Sciences in Cold and Arid Regions ›› 2019, Vol. 11 ›› Issue (1): 62-80.doi: 10.3724/SP.J.1226.2019.00062

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How changes of groundwater level affect the desert riparian forest ecosystem in the Ejina Oasis, Northwest China

HaiYang Xi1,2,*(),JingTian Zhang1,4,Qi Feng1,2,Lu Zhang3,JianHua Si1,2,TengFei Yu1,2   

  1. 1. Key Laboratory of Ecohydrology of Inland River Basin, Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2. Alashan Desert Eco-hydrology Experimental Research Station, Ejina, Inner Mongolia 735400, China
    3. CSIRO Land and Water Flagship, Canberra, ACT 2601, Australia
    4. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-08-23 Accepted:2018-10-30 Online:2019-02-01 Published:2019-03-22
  • Contact: HaiYang Xi
  • About author:HaiYang Xi, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. No. 320, West Donggang Road, Lanzhou, Gansu 730000, China. Tel: +86-931-4967139;


Groundwater is a key factor controlling the growth of vegetation in desert riparian systems. It is important to recognise how groundwater changes affect the riparian forest ecosystem. This information will not only help us to understand the ecological and hydrological process of the riparian forest but also provide support for ecological recovery of riparian forests and water-resources management of arid inland river basins. This study aims to estimate the suitability of the Water Vegetation Energy and Solute Modelling (WAVES) model to simulate the Ejina Desert riparian forest ecosystem changes, China, to assess effects of groundwater-depth change on the canopy leaf area index (LAI) and water budgets, and to ascertain the suitable groundwater depth for preserving the stability and structure of desert riparian forest. Results demonstrated that the WAVES model can simulate changes to ecological and hydrological processes. The annual mean water consumption of a Tamarix chinensis riparian forest was less than that of a Populus euphratica riparian forest, and the canopy LAI of the desert riparian forest should increase as groundwater depth decreases. Groundwater changes could significantly influence water budgets for T. chinensis and P. euphratica riparian forests and show the positive and negative effects on vegetation growth and water budgets of riparian forests. Maintaining the annual mean groundwater depth at around 1.7?2.7 m is critical for healthy riparian forest growth. This study highlights the importance of considering groundwater-change impacts on desert riparian vegetation and water-balance applications in ecological restoration and efficient water-resource management in the Heihe River Basin.

Key words: groundwater changes, desert riparian forest, Ejina Oasis, WAVES, leaf area index (LAI), water budgets

Figure 1

Locations of field experimental sites in the study area: (a) the Heihe River Basin, (b) the lower reaches of the Heihe River Basin, (c) T. chinensis forest experimental site, and (d) P. euphratica forest experimental site"

Table 1

Names, units, and parameter value of key physiological parameters of the WAVES model"

No. Parameters T. chinensis forest P. euphratica forest
1 1 minus albedo of the canopy (?) 0.7 0.8
2 1 minus albedo of the soil albedo (?) 0.90 0.90
3 Rainfall-interception coefficient (m/(d?LAI)), 0.0002 0.0003
4 Light-extinction coefficient (?) ?0.90 ?0.85
5 Maximum carbon-assimilation rate (kg C/(m2·d)) 0.019 0.020
6 Slope parameter (?) 1.2 1.2
7 Maximum water potential (m) ?400 ?400
8 IRM weighting of water (?) 3.0 2.5
9 IRM weighting of nutrients (?) 1 1
10 Ratio of stomatal to mesophyll conductance (?) 0.2 0.2
11 Temperature when the growth rate is half of the optimum (°C) 10 10
12 Temperature when the growth rate is the optimum (°C) 30 30
13 Year-day of germination (d) 10 30
14 Degree-daylight hours of the growing season (°C·h) 48,000 48,000
15 Saturation-light intensity (μmol/(m2·d)) 1,000 1,300
16 Maximum rooting depth (m) 1.8 2.0
17 Specific leaf area (LAI/(kg C)) 6.0 10.5
18 Leaf-respiration coefficient (kg C/(kg C)) 0.0006 0.0008
19 Stem-respiration coefficient (kg C/(kg C)) 0.0001 0.0005
20 Root-respiration coefficient (kg C/(kg C)) 0.0005 0.0005
21 Leaf mortality (proportion leaf C/d) 0.0020 0.0013
22 Aboveground partitioning factor (?) 0.70 0.39
23 Salt-sensitivity factor (?) 0.3 0.6
24 Aerodynamic resistance (s/m) 10 20
25 Crop-harvest index (?) 0 0
26 Crop-harvest factor (?) 0 0

