Applied Water Science

, Volume 7, Issue 5, pp 2245–2253 | Cite as

Investigating causes of changes in runoff using hydrological simulation approach

  • Guoqing Wang
  • Jianyun Zhang
  • Xuemei Li
  • Zhenxin Bao
  • Yanli Liu
  • Cuishan Liu
  • Ruimin He
  • Junsong Luo
Open Access
Original Article


Stream flow plays a crucial role in environmental, social and economic contexts. It is of significance to investigate the causes of change in runoff for better water resources management. This study detects the variation trend of recorded runoff of the Gushan River, a tributary of the Yellow River located on the Loess Plateau with severe soil and water losing, and investigates the impacts of climate change and human activities on runoff using hydrological simulation approach. Results show that the recorded runoff at Gaoshiya station on the Gushan River has experienced a significant declining trend from 1954–2013 with an abrupt change occurring in 1973. SimHyd rainfall runoff model performs well for monthly discharge simulation with Nash–Sutcliffe coefficient of 82.6 % and relative error of 0.32 %. Runoff depth over the catchment in 1980–2013 reduced by 52.4 mm compared to the previous period, in which human activities and climate change contribute 61.5 and 38.5 % of the total runoff reduction, respectively. However, the human-induced impact tends to increase. Therefore, efforts to improve the ecology of the Loess Plateau should give sufficient attention to the impacts of human activity.


Hydrological modeling Climate change Human activity Water resources Impact assessment 


Water is a fundamental element for supporting human life as well as ecological systems, which is therefore of crucial importance in environmental, social and economic contexts, particularly for the arid area (Li and Sheng 2011). Variability of stream flow presents a challenge to the security of water supply and thus has attracted loads of attention from local communities (Zhang et al. 2013).

Due to climate change and intensive human activities in recent decades, the runoff of many rivers in the world has been changing. About 22 % of the world’s rivers were shown to have a significant decrease in annual runoff because of increasing water consumptions and diversion (Walling and Fang 2003). Recorded runoff series from 1950 to 2010 of six major rivers in China were characterized by a general decreasing trend with significant decrease trends occurring in the northern major rivers (Wang et al. 2013). For example, the Yellow River ran dry for 226 days in 1997. It has experienced continuously declining in runoff during the recent decades, which has resulted in huge economic loss and brought a great challenge to water resources and environmental management (Zhang and Wang 2014).

Climate dominates regional hydrological regime. Changes in precipitation and temperature have, therefore, direct influences on stream flow (Zhang and Wang 2007). McFarlane et al. (2012) used GCM projections to simulate water yields in south-western Australia over the coming decades and found the surface water yields might decrease by 24 % due to climate change. Wang et al. (2016) investigated the sensitivity of stream flow to hypothetical climate changes in climatically different climate zones in China, and found runoff is highly sensitive to changes of precipitation and temperature. Previous studies indicate that 10 % change in precipitation will result in 15–25 % change in runoff, whereas a 2 °C rise in temperature will lead to 5–12 % decrease in runoff (Roger et al. 2005; Jones et al. 2005; Benedikt et al. 2007; Lorena et al. 2010; Bao et al. 2012a; Wang et al. 2016). Human activities, such as land cover change, urbanization, and water conservation projects, impose significant impacts on runoff through changing the runoff generation condition and therefore altering the regional hydrologic cycle (Zhang and Wang 2014). The current extent human land cover change has increased global runoff by 7.6 % and reduced global annual average terrestrial evapotranspiration by 5 % which is approximately equivalent in volume to annual global water withdrawals (Shannon et al. 2013). Water consumption for agriculture and industry has direct impacts on runoff through pumping water from stream for irrigation and meeting demand of industry. Water consumption for agriculture accounts for over 70 % of total water consumption in China (Zhang 2005). Climate change and human activities are impacting runoff simultaneously. In this context, separating and quantifying the contributions of climate change and human activities to runoff changes are a challenging problem (Wang et al. 2008).

