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Plant Science Today (PST)

Review Articles

Vol. 12 No. sp4 (2025): Recent Advances in Agriculture by Young Minds - III

Global hydrological model: A comprehensive review of types, applications and uncertainties

DOI
https://doi.org/10.14719/pst.5960
Submitted
19 October 2024
Published
11-05-2025 — Updated on 13-11-2025
Versions

Abstract

The rapid growth of industrialization and urbanization has significantly impacted our natural resources, such as air, water, and soil. Many problems have been brought about by these changes, such as drought, more frequent flooding, pollution, climate change, biodiversity loss, the extinction of numerous plant and animal species, changes in land cover and usage, and deterioration of the land. Extensive research has been conducted to understand how these physical changes affect natural resources, particularly using the hydrological models. As a result, a trustworthy hydrological model that can provide outcomes in line with observable parameters is essential. This comprehensive analysis highlights the vital role of hydrological modelling plays in forecasting floods, managing water supplies, and simulating ecosystems. This review explains the mathematical underpinnings and applicability of hydrological models for various system aspects by classifying them into empirical, conceptual, and physically-based frameworks. To furnish details with a comprehensive understanding of model robustness and dependability, this study encompasses calibration methodologies and uncertainty analysis frameworks. Furthermore, it meticulously elucidates the diverse sources of uncertainty inherent in hydrological modelling, thereby providing a nuanced perspective on the subject. It also presents a summary of well-known global hydrological models, emphasizing their goals, history, and contributions to our knowledge of biogeochemical cycles, climate change effects, and water shortage dynamics.

References

  1. 1. Dirmeyer PA. The terrestrial segment of soil moisture-climate coupling. Geophys Res Lett. 2011;38(16): https://doi.org/10.1029/2011GL048268
  2. 2. Koster RD, Suarez MJ, Higgins RW, Van den Dool HM. Observational evidence that soil moisture variations affect precipitation. Geophys Res Lett. 2003;30(5): https://doi.org/10.1029/2002GL016571
  3. 3. Munro R, Lyons W, Shao Y, Wood M, Hood L, Leslie L. Modelling land surface-atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture. Environ Model Softw. 1998;13(3-4):333‒39. https://doi.org/10.1016/S1364-8152(98)00038-3
  4. 4. Milly P, Cazenave A, Famiglietti JS, Gornitz V, Laval K, Lettenmaier DP, et al. Terrestrial water-storage contributions to sea-level rise and variability. Understanding Sea-Level Rise and Variability. 2010;226‒55. https://doi.org/10.1002/9781444323276.ch8
  5. 5. Freeman MC, Pringle CM, Jackson CR. Hydrologic connectivity and the contribution of stream headwaters to ecological integrity at regional scales 1. J Ameri Water Res Asso. 2007;43(1):5‒14. https://doi.org/10.1111/j.1752-1688.2007.00002.x
  6. 6. Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, et al. Global consequences of land use. Sci. 2005;309(5734):570‒74. https://doi.org/10.1126/science.1111772
  7. 7. Fekete BM, Wisser D, Kroeze C, Mayorga E, Bouwman L, Wollheim WM, et al. Millennium ecosystem assessment scenario drivers (1970-2050): climate and hydrological alterations. Glob Biogeochem Cycles. 2010;24(4): https://doi.org/10.1029/2009GB003593
  8. 8. Islam MS, Oki T, Kanae S, Hanasaki N, Agata Y, Yoshimura K. A grid-based assessment of global water scarcity including virtual water trading. Water Resour Mgt. 2007;21:19‒33. https://doi.org/10.1007/s11269-006-9038-y
  9. 9. Alcamo J. Environmental futures: the practice of environmental scenario analysis. Elsevier; 2008
  10. 10. Alcamo J. Managing the glob water sys. Princeton Guide of Ecology Princeton University Press, Princeton, NJ; 2009 https://doi.org/10.1515/9781400833023.701
  11. 11. Rost S, Gerten D, Heyder U. Human alterations of the terrestrial water cycle through land management. Adv in Geosci. 2008;18:43‒50. https://doi.org/10.5194/adgeo-18-43-2008
  12. 12. Wheater H, Jakeman A, Beven K. Progress and directions in rainfall-runoff modelling. John Wiley and Sons Ltd; 1993. 101‒132
  13. 13. Beven K. Environmental modelling: an uncertain future: CRC Press; 2018https://doi.org/10.1201/9781482288575
  14. 14. Wagener T, Wheater H, Gupta HV. Rainfall-runoff modelling in gauged and ungauged catchments. World Scientific; 2004 https://doi.org/10.1142/9781860945397
  15. 15. Sorooshian S, Hsu K-l, Coppola E, Tomassetti B, Verdecchia M, Visconti G. Hydrological modelling and the water cycle: coupling the atmospheric and hydrological models: Springer Science and Business Media; 2008 https://doi.org/10.1007/978-3-540-77843-1
  16. 16. Singh A. A concise review on introduction to hydrological models. Global Research and Development J for Eng. 2018;3(10):14‒19.
