Yamen Hoque

ABSTRACT The importance of uncertainty inherent in measured calibration/validation data is frequently stated in literature, but it is not often considered in calibrating and evaluating hydrologic and water quality models. This is due to... more

ABSTRACT The importance of uncertainty inherent in measured calibration/validation data is frequently stated in literature, but it is not often considered in calibrating and evaluating hydrologic and water quality models. This is due to the limited amount of data available to support relevant research and the limited scientific guidance on the impact of measurement uncertainty. In this study, the impact of considering measurement uncertainty during model auto-calibration was investigated in a case study example using previously published uncertainty estimates for streamflow, sediment, and NH4-N. The results indicated that inclusion of measurement uncertainty during the autocalibration process does impact model calibration results and predictive uncertainty. The level of impact on model predictions followed the same pattern as measurement uncertainty: streamflow &lt; sediment &lt; NH4-N; however, the direction of that impact (increasing or decreasing) was not consistent. In addition, inclusion rate and spread results did not indicate a clear relationship between predictive uncertainty and the magnitude of measurement uncertainty. The purpose of this study was not to show that inclusion of measurement uncertainty produces better calibration results or parameter estimation. Rather, this study demonstrated that uncertainty in measured calibration/validation data can play a crucial role in parameter estimation during autocalibration and that this important source of predictive uncertainty should be not be ignored as it is in typical model applications. Future modeling applications related to watershed management or scenario analysis should consider the potential impact of uncertainty in measured calibration/validation data, as model predictions influence decision-making, policy formulation, and regulatory action.

Research Interests:
Engineering and Mathematical Sciences

Risk indices such as reliability–resilience–vulnerability (R–R–V) have been proposed to assess watershed health. In this study, the spatial scaling behavior of R–R–V indices has been explored for five agricultural watersheds in the... more

Risk indices such as reliability–resilience–vulnerability (R–R–V) have been proposed to assess watershed health. In this study, the spatial scaling behavior of R–R–V indices has been explored for five agricultural watersheds in the midwestern United States. The study was conducted using two different measures of spatial scale: (i) the ratio of contributing upland area to area required for channel initiation (FA), and (ii) Strahler stream order. It was found that R–R–V indices do change with spatial scale, but a representative watershed-specific threshold FA value exists for these indices to achieve stable values. Scaling with Strahler stream order is feasible if the watershed possesses a tree-like stream network. As an example of anthropogenic influences, this study also examined the role of BMPs placed within an agricultural watershed via a cost-effective optimization scheme on the evolution of R–R–V values with scale. While the placement of BMPs achieved reductions in concentrations and/or loads of constituents, they may not significantly change watershed risk measures, but are likely to cause significant reduction in vulnerability. If primarily upland BMPs are placed in a diffuse manner throughout the watershed, there might not be a significant change in the scaling behavior of R–R–V values.

Publication Date: 2014

Publication Name: Ecological Indicators

Publication Date: Nov 2013

Publication Name: Water Quality, Exposure and Health

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Publisher: ncbi.nlm.nih.gov

Publication Date: 2012

Publication Name: Journal of environmental management

Research Interests:
Resilience, Water quality, Environmental Management, Environmental Monitoring, Risk assessment, and 9 moreVulnerability, Multidisciplinary, Uncertainty, Water Supply, Drinking Water, Reliability, Water Quality, Risk Assessment, and Water Movements

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Ground water management models require the repeated solution of a simulation model to identify an optimal solution to the management problem. Limited precision in simulation model calculations can cause optimization algorithms to produce... more

Ground water management models require the repeated solution of a simulation model to identify an optimal solution to the management problem. Limited precision in simulation model calculations can cause optimization algorithms to produce erroneous solutions. Experiments are conducted on a transient field application with a streamflow depletion control management formulation solved with a response matrix approach. The experiment consists of solving the management model with different levels of simulation model solution precision and comparing the differences in optimal solutions obtained. The precision of simulation model solutions is controlled by choice of solver and convergence parameter and is monitored by observing reported budget discrepancy. The difference in management model solutions results from errors in computation of response coefficients. Error in the largest response coefficients is found to have the most significant impact on the optimal solution. Methods for diagnosing the adequacy of precision when simulation models are used in a management model framework are proposed.