In recent years, hydrologists have made great efforts to attribution analysis of changes in runoff (Wang et al. 2008; Bao et al. 2012b; Zhang and Wang 2014). The traditional approaches to study the impacts of human activities on stream flows are based on field investigation and hydrological experiments which usually cost huge of human and financial resources (Xu et al. 2009; Zhang and Wang 2014). Hydrological models have been often used for investigating the impacts of climate change on runoff changes in recent years (Wang et al. 2012; Hidalgo et al. 2013; Arnell and Gosling 2013; Szepszo et al. 2014). Typically, Wang et al. (2008) applied a simple water balance model to the Sanchuan River catchment to naturalize the runoff in human-disturbed period and regarded the difference between naturalized runoff and recorded runoff as the contribution induced by human activities to runoff change. The above method was further applied to other catchments by many other hydrologists (Bao et al. 2012b; Zhan et al. 2013; Ma et al. 2014; Guo et al. 2014). For example, with the method, Ma et al. (2014) found climate change has led to a stream flow increase of 40.8 m3/s per year for the Huai River basin, while human activities have resulted in a stream flow decrease of 33.51 m3/s per year. Moreover, studying the drivers of changes in runoff of the Luan River over the period of 1958–2008, Zeng et al. (2015) found climate-induced runoff reduction accounted for 28.3–46.8 % of the total runoff reduction while human activities played a principal role in decreases of runoff.

The Loess Plateau is located in semi-arid climate zone where water shortage is a main water issue due to dry climatic condition. It was reported that streams run dry for most rivers on the Loess Plateau, particularly in dry seasons (Xu et al. 2009). It is of significance to investigate quantitatively reasons of decreases in runoff for water resources management of the Loess Plateau. Taking the Gushan River, the most typical river catchment on the Loess Plateau with severe soil and water losing, as a case, the objectives of this study are: (1) to detect the variation trend of runoff series of the Gushan River and identify the natural period of runoff series, (2) to investigate the principal drivers of runoff variation, and then quantitatively evaluate the contribution of climate change and human activities to changes in runoff of the Gushan River.

Data and methods

Description of the Gushan River catchment

The Gushan River is the first-order tributary of the middle Yellow River, originating from Tanger Banner of the Inner Mongolia. The main stream runs eastward to join the Yellow River at Fugu county of Shaanxi province. The drainage area of the catchment is 1,263 km2 with a main stream length of 79.4 km. Gaoshiya station is the most downstream hydrometric station on the Gushan River, established in 1953. Four well-gaged rainfall stations within the catchment with a long record were selected. The locations of the rainfall stations and the Gaoshiya hydrometric station are shown in Fig. 1.
Fig. 1

River system and locations of rain gauges and the Gaoshiya hydrometric station of the Gushan River catchment

The Gushan River catchment is in the East Asia Monsoon climate zone and is characterized by low precipitation and high potential evaporation. The distribution of precipitation is highly uneven in time and space. Due to dry climate condition, thick porous soil layer, sparse vegetation coverage and high-intensity rain storm, the Gushan River is short of water resources and heavy in soil erosion. The multiple-year average annual runoff and sediment yield at the Gaoshiya station are 67.85 million m3 and 19.2 million t, among which over 70 % of the annual runoff and over 80 % sediment yield are from flood seasons from July to September.

Major human activities (i.e., soil and water conservation measures) have attempted to combat with severe soil and water losses, with the most intensive activities emerging since the late of 1970s. As of 2011, local citizens had constructed 2 substantial dams on the main stream with total storage capacity of 5.3 million m3, 852 check dams on tributaries with total controlled area of over 61 km2. The implemented soil and water conservation measures in the catchment include 39.54 km2 of terraced land, 15.78 km2 of newly formed dam-land, 370.1 km2 of forestation, and 174.4 km2 of grassland (NBSC 2013). Substantial dams and check dams could affect runoff directly by storing water in reservoirs. However, the other soil and water conservation measures (e.g. terracing land, reforestation, etc.) have indirect impacts on runoff by changing land cover.

Quantitative method for attribution analysis of runoff change

Intensive human activities will lead to an abrupt change in runoff series. Taking runoff before the abrupt change point as the baseline, the difference between the runoff after the change point and the baseline includes two parts, human-induced runoff change and climate-induced runoff change. Hydrological model is usually calibrated with data series in natural period and then employed to naturalize runoff in human-disturbed period. The difference between recorded runoff and naturalized runoff in human-disturbed period is induced by human activities (Wang et al. 2008).