  17. 17. Bicknell BR, Imhoff JC, Kittle Jr JL, Donigian Jr AS, Johanson RC. Hydrological simulation program-FORTRAN user's manual for version 11. Environmental Protection Agency Report No EPA/600/R-97/080 US Environmental Protection Agency, Athens, Ga; 1997
  18. 18. Neitsch S, Arnold J, Srinivasan R. Pesticides fate and transport predicted by the soil and water assessment tool (SWAT). Atrazine, Metolachlor and Trifluralin in the Sugar Creek Watershed: BRC Report. 2002;3.
  19. 19. Lindström G, Johansson B, Persson M, Gardelin M, Bergström S. Development and test of the distributed HBV-96 hydrological model. J Hydrol. 1997;201(1-4):272‒88. https://doi.org/10.1016/S0022-1694(97)00041-3
  20. 20. Devia GK, Ganasri BP, Dwarakish GS. A review on hydrological models. Aquatic Procedia. 2015;4:1001‒07. https://doi.org/10.1016/j.aqpro.2015.02.126
  21. 21. Refsgaard JC, Knudsen J. Operational validation and intercomparison of different types of hydrological models. Water Resour Res. 1996;32(7):2189‒202. https://doi.org/10.1029/96WR00896
  22. 22. Wheater HS. Hydrological processes in arid and semi-arid areas. Hydrology of Wadi systems. 2002;5‒22.
  23. 23. Dunn SM, Freer J, Wieler M, Kirkby M, Seibert J, Quinn PF, et al. Conceptualization in catchment modelling: simply learning. Hydrological Processes. 2008;22(13):2389‒93. https://doi.org/10.1002/hyp.7070
  24. 24. Fenicia F, Savenije HH, Matgen P, Pfister L. Understanding catchment behavior through stepwise model concept improvement. Water Resour Res. 2008;44(1): https://doi.org/10.1029/2006WR005563
  25. 25. Van Werkhoven K, Wagener T, Reed P, Tang Y. Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Adv in Water Resour. 2009;32(8):1154‒69. https://doi.org/10.1016/j.advwatres.2009.03.002
  26. 26. McIntyre N, Al-Qurashi A. Performance of ten rainfall-runoff models applied to an arid catchment in Oman. Environ Model Softw. 2009;24(6):726‒38. https://doi.org/10.1016/j.envsoft.2008.11.001
  27. 27. Beven K. Changing ideas in hydrology-the case of physically based models. J Hydrol. 1989;105(1-2):157‒72. https://doi.org/10.1016/0022-1694(89)90101-7
  28. 28. Orellana B, Pechlivanidis I, McIntyre N, Wheater H, Wagener T. A toolbox for the identification of parsimonious semi-distributed rainfall-runoff models: Application to the upper lee catchment.
