Browsing by Author "Mishra, Anurag"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Estimating Uncertainty in HSPF based Water Quality Model: Application of Monte-Carlo Based Techniques
    Mishra, Anurag (Virginia Tech, 2011-07-28)
    To propose a methodology for the uncertainty estimation in water quality modeling as related to TMDL development, four Monte Carlo (MC) based techniques—single-phase MC, two-phase MC, Generalized Likelihood Uncertainty Estimation (GLUE), and Markov Chain Monte Carlo (MCMC) —were applied to a Hydrological Simulation Program–FORTRAN (HSPF) model developed for the Mossy Creek bacterial TMDL in Virginia. Predictive uncertainty in percent violations of instantaneous fecal coliform concentration criteria for the prediction period under two TMDL pollutant allocation scenarios was estimated. The average percent violations of the applicable water quality criteria were less than 2% for all the evaluated techniques. Single-phase MC reported greater uncertainty in percent violations than the two-phase MC for one of the allocation scenarios. With the two-phase MC, it is computationally expensive to sample the complete parameter space, and with increased simulations, the estimates of single and two-phase MC may be similar. Two-phase MC reported significantly greater effect of knowledge uncertainty than stochastic variability on uncertainty estimates. Single and two-phase MC require manual model calibration as opposed to GLUE and MCMC that provide a framework to obtain posterior or calibrated parameter distributions based on a comparison between observed and simulated data and prior parameter distributions. Uncertainty estimates using GLUE and MCMC were similar when GLUE was applied following the log-transformation of observed and simulated FC concentrations. GLUE provides flexibility in selecting any model goodness of fit criteria for calculating the likelihood function and does not make any assumption about the distribution of residuals, but this flexibility is also a controversial aspect of GLUE. MCMC has a robust formulation that utilizes a statistical likelihood function, and requires normal distribution of model errors. However, MCMC is computationally expensive to apply in a watershed modeling application compared to GLUE. Overall, GLUE is the preferred approach among all the evaluated uncertainty estimation techniques, for the application of watershed modeling as related to bacterial TMDL development. However, the application of GLUE in watershed-scale water quality modeling requires further research to evaluate the effect of different likelihood functions, and different parameter set acceptance/rejection criteria.
  • Thumbnail Image
    Generalized Likelihood Uncertainty Estimation and Markov Chain Monte Carlo Simulation to Prioritize TMDL Pollutant Allocations
    Mishra, Anurag; Ahmadisharaf, Ebrahim; Benham, Brian L.; Wolfe, Mary Leigh; Leman, Scotland C.; Gallagher, Daniel L.; Reckhow, Kenneth H.; Smith, Eric P. (2018-12)
    This study presents a probabilistic framework that considers both the water quality improvement capability and reliability of alternative total maximum daily load (TMDL) pollutant allocations. Generalized likelihood uncertainty estimation and Markov chain Monte Carlo techniques were used to assess the relative uncertainty and reliability of two alternative TMDL pollutant allocations that were developed to address a fecal coliform (FC) bacteria impairment in a rural watershed in western Virginia. The allocation alternatives, developed using the Hydrological Simulation Program-FORTRAN, specified differing levels of FC bacteria reduction from different sources. While both allocations met the applicable water-quality criteria, the approved TMDL allocation called for less reduction in the FC source that produced the greatest uncertainty (cattle directly depositing feces in the stream), suggesting that it would be less reliable than the alternative, which called for a greater reduction from that same source. The approach presented in this paper illustrates a method to incorporate uncertainty assessment into TMDL development, thereby enabling stakeholders to engage in more informed decision making.