Using the hydrological simulation approach to analyze attribution of runoff change, one key step is the selection of baseline which is often regarded as a natural period of a runoff series before abrupt change. Sequential Cluster Analysis is a commonly used statistical method to detect abrupt change point of runoff series by pursuing the minimum sum of squared deviations Sn(t) for the series before and after the abrupt change point, viz. Sn*(t) = min [Sn(t)] (Wang et al. 2008; Zhang and Wang 2014).

Hydrological model could be used to detect abrupt change of s runoff series by analyzing simulation errors of hydrological processes. Wang et al. (2015) proposed a simulation-based method for the detection of a natural epoch in a runoff series using hydrological modeling. The method assumes that human activities will lead to systematic simulation errors of hydrological processes, thus the accumulated standard simulation errors Sum(t) will continuously increase or decrease after an abrupt change point in a runoff series induced by human activities. The abrupt change induced by human activities will occur at a point, after which Sum(t) will be continuously greater or less than zero. The time series before the point could then be treated as a natural series.

In this study, both Sequential Cluster Analysis and simulation-based abrupt change detection method were employed to diagnose natural period of runoff series.

Description of SimHyd rainfall runoff model

Conceptual hydrological models are useful in assessing the impacts of environmental changes on regional hydrology (Wang et al. 2008; Bao et al. 2012a). In this paper, we employed SimHyd rainfall runoff model which has already been applied successfully in some semi-arid or semi-humid basins located in the USA, Australia, and other countries to naturalize stream flow (Chiew et al. 2002; Wang et al. 2015). The structure of the SimHyd model is shown in Fig. 2.
Fig. 2

Schematic diagram of the SimHyd rainfall runoff model

The SimHyd model estimates daily or monthly stream flow from daily or monthly rainfall and potential evaporation (Pan evaporation) data. The model divides runoff into three components: surface flow, interflow, and base flow. The surface flow is infiltration excess runoff. Interflow is estimated as a linear function of the soil wetness. Base flow is simulated as a linear recession from the groundwater store. The infiltration rate is a core of the model and expressed as a negative exponential function of soil wetness.

Evapotranspiration losses include two parts: the first part is from interception of vegetation cover and is calculated with the potential evaporation rate; the second part is from soil moisture storage and estimated as a linear function of the soil wetness, but cannot exceed the atmospherically controlled rate of areal potential evapotranspiration.

There are seven hydrological parameters in the model that need to be calibrated with recorded daily or monthly stream flow (Table 1). Plenty of measures are available to evaluate model performance, among which the Nash–Sutcliffe efficiency coefficient (NSE) and the relative error of volumetric fit (RE) are the most effective ones and have been widely applied in practices for model calibration (Nash and Sutcliffe 1970; Zhang and Wang 2007). A good simulation result will have NSE close to 1 and RE close to 0. The two measures are expressed mathematically as follows:
$$\text{NSE} = 1 - \frac{{\sum\limits_{i = 1}^{N} {\left( {Q_{oi} - Q_{ai} } \right)}^{2} }}{{\sum\limits_{i = 1}^{N} {\left( {Q_{oi} - \overline{{Q_{oi} }} } \right)}^{2} }}$$
$$\text{RE} = {{\sum\limits_{i = 1}^{N} {\left( {Q_{ai} - Q_{oi} } \right)} } \mathord{\left/ {\vphantom {{\sum\limits_{i = 1}^{N} {\left( {Q_{ai} - Q_{oi} } \right)} } {\sum\limits_{i = 1}^{N} {Q_{oi} } }}} \right. \kern-0pt} {\sum\limits_{i = 1}^{N} {Q_{oi} } }} \times 100\,\%$$
where Q oi and Q ai are observed and simulated runoff in month i, respectively; N is the number of all samples; \(\overline{{Q_{oi} }}\) is multiple-year average of recorded runoff.
Table 1

Calibrated model parameters of the Gushan River catchment

Model parameters


Default minimum

Default maximum

Calibrated value


Interception store capacity/mm





Maximum infiltration loss/mm





Infiltration loss exponent





Soil moisture store capacity/mm





Constant of proportionality in interflow equation





Constant of proportionality in groundwater recharge equation





Baseflow linear recession parameter




Results and discussion

Inter-annual variability of precipitation, temperature and recorded runoff during 1954–2013

