, Volume 15, Issue 5, pp 775–791 | Cite as

Assessing the Role of Parameter and Input Uncertainty in Ecohydrologic Modeling: Implications for a Semi-arid and Urbanizing Coastal California Catchment

  • Catherine A. Shields
  • Christina L. Tague


Ecohydrologic models are a key tool in understanding plant–water interactions and their vulnerability to environmental change. Although implications of uncertainty in these models are often assessed within a strictly hydrologic context (for example, runoff modeling), the implications of uncertainty for estimation of vegetation water use are less frequently considered. We assess the influence of commonly used model parameters and inputs on predictions of catchment-scale evapotranspiration (ET) and runoff. By clarifying the implications of uncertainty, we identify strategies for insuring that the quality of data used to drive models is considered in interpretation of model predictions. Our assessment also provides insight into unique features of semi-arid, urbanizing watersheds that shape ET patterns. We consider four sources of uncertainty: soil parameters, irrigation inputs, and spatial extrapolation of both point precipitation and air temperature for an urbanizing, semi-arid coastal catchment in Santa Barbara, CA. Our results highlight a seasonal transition from soil parameters to irrigation inputs as key controls on ET. Both ET and runoff show substantial sensitivity to uncertainty in soil parameters, even after parameters have been calibrated against observed streamflow. Sensitivity to uncertainty in precipitation manifested primarily in winter runoff predictions, whereas sensitivity to irrigation manifested exclusively in modeled summer ET. Neither ET nor runoff was highly sensitive to uncertainty in spatial interpolation of temperature. Results argue that efforts to improve ecohydrologic modeling of vegetation water use and associated water-limited ecological processes in these semi-arid regions should focus on improving estimates of anthropogenic outdoor water use and explicit accounting of soil parameter uncertainty.


Ecohydrology Modeling Urban ecology Plant–water interactions Semiarid Sensitivity analysis 



This research was supported by a National Science Foundation Graduate Research Fellowship, and by the Santa Barbara Coastal Long-Term Ecological Research project, funded by the National Science Foundation (OCE-9982105 and OCE-0620276). We thank the two anonymous reviewers whose extensive and thoughtful comments greatly contributed to the quality of the final manuscript.


