Advertisement

Water Resources Management

, Volume 32, Issue 9, pp 3189–3202 | Cite as

Budyko’s Based Method for Annual Runoff Characterization across Different Climatic Areas: an Application to United States

  • Domenico Caracciolo
  • D. Pumo
  • F. Viola
Article
  • 181 Downloads

Abstract

Runoff data knowledge is of fundamental importance for a wide range of hydrological, ecological, and socioeconomic applications. The reconstruction of annual runoff is a fundamental task for several activities related to water resources management, especially for ungauged basins. At catchment scales, the Budyko’s framework provides an extremely useful and, in some cases, accurate estimation of the long-term partitioning of precipitation into evapotranspiration and runoff as a function of the prevailing climatic conditions. Recently the same long-term partitioning rules have been successfully used to describe water partitioning also at the annual scale and calculate the annual runoff distribution within a simple analytic framework in arid and semi-arid basins. One of the main advantages of the latter method is that only annual precipitation and potential evapotranspiration statistics, and the Fu’s equation parameter ω are required to obtain the annual runoff probability distribution. The aim of this study is to test the limit and potentialities of the aforementioned method under different climatic conditions. To this aim, the model is applied to more than four hundred basins located in the United States. Catchments were grouped into five different samples, following the subdivision of the continental region in five homogeneous climatic zones according to Köppen-Geiger classification. The theoretical probability distribution of annual runoff at each basin has been compared with that derived from historical observations. The results confirm the capability of the tested technique to reproduce the empirical annual runoff distributions with similar and satisfactory performances across different areas, revealing a good option also in cases characterized by climate and hydrological conditions very different from those hypothesized during the original analytical model design, thus extending the geographical and conceptual limits of applicability of the framework.

Keywords

Budyko’s curve MOPEX data Annual runoff distribution Annual water balance Water resources assessment 

