Research Articles

Increasing probability of concurrent drought between the water intake and receiving regions of the Hanjiang to Weihe River Water Diversion Project, China

  • WANG Xiaohong ,
  • LIU Xianfeng , * ,
  • SUN Gaopeng
  • School of Geography and Tourist, Shaanxi Normal University, Xi’an 710119, China
* Liu Xianfeng (1986-), Associate Professor, specialized in climate change and ecological hydrology. E-mail:

Wang Xiaohong (1998-), Master Candidate, specialized in remote sensing of ecological environment. E-mail:

Received date: 2021-12-24

  Accepted date: 2022-03-14

  Online published: 2022-12-25

Supported by

National Natural Science Foundation of China(42171095)

National Natural Science Foundation of China(41801333)

Natural Science Foundation of Shaanxi Province(2020JQ-417)

Social Science Foundation of Shaanxi Province(2020D039)


Water diversion projects are an effective measure to mitigate water shortages in water-limited areas. Understanding the risk of such projects increasing concurrent drought between the water intake and receiving regions is essential for sustainable water management. This study calculates concurrent drought probability between the water intake and receiving regions of the Hanjiang to Weihe River Water Diversion Project using Standardized Precipitation Index and Copula functions. Results showed an increasing trend in drought probability across both the water intake and receiving regions from 2.67% and 8.38% to 12.47% and 14.18%, respectively, during 1969-2018. The return period of concurrent drought decreased from 111.11 to 13.05 years, indicating larger risk of simultaneous drought between the two regions. Projections from CMIP6 suggested that under the SSP 2-4.5 and 5-8.5 scenarios, concurrent drought probability would increase by 2.40% and 7.72% in 2019-2050 compared to that in 1969-1990, respectively. Although increases in precipitation during 2019-2050 could potentially alleviate drought conditions relative to those during 1991-2018, high precipitation variability adds to the uncertainty about future concurrent drought. These findings provide a basis for better understanding concurrent drought and its impact on water diversion projects in a changing climate, and facilitate the establishment of adaptation countermeasures to ensure sustainable water availability.

Cite this article

WANG Xiaohong , LIU Xianfeng , SUN Gaopeng . Increasing probability of concurrent drought between the water intake and receiving regions of the Hanjiang to Weihe River Water Diversion Project, China[J]. Journal of Geographical Sciences, 2022 , 32(10) : 1998 -2012 . DOI: 10.1007/s11442-022-2033-2

1 Introduction

Uneven spatiotemporal distribution of water resources has led to water scarcity in many densely inhabited districts worldwide (Vorosmarty, 2000; Zhang et al., 2020). One of the most effective measures to mitigate water shortages and balance water resource development is to artificially reallocate water via inter-basin water diversion projects (Shao et al., 2003; Pokhrel et al., 2016; Zhou et al., 2017). These projects transfer large volumes of water from relatively humid areas (the water intake regions) to arid destinations (the water receiving regions) (de Andrade et al., 2011; Gohari et al., 2013). Traditionally, water diversion projects are operated under the presupposition that natural ecosystems fluctuate within a relatively stable range of variability, hence the water intake region is considered to always maintain relatively abundant water resources (Milly et al., 2008). However, owing to substantial climate change and intensification of human activities (Sun et al., 2014), climatic extremes such as drought have largely intensified worldwide, bringing great uncertainty to water resources that cannot be ignored (Dai, 2013; AghaKouchak et al., 2014; Martin, 2018). Thus, a supply-oriented water diversion scheme will be of questionable value in the long term since precipitation fluctuation in the water intake region may lead to severe drought and apparent decreases in streamflow as well (Liu et al., 2012; She et al., 2017). Therefore, drought conditions in the water intake region should also be considered when designing diversion schemes.
Numerous studies have focused on dynamic variations and spatial characteristics of drought in a univariate framework and have quantified drought events through different indices (Sonmez et al., 2005; Weng et al., 2015; Le et al., 2019; Noguera et al., 2020; Sivakumar et al., 2020). To more comprehensively understand drought, methods such as Copula functions are widely applied to detect drought in a multivariate framework where links can be established between different drought characteristics, such as duration, severity, and intensity (Salvadori and De Michele, 2015; Zhang et al., 2015; Amirataee et al., 2018), among various drought indices (Hao and AghaKouchak, 2013; Bazrafshan et al., 2015; Chang et al., 2016), and in combination with other extreme climate events (AghaKouchak et al., 2014; Fang et al., 2019). However, these studies have mainly concentrated on drought in a single area, and few have explored concurrent drought between two related regions (Liu et al., 2015; Zhang et al., 2017). In fact, the water intake and receiving regions form an intricate system, and studies that focus on individual areas cannot reveal the interconnection of drought events (Zhang et al., 2017). Previous studies relating to simultaneous drought in the water intake and receiving regions have primarily focused on wetness-dryness encounter, which usually uses hydrological series for frequency analyses (Huang et al., 2015; Yuan et al., 2017). However, these studies failed to recognize that runoff is also affected by the increasing uncertainties stemming from human activities and climate change (Di Baldassarre et al., 2018). Consequently, current studies are rather limited, and there is a need to further explain how the concurrence of wetness or dryness may vary with future runoff changes.
Previous drought assessment regarding inter-basin engineering in China has often aimed at large water transfer projects such as the South to North Water Diversion Project (Liu et al., 2015; Liu et al., 2018; Rogers et al., 2019; Zhang et al., 2020), and the Yangtze River to Taihu Lake Water Diversion Project (Hu et al., 2008; Li et al., 2011; Dai et al., 2020). Nevertheless, water transfer projects across relatively small drainage basins also need attention since these regions can cover key cities with fast-growing economies and dense populations. The Weihe River, the largest tributary of the Yellow River, is the most vital water source for the Guanzhong Plain in Shaanxi Province (Zhao et al., 2019). As a key region integral to the comprehensive development of Western China, the Weihe River Basin has experienced dramatically decreasing precipitation and runoff in recent decades under the influence of climate change, population growth, and urbanization, which is likely to exacerbate water shortages and pose new challenges to water resource management in the Guanzhong Plain (Guo et al., 2017; Deng et al., 2020). To cope with this, the Hanjiang to Weihe River Water Diversion Project has been under construction since 2014 (Ren et al., 2020). In the engineering demonstration phase, studies showed that water supply is to be configured in two stages so that the coordination of the water transfer process is easy and the feasibility and cost are improved (Zhao et al., 2010). However, limited research in recent years has integrated precipitation and fully considered the impact of simultaneous drought in the water intake and receiving regions on the project. Thus, investigation of concurrent drought in these two areas will provide sufficient reference for the rational design of a water resources scheduling scheme and will also be significant to regional food production and national strategic security.
This study has the following objectives: (1) separately characterize drought risk changes in the water intake and receiving regions over the last 50 years, (2) quantitatively evaluate the probability and return period of concurrent drought between these two regions, (3) project whether the concurrent drought condition is to become worse in the next 30 years, and (4) explore possible mechanisms driving the variations in concurrent drought probability under climate change.