The Intergovernmental Panel on Climate Change (IPCC) series of reports concluded that global mean air temperature showed a significant increase during the past 50 years while only slight change occurred in global mean precipitation (IPCC 2013). The Gushan River catchment is located in the Eastern-Asia Monsoon climate zone, which is generally arid to semi-arid. The average areal annual precipitation over 1954–2013 is approximately 510 mm with a higher decadal variability. Long-term variations of annual precipitation and temperature in the Gushan River catchment from 1954 to 2013 are shown in Fig. 3.
Fig. 3

Variations of annual precipitation and temperature of the Gushan River catchment over 1954–2013

Figure 3 indicates that the annual precipitation varies from 166 to 726 mm with a slightly decline trend of −1.378 mm/year over the period of 1954–2013. The annual mean temperature presents a significant rising trend of 0.015 °C/year for the whole period with an average of 8.12 °C and a variation range of 7.12–9.28 °C. Decrease in precipitation and rise in temperature with no doubt will lead to reduction in runoff.

The average annual runoff at the Gaoshiya hydrometric station over the period of 1954–2013 is 53.7 mm. Long-term variations of recorded runoff and its 5-year moving average process are shown in Fig. 4.
Fig. 4

Variations of annual runoff and its 5-year moving average process at Gaoshiya station from 1954 to 2013

Figure 4 shows that (1) the recorded runoff presents a high variability with the range from 3.06 mm in 2011 to 188.0 mm in 1959. Most of the high runoff years occurred before 1980. The observed runoff has been continuously and unprecedentedly low since 2000. (2) Runoff series exhibits a significant decreasing trend with the linear decline rate of −1.541 mm/year. (3) According to the magnitude of recorded runoff, runoff series over the years of 1954–2013 could be visibly divided into three phases: higher flow period of 1954–1979, medium flow period of 1980–1999, and low flow period of 2000–2013. Abrupt change point of runoff series induced by human activities (e.g. reservoirs and check dams construction) may probably occur in 1980 according to roughly visual judgment.

Trend variation of runoff series results from changes in climate and land coverage (e.g. reforestation, terracing, and regressing, etc.) for most cases. However, abrupt change in runoff series was mostly induced by construction of reservoir and sharply increases in water consumption due to increasing agricultural irrigation and industrial demands.

Model calibration and baseline period selection

Sequential Cluster Analysis was employed to detect the abrupt change point of runoff series. The sum of squared deviations of annual runoff versus time is shown in Fig. 5. Figure 5 shows that the local minima occurred in 1980, indicating that 1980 is detected as abrupt change point of runoff series.
Fig. 5

Time series of Sn(t) and Sum(k) at the Gaoshiya hydrometric station over 1954–2013

To ensure the consistency of the natural runoff data series, data series over 1954–1969 are used to calibrate the SimHyd rainfall runoff model (Table 1). The monthly simulated and recorded discharges at the Gaoshiya station over the period 1954–1969 are compared in Fig. 6.
Fig. 6

Monthly recorded and simulated runoff at the Gaoshiya station over 1954–1965

Figure 6 illustrates that the recorded and the simulated discharges match well in general. Statistical results indicate that the SimHyd model performs well for monthly runoff simulation at the Gaoshiya station achieving a higher simulation accuracy with a NSE of 82 % and RE of −0.32 %.

The calibrated model is then driven by the meteorological data (i.e. monthly precipitation, pan evaporation.) for the entire period of record (1954–2013), using a constant set of calibrated parameters to generate runoff estimations. The simulated runoff reflects the benchmark situation without major human influences throughout the catchment. And time series Sum(t) is then constructed, representing the differences between the simulated and the recorded runoff (Fig. 5).

The time series of Sum(t) is depicted in Fig. 5. At the early stage, it varies around zero and departs from zero after 1977 with a consistent increasing trend due to human activities decreased stream flow, which means abrupt change in runoff series at the Gaoshiya station occurred in the late 1970s. The Local Statistical Year Book series document that water engineering projects as well as soil and water conservation measures have been implemented in the Gushan River catchment since the late 1970s (NBSC 2013), which is generally in accordance with the timing of changes we detected by two methods (i.e. Sequential cluster analysis and Simulation-based method). Comprehensively considering detection results and practice of human activities in the Gushan River catchment, 1954–1979 was then treated as a natural period, which was then taken as a baseline for investigating impacts of climate change and human activities on change in runoff in this study.