Figure 2

Comparison between simulated and observed ET of T. chinensis forest from January 2011 to December 2012 (a) and (c); ET of P. euphratica forest from July 2013 to December 2014 (b) and (d)"

Figure 3

Comparison between simulated and observed leaf area index (LAI) of T. chinensis forest from 2001 to 2013 (a) and (c); LAI of P. euphratica forest from 2001 to 2014 (b) and (d)"

Figure 4

Comparison between simulated and observed soil-water content (SWC) of T. chinensis forest from January 2012 to July 2013 (a1: SWC at 0.1 m depth, a2: SWC at 0.3 m depth, a3: SWC at 0.5 m depth, a4: SWC at 0.8 m depth, a5: SWC at 1.5 m depth); SWC of P. euphratica forest from January to September 2013 (b1: SWC at 0.1 m depth, b2: SWC at 0.3 m depth, b3: SWC at 0.5 m depth, b4: SWC at 0.8 m depth, b5: SWC at 1.5 m depth)"

Table 2

Goodness-of-fit measures to compare observed and simulated ET, LAI, and soil-water content values of different depths (0.1 to 1.5 m)"

Study sites Goodness of fit ET LAI SWC 0.1 m SWC 0.3 m SWC 0.5 m SWC 0.8 m SWC 1.5 m
T. chinensis forest PBIAS (%) 8.5 ?5.2 13.6 12.2 2.3 9.3 ?10.2
R 2 0.79 0.94 0.82 0.79 0.89 0.61 0.92
NSE 0.74 0.93 0.62 0.63 0.89 0.50 0.21
RMSE 0.82 0.03 0.04 0.04 0.03 0.07 0.09
P. euphratica forest PBIAS (%) ?0.2 ?7.8 ?2.4 ?7.2 1.4 3.0 25.4
R 2 0.69 0.93 0.79 0.97 0.98 0.96 0.9
NSE 0.68 0.91 0.75 ?2.23 ?5.33 ?4.33 ?2.64
RMSE 1.03 0.04 0.01 0.02 0.02 0.03 0.10

Figure 5

Relationship between the annual mean runoff of Langxin Mountain and the groundwater depth: (a) Annual mean runoff through Langxin Mountain vs. groundwater depth at P. euphratica forest experimental site, (b) annual mean runoff through Langxin Mountain vs. groundwater depth at T. chinensis forest experimental site, (c) scatter diagram of annual mean runoff through Langxin Mountain vs. groundwater depth at P. euphratica forest experimental site, and (d) scatter diagram of annual mean runoff through Langxin Mountain vs. groundwater depth at T. chinensis forest experimental site"

Figure 6

The Pearson III frequency curve of annual mean runoff through Langxin Mountain Station"

Table 3

Four scenarios of runoff through the Langxin Mountain Station and the mean groundwater depths simulated on two experimental sites under different guaranteed rate of water transfer"

Scenarios Guaranteed rate of water transfer (P) Runoff passing by Langxin Mountain Station (×106 m3) Annual mean groundwater depth (m)
T. chinensis forest P. euphratica forest
Reference 2.01 1.90
Scenario 1 25% 609.5 1.77 1.70
Scenario 2 50% 447.9 1.92 1.82
Scenario 3 75% 318.0 2.05 1.91
Scenario 4 90% 225.7 2.13 1.97

Figure 7

Change and difference in water budgets by comparing the P. euphratica riparian forest with the T. chinensis riparian forest. Water balance of storage (a), water balance of rainfall (b), water balance of ET (c), lateral fluxes and groundwater extraction (d)"

Figure 8

Changes of soil-water content at T. chinensis forest from 2000 to 2013 (a) and P. euphratica forest from 2000 to 2014 (b)"