  1. Anderson JR, Hardy EE, Roach JT, Witmer RE. 1976. A land use and land cover classification system for use with remote sensor data. US Geological Survey, Professional Paper 964. Reston, VA: USGS.Google Scholar
  2. Austin AT, Yadijan L, Stark JM, Belnap J, Porporato A, Norton U, Ravetta DA, Schaeffer SM. 2004. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141:221–35.PubMedCrossRefGoogle Scholar
  3. Band L, Moore I. 1995. Scale-landscape attributes and geographical information systems. Hydrolo Proc 9:401–22.CrossRefGoogle Scholar
  4. Bardossy A, Das T. 2008. Influence of rainfall observation network on model calibration and application. Hydrol Earth Syst Sci 12:77–89.CrossRefGoogle Scholar
  5. Beighley R, Dunne T, Melack J. 2005. Understanding and modeling basin hydrology: interpreting the hydrogeological signature. Hydrol Proc 19:1333–53.CrossRefGoogle Scholar
  6. Beven K, Binley A. 1992. The future of distributed models—model calibration and uncertainty prediction. Hydrol Proc 6:279–98.CrossRefGoogle Scholar
  7. Beven K, Freer J. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of a complex environmental system using the GLUE methodology. J Hydrol 249:11–29.CrossRefGoogle Scholar
  8. Cao WZ, Bowden WB, Davie T, Fenemor A. 2006. Multi-variable and multi-site calibration and validation of SWAT in a large mountainous catchment with high spatial variability. Hydrol Proc 20:1057–73.CrossRefGoogle Scholar
  9. Cayan D, Maurer EP, Dettinger MD, Tyree M, Hayhoe K. 2008. Climate change scenarios for the Californian region. Clim Change 87:21–42.CrossRefGoogle Scholar
  10. Choi H, Beven K. 2007. Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework. J Hydrol 332:316–36.CrossRefGoogle Scholar
  11. County of Santa Barbara. 2007. Santa Barbara County water purveyors water shortage contingency/drought planning handbook.
  12. Dettinger M, Redmond K, Cayan D. 2004. Winter orographic precipitation ratios in the Sierra Nevada—large-scale atmospheric circulations and hydrologic consequences. J Hydrometeorol 5:1102–16.CrossRefGoogle Scholar
  13. Fuentes M, Kittel TGF, Nychka D. 2006. Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing. Contemp Stat Ecol 16:99–116.Google Scholar
  14. Fischer DT, Still CJ, Williams AP. 2009. Significance of summer fog and overcast for drought stress and ecological functioning of coastal California endemic plant species. J Biogeogr 36:783–99.CrossRefGoogle Scholar
  15. Gleick P, Haasz D, Henges-Jeck C, Srinivasan V, Wolff G, Cushing KK, Mann A. 2003. Waste not, want not: the potential for urban water conservation in California. Pacific Institute for Studies in Development, Environment, and Security, Oakland.Google Scholar
  16. Hevesi J, Flint A, Istok J. 1992. Precipitation estimation in mountainous terrain using multivariate geostatistics 2 isohyetal maps. J Appl Meteorol 31:677–88.CrossRefGoogle Scholar
  17. Holden J. 2009. Topographic controls upon soil macropore flow. Earth Surf Proc Land 34:345–51.CrossRefGoogle Scholar
  18. IPCC. 2007. Contribution of Working Groups I, II, and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland.Google Scholar
  19. Irmak S, Payero JO, Martin DL, Irmak A, Howell TA. 2006. Sensitivity analyses and sensitivity coefficients of standardized daily ASCE-Penman Monteith equation. J Irrig Drain Eng-ASCE 132:564–78.CrossRefGoogle Scholar
  20. Johnson T. 2005. Predicting residential irrigation amounts using remote sensing in Los Angeles, California. M.S. thesis. San Diego, CA: San Diego State University.Google Scholar
  21. Jones H. 1992. Plants and microclimate. 2nd edn. Cambridge: Cambridge University Press.Google Scholar
  22. Kirchner J. 2006. Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resour Res 42:W03S04.Google Scholar
  23. Kumar S, Sekhar M, Reddy DV, Kumar MSM. 2010. Estimation of soil hydraulic properties and their uncertainty: comparison between laboratory and field experiment. Hydrol Proc 24:3426–35.CrossRefGoogle Scholar
  24. Mackun P, Wilson S. 2011. Population Distribution and Change: 2000 to 2010, U.S. Census Bureau, U.S. Department of Commerce, Economics and Statistics Administration.Google Scholar
  25. Mair A, Fares A. 2011. Comparison of rainfall interpolation methods in a mountainous region of a tropical island. J Hydrol Eng 16:371–83.CrossRefGoogle Scholar
  26. Metropolitan Water District. 1996. Integrated Water Resources Plan (IRP), MWD report no. 1107.Google Scholar
  27. Moulin L, Gaume E, Obled C. 2009. Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations. Hydrol Earth Syst Sci 13:99–114.CrossRefGoogle Scholar