For the whole period of 1954–2013, the annual recorded and simulated runoffs are compared in Fig. 7. The figure suggests that the annual recorded and simulated runoffs before 1980 match well in general. However, most of the simulated runoffs in period of 1980–2013 are much higher than the recorded which explains the reduction in runoff induced by human activities to some extent. Statistical results indicate that the annual runoff and precipitation during 1980–2013 decrease by 14.2 and 62.8 % compared to 1954–1979, respectively, while temperature increase by 0.33 °C.
Fig. 7

Annual recorded and simulated runoff at the Gaoshiya station over 1954–2013

Attribution analysis of changes in runoff of the Gushan River catchment

Rainfall is a source of stream flow of the Gushan River catchment. Evaporation is another way in which water is removed from the hydrologic cycle in a catchment. Theoretically, a rise in temperature will result in an increase in potential evaporation, and accordingly, lead to a decrease in stream flow. Human activities, e.g. soil and water conservation measures, water consumption for agriculture and industry development, have direct influences on stream flow. For better understanding the potential drivers of changes in runoff with focus on periods before and after 2000, time series of the Gushan River catchment over 1954–2013 was divided into three phases of 1954–1979, 1980–1999, and 2000–2013, and annual discharges of the three phases have been plotted against areal precipitation and temperature and illustrated as in Fig. 8.
Fig. 8

Relationships of annual runoff against annual precipitation (left) and temperature (right)

Figure 8 indicates that annual runoff is highly correlated to precipitation with linear correlation coefficients ranging from 0.513 to 0.726, which implies precipitation governs water resources. However, the points plotted for runoff against precipitation for the different phases fall into the different domains. For the same precipitation, runoff in natural period of 1954–1979 is much higher than the second and third periods of 1980–1999 and 2000–2013, which means the relationship between runoff and precipitation has been changed by other drivers, e.g. human activities. Human activities induced a certain reduction in runoff. Although the points of runoff against temperatures are highly scattered, there exists decreasing trends of runoff with temperature rise for all three phases, showing discharges are weakly correlated to temperature (linear correlation coefficients ranging from 0.012 to 0.179) and implying temperature is but not a dominant driver of change in stream flow.

Taking the pre-1980 recorded runoff as the baseline, the effects of climate change (i.e. changes in precipitation and temperature) and human activities on runoff in the Gushan River catchment are analyzed through comparing baseline, naturalized and recorded runoff, and the results are presented in Table 2.
Table 2

Impacts of climate change and human activities on runoff of the Gushan River catchment


Recorded runoff (mm)

Simulated runoff (mm)

Total runoff reduction (mm)

Human-induced change

Climate-induced change

































From Table 2, four points can be concluded as follows. (1) The recorded runoff in the Gushan River catchment decreases from 83.4 mm in the baseline period to 42.8 mm in the second phase and 14.2 mm in the third phase. However, the naturalized runoff in the third phase (67.8 mm) is higher than that in the second phase (60.0 mm), and the both are much less than the baseline (83.4 mm in 1954–1979). (2) On average, annual recorded runoff over 1980–2013 decreases by 52.4 mm relative to baseline. However, the total runoff reduction increases from 40.6 mm in 1980–1999 to 69.2 mm in 2000–2013 due to the environmental changes. (3) The absolute runoff reduction induced by human activities increases from 17.2 mm in 1980–1999 to 53.6 mm in the third phase while climate-induced runoff reduction decreases from 23.4 mm in the second phase to 15.5 mm after 2000. (4) Human activities play a principal role in the runoff reduction of the Gushan River catchment during 1980–2013. The percentage of human-induced runoff reduction to total runoff reduction is approximate 61.5 %.

Human activities can come in many forms, such as land use change, agricultural and industrial development, urbanization, reforestation, etc. Different types of human activities can have different influences (i.e., direct or indirect influence) on regional water resources. These impacts should be explored in future studies.


Human activities together with climate change led to great change in runoff simultaneously. The recorded runoff at the Gaoshiya station of the Gushan River presents a significant decreasing trend with a linear decline rate of −1.541 mm/year. Human-induced abrupt change on runoff series is detected occurring in 1980 with a physically based detection method. The proposed SimHyd rainfall runoff model performs well for the simulation of natural monthly runoff over 1954–1969, with Nash and Sutcliffe efficiency criterion (NSE) of 82.6 % and the relative error of volumetric fit (RE) of 0.32 %.