2 Materials and methods

2.1 Study area

The Hanjiang to Weihe River Water Diversion Project (HWRWDP) is designed to divert 1.5 billion m3 of water from the Hanjiang River to the Weihe River annually after 2030 (Figure 1) (Liu et al., 2019). The upstream of the Hanjiang River, which supplies water for the project, spans from its origin in Ningqiang County to the Danjiangkou Reservoir (Zhou et al., 2017). The Hanjiang River Basin has high seasonal precipitation variability (700-1100 mm per annum), 70% of which is in the rainy season (May-October) (She et al., 2017). The Weihe River basin, covering 134,766 km2 in Shaanxi Province (Zhao et al., 2019), features semi-arid hydrological characteristics with average annual precipitation of 500-800 mm, which is the densest during June to October (Guo et al., 2017; Ren et al., 2020). The HWRWDP consists of two parts. The first part consists of water transfer projects including Huangjinxia Water Control Project, Sanhekou Water Control Project, and Qinling Water Conveyance Tunnel (Table A1). The second is a water distribution project with a total length of 330 km designed to supply water to key cities and industrial parks such as Xi’an and Jingwei Industrial Park (MWR-PRC, 2016a, 2016b).
Figure 1 Summary of the study area

2.2 Data

Observed monthly precipitation and temperature series in 1969-2018 were sourced from China Meteorological Data Service Centre ( Fourteen stations that uniformly and densely cover the study area were selected for analysis (seven for the water intake and seven for the water receiving basins). The average of values recorded at the seven sites were used to represent general meteorological condition and to calculate drought index in one basin. The temporal coverage of each station was no less than 99% complete (Table A2).
Simulations of monthly total precipitation and temperature in 1969-2050 were obtained from seven global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) ( These models were selected based on their strong correlation with meteorological observations (Table A3) and good performance in simulating historical precipitation and temperature in China, as previously verified (Liu et al., 2015; Zhou et al., 2020). The historical experiment and data from the Shared Socioeconomic Pathways (SSPs) were combined to project future climate. Among all the scenarios, the SSP 2-4.5 has attracted broad attention in countries aiming at sustainable development. The SSP 5-8.5, which embodies the worst high end forcing pathway, is also the focus of current research projects (Aadhar and Mishra, 2020; Wang et al., 2021). Therefore, both scenarios were chosen to help detect future concurrent drought and compare whether drought would be alleviated in a lower radiation emission scenario. The GCM outputs for one basin were averaged in each scenario since the mean value can provide more conservative estimates and decrease the uncertainties in future climate forecasts relative to a single model output (Li et al., 2019; Wang et al., 2020).

2.3 Methods

2.3.1 Drought index

There are many drought indices to choose from, among which the Standardized Precipitation Index (SPI) has obvious advantages. First, the calculation of SPI is easy and requires fewer parameters. Second, SPI is independent of land surface conditions such as climate, topography and soil, so it has stronger spatial consistency than indices such as the Palmer drought severity index (PDSI). Third, SPI considers water deficit at different time scales, so it is more flexible in characterizing drought events (Yuan et al., 2016; Yu et al., 2018; Wang, 2019; Zuo et al., 2021). Additionally, studies have indicated that SPI drought predictions in Northwest China consistently reflect the actual situation (Wang et al., 2013). Therefore, 12-month SPI is used in this study since large reservoirs on Hanjiang River have the ability to adapt to seasonal drought through self-regulation (Liu et al., 2015). Additionally, the 12-month timescale can better describe long-term variations in water resources such as surface runoff and groundwater supply (Gebrechorkos et al., 2020). The classification of drought levels by SPI is presented in Table A4.

2.3.2 Copula function

The normal, extreme value, logistic, t location-scale, and generalized extreme value distributions were chosen to fit SPI in various periods since these have been commonly applied to fit probability distribution of extreme climatic events such as drought and flood (She et al., 2016). The Kolmogorov-Smirnov test (K-S test) was used to screen acceptable distributions and to choose the optimal marginal distribution with the largest p value for SPI in one basin. The detailed selection process and results can be found in Table A5, using SPI series calculated by observed precipitation in 1969-2018 as an example.
The calculation of concurrent drought probability and corresponding return period was based on the Copula functions, which were established to simulate the dependence structure between variables because there is a certain correlation between the 12-month SPI in both regions (Table A6). Furthermore, Copula function is not constrained by specific types of marginal distribution (Nelsen, 2006). Assuming two variables X1 and X2 (the 12-month SPI in the water intake and receiving regions, respectively) with cumulative distribution functions ${{F}_{{{X}_{1}}}}({{x}_{1}})=\Pr ({{X}_{1}}\le {{x}_{1}})$and ${{F}_{{{X}_{2}}}}({{x}_{2}})=\Pr ({{X}_{2}}\le {{x}_{2}})$, the joint distribution $F({{x}_{1}},{{x}_{2}})$ can be obtained as follows:
$F({{x}_{1}},{{x}_{2}})=\Pr ({{X}_{1}}\le {{x}_{1}},{{X}_{2}}\le {{x}_{2}})=C({{F}_{{{X}_{1}}}}({{x}_{1}}),{{F}_{{{X}_{2}}}}({{x}_{2}}))$
where function C refers to the Copula function representing the bivariate dependence structure between variables X1 and X2. Since the return period of a climatic event is T years when the probability of occurrence for this event is 1/T, the joint return period of concurrent drought can be described as follows (Salvadori et al., 2011):
$T({{X}_{1}}\le {{x}_{1}},{{X}_{2}}\le {{x}_{2}})=\frac{\mu }{\Pr ({{X}_{1}}\le {{x}_{1}},{{X}_{2}}\le {{x}_{2}})}=\frac{\mu }{C({{F}_{{{X}_{1}}}}({{x}_{1}}),{{F}_{{{X}_{2}}}}({{x}_{2}}))}$
where μ is the average inter-arrival period of X1 and X2 (μ = 1 indicates that the average inter-arrival period between subsequent values is one year). When the time scale for SPI is 12 or 3 months, the corresponding μ should be 1 or 0.25 year, respectively.
Various Copula families can be chosen to quantify joint probability. We used five Copula functions namely Gaussian, t, Gumbel, Frank, and Clayton Copulas, and analyzed their fitting results with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). For example, the Frank Copula is the most appropriate fitting function for concurrent drought in 1969-2018, because it had the smallest AIC and BIC values when modeling the multivariate dependence of SPI in the two basins (Table A7).

