Journal of Hydrologic Engineering

This paper reviews 14 prevalent watershed models for their capabilities, credibility, and suitability in total maximum daily load (TMDL) development and implementation. Brief descriptions of the models, including sources, capabilities, and applicability are presented. General information such as intended watershed and simulation types, simulated outputs, uncertainty analysis capabilities, graphical user interface, and availability are also presented. Mathematical bases of the hydrologic and water quality simulations, which indicate credibility, expected performance, and accuracy, and dictate model features (e.g., structure, input data, and parameters) are presented. Routing procedures, the backbones of the models, are compared and ranked. The Gridded Surface and Subsurface Hydrologic Analysis (GSSHA) and MIKE SHE (Système Hydrologique Européen) models rank high on overland and Storm Water Management Model (SWMM) on channel/pipe flow routing as the most accurate for representation of the physical processes and also the most numerically complex. The Generalized Watershed Loading Function (GWLF) and Spreadsheet Tool for the Estimation of Pollutant Load (STEPL) rank at the bottom on both the aspects. The rest of the models are in between, although the Dynamic Watershed Simulation Model (DWSM) is computationally efficient among kinematic wave models. Notable strengths and limitations of the models for TMDL development and implementation are presented. All these provide valuable information on the models, not readily available in a concise form elsewhere, to compare and help determine relative credibility and make informed selections for TMDLs and similar studies. Future research should focus on further comparisons of the models based on other key aspects such as simulation capabilities of processes, uncertainty analysis, required resources, and performances on watersheds followed by developing better models or improving existing ones by strengthening the weaknesses found. Robust physically based algorithms, uncertainty analysis capabilities, and use of remotely sensed and high-resolution data are recommended to be part of the model improvements.

The choice of a model for total maximum daily load (TMDL) development for impaired water bodies depends mainly on the modeling objectives and system complexity. System complexity and modeling objectives, in turn, determine the required model complexity. Nonlinked or stand-alone complex models or linked models are generally selected for complex systems that consist of large watersheds with urban and rural areas and with a network of flow-controlled water impoundments (e.g., lakes and reservoirs) and estuaries. As an example, we present herein three case studies to demonstrate the role that complex nonlinked and linked models play in the development of complex TMDL studies. Case 1 is the Cannon River watershed, which uses a nonlinked complex model known as Hydrological Simulation Program—FORTRAN (HSPF), which is the core watershed model of the Better Assessment Science Integrating Point and Non-Point Sources (BASINS). Case 2 is the Chesapeake Bay watershed, which uses linked models: air-shed, watershed loading, and estuary models. Note that Case 2 uses the Chesapeake Bay Phase 5.3 Community Watershed Model, which is a version of the HSPF model. Case 3 is the Sougahatchee Creek watershed, which uses three linked models: Loading Simulation Program in C++ (LSPC), Water Quality Simulation Program (WASP), and Environmental Fluid Dynamics Code (EFDC). The results obtained from the case study reports show an absence of quantitative model performance metrics for Case 3 and limited performance results for Cases 1 and 2. For Case 2—a linked model case—model performance is only available for the loading model but not for the other linked models. The use of complex nonlinked models or linked models is not a guarantee of a good modeling practice by itself unless the models are supported by performance metrics, such as Nash–Sutcliffe efficiency (NSE) values that are greater than or equal to 0.65. Only Case 2 reported simulated water quality constituent NSE values for the watershed loading model, and reported values were less than zero. The low predictive performances gleaned from the case study reports can be attributed to inadequate system representation, model structure uncertainty, and poor data quality. Unless complex stand-alone and linked models are supported by accurate system representation and good quality input data, their application for TMDL development may not be scientifically defensible.

Urban communities use hydrologic models to plan for and assess the effectiveness of stormwater control measures. Although emphasis is placed on soils as permeable surfaces that regulate the rainfall-runoff process, representative soil hydrologic parameters for urban areas are rare. The extent to which measured and commonly simulated hydrologic data may differ is also largely uncharacterized. As part of the US EPA urban soil assessment, infiltration and drainage rates were measured in 12 cities, and the authors compared these measured data to estimates generated from the EPA National Stormwater Calculator (NSWC), United States Department of Agriculture (USDA) Soil Survey Geographic Database (SSURGO), and USDA Rosetta. The analysis highlights the overall lack of soil hydrologic data for many cities in the NSWC and SSURGO and show that common prediction algorithms for infiltration and drainage poorly represent urban soil hydraulics. Paired comparison of field-measured values and model-estimated values resulted in root-mean-square errors ranging from 23 to $173\mathrm{mm}/\mathrm{h}$. These findings are presented in the context of planning for effective stormwater and wastewater management practices, and the need for confirming simulation results with site-specific field data.

Sensitivity analysis (SA) plays a vital role in hydrologic and water quality (H/WQ) model development and reliability assessment. This paper performed the first formal global SA (GSA) evaluation of the routing submodule of the Watershed Assessment Model (WAM), a spatially distributed high-dimensional H/WQ model, using a two-step GSA approach consisting of sequential application of the elementary effects (EE) parameter screening and the variance-based technique of Sobol’. The Taylor Creek Nubbin Slough basin (S-191 basin) in southcentral Florida is taken as a test case. Multioutput EE analysis successfully screened unimportant parameters, deeming the rest (27 of 297) likely influential. Sobol’ GSA with these 27 parameters showed that for flow and nutrient outputs, most of the model variability was attributed to only 12 parameters, fewer than 5% of the original parameter set. An approach to extract additional useful information about the output–parameter relationships, interactions, and monotonic/nonmonotonic behavior from the EE analysis with minimal additional calculations was explored. The proposed criteria for identifying parameter interactions from EE analysis showed good comparison with Sobol’ analysis results.