  29. 29. Moore C, Doherty J. Role of the calibration process in reducing model predictive error. Water Resour Res. 2005;41(5): https://doi.org/10.1029/2004WR003501
  30. 30. Sorooshian S, Gupta V. Model calibration in computer models of watershed hydrology. VP Singh (Eds). Water Resour Publications, Highlands Ranch, Colorado; 1995
  31. 31. Boyle DP, Gupta HV, Sorooshian S. Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resour Res. 2000;36(12):3663‒74. https://doi.org/10.1029/2000WR900207
  32. 32. Schaefli B, Gupta HV. Do Nash values have value Hydrological processes. Hydrol Process (in press). 2007;21:2075‒80. https://doi.org/10.1002/hyp.6825
  33. 33. Nash JE, Sutcliffe JV. River flow forecasting through conceptual models part I-A discussion of principles. J Hydrol. 1970;10(3):282‒90. https://doi.org/10.1016/0022-1694(70)90255-6
  34. 34. Gupta HV, Kling H, Yilmaz KK, Martinez GF. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J Hydrol. 2009;377(1-2):80‒91. https://doi.org/10.1016/j.jhydrol.2009.08.003
  35. 35. Pechlivanidis I, Jackson B, McMillan H. The use of entropy as a model diagnostic in rainfall-runoff modelling. 5th International Congress on Environmental Modelling and Software - Ottawa, Ontario, Canada - July 2010; 2010.
  36. 36. Yapo PO, Gupta HV, Sorooshian S. Multi-objective global optimization for hydrologic models. J Hydrol. 1998;204(1-4):83‒97. https://doi.org/10.1016/S0022-1694(97)00107-8
  37. 37. Beven K. How far can we go in distributed hydrological modelling? Hydrol and Earth Sys Sci. 2001;5(1):1‒12. https://doi.org/10.5194/hess-5-1-2001
  38. 38. Gupta HV, Beven KJ, Wagener T. Model calibration and uncertainty estimation. Encycl Hydrol Sci. 2006. https://doi.org/10.1002/0470848944.hsa138
  39. 39. Bevan N, editor. What is the difference between the purpose of usability and user experience evaluation methods. Proceedings of the Workshop UXEM. Citeseer; 2009
  40. 40. Duan Q, Gupta VK, Sorooshian S. Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl. 1993;76:501‒21. https://doi.org/10.1007/BF00939380
  41. 41. Vrugt JA, Robinson BA, Hyman JM. Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput. 2008;13(2):243‒59. https://doi.org/10.1109/TEVC.2008.924428
  42. 42. Vrugt JA, Diks CG, Clark MP. Ensemble Bayesian model averaging using Markov chain Monte Carlo sampling. Environ Fluid Mech. 2008;8:579‒95. https://doi.org/10.1007/s10652-008-9106-3
  43. 43. Vrugt JA, Gupta HV, Bastidas LA, Bouten W, Sorooshian S. Effective and efficient algorithm for multi objective optimization of hydrologic models. Water Resour Res. 2003;39(8): https://doi.org/10.1029/2002WR001746
  44. 44. Perrin C, Michel C, Andréassian V. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. J Hydrol. 2001;242(3-4):275‒301. https://doi.org/10.1016/S0022-1694(00)00393-0
  45. 45. Ewen J, Parkin G. Validation of catchment models for predicting land-use and climate change impacts. 1. Method. J Hydrol. 1996;175(1-4):583‒94. https://doi.org/10.1016/S0022-1694(96)80026-6
  46. 46. Bathurst J, Ewen J, Parkin G, O'Connell P, Cooper J. Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses. J Hydrol. 2004;287(1-4):74‒94. https://doi.org/10.1016/j.jhydrol.2003.09.021
  47. 47. Wagener T, Kollat J. Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox. Environ Model Softw. 2007;22(7):1021‒33. https://doi.org/10.1016/j.envsoft.2006.06.017