3 Results

3.1 Drought variations in the water intake and receiving regions

In the water intake region, drought probability during 1969-2018 was 10.10% (a return period of 9.90 years). There were three drought years (1997, 1995, and 1986, SPI ≤ ‒1, sorted in descending order of SPI value) according to observation series. The risks of moderate, severe, and extreme droughts were 7.31%, 2.25%, and 0.54%, respectively (Figure 2a). To evaluate drought risk changes over time, two sub-periods, before and after 1991, were used for analysis since studies have suggested that the 1990s was a key period when more frequent and severe drought events were observed across Northwest China (Ma et al., 2013; Zhu et al., 2019). Figure 2b shows that drought probability in the water intake region increased from 2.67% in 1969-1990 to 12.47% in 1991-2018, indicating that drought had clearly intensified.
Figure 2 Probability distribution of 12-month SPI in the water intake (a) and receiving (c) regions during 1969- 2018. Probability density curves of 12-month SPI in the water intake (b) and receiving (d) regions in 1969-1990 (P1) and 1991-2018 (P2). The intersection of the black dashed lines represents the threshold of drought. The shaded portions and percentages represent the probability of meteorological drought (when SPI < -1).
In the water receiving region, drought probability in 1969-2018 was 11.55% (a return period of 8.66 years), with six occurrences of drought years (1997, 1995, 1977, 1986, 1979 and 2001) (Figure 2c). The risks of moderate, severe, and extreme drought were 7.08%, 2.82%, and 1.65%, respectively, which was larger than that in the water intake region (except for the moderate drought case). Figure 2d shows that drought probability exhibited a similar trend to that in the water intake region, increasing from 8.38% in 1969-1990 to 14.18% in 1991-2018. Notably, in the above three periods, drought probability in the water receiving region was always greater than that in the water intake region, suggesting that the water receiving region was more vulnerable to drought during the past 50 years.
According to the observation records, 66.67% and 50.00% of drought events occurred after 1990 in the water intake and receiving basins, respectively, most of which were severe. Moreover, drought years identified by SPI such as 1977, 1986, 1995, and 1997 correspond well with records in the Chinese Meteorological Disaster Dictionary (Shaanxi Volume) (Wen and Zhai, 2005). Thus, our findings were consistent with the observation records that drought intensified during 1991-2018 in both regions.

3.2 Concurrent drought in the water intake and receiving basins

The Frank Copula was selected as the optimal joint distribution fitted for SPI between the water intake and receiving regions during 1969-2018, 1969-1990, and 1991-2018. The probability of concurrent drought (SPI ≤ -1.0) between the two basins in 1969-2018 was 4.95% (20.20 years for the return period). The specific concurrent drought years according to observation records were 1986, 1995 and 1997 (Figure 3a). During 1969-1990 and 1991-2018, the corresponding probabilities were 0.90% and 7.66% (111.11 and 13.05 years for the return period), respectively (Figures 3b and 3c). This suggests that the risk of simultaneous drought has increased in recent years. According to previous studies, the water intake and receiving regions of HWRWDP have high consistency in wetness-dryness encounter, with a synchronous probability of more than 60%. Specifically, the probability of simultaneous dryness for both basins is greater than 20% (Wang et al., 2012; Chen et al., 2013). Additionally, research based on streamflow of typical hydrological stations revealed that 1986 and 1999 were dry years for both the Huangjinxia and Sanhekou reservoirs (Jin et al., 2019), which corresponds well with our results above.
Figure 3 Probabilities of concurrent drought between the water intake and receiving basins during (a) 1969-2018, (b) 1969-1990 and (c) 1991-2018. The intersection of the black dashed lines represents concurrent drought occurred in the two areas.

3.3 Projected concurrent drought probabilities in the future

Under the SSP 2-4.5 scenario, drought probabilities in the water intake region during 1969-1990, 1991-2018, and 2019-2050 were 7.13%, 19.12%, and 9.45% with corresponding return periods of 14.03, 5.23, and 10.58 years, respectively. For the water receiving region, drought probabilities were 6.91%, 15.88%, and 15.24% with corresponding return periods of 14.47, 6.30, and 6.56 years, respectively. Under the SSP 5-8.5 scenario, however, corresponding probabilities in the water intake basin were 9.79%, 17.53%, and 15.29% and 10.21, 5.70, and 6.54 years for the return periods, and drought risks in the water receiving region were 9.54%, 14.94%, and 16.64% with 10.48, 6.69, and 6.01 years for the return periods, respectively (Figure 4). These results imply that drought probability was generally the highest in 1991-2018 for both basins, followed by 2019-2050, except for the water receiving region under SSP 5-8.5 scenario. Moreover, relative to those in 1969-1990, drought probabilities in 1991-2050 are projected to increase under the SSP 2-4.5 and SSP 5-8.5 scenarios in almost all drought levels. For instance, concurrent drought probability in 2019-2050 would increase by 2.40% and 7.72% under the two scenarios, respectively. Thus, a potential synchronous dry condition in both basins is expected in the future (Figure 5).
Figure 4 Probability density curves of 12-month SPI in the water intake and receiving regions in 1969-1990 (a, b), 1991-2018 (c, d) and 2019-2050 (e, f) based on CMIP6 outputs. The shaded portions and percentages represent drought probabilities (When SPI smaller than -1).
Figure 5 Changes in drought probabilities (%) under the SSP 2-4.5 and SSP 5-8.5 scenarios in 1991-2018 and 2019-2050 compared to those during 1969-1990 in the water intake (a, b) and receiving regions (c, d). Variations in probability of concurrent drought are shown in e and f.
Notably, increases in drought probabilities under the SSP 5-8.5 scenario are not always greater than those under the SSP 2-4.5 scenario (e.g., the water receiving basin during 2019-2050 in Figure 5d), which could be tied up with water budget changes in that basin (Granier et al., 1999). In 2019-2050, annual precipitation variation in the water intake and receiving regions would increase significantly under the SSP 5-8.5 scenario (P < 0.1 and P < 0.05, respectively). However, a similar trend was not detected under the SSP 2-4.5 scenario (Figures A1a and A1b). Therefore, both basins become wetter at a faster rate under the SSP 5-8.5 scenario than under the SSP 2-4.5 scenario. Meanwhile, variations in annual mean temperature in both regions also increase significantly in 2019-2050 (P < 0.01, Figures A1c and A1d). Thus, increases in rainfall are mostly canceled out by rises in evaporation caused by higher temperature under the SSP 2-4.5 scenario, while precipitation offset by evapotranspiration under the SSP 5-8.5 scenario is only partial (Fu and Feng, 2014; Li et al., 2017; Miao et al., 2020). Hence, these changes could help to mitigate future drought under the SSP 5-8.5 scenario in both basins.

