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Assessing uncertainties in surface water security: An empirical multimodel approach
Author(s) -
Rodrigues Dulce B. B.,
Gupta Hoshin V.,
Mendiondo Eduardo M.,
Oliveira Paulo Tarso S.
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr016691
Subject(s) - uncertainty analysis , resampling , residual , computer science , range (aeronautics) , sensitivity analysis , environmental science , hydrograph , propagation of uncertainty , econometrics , water resources , probabilistic logic , streamflow , drainage basin , mathematics , ecology , materials science , cartography , algorithm , artificial intelligence , composite material , biology , geography , simulation
Various uncertainties are involved in the representation of processes that characterize interactions among societal needs, ecosystem functioning, and hydrological conditions. Here we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multimodel and resampling framework. We consider several uncertainty sources including those related to (i) observed streamflow data; (ii) hydrological model structure; (iii) residual analysis; (iv) the method for defining Environmental Flow Requirement; (v) the definition of critical conditions for water provision; and (vi) the critical demand imposed by human activities. We estimate the overall hydrological model uncertainty by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km 2 agricultural basin within the Cantareira water supply system in Brazil. Together, the two‐component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multimodel framework and the uncertainty estimates provided by each model uncertainty estimation approach. The range of values obtained for the water security indicators suggests that the models/methods are robust and performs well in a range of plausible situations. The method is general and can be easily extended, thereby forming the basis for meaningful support to end‐users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision‐making process.