Variation of precipitation dominates the variability of runoff. However, human activities play a principal role in runoff reduction. Recorded runoff in 1980–1999 and 2000–2013 decreases by 48.7 and 83.0 % compared to that of the baseline (1954–1979). Human-induced runoff reduction accounts for 61.5 % of the total runoff reduction over 1980–2013. The absolute human-induced runoff reduction presents increasing trend over the whole period. It is, therefore, essential to consider the impact of human activities in water resources management as well as ecological restoration on the Loess Plateau.



This study has been financially supported by the National Natural Science Foundation of China (Grand 41371063, 41330854, 41401023), the China’s National Basic Research Program on Global Change (Grant 2010CB951103). Thanks also go to the anonymous reviewers and editors for their valuable comments.


  1. Arnell NW, Gosling SN (2013) The impacts of climate change on river flow regimes at the global scale. J Hydrol 486:351–364CrossRefGoogle Scholar
  2. Bao ZX, Zhang JY, Liu JF, Wang GQ, Yan XL, Wang XJ, Zhang LR (2012a) Sensitivity of hydrological variables to climate change in the Haihe River basin, China. Hydrol Process 26(15):2294–2306CrossRefGoogle Scholar
  3. Bao ZX, Zhang JY, Wang GQ, Fu GB, He RM, Yan XL, Jin JL, Liu YL, Zhang AJ (2012b) Attribution for decreasing streamflow of the Haihe River basin, northern China: climate variability or human activities? J Hydrol 460:117–129CrossRefGoogle Scholar
  4. Benedikt N, Lindsay M, Daniel V, Rolf W, Hans PL (2007) Impacts of environmental change on water resources in the Mt. Kenya region. J Hydrol 343:266–278CrossRefGoogle Scholar
  5. Chiew FHS, Peel MC, Western AW (2002) Application and testing of the simple rainfall-runoff model SimHyd. In: Singh VP, Frevert DK (eds) Mathematical models of small watershed hydrology and applications. Water Resources Publication, Littleton, Colorado, pp 335–367Google Scholar
  6. Guo Y, Li Z, Amo-Boateng M, Deng P, Huang P (2014) Quantitative assessment of the impact of climate variability and human activities on runoff changes for the upper reaches of Weihe River. Stoch Env Res Risk Assess 28:333–346CrossRefGoogle Scholar
  7. Hidalgo HG, Amador JA, Alfaro EJ, Quesada B (2013) Hydrological climate change projections for Central America. J Hydrol 495:94–112CrossRefGoogle Scholar
  8. IPCC (2013) Climate change 2013: the physical science basis. Cambridge University Press, CambridgeGoogle Scholar
  9. Jones RN, Chiew FHS, Boughton WC, Zhang L (2005) Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Adv Water Resour 29:1419–1429CrossRefGoogle Scholar
  10. Li GY, Sheng LX (2011) Ecological water requirements of Yellow River estuary. J Northeast Normal Univ (Nat Sci Edn) 43(3):138–144 (in Chinese with an English abstract) Google Scholar
  11. Lorena L, Leonardo V, Enrique R, Goffredi L (2010) Basin-scale water resources assessment in Oklahoma under synthetic climate change scenarios using a fully distributed hydrologic model. J Hydrol Eng 15(2):107–118CrossRefGoogle Scholar
  12. Ma F, Ye AZ, Gong W, Mao YN, Miao CY, Di ZH (2014) An estimate of human and natural contributions to flood changes of the Huai River. Glob Planet Chang 119:39–50CrossRefGoogle Scholar
  13. McFarlane D, Stone R, Martens S, Thomas J, Silberstein R, Ali R, Hodgson G (2012) Climate change impacts on water yields and demands in south-western Australia. J Hydrol 475:488–498CrossRefGoogle Scholar
  14. Nash JE, Sutcliffe J (1970) River flow forecasting through conceptual models: part 1—a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  15. NBSC (National Bureau of Statistics of China) (2013) China statistical yearbook. China Statistics Press, Beijing (in Chinese)Google Scholar