4 Discussion

4.1 Possible mechanisms for the increased concurrent drought

Our results indicate that more separate and concurrent droughts will occur in both the water intake and receiving basins in the near future compared to the reference period (1969-1990), which is in line with the findings of other recent studies (Li et al., 2020; Miao et al., 2020). The changes in drought conditions, however, are considered intimately related to large-scale teleconnection oscillations (TC) under global warming (Keener et al., 2010; Zhang et al., 2017). One of the main TCs influencing climate variability in the water intake and receiving regions is ENSO, which usually leads to periodic dry and wet conditions (Zhang et al., 2019). The Arctic Oscillation (AO) also play an important role. A weakened East Asian winter monsoon during a positive AO phase could transport more moisture from the Northwest Pacific Ocean to China, resulting in warmer and more humid climate conditions, and a strengthened East Asian winter monsoon during a negative AO phase would have the opposite effect (Wu and Wang, 2002; Mao et al., 2011; Chen et al., 2013; Liu et al., 2018). Additionally, some other circulation systems such as the westerly flow and the southwesterly flow, related to the Indian summer monsoon, could affect the dry and wet conditions in Northwest China, including in the study area (Liu et al., 2016; Zhang et al., 2019).
In 1991-2018, precipitation variability in both regions increased simultaneously in 32.14% of the 28 years (scattered points falling in the second quadrant) relative to the multi-year average during 1969-1990 under both the SSP 2-4.5 and SSP 5-8.5 scenarios (Figure A2a). The corresponding proportions during 2019-2050 under both scenarios were 15.62% and 28.13%, respectively, showing that there are 80.09% more years with increased precipitation fluctuation in both basins under the SSP 5-8.5 scenario (Figure A2b). Larger annual variability suggests an unevenly distributed precipitation and more frequent pluvial and drought events in both the water intake and receiving regions in the future (Ukkola et al., 2020).

4.2 Influences of concurrent drought on water diversion

The general scheme of HWRWDP is to firstly divert water from the Huangjinxia Reservoir. If the water supply cannot meet the needs of Guanzhong Plain, it will be supplemented by the Sanhekou Reservoir. Otherwise, excess water will be transported to the Sanhekou Reservoir for storage (Du et al., 2017). Therefore, when concurrent drought occurs, water storage in reservoirs in the upstream of the Hanjiang River would be critical in alleviating water shortages. Simulations from existing studies show that around 1986 and 1999, when concurrent drought occurred, as the inflow into parallel reservoirs decreased in the water intake region, the water available for diverting was between 0.99 and 1.36 km³, so the shortfall may be between 0.14 and 0.51 km³ (Jin et al., 2019). Besides, in 2000-2019, water storage of large and medium-sized reservoirs in the upstream of the Hanjiang River in Shaanxi Province fluctuated between 2.1 and 3.6 km³. Thus, the 1.5 km³ of diverted water accounts for 52.43% of the mean water storage. However, annual water consumption in the water intake and receiving regions has increased significantly by 15% and 12% in 2000-2019, respectively (P < 0.01, Figure A3). Therefore, the proportion for water diverting above is already too high given the increasing water demand in the water intake region. If concurrent drought occurs as in 1995 when rainfall in the water intake region decreased by more than 60% (CMA, 1995), it would be difficult to guarantee water transfer considering to the existing production and domestic water needs in the region.
In addition to HWRWDP, the water resources allocation project in Northern Hubei and the South to North Water Diversion Project collect water from the Hanjiang River, with annual amounts projected to reach 0.77 km3 and 13 km3 after 2030, respectively. Therefore, simultaneous drought in the water intake and receiving regions of HWRWDP will lead to excessive water withdrawals in the upstream of the Hanjiang River to guarantee basic domestic needs, limiting water availability for the other two projects. Thus, 15.27 km3 of diverted water must be guaranteed even in concurrent drought years, and the resulting heavily reduced runoff in the lower reaches of the Hanjiang River is likely to aggravate environmental problems such as algal blooms, which once occurred in the 1990s triggering massive fish kills (Stone and Jia, 2006; Zhou et al., 2017). Studies have also demonstrated that after meeting the water demand of the above projects after 2030, the maximum population and economic scale that the remaining water resources in Hanjiang River can carry will not be enough to maintain local sustainable development. Therefore, it will be difficult to coordinate water resources for all water diversion projects around the basin in the coming concurrent drought years (Chang et al., 2020).
To replenish water and mitigate ecological problems in the lower reaches of the Hanjiang River, the Yangtze to Hanjiang River Water Diversion Project was proposed (HPDWR, 2014). Meanwhile, the Shaanxi government has also considered diverting water from the Jialing River to the Hanjiang River to improve the grim water situation (SPDWR, 2020). Given the increasing simultaneous drought between the Hanjiang River and the Weihe River, these compensation projects can serve as effective adaptive measures. Moreover, saving water and improving water-use efficiency are essential to cope with future concurrent drought so the dependence on water diversion projects can be reduced and regional water security can be greatly improved. In short, policies that not only focus on the current water crisis but also encourage forward-thinking measures for scientific and reasonable water-use planning are urgently needed (Morris and Bucini, 2016).