  48. 48. Matott LS, Babendreier JE, Purucker ST. Evaluating uncertainty in integrated environmental models: A review of concepts and tools. Water Resour Res. 2009;45(6): https://doi.org/10.1029/2008WR007301
  49. 49. Helton JC, Davis FJ. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf. 2003;81(1):23‒69. https://doi.org/10.1016/S0951-8320(03)00058-9
  50. 50. Tang Y, Reed P, Wagener T, Van Werkhoven K. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrology and Earth System Sci. 2007;11(2):793‒817. https://doi.org/10.5194/hess-11-793-2007
  51. 51. McIntyre N, Jackson B, Wade A, Butterfield D, Wheater H. Sensitivity analysis of a catchment-scale nitrogen model. J Hydrology. 2005;315(1-4):71‒92. https://doi.org/10.1016/j.jhydrol.2005.04.010
  52. 52. Saltelli A. Making best use of model evaluations to compute sensitivity indices. Computer Physics Commun. 2002;145(2):280‒97. https://doi.org/10.1016/S0010-4655(02)00280-1
  53. 53. Helton JC, Davis F. Illustration of sampling‐based methods for uncertainty and sensitivity analysis. Risk Analysis. 2002;22(3):591‒622. https://doi.org/10.1111/0272-4332.00041
  54. 54. Hornberger GM, Spear RC. Approach to the preliminary analysis of environmental systems. J Environ Mgmt. 1981;12(1):7‒18.
  55. 55. Freer J, Beven K, Ambroise B. Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach. Water Resour Res. 1996;32(7):2161‒73. https://doi.org/10.1029/95WR03723
  56. 56. Vrugt JA, Robinson BA. Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging. Water Resour Res. 2007;43(1): https://doi.org/10.1029/2005WR004838
  57. 57. Wagener T, Gupta HV. Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Risk Assess. 2005;19:378‒87. https://doi.org/10.1007/s00477-005-0006-5
  58. 58. Refsgaard JC, Christensen S, Sonnenborg TO, Seifert D, Højberg AL, Troldborg L. Review of strategies for handling geological uncertainty in groundwater flow and transport modeling. Adv in Water Resour. 2012;36:36‒50. https://doi.org/10.1016/j.advwatres.2011.04.006
  59. 59. Rojas R, Kahunde S, Peeters L, Batelaan O, Feyen L, Dassargues A. Application of a multimodel approach to account for conceptual model and scenario uncertainties in groundwater modelling. J Hydrol. 2010;394(3-4):416‒35. https://doi.org/10.1016/j.jhydrol.2010.09.016
  60. 60. Zeng X, Wang D, Wu J, Zhu X, Wang L, Zou X. Uncertainty evaluation of a groundwater conceptual model by using a multi model averaging method. Human and Ecological Risk Assessment. 2015;21(5):1246‒58. https://doi.org/10.1080/10807039.2014.957945
  61. 61. Butts MB, Payne JT, Kristensen M, Madsen H. An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation. J Hydrol. 2004;298(1-4):242‒66. https://doi.org/10.1016/j.jhydrol.2004.03.042
  62. 62. Doherty J, Simmons C. Groundwater modelling in decision support: reflections on a unified conceptual framework. Hydrogeol J. 2013;21(7): https://doi.org/10.1007/s10040-013-1027-7
  63. 63. Refsgaard JC, Van der Sluijs JP, Brown J, Van der Keur P. A framework for dealing with uncertainty due to model structure error. Adv in Water Resour. 2006;29(11):1586‒97. https://doi.org/10.1016/j.advwatres.2005.11.013
  64. 64. Højberg A, Refsgaard J. Model uncertainty-parameter uncertainty versus conceptual models. Water Sci Tech. 2005;52(6):177‒86. https://doi.org/10.2166/wst.2005.0166
  65. 65. Rojas R, Feyen L, Dassargues A. Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging. Water Resour Res. 2008;44(12): https://doi.org/10.1029/2008WR006908
