped at far greater rates than can be replenished naturally (Famiglietti, 2014). To consider the influence of groundwater extraction on groundwater drought and propagation in the PRB, we obtained the annual groundwater supply data of the PRB from the Ministry of Water Resources of China. Fig. 9a shows that the annual groundwater supply in the PRB tends to decline. Especially after 2008, the continuous decline is due to the restriction of groundwater exploitation in some provinces and regions, and the protection measures of groundwater resources such as sealing up self-provided wells (Wang et al., 2015). For comparison, we calculated annual average GWSA-DSI based on its monthly series, and found that there was a significant negative correlation (r = −0.83, p < 0.01) with groundwater supply (Fig. 9b). It shows that the

3 2

Index

1 0 -1

Exceptional Extreme Moderate GWSA-DSI SPI-8

-2 -3

-4 2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Fig. 5. The propagation change of meteorological to groundwater drought in the PRB (different color bands indicate the degree of groundwater drought). 6

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Fig. 6. The correlation coefficients between the monthly GWSA-DSI and SPI series at various time scales in the PRB.

5.2. Impact of intensified water cycle on drought propagation

Table 2 The propagation time of meteorological to groundwater drought in different seasons in the PRB. Summer

Autumn

Winter

7 0.86 p < 0.01

10 0.76 p < 0.01

14 0.76 p < 0.01

17 0.83 p < 0.01

Time scale (SPI)

artificial extraction of groundwater is an important factor to aggravate groundwater drought, which is similar to the depletion of groundwater in northern India (Rodell et al., 2009) and California Valley (Famiglietti et al., 2011). Meanwhile, it also explains why the groundwater drought in the PRB has been alleviated after 2009 (Fig. 5). In addition, the correlation between groundwater supply and propagation time was 0.19 (p = 0.55), indicating that extraction of groundwater had no significant acceleration effect on drought propagation (Fig. 9c). Because of the acceleration of meteorological propagation to groundwater drought, groundwater exploitation cannot be considered solely. Further, the meteorological and underlying surface elements (precipitation, evapotranspiration and soil moisture) that connect precipitation and groundwater are the main factors (Fig. 8). In general, the artificial extraction of groundwater can significantly aggravate groundwater drought, but the acceleration of the propagation time of meteorological to groundwater drought in the PRB should be concerned with the factors of climate change and aggravation of water cycle.

16

0.8

14

0.7

12

0.6

10

0.5

8

0.4

6

0.3

4

0.2

2

0.1

0 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

7

Cor relation coefficient

Propagation time Corresponding correlation Corresponding significance

Spring

Because similar to traditional droughts, a change in groundwater drought is not only associated with the change in terrestrial hydrological cycle, but also influenced by the change in terrestrial energy cycle (and the related heatwaves) as well as the interaction between the land and atmosphere (Dai, 2013). Under global warming. The risk of heatwave is increasing (Meehl and Tebaldi, 2004), and the terrestrial hydrological cycle is also expected to be intensified with increasing water vapor feedback (Huntington, 2006), leading to the changes in precipitation, soil moisture and ET, as well as characteristics of climate extremes. The concurrent drought and heatwave events with low soil moisture (Fig. 10e) and high ET are recently termed as “flash drought” (Yuan et al., 2015). Wang et al. (2016) found that flash drought mainly occurred in South China, with an average of 16–24 times per decade, and the number of occurrences showed an upward trend. Although the temperature has been relatively lower in the whole series (Fig. 10c), this is because the global mean temperature remained flat during the first decade of the 21st century, which is related to the intermittent period of global warming (Foster and Rahmstorf, 2011). However, the decreasing temperature was compensated by the accelerated drying trends of soil moisture and enhanced ET, leading to an acceleration of flash droughts during the warming hiatus (Wang et al., 2016). As a result, more serious drought events tend to occur during this period. In addition, precipitation is another major factor that aggravates the propagation of drought. When considering precipitation magnitude, we further consider the precipitation-concentration degree (PCD). Prior to 2011, the higher PCD indicates that the annual precipitation distribution in the PRB is uneven (Fig. 10b). At the same time, relevant studies Fig. 7. The propagation time, correlation (all pass the significance test of 0.01) and trend of meteorological to groundwater drought under different time scales sliding (blue and orange sliding for 3 and 5 years respectively). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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16

16

r= -0.76, p<0.01

12

Propagation time

Propagation time

r= 0.79, p<0.01

8

4

0 0 .5

0 .5 5

0.6

ET rate

12

8

4

0 1300

1400

1500

16

16

r= -0.26, p=0.41

12

Propagation time

Propagation time

r= -0.48, p=0.12

8

4

64

65

66

67

12

8

4

0 700

0 68

750

Soil moisture (mm)

800

850

r= 0.02, p=0.95

r= -0.30, p=0.35 Propagation time

12

8

4

0 291

ET (mm)

16

16

Propagation time

1600

Precipitation (mm)

291.3

Temperature (k)

291.6

291.9

12

8

4

0 0.67

0.69

0.71

NDVI

0.73

0.75

Fig. 8. The relationship between propagation time and meteorological (ET rate, precipitation and temperature) and underlying surface factors (soil moisture and NDVI). All factors were processed by sliding window for 3 years.