4.3 Uncertainties and future directions

The results of our study are constrained by several uncertainties. First, the number of meteorological sites used for analysis is relatively small because data continuity was prioritized. Second, precipitation and temperature projections are uncertain due to different emission scenarios and GCMs. Third, although SPI helps to quantify concurrent drought, it only concentrates on meteorological drought. To establish more comprehensive drought indicators that incorporate elements across the atmosphere, hydrosphere, and biosphere, based on specific models, would facilitate a better understanding of the onset, propagation, and termination of drought.
The increasing concurrent drought exposes the Weihe River Basin, a key food producing region, to potential economic losses. In future studies, data with higher spatial resolution is essential to achieve more detailed evaluation around sub-regions with pivotal ecological value in the Basin. This would provide powerful support for policy interventions such as crop insurance and supply distribution. Moreover, water resource scheduling models that incorporate concurrent drought need to be explored to better manage multiple priorities including water supply, ecological restoration, flood control, and other water-related objectives.

5 Conclusions

In this study, concurrent drought risks between the water intake and receiving basins of HWRWDP was evaluated based on SPI and Copula functions. Our results show that both regions have historically experienced intensified separate and concurrent droughts. Additionally, the risks of concurrent drought will increase in the future according to new CMIP6 projects. Furthermore, our results highlight that large variations in precipitation and temperature pose great uncertainty in future drought predictions. Thus, adaptation measures are urgently required to improve water security in a changing climate. Our findings should be of great significance in objectively evaluating the effectiveness of HWRWDP and provide a reliable reference for water sources management once the project is fully operational.

AcknowledgmentsAppendix1 Appendix tables

The authors would like to thank China Meteorological Data Service Centre for providing the observed monthly precipitation and temperature data, the Earth System Grid Federation (ESGF) for archiving the CMIP6 data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. The authors would also like to thank for the support of precipitation data from ‘National Earth System Science Data Center, National Science & Technology Infrastructure of China (’.
Supplementary data to this article are also provided.
Table A1 Brief description of the subprojects of the Hanjiang to Weihe River Water Diversion Project
Name of the
Location Water storage (106 m3) Maximum height (m) Designed discharge (m³/s) Function
Huangjinxia Water Control Project Upper Hanjiang, 62 km from Yang County 229 68 70 Water supply, power generation and improvement of navigation conditions
Sanhekou Water Control Project 2 km from the junction of Jiaoxi River, Pu River and Wenshui River 710 145 50 Regulating and storing water
Qinling water conveyance tunnel Connect the Sanhekou Water Control Project and the Huangchi River in Zhouzhi County / / 70 Transport water
Table A2 Basic information of meteorological stations
Name ID Longitude Latitude Elevation (m)
Changwu 53929 35°12'N 107°48'E 1206.5
Tongchuan 53947 35°05'N 109°04'E 978.9
Baoji 57016 34°21'N 107°08'E 612.4
Fengxiang 57025 34°31'N 107°23'E 781.1
Yaoxian 57037 34°56'N 108°59'E 710.0
Huashan 57046 34°29'N 110°05'E 2064.9
Lueyang 57106 33°19'N 106°09'E 794.2
Hanzhong 57127 33°04'N 107°02'E 509.5
Foping 57134 33°31'N 107°59'E 827.2
Shangzhou 57143 33°52'N 109°58'E 742.2
Zhenan 57144 33°26'N 109°09'E 693.7
Shiquan 57232 33°03'N 108°16'E 484.9
Wugong 57234 34°15'N 108°13'E 447.8
Ankang 57245 32°43'N 109°02'E 290.8
Table A3 CMIP6 models and corresponding variables used in this study along with Pearson correlation coefficient with site observations at a significance level of p < 0.01. Only one ensemble member (r1i1p1f1) was used for each model. All the GCM outputs were interpolated into 0.5° × 0.5° for further analysis
Model abbreviation Institute ID Horizontal resolution
(°lon × °lat)
Study area
Water intake Water receiving
BCC-CSM2-MR BCC 1.125, 1.125 (gn) r1i1p1f1 pr 0.5938 0.5388
BCC-CSM2-MR BCC 1.125, 1.125 (gn) r1i1p1f1 ta 0.9732 0.9738
CNRM-CM6-1 CNRM-CERFACS 1.406, 1.406 (gr) r1i1p1f1 pr 0.4633 0.4126
FGOALS-f3-L LASG-CESS 1.25, 1 (gr) r1i1p1f1 pr 0.5778 0.5135
GFDL-ESM4 NOAA GFDL 1.25, 1 (gr1) r1i1p1f1 pr 0.5447 0.5310
MIROC6 MIROC 1.406, 1.406 (gn) r1i1p1f1 pr 0.5403 0.4820
MRI-ESM2-0 MRI 1.125, 1.125 (gn) r1i1p1f1 pr 0.4598 0.4101
MPI-ESM1-2-LR MPI-M 1.875, 1.875 (gn) r1i1p1f1 ta 0.9739 0.9735
Table A4 Classification of drought according to SPI
Index value Category
Extreme drought SPI ≤ ‒2.0
Severe drought -2.0 < SPI ≤ ‒1.5
Moderate drought -1.5 < SPI ≤ ‒1.0
Near normal -1.0 < SPI ≤ 1.0
Moderately wet 1.0 < SPI ≤ 1.5
Very wet 1.5 < SPI ≤ 2.0
Extremely wet SPI > 2.0
Table A5 Goodness-of-fit values of the five probability distributions of SPI in both areas in 1969-2018
Region Margin p value
Water intake Normal 0.9837
Logistic 0.9057
Generalized extreme value 0.9675
t location-scale 0.9762
Extreme value 0.3369
Water receiving Normal 0.9246
Logistic 0.9627
Generalized extreme value 0.8944
t location-scale 0.9389
Extreme value 0.3159
Table A6 The correlation coefficient of SPI in the water intake and receiving regions
Data source Period Pearson (rn) Spearman (ρn) Kendall (τn)
Observation 1969-2018 0.8154 0.7565 0.5902
Simulation under SSP 2-4.5 1969-2050 0.8016 0.7634 0.5748
Simulation under SSP 5-8.5 1969-2050 0.8295 0.8231 0.6387
Table A7 Goodness-of-fit values of Copula functions between the optimal marginal distributions in the two regions
Period 1969-2018 1969-1990 1991-2018
Copulas t -2291.16 -2271.69 -1887.2 -1867.7 -2071.8 -2052.3
Gaussian -2297.85 -2278.38 -1887.8 -1868.4 -2074.4 -2055.0
Clayton -2240.16 -2220.69 -1885.5 -1866.0 -2045.9 -2026.4
Gumbel -2387.53 -2368.06 -1942.7 -1923.3 -2129.4 -2109.9
Frank -2398.88 -2379.41 -1974.7 -1955.2 -2172.2 -2152.7