  66. 66. Troin M, Arsenault R, Martel J-L, Brissette F. Uncertainty of hydrological model components in climate change studies over two Nordic Quebec catchments. J Hydrometeorol. 2018;19(1):27‒46. https://doi.org/10.1175/JHM-D-17-0002.1
  67. 67. Balin D, Lee H, Rode M. Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling. Water Resour Res. 2010;46(11): https://doi.org/10.1029/2009WR007848
  68. 68. Kavetski D, Kuczera G, Franks SW. Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resour Res. 2006;42(3): https://doi.org/10.1029/2005WR004376
  69. 69. Ajami NK, Duan Q, Sorooshian S. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter and model structural uncertainty in hydrologic prediction. Water Resour Res. 2007;43(1): https://doi.org/10.1029/2005WR004745
  70. 70. Hagedorn R, Doblas-Reyes FJ, Palmer TN. The rationale behind the success of multi-model ensembles in seasonal forecasting-I. Basic concept. Tellus A: Dyn Meteorol Oceanogr. 2005;57(3):219‒33.https://doi.org/10.1111/j.1600-0870.2005.00103.x
  71. 71. Moges E, Jared A, Demissie Y, Yan E, Mortuza R, Mahat V, editors. Bayesian augmented L-moment approach for regional frequency analysis. World Environ Water Resour Congr. American Society of Civil Engineers Reston, VA; 2018 https://doi.org/10.1061/9780784481417.016
  72. 72. Velázquez J, Anctil F, Perrin C. Performance and reliability of multi model hydrological ensemble simulations based on seventeen lumped models and a thousand catchments. Hydrology and Earth System Sci. 2010;14(11):2303‒17. https://doi.org/10.5194/hess-14-2303-2010
  73. 73. Georgakakos KP, Seo D-J, Gupta H, Schaake J, Butts MB. Towards the characterization of streamflow simulation uncertainty through multi model ensembles. J Hydrol. 2004;298(1-4):222‒41. https://doi.org/10.1016/j.jhydrol.2004.03.037
  74. 74. Arsenault R, Gatien P, Renaud B, Brissette F, Martel J-L. A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation. J Hydrol. 2015;529:754‒67. https://doi.org/10.1016/j.jhydrol.2015.09.001
  75. 75. Moges E, Demissie Y, Larsen L, Yassin F. Review: Sources of hydrological model uncertainties and advances in their analysis. Water. 2021;13(1):28. https://doi.org/10.3390/w13010028
  76. 76. Vörösmarty CJ, Federer CA, Schloss AL. Potential evaporation functions compared on US watersheds: Possible implications for global-scale water balance and terrestrial ecosystem modeling. J Hydrol. 1998;207(3-4):147‒69. https://doi.org/10.1016/S0022-1694(98)00109-7
  77. 77. Fekete BM, Gibson JJ, Aggarwal P, Vörösmarty CJ. Application of isotope tracers in continental scale hydrological modeling. J Hydrol. 2006;330(3-4):444‒56. https://doi.org/10.1016/j.jhydrol.2006.04.029
  78. 78. Wisser D, Frolking S, Douglas EM, Fekete BM, Vörösmarty CJ, Schumann AH. Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets. Geophys Res Lett. 2008;35(24): https://doi.org/10.1029/2008GL035296
  79. 79. Brakenridge GR, Cohen S, Kettner AJ, De Groeve T, Nghiem SV, Syvitski JP, et al. Calibration of satellite measurements of river discharge using a global hydrology model. J hydrol. 2012;475:123‒36. https://doi.org/10.1016/j.jhydrol.2012.09.035
  80. 80. Hagemann S, Gates LD. Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model. J Geophys Res: Atmo. 2001;106(D2):1503‒10. https://doi.org/10.1029/2000JD900568
  81. 81. Meigh J, McKenzie A, Sene K. A grid-based approach to water scarcity estimates for eastern and southern Africa. Water Resour Manage. 1999;13:85‒115. https://doi.org/10.1023/A:1008025703712