References

  1. Abatzoglou JT, Ficklin DL (2017) Climatic and physiographic controls of spatial variability in surface water balance over the contiguous United States using the Budyko relationship. Water Resour Res 53(9): 7630–7643Google Scholar
  2. Berghuijs W, Woods R, Hrachowitz M (2014) A precipitation shift from snow towards rain leads to a decrease in streamflow. Nat Clim Chang 4:583–586CrossRefGoogle Scholar
  3. Budyko (1961) The heat balance of the earth's surface. Sov Geogr 2:3–13Google Scholar
  4. Budyko (1974) Climate and life. Academic, San DiegoGoogle Scholar
  5. Caracciolo D, Deidda R, Viola F (2017) Analytical estimation of annual runoff distribution in ungauged seasonally dry basins based on a first order Taylor expansion of the Fu’s equation. Adv Water Resour 109:320–332CrossRefGoogle Scholar
  6. Caracciolo D, Istanbulluoglu E, Noto LV, Collins SL (2016) Mechanisms of shrub encroachment into Northern Chihuahuan Desert grasslands and impacts of climate change investigated using a cellular automata model. Adv Water Resour 91:46–62Google Scholar
  7. Carmona AM, Sivapalan M, Yaeger MA, Poveda G (2014) Regional patterns of interannual variability of catchment water balances across the continental U.S.: a Budyko framework. Water Resour Res 50:9177–9193.  https://doi.org/10.1002/2014wr016013 CrossRefGoogle Scholar
  8. Dai A (2008) Temperature and pressure dependence of the rain-snow phase transition over land and ocean. Geophys Res Lett 35(12):L12802.  https://doi.org/10.1029/2008GL033295
  9. Donohue RJ, Roderick ML, McVicar TR (2007) On the importance of including vegetation dynamics in Budyko's hydrological model. Hydrol Earth Syst Sci 11:983–995CrossRefGoogle Scholar
  10. Duan Q, Schaake J, Andréassian V, Franks S, Goteti G, Gupta HV, Gusev YM, Habets F, Hall A, Hay L, Hogue T, Huang M, Leavesley G, Liang X, Nasonova ON, Noilhan J, Oudin L, Sorooshian S, Wagener T, Wood EF (2006) Model parameter estimation experiment (MOPEX): an overview of science strategy and major results from the second and third workshops. J Hydrol 320:3–17CrossRefGoogle Scholar
  11. Essou GR, Arsenault R, Brissette FP (2016) Comparison of climate datasets for lumped hydrological modeling over the continental United States. J Hydrol 537:334–345CrossRefGoogle Scholar
  12. Fu BP (1981) On the calculation of the evaporation from land surface. Sci Atmos Sin 5:23–31Google Scholar
  13. Gentine P, D'Odorico P, Lintner BR, Sivandran G, Salvucci G (2012) Interdependence of climate, soil, and vegetation as constrained by the Budyko curve. Geophys Res Lett 39(19).  https://doi.org/10.1029/2012GL053492
  14. Greve P, Gudmundsson L, Orlowsky B, Seneviratne SI (2015) Introducing a probabilistic Budyko framework. Geophys Res Lett 42:2261–2269CrossRefGoogle Scholar
  15. 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
  16. Li D, Pan M, Cong Z, Zhang L, Wood E (2013) Vegetation control on water and energy balance within the Budyko framework. Water Resour Res 49:969–976CrossRefGoogle Scholar
  17. Milly PC, Dunne KA, Vecchia AV (2005) Global pattern of trends in streamflow and water availability in a changing climate. Nature 438:347–350CrossRefGoogle Scholar
  18. Pike J (1964) The estimation of annual run-off from meteorological data in a tropical climate. J Hydrol 2:116–123CrossRefGoogle Scholar
  19. Pumo D, Viola F, Noto LV (2016) Generation of natural runoff monthly series at ungauged sites using a regional regressive model. Water (Switzerland) 8. doi: https://doi.org/10.3390/w8050209
  20. Pumo D, Arnone E, Francipane A, Caracciolo D, Noto L (2017) Potential implications of climate change and urbanization on watershed hydrology. J Hydrol 554:80–99CrossRefGoogle Scholar
  21. Renner M, Bernhofer C (2012) Applying simple water-energy balance frameworks to predict the climate sensitivity of streamflow over the continental United States. Hydrol Earth Syst Sci 16:2531–2546CrossRefGoogle Scholar
  22. Schaake J, Cong S, Duan Q (2006) The US MOPEX data set. IAHS Publ 307:9Google Scholar
  23. Seager R, Ting M, Li C, Naik N, Cook B, Nakamura J, Liu H (2013) Projections of declining surface-water availability for the southwestern United States. Nat Clim Chang 3:482–486CrossRefGoogle Scholar
  24. Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94CrossRefGoogle Scholar
  25. Turc L (1954) Le bilan d’eau des sols. Relation entre la precipitation, l’evaporation et l’ecoulement. Ann Agron 5:491–569Google Scholar
  26. Vangelis H, Spiliotis M, Tsakiris G (2011) Drought severity assessment based on bivariate probability analysis. Water Resour Manag 25:357–371CrossRefGoogle Scholar
  27. Viola F, Francipane A, Caracciolo D, Pumo D, La Loggia G, Noto LV (2016) Co-evolution of hydrological components under climate change scenarios in the Mediterranean area. Sci Total Environ 544:515–524.  https://doi.org/10.1016/j.scitotenv.2015.12.004 CrossRefGoogle Scholar
  28. Viola F, Caracciolo D, Forestieri A, Pumo D, Noto L (2017) Annual runoff assessment in arid and semiarid Mediterranean watersheds under the Budyko’s framework. Hydrol Process 31:1876–1888CrossRefGoogle Scholar
  29. Xu X, Liu W, Scanlon BR, Zhang L, Pan M (2013) Local and global factors controlling water-energy balances within the Budyko framework. Geophys Res Lett 40:6123–6129CrossRefGoogle Scholar
  30. Yang D, Shao W, Yeh PJF, Yang H, Kanae S, Oki T (2009) Impact of vegetation coverage on regional water balance in the nonhumid regions of China. Water Resour Res 45(7):W00A14.  https://doi.org/10.1029/2008WR006948

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria Civile, Ambientale e Architettura (DICAAR)Università di CagliariCagliariItaly
  2. 2.Regional Environmental Protection Agency of Sardinia (ARPAS)CagliariItaly
  3. 3.Dipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali (DICAAM)Università di PalermoPalermoItaly

Personalised recommendations