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Uncertainty of hydrologic simulation, and its impact on the design and the effectiveness of water conservation structures
, Vema V.K., Chaubey I.
Published in Springer
2020
Volume: 34
   
Issue: 7
Pages: 973 - 991
Abstract
The uncertainties associated with the simulation models are often ignored in operational hydrology. While many methods are available for evaluation of simulation uncertainty, most of them focus on construction of prediction bands, which alone may not be sufficient to make effective decisions. This is a serious concern in watershed management planning, especially in cases where the models are uncalibrated due to unavailability of observations. This study addressed uncertainty in hydrologic modeling, and its consideration in check dam design decisions. Size of the check dams were determined using a simulation–optimization framework by considering dual objectives of maximizing water availability for agriculture and minimizing the adverse effects on downstream reaches. The optimizer suggested distinct Pareto-optimal-front for different parameter sets of the model (in turn resulting in different simulations), indicating the influence of parametric uncertainty on the design. An analysis of the optimal solutions suggested varying check dam sizes (0.5–1.5 m) for similar objective function value, which plausibly indicate an economic impact. Nonetheless, the effectiveness of the structure (in terms of simulated wet and dry period lengths) did not exhibit significant variability across the designs (average wet period length of > 100 days). The median of the streamflow ensemble provided satisfactory performance (> 100 days wet period length and only 11–25% reduction of flow to downstream) and could be a viable choice for implementation. The results suggest that parametric uncertainty that is propagated to prediction uncertainty significantly influences the final design decisions and calls for careful assessment prior to implementation. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetStochastic Environmental Research and Risk Assessment
PublisherData powered by TypesetSpringer
ISSN14363240
Open AccessNo