  82. 82. Meigh J, Tate E, editors. The Gwava model-development of a global-scale methodology to assess the combined impact of climate and land use changes. EGS General Assembly Conference Abstracts; 2002
  83. 83. Gosling SN, Bretherton D, Haines K, Arnell NW. Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sci. 2010;368(1926):4005‒21. https://doi.org/10.1098/rsta.2010.0164
  84. 84. Arnell NW, Gosling SN. The impacts of climate change on river flow regimes at the global scale. J Hydrol. 2013;486:351‒64. https://doi.org/10.1016/j.jhydrol.2013.02.010
  85. 85. Nijssen B, Schnur R, Lettenmaier DP. Global retrospective estimation of soil moisture using the variable infiltration capacity land surface model, 1980-93. J Climate. 2001;14(8):1790‒808. https://doi.org/10.1175/1520-0442(2001)014<1790:GREOSM>2.0.CO;2
  86. 86. Nijssen B, O'Donnell GM, Lettenmaier DP, Lohmann D, Wood EF. Predicting the discharge of global rivers. J Climate. 2001;14(15):3307‒23. https://doi.org/10.1175/1520-0442(2001)014<3307:PTDOGR>2.0.CO;2
  87. 87. Irannejad P, Henderson-Sellers A. Evaluation of AMIP II global climate model simulations of the land surface water budget and its components over the GEWEX-CEOP regions. J Hydrometeorol. 2007;8(3):304‒26. https://doi.org/10.1175/JHM579.1
  88. 88. Milly P, Shmakin A. Global modeling of land water and energy balances. Part I: The land dynamics (LaD) model. J Hydrometeoro. 2002;3(3):283‒99. https://doi.org/10.1175/1525-7541(2002)003<0283:GMOLWA>2.0.CO;2
  89. 89. Shmakin A, Milly P, Dunne K. Global modeling of land water and energy balances. Part III: Interannual variability. J Hydrometeoro. 2002;3(3):311‒21. https://doi.org/10.1175/1525-7541(2002)003<0311:GMOLWA>2.0.CO;2
  90. 90. Bierkens M, Van Beek L. Seasonal predictability of European discharge: NAO and hydrological response time. J Hydrometeoro. 2009;10(4):953‒68. https://doi.org/10.1175/2009JHM1034.1
  91. 91. Wada Y, Van Beek LP, Van Kempen CM, Reckman JW, Vasak S, Bierkens MF. Global depletion of groundwater resources. Geophys Res Lett. 2010;37(20):L20402. https://doi.org/10.1029/2010GL044571
  92. 92. Petrescu A, Van Beek L, Van Huissteden J, Prigent C, Sachs T, Corradi C, et al. Modeling regional to global CH4 emissions of boreal and arctic wetlands. Glo Biogeoche Cycles. 2010;24(4): https://doi.org/10.1029/2009GB003610
  93. 93. Weiland FS, Van Beek L, Weerts A, Bierkens M. Extracting information from an ensemble of GCMs to reliably assess future global runoff change. J Hydrol. 2012;412:66‒75. https://doi.org/10.1016/j.jhydrol.2011.03.047
  94. 94. Van Beek L, Wada Y, Bierkens MF. Global monthly water stress: 1. Water balance and water availability. Water Resour Res. 2011;47(7): https://doi.org/10.1029/2010WR009791
  95. 95. Wada Y, Van Beek L, Viviroli D, Dürr HH, Weingartner R, Bierkens MF. Global monthly water stress: 2. Water demand and severity of water stress. Water Resour Res. 2011;47(7): https://doi.org/10.1029/2010WR009792
  96. 96. De Graaf I, Van Beek L, Wada Y, Bierkens M. Dynamic attribution of global water demand to surface water and groundwater resources: Effects of abstractions and return flows on river discharges. Adv in Water Resour. 2014;64:21‒33. https://doi.org/10.1016/j.advwatres.2013.12.002