  • Thumbnail Image
    Nutrient and Bacterial Transport From Agricultural Lands Fertlized With Different Animal Manures
    Mishra, Anurag (Virginia Tech, 2003-12-04)
    The increase of animal agriculture coupled with excess manure production, and the reduced availability of land has led to the over application of animal manure on agricultural fields. The excessive application of manure is responsible for nutrient and bacterial pollution of downstream waterbodies. Manure application based on the crop phosphorus (P) requirements has been recommended as a viable method to reduce nutrient pollution. A plot scale study was conducted to measure the loss of nutrients and bacterial transport in runoff from cropland treated with poultry litter, dairy manure and inorganic fertilizer according to the P requirements of the crop. Three simulated rainfall events were conducted 1, 2 and 35 days after planting of corn. Highest P and N concentrations were observed in the runoff from plots treated with poultry litter, followed by dairy manure and inorganic fertilizer. The poultry litter treated plots exhibited highest concentrations of bioavailable P in the runoff, compared to all other treatments. The P from poultry litter treated plots was also mostly in the soluble form, which underscores the need to control the runoff from cropland in order to decrease the P losses from the poultry litter treated fields. The edge of the field nutrient concentrations observed in this study were high enough to cause severe to moderate eutrophication problems in downstream waterbodies unless they are diluted. In general, nutrient concentrations were lower during the second simulated event, compared with those from the first event. A significant reduction in the nutrient concentrations of runoff was observed from the second to the third simulated event for all the treatments. This reduction was attributed to the loss of nutrients by natural rainfall-runoff events during the time period between the second and the third simulated rainfall event, plant uptake of nutrients, sorption and leaching processes. The indicator bacteria analyzed in the present study were fecal Coliform (FC), Escherichia Coli (E.Coli) and Enterococcus (ENT). The bacterial concentrations reported in the runoff for the first and second simulated events were 104 to 105 times higher than the federal and state limits for primary contact recreation waters. No significant effect of treatments was observed on the bacterial concentrations in runoff. The highest concentrations were observed for FC, followed by ENT and EC in the runoff. The ratio of bacteria removed in runoff to the bacteria applied also followed the above trend. The concentrations of bacteria generally increased from the first to second simulated event; unlike the nutrients. However, the bacterial concentrations dropped significantly from second to the third simulated rainfall event to the levels lower than those designated for primary contact recreation water limits. This reduction was attributed to the washing away of bacteria by the heavy rainfall-runoff events in the period between second and third simulated rainfall events and the die-off of bacteria. The results reported from this study suggest that the manure application based on crop P requirements can also be a significant source of nutrient pollution and should be coupled with other best management practices (BMPs) also to reduce nutrient pollution. The results also suggest that the manure treated cropland can be a source for significant indicator bacterial pollution and appropriate BMPs are required to mitigate their effect.
  • Thumbnail Image
    Two-phase Monte Carlo simulation for partitioning the effects of epistemic and aleatory uncertainty in TMDL modeling
    Mishra, Anurag; Ahmadisharaf, Ebrahim; Benham, Brian L.; Gallagher, Daniel L.; Reckhow, Kenneth H.; Smith, Eric P. (ASCE, 2018-10-29)
    A two-phase Monte Carlo simulation (TPMCS) uncertainty analysis framework is used to analyze epistemic and aleatory uncertainty associated with simulated exceedances of an in-stream fecal coliform (FC) water quality criterion when using the Hydrological Simulation Program-FORTRAN (HSPF). The TPMCS framework is compared with a single-phase or standard Monte Carlo simulation (SPMCS) analysis. Both techniques are used to assess two total maximum daily load (TMDL) pollutant allocation scenarios. The application of TPMCS illustrates that cattle directly depositing FC in the stream is a greater source of epistemic uncertainty than FC loading from cropland overland runoff, the two sources specifically targeted for reduction in the allocation scenario. This distinction is not possible using SPMCS. Although applying the TPMCS framework involves subjective decisions about how selected model parameters are considered within the framework, this uncertainty analysis approach is transparent and the results provide information that can be used by decision makers when considering pollution control measure implementation alternatives, including quantifying the level of confidence in achieving applicable water quality standards. © American Society of Civil Engineers (ASCE).