  16. Roger N, Francis HS, Walter C, Zhang L (2005) Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Adv Water Resour 29:1419–1429Google Scholar
  17. Shannon MS, Agnès D, Jan P (2013) The impact of global land-cover change on the terrestrial water cycle. Nat Clim Chang 3:385–390Google Scholar
  18. Szepszo G, Lingemann I, Klein B, Kovacs M (2014) Impact of climate change on hydrological conditions of Rhine and Upper Danube rivers based on the results of regional climate and hydrological models. Nat Hazards 72:241–262CrossRefGoogle Scholar
  19. Walling DE, Fang D (2003) Recent trends in the suspended sediment loads of the world’s rivers. Glob Planet Chang 39:111–125CrossRefGoogle Scholar
  20. Wang GQ, Zhang JY, He RM, Jiang NQ, Jing XA (2008) Runoff reduction due to environmental changes in the Sanchuanhe River basin. Int J Sedim Res 23:174–180CrossRefGoogle Scholar
  21. Wang GQ, Zhang JY, Jin JL, Thomas C, Roger C, Bao ZX, Liu CS, Liu YL, Yan XL (2012) Assessing water resources in China using PRECIS and VIC model. Hydrol Earth Syst Sci 16(1):231–240CrossRefGoogle Scholar
  22. Wang GQ, Yan XL, Zhang JY, Liu CS, Jin JL, Bao ZX (2013) Detecting evolution trends in the recorded runoffs from the major rivers in China during 1950–2010. J Water Clim Chang 4(3):252–264CrossRefGoogle Scholar
  23. Wang GQ, Zhang JY, Pagano TC, Liu YL, Liu CS, Bao ZX, Jin JL, He RM (2015) Using hydrologic simulation to detect human-disturbed epoch in runoff series. Water Sci Technol 71(5):691–700CrossRefGoogle Scholar
  24. Wang GQ, Zhang JY, He RM, Liu CS, Ma T, Bao ZX, Liu YL (2016) Runoff sensitivity to climate change for hydro-climatically different catchments in China. DOI, Stoch Environ Res Risk Assess. doi: 10.1007/s00477-016-1218-6 Google Scholar
  25. Xu JH, Li XY, Chen JJ, Gao YJ, Li M (2009) Impact of water conservancy projects on storm flood and sediment yield in the middle reaches of the Yellow River. Yellow River Press, ZhengzhouGoogle Scholar
  26. Zeng SD, Zhan CS, Sun FB, Du H, Wang FY (2015) Effects of climate change and human activities on surface runoff in the Luan River Basin. Adv Meteorol 1–12Google Scholar
  27. Zhan C, Niu C, Song X, Xu C (2013) The impacts of climate variability and human activities on streamflow in Bai River basin, northern China. Hydrol Res 44(5):875–885CrossRefGoogle Scholar
  28. Zhang HL (2005) Strategy study for water management in China. Southeast University Press, NanjingGoogle Scholar
  29. Zhang JY, Wang GQ (2007) Impacts of climate change on hydrology and water resources. Science Press, Beijing (in Chinese) Google Scholar
  30. Zhang JY, Wang GQ (2014) Variation of stream flow and quantitative identification for attribution. Science Press, Beijing (in Chinese) Google Scholar
  31. Zhang Q, Singh VP, Li JF (2013) Eco-hydrological requirements in arid and semi-arid regions: the Yellow River in China as a case study. J Hydraul Eng. doi: 10.1061/(ASCE)HE.1943-5584.0000653 Google Scholar

Copyright information

© The Author(s) 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Guoqing Wang
    • 1
    • 2
  • Jianyun Zhang
    • 1
    • 2
  • Xuemei Li
    • 3
  • Zhenxin Bao
    • 1
    • 2
  • Yanli Liu
    • 1
    • 2
  • Cuishan Liu
    • 1
    • 2
  • Ruimin He
    • 1
    • 2
  • Junsong Luo
    • 4
  1. 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringNanjing Hydraulic Research InstituteNanjingChina
  2. 2.Research Center for Climate ChangeMinistry of Water ResourcesNanjingChina
  3. 3.Hydrology BureauYellow River Conservancy CommissionZhengzhouChina
  4. 4.Chengdu University of TechnologyChengduChina

Personalised recommendations