  28. Miller AE, Schimel JP, Meixner T, Sickman JO, Melack JM. 2005. Episodic rewetting enhances carbon and nitrogen release from chaparral soils. Soil Biol Biogeochem 37:2195–204.CrossRefGoogle Scholar
  29. Nandakumar N, Mein R. 1997. Uncertainty in rainfall-runoff model simulation and the implications for predicting the hydrologic effects of land-use change. J Hydrol 192:211–32.CrossRefGoogle Scholar
  30. Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10:282–90.CrossRefGoogle Scholar
  31. Nieber J, Sidle R. 2010. How do disconnected macropores in sloping soils facilitate preferential flow? Hydrol Proc 24:1582–94.CrossRefGoogle Scholar
  32. Ochoa-Hueso R, Manrique E. 2010. Nitrogen fertilization and water supply affect germination and plant establishment of the soil seed bank present in a semi-arid Mediterranean scrubland. Plant Ecol 210:263–73.CrossRefGoogle Scholar
  33. Paul M, Meyer J. 2001. Streams in the urban landscape. Annu Rev Ecol Syst 32:333–65.CrossRefGoogle Scholar
  34. Priestley C, Taylor R. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–2.CrossRefGoogle Scholar
  35. Running S, Nemani R, Hungerford R. 1987. Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis. Can J For Res 17:472–83.CrossRefGoogle Scholar
  36. Serat-Capdevila A, Scott RL, Shuttleworth WJ, James W. 2011. Estimating evapotranspiration under warmer climates: insights from a semi-arid riparian system. J Hydrol 399:1–11.CrossRefGoogle Scholar
  37. Sivapalan M. 2009. The secret to ‘doing better hydrological science’: change the question!. Hydrol Proc 23:1391–6.CrossRefGoogle Scholar
  38. Spruill CA, Workman SR, Taraba JL. 2000. Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Trans ASAE 43:1431–9.Google Scholar
  39. Tague C, Pohl M. 2008. The utility of physically based hydrologic modeling in ungaged urban streams. Ann Assoc Am Geogr 93:1–16.Google Scholar
  40. Tague C, McMichael C, Hope A, Choate J, Clark R. 2004. Application of the RHESSys model to a California semiarid shrubland watershed. JAWRA J Am Wat Resour Assoc 40:575–89.CrossRefGoogle Scholar
  41. Tague C, Seaby L, Hope A. 2009. Modeling the eco-hydrologic response of a Mediterranean type ecosystem to the combined impacts of projected climate change and altered fire frequencies. Clim Change 93:137–55.CrossRefGoogle Scholar
  42. Tague C, Band L. 2004. RHESSys: regional hydro-ecologic simulation system—an object-oriented approach to spatially distributed modeling of carbon, water, and nutrient cycling. Earth Interact 8:1–42.CrossRefGoogle Scholar
  43. van Wijk M. 2011. Understanding plant rooting patterns in semi-arid ecosystems: an integrated model analysis of climate, soil type, and plant biomass. Glob Ecol Biogeogr 20:331–42.CrossRefGoogle Scholar
  44. Wagener T, Wheater HS. 2006. Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty. J Hydrol 320:132–54.CrossRefGoogle Scholar
  45. Ward A, Trimble S, Wolman M. 1994. Environmental hydrology. 2nd edn. Boca Raton: CRC Press.Google Scholar
  46. White MA, Thornton PE, Running SW, Nemani RR. 1997. Parametrization and sensitivity of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interact 4:1–85.CrossRefGoogle Scholar
  47. White M, Greer K. 2006. The effects of watershed urbanization on the stream hydrology and riparian vegetation of Los Penasquitos Creek, California. Landsc Urban Plan 74:125–38.CrossRefGoogle Scholar
  48. Williams A, Still CJ, Fischer DT, Leavitt SW. 2008. The influence of summertime fog and overcast clouds on the growth of a coastal Californian pine: a tree-ring study. Oecologia 156:601–11.PubMedCrossRefGoogle Scholar
  49. Winsemius HC, Savenjie HHG, Bastiaanssen WGM. 2008. Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins? Hydrol Earth Syst Sci 12:1403–13.CrossRefGoogle Scholar
  50. Winsemius H, Schaefli B, Montanari A, Savenije HHG. 2009. On the calibration of hydrological models in ungauged basins: a framework for integrating hard and soft hydrological information. Wat Resour Res 45:W12422.CrossRefGoogle Scholar
  51. Xiang SR, Doyle A, Holden PA, Schimel JP. 2008. Drying and rewetting effects on C and N mineralization and microbial activity in surface and subsurface California grassland soils. Soil Biol Biogeochem 40:2281–9.CrossRefGoogle Scholar
  52. Zhu AX, Hudson B, Burt J, Lubich K, Simonson D. 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Sci Soc Am J 65:1463–72.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Bren School of Environmental Science and ManagementUniversity of CaliforniaSanta BarbaraUSA

Personalised recommendations