2 Appendix figures

Figure A1 Variations in annual precipitation and mean temperature in the water intake (a, c) and water receiving (b, d) basins in 1969-2050 compared to multi-year average in 1969-1990. The solid lines and shaded areas indicate the averages and extreme value of CMIP6 outputs.
Figure A2 Variations in precipitation variability in the water intake versus water receiving basins in 1991-2018 (a), 2019-2050 (b) relative to the multi-year average in 1970-1990. The steps for calculating precipitation variability are as follows: 1) 12-month SPI series in the two regions in 1969-2050 are calculated based on monthly precipitation under the SSP 2-4.5 and 5-8.5 scenarios, respectively; 2) calculate the standard deviation of 12-month SPI in each year from 1970-2050, which represents the precipitation variability in the corresponding year. The precipitation variability in 1969 is ignored because 12-month SPI in 1969 has only one value.
Figure A3 Annual water consumption in the water intake and receiving regions in 2000-2019 (Collected from the Annual Bulletin of Water Resources in Shaanxi Province, available at:
Aadhar S, Mishra V, 2020. On the projected decline in droughts over South Asia in CMIP6 multimodel ensemble. Journal of Geophysical Research Atmospheres, 125(20): e2020JD033587.

AghaKouchak A, Cheng L Y, Mazdiyasni O et al., 2014. Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophysical Research Letters, 41(24): 8847-8852.


Amirataee B, Montaseri M, Rezaie H, 2018. Regional analysis and derivation of Copula-based drought Severity-Area-Frequency curve in Lake Urmia basin, Iran. Journal of Environmental Management, 206: 134-144.


Bazrafshan J, Nadi M, Ghorbani K, 2015. Comparison of empirical Copula-based Joint Deficit Index (JDI) and multivariate standardized precipitation index (MSPI) for drought monitoring in Iran. Water Resources Management, 29(6): 2027-2044.


Chang J X, Li Y Y, Wang Y M et al., 2016. Copula-based drought risk assessment combined with an integrated index in the Wei River Basin, China. Journal of Hydrology, 540: 824-834.


Chang W J, Dong X, Ma H B et al., 2020. Basin water resources carrying capacity and affordable water transfer scale in consideration of water consumption. Journal of Yangtze River Scientific Research Institute, 37(9): 8-12, 17. (in Chinese)

Chen R Z, Sang Y F, Wang Z G et al., 2013. Influence of rich-poor precipitation on water resource allocation of the Hanjiang-to-Weihe River Water Transfer Project. Resources Science, 35(8): 1577-1583. (in Chinese)

Chen W, Lan X Q, Wang L et al., 2013. The combined effects of the ENSO and the Arctic Oscillation on the winter climate anomalies in East Asia. Chinese Science Bulletin, 58(12): 1355-1362.


China Meteorological Administration (CMA), 1995. Shaanxi has a rare drought this year. Accessed 15 October 2021.

Dai A G, 2013. Increasing drought under global warming in observations and models. Nature Climate Change, 3(1): 52-58.


Dai J Y, Wu S Q, Wu X F et al., 2020. Impacts of a large river-to-lake water diversion project on lacustrine phytoplankton communities. Journal of Hydrology, 587: 124938.


de Andrade J G P, Barbosa P S F, Souza L C A et al., 2011. Interbasin water transfers: The Brazilian experience and international case comparisons. Water Resources Management, 25(8): 1915-1934.


Deng W J, Song J X, Sun H T et al., 2020. Isolating of climate and land surface contribution to basin runoff variability: A case study from the Weihe River Basin, China. Ecological Engineering, 153: 105904.


Di Baldassarre G, Wanders N, AghaKouchak A et al., 2018. Water shortages worsened by reservoir effects. Nature Sustainability, 1(11): 617-622.


Du X Z, Bai T, Ma X et al., 2017. Study on water transfer mode of reservoir group within water-transferring region for Hanjiang-to-Weihe River Valley Water Diversion Project. Water Resources and Hydropower Engineering, 48(8): 2-7, 136. (in Chinese)

Fang W, Huang S Z, Huang G H et al., 2019. Copulas-based risk analysis for inter-seasonal combinations of wet and dry conditions under a changing climate. International Journal of Climatology, 39(4): 2005-2021.


Fu Q, Feng S, 2014. Responses of terrestrial aridity to global warming. Journal of Geophysical Research: Atmospheres, 119(13): 2014JD021608.

Gebrechorkos S H, Huelsmann S, Bernhofer C, 2020. Analysis of climate variability and droughts in East Africa using high-resolution climate data products. Global and Planetary Change, 186: 103130.


Gohari A, Eslamian S, Mirchi A et al., 2013. Water transfer as a solution to water shortage: A fix that can Backfire. Journal of Hydrology, 491: 23-39.


Granier A, Bréda N, Biron P et al., 1999. A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands. Ecological Modelling, 116(2): 269-283.


Guo A J, Chang J X, Liu D F et al., 2017. Variations in the precipitation-runoff relationship of the Weihe River Basin. Hydrology Research, 48(1): 295-310.


Hao Z C, AghaKouchak A, 2013. Multivariate Standardized Drought Index: A parametric multi-index model. Advances in Water Resources, 57: 12-18.


Hu W P, Zhai S J, Zhu Z C et al., 2008. Impacts of the Yangtze River water transfer on the restoration of Lake Taihu. Ecological Engineering, 34(1): 30-49.


