Research & Funding

Hydrological Modeling

Second-order autoregressive model based likelihood function for calibration and uncertainty analysis of SWAT model

Arpana Rani Datta and Tirupati Bolisetti

Abstract

Second order autoregressive [AR(2)] model has been adopted in the likelihood function to calibrate the Soil and Water Assessment Tool (SWAT) model for the Canard River watershed,Southwestern Ontario, Canada. The Bayesian approach is used for uncertainty analysis of SWAT modeling. The performance of AR(2) model for uncertainty estimation is evaluated by the index called Percentage of observations bracketed by the Unit Confidence Interval (PUCI) for 95% confidence limits. The results are compared with the Simple Least Square (SLS) method of calibration. In the SLS method, the modeling errors are assumed to be uncorrelated. The study reveals that the model parameter uncertainty is high and there exists local optimum values in the parameter space. The reliability of streamflow simulation uncertainty due to parameter uncertainty is increased when AR(2) model is implemented in the calibration process. The comparison of PUCI values between AR(2) method and SLS method show that the estimation of streamflow simulation uncertainty is more reliable in AR(2) model based calibration method. But the lower values of PUCI indicate very high uncertainty in 95% confidence limits estimation. The residuals are observed to have non-normal distribution with non-constant variance. Therefore, appropriate transformation of data might improve the uncertainty estimation. The model structural uncertainty is high for simulating streamflow in the study area during low and high flow periods. Therefore, the study suggests applying separate statistical error models in the likelihood function for representing the modelling errors in low and high flow periods.

Application of variance decomposition approach in the uncertainty analysis of a hydrological model

Arpana Rani Datta and Tirupati Bolisetti

Abstract

Decomposition of uncertainty into individual sources is important for understanding the risks of decisions made under the modelling uncertainty. This paper has applied the predictive uncertainty analysis and variance decomposition (VD) approach for quantifying hydrological modelling uncertainty. The VD analysis is used for quantifying the contribution of various sources of uncertainty to total modelling uncertainty. The goal is to increase the reliability of predictive uncertainty analysis by the inclusion of the results of VD analysis. The approach is evaluated by analyzing uncertainty of the Soil and Water Assessment Tool (SWAT) model for a watershed of Southwestern Ontario, Canada. Three uncertainty analyzing frameworks are employed for quantifying modelling uncertainty by both predictive uncertainty analysis and VD approach. The contribution of parameter uncertainty to total error variance is expressed by the percentage of total variance explained by parameter uncertainty and this contribution is quantified for three uncertainty analyzing frameworks. The contributions of other uncertainties excluding model parameters and precipitation uncertainties to total error variance are quantified by applying the VD approach. The results obtained from predictive uncertainty and VD analyses are observed to be consistent. The underlying hypotheses of each uncertainty framework are also verified for the reliability of the uncertainty analysis.

Can. J. Civ. Eng. 40: 373–381 (2013) dx.doi.org/10.1139/cjce-2012-0337

Automated Linear and Nonlinear Reservoir Approaches for Estimating Annual Base Flow

Arpana Rani Datta; Tirupati Bolisetti; and Ram Balachandar

Abstract

Three automated base flow separation techniques based on linear and nonlinear reservoir approaches are used to identify the seasonal variation of base flow and to quantify the annual base flow for three subwatersheds of the Essex region in Southwestern Ontario, Canada. Significant differences in annual base flow estimated by linear and nonlinear reservoir algorithms are observed. In the nonlinear reservoir approach, the recession parameter is considered to be a seasonally variable parameter. The nonlinear reservoir approach fits streamflow recession better than the linear reservoir approach. The steeper slopes of seasonal flow duration curves in the 90% to 100% flow exceedance range show that the groundwater contribution to streamflow is relatively small in the study area. The precipitation-streamflow relationships show faster response of base flow during the period of high recharge. All of the methods show similar base flow estimation during the period of high evapotranspiration losses. The nonlinear reservoir approach represents the base flow response to precipitation better than the other methods. Therefore, the annual base flow estimated by the nonlinear reservoir approach is considered as the most reasonable estimation for the formulation of water budget of the study area. The method quantifies the occurrence of average annual baseflow as 34% of average annual streamflow.

DOI: 10.1061/(ASCE)HE.1943-5584.0000450. © 2012 American Society of Civil Engineers.

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Climate change impact assessment

Hydrologic modelling to assess the climate change impacts in a Southern Ontario watershed

Masihur Rahman, Tirupati Bolisetti, and Ram Balachandar

Abstract

In Southern Ontario, the Canard River watershed is the largest subwatershed of the Detroit River watershed on the Canadian side. The Soil and Water Assessment Tool (SWAT) model was implemented in the Canard River Watershed to understand the hydrologic regime and assess the impacts of potential future climate change on the hydrology of the watershed. The SWAT model was calibrated and validated against observed streamflow data. The Nash-Suttcliffe efficiencies of the model for monthly streamflow predictions were 0.81 and 0.83, respectively, during the calibration and validation periods. The LARS-WG, weather generator was employed to generate daily future weather data at local scale using the Canadian Regional Climate Model (CRCM) outputs under SRES A2 scenario for the years 2041 to 2070. It was found from the model results that the average annual streamflow could be increased by 12% compared to that over the base period from 1961 to 1990. The results also indicated that streamflow would be increased significantly in spring and winter, but would be decreased in fall due to the projected future climate change scenarios.

Can. J. Civ. Eng. 39: 91–103 (2012)

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Climate change adaptation

Research Title: Sustainable Stormwater Management: Low Impact Development for Urban Climate Change Adaptation

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Non-point source pollutant loading modeling

• Ground Source Heating Systems

• Evaporation from porous surfaces
• Applications

• Chemical Grouting
• Grout Injection for a barrier

Bridge Pier Scour

• Masihur Rahman
• Saranya Jeyalakshmi

• Rahuk Narula (2021)
• Tejith Pogakula (2021)
• Kirankumar Galla (2021)