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.

In this study, multiscale characterization of hydroclimatic time series was performed using the Hilbert-Huang Transform (HHT) approach. First, to investigate the possible teleconnections of monthly inflows into Hirakud reservoir in India, and two potential large-scale climate oscillations, El Niño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO), the corresponding time series were decomposed using the multivariate empirical mode decomposition (MEMD) method, and then running correlation analysis, namely time-dependent intrinsic correlation (TDIC) analysis, was applied. TDIC analysis showed that there exists a long-range negative correlation between EQUINOO and reservoir inflow in most of the time scales, whereas a positive relation prevails between ENSO and inflow at the biannual time scale in particular. TDIC analysis further proved that the association between large-scale climate oscillations and reservoir inflow is not always unique but associated with localized reversals in the nature of correlation in the time domain; also, both the nature and strength of the association vary with time scales. Stemming from this finding, this paper proposes an innovative approach that combines MEMD and stepwise linear regression (SLR) methods for prediction of reservoir inflows. In this approach, the different modes obtained through MEMD are predicted independently by SLR fitting, considering the statistically significant inputs at respective time scales, and the final summation of the predicted modes gives the monthly inflows. A statistical performance evaluation based on multiple criteria showed that the proposed MEMD-SLR approach displayed better performance, with a high $R^2$ of 0.905 and low values of root mean square error (RMSE) and bias for validation data over the EMD-SLR, M5 model tree, and multiple linear regression models for inflow prediction, including significant improvement in prediction of high inflows.

Recent studies propose different metrics for hydrologic design under nonstationarity, such as the effective return level, the expected waiting time (EWT)-based return level, the expected number of events (ENE)-based return level, the design life level (DLL), and the minimax design level (MDL). In this study, we formalize a method to test the credibility of such metrics in (1) developing precipitation intensity-duration-frequency relationships, (2) at-site design flood estimation, and (3) regional flood frequency analysis. The test relies on asymptotic normality assumptions and applies to the mean of the estimated return levels. Our results show that, based on historical records, point estimates or means of nonstationary design quantiles in all three applications are not significantly different from their traditional stationary counterparts when the associated uncertainties are considered. For example, in the application of at-site design flood estimation, although the estimated stationary 100-year flood is 32% and 29% lower in magnitude than the EWT- and ENE-based nonstationary 100-year return level, respectively, such a difference is not statistically significant. Further, enhanced model complexity is found to result in increased uncertainty in design levels under nonstationarity to at least twice the range obtained from a stationary analysis.

Flooding causes serious loss of life and property, especially in highly urbanized regions. The driving mechanisms of flooding should be investigated in detail and permanent precautions should be taken. One of the latest fatal events occurred in Ayamama River in Istanbul on September 9, 2009. As a result of this flood, 32 people died and 50 people were injured. In addition, many structures were damaged, which influenced the economy significantly. In this study, a hydrodynamic model of the watershed was generated by using the Watershed Modeling System (WMS) (version 9.1) and the US Environmental Protection Agency’s Storm Water Management Model (EPA SWMM) (version 5) in light of observations after the September 2009 flood event. The flood depth and volume belonging to the flood event were calculated using the EPA SWMM model. In addition, the flood hazard map was produced using the WMS. The validation of the models was made based on field observations and event reports. The results obtained by the two models showed that the flood depth reached approximately 11 m at the critical regions, which are highly prone to flooding on the Ayamama watershed. Moreover, the flood extent reaches about 1,200 m downstream of the watershed. The related flood hazard map was produced and assessed to predict future events and flood management. Moreover, the capabilities of the two different computer models are evaluated by comparing the output of the models. It is concluded that an especially high number of constructions of the floodplain in Ayamama region act as a big threat on flooding.

Seepage from a tailings dam associated with a copper mine located in Ganzhou, Jiangxi Province, China, may affect surrounding groundwater quality during mine operations. A groundwater environmental impact assessment (EIA) is an important aspect of China’s current EIA system, and is used to protect groundwater quality. Water-quality monitoring and analyses were performed to assess the impacts of this mining site. To understand the current status of groundwater quality in the area, water-quality sampling was conducted at monitoring points in the tailings-reservoir area. In addition to measuring physicochemical parameters, such as pH, 11 major cations and anions (${\mathrm{Na}}^{+}$, ${\mathrm{K}}^{+}$, ${\mathrm{Ca}}^{2+}$, ${\mathrm{Mg}}^{2+}$, ${\mathrm{NH}}_3-\mathrm{N}$, ${\mathrm{Cl}}^{-}$, ${\mathrm{F}}^{-}$, ${\mathrm{HCO}}_3^{-}$, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{CO}}_3^{2-}$, and ${\mathrm{Cr}}^{6+}$) and eight heavy metals (Cu, Pb, Zn, Cd, Hg, Mn, As, and Ag) were analyzed using the standard index method. A majority of the groundwater-quality parameters met the current China Class III Groundwater Quality Standards. The tailings dam under abnormal conditions could damage the dam body and cause damage to the antiseepage layer. Based on this study, the design construction and operation of the tailings pond is predicted to meet requirements ensuring that the tailings pond will not affect surrounding groundwater quality.