  97. 97. Gerten D, Hoff H, Bondeau A, Lucht W, Smith P, Zaehle S. Contemporary "green" water flows: Simulations with a dynamic global vegetation and water balance model. Physics and Chemistry of the Earth, Parts A/B/C. 2005;30(6-7):334‒38. https://doi.org/10.1016/j.pce.2005.06.002
  98. 98. Bondeau A, Smith PC, Zaehle S, Schaphoff S, Lucht W, Cramer W, et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glo Change Bio. 2007;13(3):679‒706. https://doi.org/10.1111/j.1365-2486.2006.01305.x
  99. 99. Müller C, Lucht W. Robustness of terrestrial carbon and water cycle simulations against variations in spatial resolution. J Geophy Res: Atmospheres. 2007;112(D6): https://doi.org/10.1029/2006JD007875
  100. 100. Wada Y, Wisser D, Bierkens MF. Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth System Dynamics. 2014;5(1):15‒40. https://doi.org/10.5194/esd-5-15-2014
  101. 101. Gong L, Widen-Nilsson E, Halldin S, Xu C-Y. Large-scale runoff routing with an aggregated network-response function. J Hydrol. 2009;368(1-4):237‒50. https://doi.org/10.1016/j.jhydrol.2009.02.007
  102. 102. Li L, Ngongondo CS, Xu C-Y, Gong L. Comparison of the global TRMM and WFD precipitation datasets in driving a large-scale hydrological model in southern Africa. Hydrol Res. 2013;44(5):770‒88. https://doi.org/10.2166/nh.2012.175
  103. 103. Hanasaki N, Kanae S, Oki T, Masuda K, Motoya K, Shirakawa N, et al. An integrated model for the assessment of global water resources-Part 2: Applications and assessments. Hydrol and Earth Sys Sci. 2008;12(4):1027‒37. https://doi.org/10.5194/hess-12-1007-2008 https://doi.org/10.5194/hess-12-1027-2008
  104. 104. Hanasaki N, Inuzuka T, Kanae S, Oki T. An estimation of global virtual water flow and sources of water withdrawal for major crops and livestock products using a global hydrological model. J Hydrol. 2010;384(3-4):232‒44. https://doi.org/10.1016/j.jhydrol.2009.09.028
  105. 105. Hanasaki N, Fujimori S, Yamamoto T, Yoshikawa S, Masaki Y, Hijioka Y, et al. A global water scarcity assessment under Shared Socio-economic Pathways-Part 2: Water availability and scarcity. Hydrol and Earth Sys Sci. 2013;17(7):2393‒413. https://doi.org/10.5194/hess-17-2393-2013.
  106. 106. Alcamo J, Henrichs T. Critical regions: A model-based estimation of world water resources sensitive to global changes. Aquatic Sci. 2002;64:352‒62. https://doi.org/10.1007/PL00012591
  107. 107. Döll P, Siebert S. Global modeling of irrigation water requirements. Water Resour Res. 2002;38(4):8-1-8-10. https://doi.org/10.1029/2001WR000355
  108. 108. Döll P, Kaspar F, Lehner B. A global hydrological model for deriving water availability indicators: model tuning and validation. J Hydrol. 2003;270(1-2):105‒34. https://doi.org/10.1016/S0022-1694(02)00283-4
  109. 109. Werth S, Güntner A, Petrovic S, Schmidt R. Integration of GRACE mass variations into a global hydrological model. Earth and Planetary Sci Lett. 2009;277(1-2):166‒73. https://doi.org/10.1016/j.epsl.2008.10.021
  110. 110. Döll P, Fiedler K, Zhang J. Global-scale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrol and Earth Sys Sci. 2009;13(12):2413‒32. https://doi.org/10.5194/hess-13-2413-2009
  111. 111. Alkama R, Decharme B, Douville H, Becker M, Cazenave A, Sheffield J, et al. Global evaluation of the ISBA-TRIP continental hydrological system. Part I: Comparison to GRACE terrestrial water storage estimates and in situ river discharges. J Hydrometeoro. 2010;11(3):583‒600. https://doi.org/10.1175/2010JHM1211.1

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