Huang X R, Zhao J W, Yang P P, 2015. Wet-dry runoff correlation in western route of South-to-North Water Diversion Project, China. Journal of Mountain Science, 12(3): 592-603.


Hubei Provincial Department of Water Resources (HPDWR), 2014. Jingmen Bureau emergency monitoring for the operation of the Yangtze-to-Hanjiang water diversion project. Accessed 15 October 2021.

Jin W T, Wang Y M, Bai T et al., 2019. Multi-objective operation and decision making of parallel reservoirs for Hanjiang-to-Weihe water diversion project in dry years. Journal of Hydroelectric Engineering, 38(2): 68-81. (in Chinese)

Keener V W, Feyereisen G W, Lall U et al., 2010. El-Niño/Southern Oscillation (ENSO) influences on monthly NO3 load and concentration, stream flow and precipitation in the Little River Watershed, Tifton, Georgia (GA). Journal of Hydrology, 381(3/4): 352-363.


Le P V V, Phan-Van T, Mai K V et al., 2019. Space-time variability of drought over Vietnam. International Journal of Climatology, 39(14): 5437-5451.


Li L C, Yao N, Li Y et al., 2019. Future projections of extreme temperature events in different sub-regions of China. Atmospheric Research, 217: 150-164.


Li S Y, Miao L J, Jiang Z H et al., 2020. Projected drought conditions in Northwest China with CMIP 6 models under combined SSPs and RCPs for 2015-2099. Advances in Climate Change Research, 11(3): 210-217.


Li Y P, Acharya K, Yu Z B, 2011. Modeling impacts of Yangtze River water transfer on water ages in Lake Taihu, China. Ecological Engineering, 37(2): 325-334.


Li Z, Chen Y N, Fang G H et al., 2017. Multivariate assessment and attribution of droughts in Central Asia. Scientific Reports, 7: 1316.


Liu F, Tang C, Ma T H et al., 2019. Characterizing rockbursts along a structural plane in a tunnel of the Hanjiang-to-Weihe River Diversion Project by microseismic monitoring. Rock Mechanics and Rock Engineering, 52(6): 1835-1856.


Liu H, Yin J, Feng L, 2018. The dynamic changes in the storage of the Danjiangkou Reservoir and the influence of the South-North Water Transfer Project. Scientific Reports, 8: 8710.


Liu X M, Liu C M, Luo Y Z et al., 2012. Dramatic decrease in streamflow from the headwater source in the central route of China’s water diversion project: Climatic variation or human influence? Journal of Geophysical Research: Atmospheres, 117: D06113.

Liu X M, Luo Y Z, Yang T T et al., 2015. Investigation of the probability of concurrent drought events between the water source and destination regions of China’s water diversion project. Geophysical Research Letters, 42(20): 8424-8431.


Liu Z Y, Menzel L, Dong C Y et al., 2016. Temporal dynamics and spatial patterns of drought and the relation to ENSO: A case study in Northwest China. International Journal of Climatology, 36(8): 2886-2898.


Liu Z Y, Zhang X, Fang R H, 2018. Multi-scale linkages of winter drought variability to ENSO and the Arctic Oscillation: A case study in Shaanxi, North China. Atmospheric Research, 200: 117-125.


Ma M W, Song S B, Ren L L et al., 2013. Multivariate drought characteristics using trivariate Gaussian and Student t copulas. Hydrological Processes, 27(8): 1175-1190.


Mao R, Gong D Y, Yang J et al., 2011. Linkage between the Arctic Oscillation and winter extreme precipitation over central-southern China. Climate Research, 50(2): 187-201.


Martin E R, 2018. Future projections of global pluvial and drought event characteristics. Geophysical Research Letters, 45(21): 11913-11920.

Miao L J, Li S Y, Zhang F et al., 2020. Future drought in the dry lands of Asia under the 1.5 and 2.0 °C warming scenarios. Earth’s Future, 8(6): UNSP e2019EF001337.

Milly P C D, Betancourt J, Falkenmark M et al., 2008. Climate change-stationarity is dead: Whither water management? Science, 319(5863): 573-574.


Ministry of Water Resources of the People’s Republic of China (MWR-PRC), 2016a. The proposal of the second phase project of the Han-to-Wei Water Diversion Project passed the review of the Ministry of Water Resources. Accessed 15 October 2021.

Ministry of Water Resources of the People’s Republic of China (MWR-PRC), 2016b. The dam for the Sanhekou Water Control Project of the Han-to-Wei Water Diversion Project has officially started to pour. Accessed 15 October 2021.

Morris K S, Bucini G, 2016. California’s drought as opportunity: Redesigning U.S. agriculture for a changing climate. Elementa: Science of the Anthropocene, 4: 1-12.


Nelsen R B, 2006. An Introduction to Copulas. New York: Springer-Verlag, 272pp.

Noguera I, Domínguez-Castro F, Vicente-Serrano S M, 2020. Characteristics and trends of flash droughts in Spain, 1961-2018. Annals of the New York Academy of Sciences, 1472(1): 155-172.

Pokhrel Y N, Hanasaki N, Wada Y et al., 2016. Recent progresses in incorporating human land-water management into global land surface models toward their integration into Earth system models. Wiley Interdisciplinary Reviews: Water, 3(4): 548-574.

Ren K, Huang S Z, Huang Q et al., 2020. Assessing the reliability, resilience and vulnerability of water supply system under multiple uncertain sources. Journal of Cleaner Production, 252: 119806.


Rogers S, Chen D, Jiang H et al., 2019. An integrated assessment of China’s South-North Water Transfer Project. Geographical Research, 58(1): 49-63.


Salvadori G, De Michele C, 2015. Multivariate real-time assessment of droughts via Copula-based multi-site hazard trajectories and fans. Journal of Hydrology, 526: 101-115.


Salvadori G, De Michele C, Durante F, 2011. On the return period and design in a multivariate framework. Hydrology and Earth System Sciences, 15(11): 3293-3305.


Shaanxi Provincial Department of Water Resources (SPDWR), 2020. Reply letter to the 3rd meeting of the 12th Provincial CPPCC No.894. Accessed 15 October 2021.