Figure 9

Modeled-effect groundwater changes on ET, rainfall infiltration, and storage at T. chinensis forest experimental site. Time series from 2000 to 2013 (a) and annual means (b)"

Figure 10

Modeled-effect groundwater changes on ET, rainfall infiltration, and storage at P. euphratica forest experimental site. Annual variations from 2000 to 2014 (a) and annual mean variations (b)"

Figure 11

Effect of groundwater changes on LAI. Annual variations from 2000 to 2013 at T. chinensis forest experimental site (a); annual mean variations at T. chinensis forest experimental site (b); annual variations from 2000 to 2014 at P. euphratica forest experimental site (c); annual mean variations at P. euphratica forest experimental site (d)"

Figure 12

Shift of water budgets due to groundwater changes at T. chinensis forest experimental site (a); and at P. euphratica forest experimental site (b)"

Figure 13

Scatter relationship plot between annual mean of groundwater depth and LAI under different scenarios at T. chinensis forest experimental site (a); and P. euphratica forest experimental site (b)"

Ayars JE , Christen EW , Soppe RW , et al. , 2006. The resource potential of in-situ shallow ground water use in irrigated agriculture: a review. Irrigation Science, 24(3): 147−160. DOI: 10.1007/s00271-005-0003-y.
doi: 10.1007/s00271-005-0003-y.
Chambel A , 2006. Groundwater in semi-arid mediterranean areas: desertification, soil salinization and ecosystems.In: Baba A, Howard KWF, Gunduz Oeds. Groundwater and Ecosystems. (eds.). Dordrecht: Springer, pp.47-58.DOI: 10.1007/1-4020-4738-X_4.
doi: 10.1007/1-4020-4738-X_4.
Chen YN , Wang Q , Li WH , et al. , 2006. Rational groundwater table indicated by the eco-physiological parameters of the vegetation: a case study of ecological restoration in the lower reaches of the Tarim River. Chinese Science Bulletin, 51(S1): 8−15. DOI: 10.1007/s11434-006-8202-3.
doi: 10.1007/s11434-006-8202-3.
Chen YN , Li WH , Xu CC , et al. , 2015. Desert riparian vegetation and groundwater in the lower reaches of the Tarim River Basin. Environmental Earth Sciences, 73(2): 547−558. DOI: 10.1007/s12665-013-3002-y.
doi: 10.1007/s12665-013-3002-y.
Cheng L , Zhang L , Wang YP , et al. , 2014a. Quantifying the effects of elevated CO2 on water budgets by combining FACE data with an ecohydrological model. Ecohydrology, 7(6): 1574−1588. DOI: 10.1002/eco.1478.
doi: 10.1002/eco.1478.
Cheng L , Zhang L , Wang YP , et al. , 2014b. Impacts of elevated CO2, climate change and their interactions on water budgets in four different catchments in Australia. Journal of Hydrology, 519: 1350−1361. DOI: 10.1016/j.jhydrol. 2014.09.020.
doi: 10.1016/j.jhydrol. 2014.09.020.
Crowley GM, 1994. Groundwater rise, soil salinization and the decline of Casuarina in southeastern Australia during the late quaternary. Australian Ecology, 19(4): 417−424. DOI: 10. 1111/j.1442-9993.1994.tb00507.x.
doi: 10. 1111/j.1442-9993.1994.tb00507.x.
Dawes WR , Short DL , 1993. The efficient numerical solution of differential equations for coupled water and solute dynamics: the WAVES model. Technical Memorandum-CSIRO, Australia, Division of Water Resources, 93(18). Procite: 75f60280-d9ac-4f71-aacf-cd5454e3284e.
Doody TM , Holland KL , Benyon RG , et al. , 2009. Effect of groundwater freshening on riparian vegetation water balance. Hydrological Processes, 23(24): 3485−3499. DOI: 10. 1002/hyp.7460.
doi: 10. 1002/hyp.7460.
Eklundh L , Jönsson P , 2012. IMESAT 3.1 software manual. Lund, Sweden: Lund University.