of drought propagation, cross-wavelet analysis of the teleconnection factors and the main factors (ET rate, precipitation, PCD and soil moisture) affecting drought propagation were carried out. Fig. 11a shows that the significant influence of AO on each factor is mainly concentrated on the scale of 1–2 years. ENSO had no significant effect on PCD, but had significant correlation with other factors in 2–7 years (Fig. 11b), which was consistent with the periodic variation of ENSO (Dash et al., 2013). Compared with AO and ENSO, the influence of PDO on factors is mainly concentrated on the scale of 1–4 years, showing a significant positive correlation between precipitation and soil moisture, and a significant negative correlation between ET rate and PCD (Fig. 11c). In addition, the influence of PDO on the factors is mainly on the scale of 1993–2000 and 2006–2013, because at the end of the 20th century, PDO changed from warm phase to cold phase, while during the negative phase, the precipitation intensity in southern China decreased significantly (Huang et al., 2017a,b). Lack of rainfall usually occurs in conjunction with cloud reduction associated with anticyclone circulation patterns, resulting in a strong increase in net radiation under dry conditions and an increase in ET rates (Teuling et al., 2013). As a result,

(Duan et al., 2017) show that the extreme precipitation level and frequency in the PRB have increased significantly since the 21st century. In addition, the increase of extreme high temperature events and the decrease of extreme low temperature events aggravate the water cycle in the basin and the response time of groundwater to meteorological drought (Hundecha and Bárdossy, 2005; Gornall et al., 2010). It is worth noting that after 2011, the increase of precipitation (Fig. 10a) lead to the decrease of ET rate (Fig. 10d). However, the decrease of PCD (p < 0.05) indicates that precipitation in the basin distributes evenly during the year, which results in high humidity in the year, and the accompanying high temperature further increases the risk of heat wave (Peterson et al., 2013). In general, the flash drought and heat wave events caused by global warming and the intensification of water cycle have further aggravated the propagation drought.

5.3. The influence of teleconnection factors on dynamic propagation of drought To reveal how the atmospheric circulation anomaly affects the time 8

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4.5

Grounwater supply (km3)

(a) 4

3.5

3

2.5

2 2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

4.5

r = 0.19, p= 0.55

r = -0.83, p< 0.01

Groundwater supply (km3)

Groundwater supply (km3)

4.5 4 3.5 3 2.5

(b) 2 -1.5

-1

-0.5

0

0.5

1

1.5

4 3.5 3 2.5

(c) 2 0

GWSA-DSI

5

10

15

Propagation time

Fig. 9. Groundwater supply change in the PRB from 2003 to 2015 (a) and its relationship with GWSA-DSI (b) and propagation time (c) in 3-year sliding window, respectively.

Precipitation (mm)

2000

(a)

PCD

1000 0.6

Temperature (K)

0.2 294

(b)

(c)

290

ET rate

0.8

(d)

Soil moisture (cm)

0.3 70

(e)

62

1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Fig. 10. Changes of precipitation (a), PCD (b), temperature (c), ET (d) and soil moisture (e) in the PRB from 1979 to 2015 (black dotted line is average, red is trend line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 9

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ENSO (b)

PDO (c)

PCD

Soil moisture

Precipitation

ET rate

AO (a)

Fig. 11. The cross-wavelet transforms of ET rate, precipitation, soil moisture and PCD in PRB from 1979 to 2015 with AO (a), ENSO (b) and PDO (c), respectively. The 5% significance level against red noise is exhibited as a thick contour, and the relative phase relationship is denoted as arrows. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

propagation. Increasing soil moisture accelerates the response of groundwater to precipitation in the surplus period due to the stored-full runoff mechanism, whilst intensifying ET rate and heat wave facilitate the drought propagation in the deficit period. Compared with AO and ENSO, PDO has more significant effects on temperature, precipitation and ET rate, and is the main driving force affecting the drought propagation. Generally, the findings of this study help to reveal the dynamic and influence factors of drought propagation, thus being useful and valuable for local drought mitigation.

soil moisture anomalies were magnified and drought was exacerbated. Meanwhile, the precipitation in the PRB is mainly affected by the East Asian summer monsoon and the South Asian summer monsoon (Zhao et al., 2014a,b). Because of the geographical pattern of the PRB, which is high in the north and low in the south and high in the west and low in the east, this geographical pattern is conducive to the flow of water vapor to the mainland. Besides, the increasing frequency of extreme precipitation in the PRB is mainly affected by the negative phase of the PDO (Zhao et al., 2014a,b), which increases the risk of flash drought and heat wave in the PRB. As the most significant interdecadal change signal in the North Pacific, PDO is the most important factor regulating the teleconnection between ENSO and climate in East Asia (Chen et al., 2013). Especially in the 21st century, the phase transformation and amplitude fluctuation of PDO and ENSO are obviously enhanced, which intensifies the changes of meteorological elements such as temperature, precipitation and evaporation (Chen and Grasby, 2014). Thus, the propagation time from meteorological drought to hydrological drought in the PRB was accelerated.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements

6. Conclusion We appreciate the constructive comments and suggestions of the editors and the anonymous reviewers. This study was jointly funded by the National Natural Science Foundation of China (grant number 51709221, 51879213), National Key Research and Development Program of China (grant number 2017YFC0405900), the Planning Project of Science and Technology of Water Resources of Shaanxi (grant numbers 2015slkj-27 and 2017slkj-19), the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research, grant number IWHR-SKL-KF201803) and the Doctorate Innovation Funding of Xi'an University of Technology (grant number 310-252071712).

The GWSA-DSI proposed in this paper is based on GRACE-DSI, which can effectively identify and capture groundwater drought in the PRB. The propagation time of meteorological to groundwater drought in the PRB from 2002 to 2015 was 8 months. Through sliding in different time scales, it was found that the time of drought propagation has a significant deceasing trend (p < 0.01), indicating that the water cycle in the PRB was accelerating. Besides, artificial exploitation of groundwater in the PRB will aggravate the groundwater drought, but it has no significant effect on the propagation time. The ET rate (r = 0.79, p < 0.01), precipitation (r = −0.76, p < 0.01) and soil moisture (r = −0.48, p = 0.12) are the main factors affecting drought 10

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