Water Resources Management

, Volume 33, Issue 1, pp 189–205 | Cite as

Spatial Scale Resolution of Prognostic Hydrological Models: Simulation Performance and Application in Climate Change Impact Assessment

  • Mohsen NasseriEmail author
  • Banafsheh Zahraie
  • Ardalan Tootchi


In this paper, long-term hydrological response of a watershed to climate change was investigated taking into account the spatial scale effect on the performance of hydrological models. A water balance model was used in which variations of soil moisture, snow budget, deep infiltration and interactions with groundwater resources were modeled. Four various combinations of sub-catchment delineation, altitudinal discretization and division into square-shaped grids were tested for semi-distributed water balance modeling of a basin located in southwest of Iran, namely Roodzard Basin, with arid and semiarid climate based on Köppen-Geiger climate classification. The results showed improvement in the model performances when spatial variations of the meteorological data and topographic characteristics of the basin were incorporated in the modeling process. The effects of spatial scale resolution dependency were evaluated in projecting streamflow for various climate change scenarios. The results showed that finer resolution of grid cells in the semi-distributed model does not necessarily result in more accurate estimation of monthly streamflows and altitudinal discretization provides almost same accuracy as the results of grid-based models. Moreover, probability distribution of projections obtained from water balance models for A2 and B2 of Special Report on Emissions Scenarios (SRES) scenarios presented less coefficient of variation and skewness compared with historical observations.


Hydro-climatic processes Water balance model Climate change Spatial scale 


Compliance with ethical standards

Conflict of Interest


Supplementary material

11269_2018_2096_MOESM1_ESM.docx (60 kb)
ESM 1 (DOCX 60 kb)