Fan XM , Pedroli B , Liu GH , et al. , 2011. Potential plant species distribution in the Yellow River Delta under the influence of groundwater level and soil salinity. Ecohydrology, 4(6): 744−756. DOI: 10.1002/eco.164.
doi: 10.1002/eco.164.
Feng Q , Peng JZ , Li JG , et al. , 2012. Using the concept of ecological groundwater level to evaluate shallow groundwater resources in hyperarid desert regions. Journal of Arid Land, 4(4): 378−389. DOI: 10.3724/SP.J.1227.2012.00378.
doi: 10.3724/SP.J.1227.2012.00378.
Fu AH , Chen YN , Li WH , 2014. Water use strategies of the desert riparian forest plant community in the lower reaches of Heihe River Basin, China. Science China Earth Sciences, 57(6): 1293−1305. DOI: 10.1007/s11430-013-4680-8.
doi: 10.1007/s11430-013-4680-8.
Fu BH , Burgher I , 2015. Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater. Journal of Arid Environments, 113: 59−68. DOI: 10.1016/j.jaridenv.2014.09.010.
doi: 10.1016/j.jaridenv.2014.09.010.
Han M , Zhao CY , Šimůnek J , et al. , 2015. Evaluating the impact of groundwater on cotton growth and root zone water balance using Hydrus-1D coupled with a crop growth model. Agricultural Water Management, 160: 64−75. DOI: 10.1016/j.agwat. 2015.06.028.
doi: 10.1016/j.agwat. 2015.06.028.
Hao XM , Li WH , Huang X , et al. , 2010. Assessment of the groundwater threshold of desert riparian forest vegetation along the middle and lower reaches of the Tarim River, China. Hydrological Processes, 24(2): 178−186. DOI: 10.1002/hyp.7432.
doi: 10.1002/hyp.7432.
Hose GC , Bailey J , Stumpp C , et al. , 2014. Groundwater depth and topography correlate with vegetation structure of an upland peat swamp, Budderoo Plateau, NSW, Australia. Ecohydrology, 7(5): 1392−1402. DOI: 10.1002/eco.1465.
doi: 10.1002/eco.1465.
Hou T , Zhu YH , Lu HS , et al. , 2011. Modelling capillary rise of crop land under different groundwater level. Hydrological Cycle and Water Resources Sustainability in Changing Environments, 350: 212−218.
Jansson R , Laudon H , Johansson E , et al. , 2007. The importance of groundwater discharge for plant species number in riparian zones. Ecology, 88(1): 131−139. DOI: 10.1890/0012-9658(2007)88 [131:TIOGDF]2.0.CO;2.
doi: 10.1890/0012-9658(2007)88
Jia YH , Zhao CY , Zhou L , et al. , 2009. Estimation of Leaf Area Index using remote sensing in the groundwater-fluctuating belt in lower reaches of Heihe River, Northwest China. In: Proceedings of 2009 International Conference on Environmental Science and Information Application Technology. Wuhan, China: IEEE, pp. 462−465. DOI: 10.1109/ESIAT. 2009.403.
doi: 10.1109/ESIAT. 2009.403.
Jönsson P , Eklundh L , 2004. TIMESAT−a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30(8): 833−845. DOI: 10.1016/j.cageo. 2004.05.006.
doi: 10.1016/j.cageo. 2004.05.006.
Jorenush MH , Sepaskhah AR , 2003. Modelling capillary rise and soil salinity for shallow saline water table under irrigated and non-irrigated conditions. Agricultural Water Management, 61(2): 125−141. DOI: 10.1016/S0378-3774(02)00176-2.
doi: 10.1016/S0378-3774(02)00176-2.
Kuglerová L , Jansson R , Ågren A , et al. , 2014. Groundwater discharge creates hotspots of riparian plant species richness in a boreal forest stream network. Ecology, 95(3): 715−725. DOI: 10.1890/13-0363.1.
doi: 10.1890/13-0363.1.
Lamontagne S , Cook PG , O'Grady A , et al. , 2005. Groundwater use by vegetation in a tropical savanna riparian zone (Daly River, Australia). Journal of Hydrology, 310(1−4): 280−293. DOI: 10.1016/j.jhydrol.2005.01.009.
doi: 10.1016/j.jhydrol.2005.01.009.