Shao X J, Wang H, Wang Z Y, 2003. Interbasin transfer projects and their implications: A China case study. International Journal of River Basin Management, 1(1): 5-14.


She D X, Mishra A K, Xia J et al., 2016. Wet and dry spell analysis using Copulas. International Journal of Climatology, 36(1): 476-491.


She D X, Xia J, Shao Q X et al., 2017. Advanced investigation on the change in the streamflow into the water source of the middle route of China’s water diversion project. Journal of Geophysical Research: Atmospheres, 122(13): 6950-6961.

Sivakumar V L, Ramalakshmi M, Krishnappa R R et al., 2020. An integration of geospatial technology and standard precipitation index (SPI) for drought vulnerability assessment for a part of Namakkal district, South India. Materials Today: Proceedings, 33: 1206-1211.


Sonmez F K, Komuscu A U, Erkan A et al., 2005. An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Natural Hazards, 35(2): 243-264.


Stone R, Jia H, 2006. Hydroengineering: Going against the flow. Science, 313(5790): 1034-1037.


Sun Y, Tian F Q, Yang L et al., 2014. Exploring the spatial variability of contributions from climate variation and change in catchment properties to streamflow decrease in a mesoscale basin by three different methods. Journal of Hydrology, 508: 170-180.


Ukkola A M, De Kauwe M G, Roderick M L et al., 2020. Robust future changes in meteorological drought in CMIP6 Projections despite uncertainty in precipitation. Geophysical Research Letters, 47(11): e2020GL087820.

Vorosmarty C J, Green P, Salisbury J et al., 2000. Global water resources: Vulnerability from climate change and population growth. Science, 289(5477): 284-288.


Wang J T, 2019. Analysis on spatiotemporal pattern of SPI drought based on meteorological index in North China. Research of Soil and Water Conservation, 26(4): 203-207. (in Chinese)

Wang T, Tu X J, Singh V P et al., 2021. Global data assessment and analysis of drought characteristics based on CMIP6. Journal of Hydrology, 596: 126091.


Wang W, Zhong Y H, Lei X H et al., 2012. Synchronous-asynchronous encounter probability of rich-poor precipitation between water source area and water receiving area of the Hanjiang-to-Weihe River Water Transfer Project. South-to-North Water Diversion and Water Science & Technology, 10(5): 23-26, 36. (in Chinese)

Wang X Y, Zhou L, Li C et al., 2020. Temporal and spatial evolution trends of drought in northern Shaanxi of China: 1960-2100. Theoretical and Applied Climatology, 139(3/4): 965-979.


Wang Z L, Wang J S, Li Y H et al., 2013. Comparison of application between generalized extreme value index and standardized precipitation index in Northwest China. Plateau Meteorology, 32(3): 839-847. (in Chinese)


Wen K G, Zhai Y A, 2005. Chinese Meteorological Disasters Dictionary:Shaanxi Volume. Beijing: China Meteorological Press, 211 pp.

Weng B S, Yan D H, Wang H et al., 2015. Drought assessment in the Dongliao River basin: Traditional approaches vs. generalized drought assessment index based on water resources systems. Natural Hazards and Earth System Sciences, 15(8): 1889-1906.

Wu B Y, Wang J, 2002. Winter Arctic Oscillation, Siberian High and East Asian winter monsoon. Geophysical Research Letters, 29(19): 1897.

Yu F L, Zhai S Y, Wang Z et al., 2018. Spatial and temporal variation characteristics of drought during rice growth based on SPI in Southwest China from 1960 to 2012. Scientia Geographica Sinica, 38(5): 808-817. (in Chinese)


Yuan X F, Jian J S, Jiang G, 2016. Spatiotemporal variation of precipitation regime in China from 1961 to 2014 from the standardized precipitation index. ISPRS International Journal of Geo-Information, 5(11): 194.


Yuan Z, Xu J J, Huo J J et al., 2017. Drought-waterlog encounter probability research between the diversion region and benefited region in the Middle Route of South-to-North Water Transfer Project. IOP Conference Series. Earth and Environmental Science, 82: 012044.

Zhang C, Duan Q Y, Yeh P J F et al., 2020. The effectiveness of the South-to-North Water Diversion Middle Route Project on water delivery and groundwater recovery in North China Plain. Water Resources Research, 56(10): e2019WR026759.

Zhang D, Chen P, Zhang Q et al., 2017. Copula-based probability of concurrent hydrological drought in the Poyang lake-catchment-river system (China) from 1960 to 2013. Journal of Hydrology, 553: 773-784.


Zhang D D, Yan D H, Lu F et al., 2015. Copula-based risk assessment of drought in Yunnan province, China. Natural Hazards, 75(3): 2199-2220.


Zhang R, Chen Z Y, Xu L J et al., 2019. Meteorological drought forecasting based on a statistical model with machine learning techniques in Shaanxi province, China. Science of The Total Environment, 665: 338-346.


Zhang R B, Shang H M, Yu S L et al., 2017. Tree-ring-based precipitation reconstruction in southern Kazakhstan, reveals drought variability since A.D. 1770. International Journal of Climatology, 37(2): 741-750.

Zhao J, Shi S J, Li H E, 2010. Preliminary study on optimizing method of scheme in inter-basin water transfer project. Agricultural Research in the Arid Areas, 28(2): 214-218, 248. (in Chinese)

Zhao P P, Lu H S, Yang H C et al., 2019. Impacts of climate change on hydrological droughts at basin scale: A case study of the Weihe River Basin, China. Quaternary International, 513: 37-46.


Zhou T J, Chen Z M, Zou L W et al., 2020. Development of climate and earth system models in China: Past achievements and new CMIP6 results. Journal of Meteorological Research, 34(1): 1-19.


Zhou Y L, Guo S L, Hong X J et al., 2017. Systematic impact assessment on inter-basin water transfer projects of the Hanjiang River Basin in China. Journal of Hydrology, 553: 584-595.


Zhu Y L, Liu Y, Wang H J et al., 2019. Changes in the interannual summer drought variation along with the regime shift over Northwest China in the Late 1980s. Journal of Geophysical Research: Atmospheres, 124(6): 2868-2881.


Zuo D D, Hou W, Wu H et al., 2021. Feasibility of calculating standardized precipitation index with short-term precipitation data in China. Atmosphere, 12(5): 603.