  1. Asadieh B, Krakauer NY (2017) Global change in streamflow extremes under climate change over the 21st century. Hydrol Earth Syst Sci 21:5863–5874CrossRefGoogle Scholar
  2. Beven K (1989) Changing ideas in hydrology - the case of physically-based models. J Hydrol 105:157–172CrossRefGoogle Scholar
  3. Chen X, David Chen Y, Xu C-y (2007) A distributed monthly hydrological model for integrating spatial variations of basin topography and rainfall. Hydrol Process 21:242–252CrossRefGoogle Scholar
  4. Dunne KA (1996) Global distribution of plant extractable water capacity of soil. Int J Climatol 16:841–859CrossRefGoogle Scholar
  5. Grayson RM (1992) Physically based hydrologic modeling: 1. A terrain-based model for investigative purposes. Water Resour Res 28:2639–2658CrossRefGoogle Scholar
  6. Guo S, Chen H, Zhang H, Xiong L, Liu P, Wang G (2005) A Semi-Distributed Monthly Water Balance Model and Its Application in Climate Change Impact study in the Middle and Lower Yellow River Basin. Water Int 30(2):250–260CrossRefGoogle Scholar
  7. Hlavcova, K., Cunderlik, J. (1998). Impact of climate change on the seasonal distribution of runoff in mountainous basins in Slovakia, Hydrology, Processding of Water Resources and Ecology in Headwaters, 1AHS Publ. no. 248, pp. 39–46Google Scholar
  8. Im S-S, Kim H-S, Sep B-H (2001) A study on Computation Methods of Monthly Runoff by Water Balance Method. J Korea Water Resour Assoc 34(6):713–724Google Scholar
  9. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World Map of the Köppen-Geiger climate classification updated. Meteorol Z 15:259–263CrossRefGoogle Scholar
  10. Kour R, Patel N, Krishna AP (2016) Climate and hydrological models to assess the impact of climate change on hydrological regime: a review. Arab J Geosci 9:544. CrossRefGoogle Scholar
  11. Kuchar L, Szalińska W, Iwański S, Jelonek L (2014) A modeling framework to assess the impact of climate change on a river runoff. Meteorol Hydrol Water Manag 2(2):49–64CrossRefGoogle Scholar
  12. Lee H, McIntyre N, Wheater H, Young A (2005) Selection of conceptual models for regionalization of the rainfall-runoff relationship. J Hydrol 312(1–4):125–147CrossRefGoogle Scholar
  13. Ljung L (1999) System Identification: Theory for the User. Prentice-Hal PTR, Upper Saddle RiverGoogle Scholar
  14. Mishra A, Hata T (2006) A grid-based runoff generation and flow routing model for upper blue Nile basin. Hydrol Sci J 51(2):191–206CrossRefGoogle Scholar
  15. Nasseri M, Zahraie B, Ajami NK, Solomatine DP (2014a) Monthly Water Balance Modeling: Probabilistic, Possibilistic and Hybrid Methods for Model Combination and Ensemble Simulation. J Hydrol 511:675–691CrossRefGoogle Scholar
  16. Nasseri M, Ansari A, Zahraie B (2014b) Uncertainty Assessment of Hydrological Models with Fuzzy Extension Principle: Evaluation of a New Arithmetic Operator. Water Resour Res 50:1095–1111. CrossRefGoogle Scholar
  17. Nasseri M, Zahraie B, Forouhar L (2017) A Comparison Between Direct and Indirect Frameworks to Evaluate Impacts of Climate Change on Streamflows: Case Study of Karkheh River Bsin in Iran. J Water Clim Chang
  18. Olea RA (2006) A six-step practical approach to semivariogram modeling. Stoch Env Res Risk A 20:307–318CrossRefGoogle Scholar
  19. Panagouliaa D, Dimoub G (1997) Linking space–time scale in hydrological modelling with respect to global climate change: Part 1. Models, model properties, and experimental design. J Hydrol 194(1–4):15–37. CrossRefGoogle Scholar
  20. Pechilivanidis IM (2010) Calibration of the semi-distributed PDM rainfall-runoff model in the upper LEE cathcment, UK. J Hydrol 386:198–209CrossRefGoogle Scholar
  21. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644. CrossRefGoogle Scholar
  22. Piniewski M, Voss F, Bärlund I, Okruszko T, Kundzewicz ZW (2013) Effect of modelling scale on the assessment of climate change impact on river runoff. Hydrol Sci J 58(4):737–754. CrossRefGoogle Scholar
  23. Piniewski M, Meresa HK, Romanowicz R, Osuch M, Szcześniak KI, Okruszko T, Mezghani A, Kundzewicz ZW (2017) What can we learn from the projections of changes of flow patterns? Results from Polish case studies. Acta Geophysica 65(4):809–827CrossRefGoogle Scholar
  24. Rinaldo A, Marani A, Rigon R (1991) Geomorphological disspersion. Water Resour Res 27(4):513–525CrossRefGoogle Scholar
  25. USDA-SCS (1985) National Engineering Handbook. Section 4-Hydrology. USDA-SCS, Washington DC.Google Scholar
  26. Saghafian B, Ghasemi SA, Nasseri M (2018) Backcasting Long Term Climate Data: Evaluation of Hypothesis. Theor Appl Climatol 134(3–4):717–726. CrossRefGoogle Scholar
  27. Solomatine DP, Wagener T (2011) Hydrological Modeling. In: Wilderer P (ed) Treatise on Water Science, vol 2. Academic Press, Oxford, pp 435–457CrossRefGoogle Scholar
  28. Smith MB, Koren V, Reed S, Zhang Z, Zhang Y, Moreda F, Cui Z, Mizukami N, Anderson EA, Cosgrove BA (2012) The distributed model intercomparison project–Phase 2: Motivation and design of the Oklahoma experiments. J Hydrol 418–419:3–16CrossRefGoogle Scholar
  29. Tavakol-Davani H, Nasseri M, Zahraie B (2012) Improved statistical downscaling of daily precipitation using SDSM platform and data-mining methods. Int J Climatol 33(11):2561–2578CrossRefGoogle Scholar
  30. Tong, H. (1983). Threshold models in non-linear time series analysis. Springer.Google Scholar
  31. Vörösmarty MB (1989) A continental-scale model of water balance and fluvial transport: application to South America. Glob Biogeochem Cycles 3:241–265CrossRefGoogle Scholar
  32. XU CH-Y (1999) Climate Change and Hydrologic Models: A Review of Existing Gaps and Recent Research Developments. Water Resour Manag 13:369–382CrossRefGoogle Scholar
  33. Zhao RJ (1992) The Xinanjiang model applied in China. J Hydrol 135:371–381CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Mohsen Nasseri
    • 1
    Email author
  • Banafsheh Zahraie
    • 2
  • Ardalan Tootchi
    • 3
  1. 1.School of Civil Engineering, College of EngineeringUniversity of TehranTehranIran
  2. 2.School of Civil Engineering, Center of Excellence in Infrastructure Engineering and Management, College of EngineeringUniversity of TehranTehranIran
  3. 3.UMR 7619 METIS, Sorbonne Universités, UPMC, CNRS, EPHEParisFrance

Personalised recommendations