Li WH , Zhou HH , Fu AH , et al. , 2013. Ecological response and hydrological mechanism of desert riparian forest in inland river, northwest of China. Ecohydrology, 6(6): 949−955. DOI: 10.1002/eco.1385.
doi: 10.1002/eco.1385.
Ma XD , Chen YN , Zhu CG , et al. , 2011. The variation in soil moisture and the appropriate groundwater table for desert riparian forest along the Lower Tarim River. Journal of Geographical Sciences, 21(1): 150−162. DOI: 10.1007/s11442-011-0835-8.
doi: 10.1007/s11442-011-0835-8.
Mahoney JM , Rood SB , 1992. Response of a hybrid poplar to water table decline in different substrates. Forest Ecology and Management, 54(1−4): 141−156. DOI: 10.1016/0378-1127(92)90009-X.
doi: 10.1016/0378-1127(92)90009-X.
Marohn C , Distel A , Dercon G , et al. , 2012. Impacts of soil and groundwater salinization on tree crop performance in post-tsunami Aceh Barat, Indonesia. Natural Hazards and Earth System Sciences, 12(9): 2879−2891. DOI: 10.5194/nhess-12-2879-2012.
doi: 10.5194/nhess-12-2879-2012.
Muñoz-Reinoso JC, 2001. Vegetation changes and groundwater abstraction in SW Doñana, Spain. Journal of Hydrology, 242(3−4): 197−209. DOI: 10.1016/S0022-1694(00)00397-8.
doi: 10.1016/S0022-1694(00)00397-8.
Oomes MJM , Olff H , Altena HJ , 1996. Effects of vegetation management and raising the water table on nutrient dynamics and vegetation change in a wet grassland. Journal of Applied Ecology, 33: 576−588. DOI: 10.2307/2404986.
doi: 10.2307/2404986.
ORNL DAAC, 2018. MODIS Collection 5 Land Products Global Subsetting and Visualization Tool. ORNLDAAC, OakRidge, Tennessee, USA. Accessed on September 7, 2015. Subset obtained for MOD15A2 product at 41.9943N,101.1372E, time period: 2000-02-18 to 2015-08-21, and subset size:7×7 km. DOI:
doi: 10.3334/ORNLDAAC/1241.
Pang ZH , Huang TM , Chen YN , 2010. Diminished groundwater recharge and circulation relative to degrading riparian vegetation in the middle Tarim River, Xinjiang Uygur, Western China. Hydrological Processes,24(2): 147−159. DOI: 10.1002/hyp.7438.
doi: 10.1002/hyp.7438.
Perry LG , Andersen DC , Reynolds LV, et al. , 2012. Vulnerability of riparian ecosystems to elevated CO2 and climate change in arid and semiarid western North America. Global Change Biology, 18(3): 821−842. DOI: 10.1111/j.1365-248 6.2011.02588.x.
doi: 10.1111/j.1365-248 6.2011.02588.x.
Sabo JL , Sponseller R , Dixon M , et al. , 2005. Riparian zones increase regional species richness by harboring different, notmore, species. Ecology, 86(1): 56−62. DOI: 10.1890/04-0668.
doi: 10.1890/04-0668.
Scott RL , Cable WL , Huxman TE , et al. , 2008. Multiyear riparian evapotranspiration and groundwater use for a semiarid watershed. Journal of Arid Environments, 72(7): 1232−1246. DOI: 10.1016/j.jaridenv.2008.01.001.
doi: 10.1016/j.jaridenv.2008.01.001.
Scott RL, 2010. Using watershed water balance to evaluate the accuracy of eddy covariance evaporation measurements for three semiarid ecosystems. Agricultural and Forest Meteorology, 150(2): 219−225. DOI: 10.1016/j.agrformet. 2009. 11.002.
doi: 10.1016/j.agrformet. 2009. 11.002.
Silberstein RP , Dawes WR , Bastow TP , et al. , 2013. Evaluation of changes in post-fire recharge under native woodland using hydrological measurements, modelling and remote sensing. Journal of Hydrology, 489: 1−15. DOI: 10.1016/j.jhydrol.2013.01.037.
doi: 10.1016/j.jhydrol.2013.01.037.
Smettem KRJ , Waring RH , Callow JN , et al. , 2013. Satellite-derived estimates of forest leaf area index in southwest Western Australia are not tightly coupled to interannual variations in rainfall: implications for groundwater decline in a drying climate. Global Change Biology, 19(8): 2401−2412. DOI: 10.1111/gcb.12223.
doi: 10.1111/gcb.12223.
Sommer B , Froend R , 2014. Phreatophytic vegetation responses to groundwater depth in a drying mediterranean-type landscape. Journal of Vegetation Science, 25(4): 1045−1055. DOI: 10.1111/jvs.12178.
doi: 10.1111/jvs.12178.
Soylu ME , Kucharik CJ , Loheide II SP , 2014. Influence of groundwater on plant water use and productivity: development of an integrated ecosystem—Variably saturated soil water flow model. Agricultural and Forest Meteorology, 189−190: 198−210. DOI: 10.1016/j.agrformet. 2014. 01.019.
doi: 10.1016/j.agrformet. 2014. 01.019.
Stromberg JC , Tiller R , Richter B , 1996. Effects of groundwater decline on riparian vegetation of semiarid regions: the San Pedro, Arizona. Ecological Applications, 6(1): 113−131. DOI: 10.2307/2269558.
doi: 10.2307/2269558.
Vogt T , Schirmer M , Cirpka OA , 2012. Investigating riparian groundwater flow close to a losing river using diurnal temperature oscillations at high vertical resolution. Hydrology and Earth System Sciences, 16(2): 473−487. DOI: 10.5194/hess-16-473-2012.
doi: 10.5194/hess-16-473-2012.
Williams DG , Cable W , Hultine K , et al. , 2004. Evapotranspiration components determined by stable isotope, sap flow and eddy covariance techniques. Agricultural and Forest Meteorology, 125(3−4): 241−258. DOI: 10.1016/j.agrformet. 2004.04.008.
doi: 10.1016/j.agrformet. 2004.04.008.
Wilson KB , Hanson PJ , Mulholland PJ , et al. , 2001. A comparison of methods for determining forest evapotranspiration and its components: sap-flow, soil water budget, eddy covariance and catchment water balance. Agricultural and Forest Meteorology, 106(2): 153−168. DOI: 10.1016/S0168-1923(00)00199-4.
doi: 10.1016/S0168-1923(00)00199-4.
Xi HY , Feng Q , Zhang L , et al. , 2016. Effects of water and salinity on plant species composition and community succession in Ejina Desert Oasis, northwest China. Environmental Earth Sciences, 75(2): 138. DOI: 10.1007/s12665-015-4823-7.
doi: 10.1007/s12665-015-4823-7.
Yang YH , Chen YN , Li WH , 2009. Relationship between soil properties and plant diversity in a desert riparian forest in the lower reaches of the Tarim River, Xinjiang, China. Arid Land Research and Management,23(4): 283−296. DOI: 10.1080/15324980903231991.
doi: 10.1080/15324980903231991.
Zambrano-Bigiarini M, 2011. Goodness-of-fit measures to compare observed and simulated values with hydroGOF. DOI:
Zhang L , Dawes WR , Hatton TJ , 1996. Modelling hydrologic processes using a biophysically based mode-application of WAVES to FIFE and HAPEX-MOBILHY. Journal of Hydrology, 185(1−4): 147−169. DOI: 10.1016/0022-1694(95)03006-9.
doi: 10.1016/0022-1694(95)03006-9.
Zhang L , Dawes WR , 1998. WAVES: an integrated energy and water balance model. Canberra: CSIRO Land and Water.
Zhao CY , Cheng GD , Nan ZR , et al. , 2007. Relationship between vegetation distribution and groundwater level in the lower reaches of Heihe River basin, China. In: Proceedings of 2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona, Spain: IEEE, pp. 3963−3966. DOI: 10.1109/IGARSS.2007.4423716.
doi: 10.1109/IGARSS.2007.4423716.
Zhao Y , Zhao CY , Xu ZL , et al. , 2012. Physiological responses of Populus euphratica Oliv. to groundwater table variations in the lower reaches of Heihe River, Northwest China. Journal of Arid Land, 4(3): 281−291. DOI: 10.3724/SP.J.1227. 2012.00281.
doi: 10.3724/SP.J.1227. 2012.00281.
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