Journal of Hydrologic Engineering

Fractures and barriers exist in hydrocarbon reservoirs. It is essential to evaluate the influence of fractures and barriers on porous flow in many research domains like petroleum engineering and disposal of nuclear waste. Fractures can play the role of highly conductive channels in the flow fields, while barriers can act as the walls blocking fluids. The extended finite-element method (XFEM) is effective in addressing the porous flow problems involving arbitrary geometries of fractures and barriers that do not need to conform to the mesh. This paper uses the XFEM to deal with the discontinuity problems of porous flow caused by highly permeable fractures, lowly permeable fractures, and impermeable barriers. The authors further improve the previous fracture-matrix transfer flow equations by differentiating the pressure at the fracture center from the average pressure in the fracture cross section, and finally combine the fracture-matrix transfer flow equations with a boundary layer theory to better model fluid flow in the case of anisotropically permeable fractures. Numerical results demonstrate that the XFEM is effective and applicable in solving the discontinuity problems of porous flow due to barriers and fractures. In addition, the introduction of the boundary layer can improve the accuracy in modeling anisotropically permeable fractures in which the tangential permeability is far larger than the normal permeability.

In this study, a new approach is developed to compute groundwater flow rate and hydraulic head distribution in a two-dimensional discrete fracture network (DFN) in which both laminar and inertial flows coexist in individual fractures. The potential errors in both flow rate and hydraulic head, from the approach based on uniform flow assumption in relation to DFN and flow characteristics, are examined. The cubic law is used to calculate hydraulic head distribution and flow behaviors in fractures in which flow is laminar, whereas Forchheimer’s law is used to quantify inertial flow behaviors. The Reynolds number is used to distinguish flow characteristics in individual fractures. The combination of linear and nonlinear equations is solved iteratively to determine flow rates in all fractures and hydraulic heads at all intersections. Applying the cubic law in all fractures regardless of actual flow conditions overestimates the flow rate when inertial flow may exist whereas applying Forchheimer’s law indiscriminately underestimates the flow rate in the network. The contrast of apertures of large and small fractures in the DFN has significant influence on the potential error of using only the cubic law or Forchheimer’s law. Both the cubic law and Forchheimer’s law simulate similar hydraulic head distributions, as the main difference between these two approaches lies in predicting different flow rates. Fracture irregularity does not significantly affect the potential errors from using only the cubic law or Forchheimer’s law if the network configuration remains similar. The relative density of fractures does not significantly affect the relative performance of the cubic law or Forchheimer’s law.

The performance of a hydrological model depends strongly on the calibration procedure, and in particular on the goodness-of-fit measure used. It is widely recognized that traditional goodness-of-fit measures such as the Nash-Sutcliffe efficiency (NSE) are biased toward securing a particular aspect of a hydrograph (high flows, in the case of the NSE). This paper proposes a new strategy for model calibration that evaluates the ability of the model to simulate the complete shape, timing, and variability of the observed hydrographs. The methodology is based on the comparison of the simulated and observed whole annual hydrograph as a single curve using the functional data analysis (FDA) framework. FDA is a recent statistical framework that considers observations as curves or functions. The hydrograph is a particular example of such functions. The proposed approach is applied to calibrate the CEQUEAU model on the Lac St-Jean drainage basin (Quebec, Canada) and is compared with a traditional approach using NSE. Both calibrations yield to similar results for high flows, with NSE of 0.89 during calibration and 0.94 during the validation period. The results show an improvement for winter low-flow bias by 10% over the traditional calibration using NSE. Moreover, the application of the functional Student’s test suggests that winter flows simulated by the model calibrated with NSE are significantly different, whereas flows simulated by the model calibrated with the proposed approach are accurate for almost all periods of the year.

This study develops a new analytical model to simulate the infiltration and runoff with a crust surface. The new model consists of two parts: (1) an infiltration capacity model using a linear rate-moisture relation (RMR) similar to the flux-concentration relation (FCR) for homogeneous soil, but considering the changing saturation over time at the interface and unsaturated flow in the subsoil layer; and (2) an infiltration and runoff model under rainfall events based on the time compression approximation (TCA) using the developed infiltration capacity in part (1). Its performance is evaluated by comparing to the solutions of the Richards equation and two field experiments under different rainfall patterns. Results show that the new model can well predict the infiltration capacity and wetting front profiles with slight underestimation of infiltration. The modeled runoff closely agrees with field measurements. The new model extends the FCR and TCA with the prediction of unsaturated flow. The model can use any form of soil hydraulic model and is computationally stable without numerically solving the Richards equation, which facilitates large-scale predictions of the rainfall-runoff process.

Analyses of hydrologic forecasts from the US National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) (NOAA/NWS) Ohio River Community Hydrologic Engineering Center River Analysis System (HEC-RAS) Model for an approximate 12-month hindcast model validation period using observed precipitation and an independent five-month operational, real-time forecast period using quantitative precipitation forecast (QPF) are presented. The significant role the model played during forecast operations over the major April–May 2011 flooding period along the lower Ohio River and Mississippi River is also discussed. The model is one of many models used within the flood early warning system (FEWS)-based NWS community hydrologic prediction system (CHPS) at the NWS Ohio River Forecast Center (OHRFC). The model was developed as a cooperative effort involving the OHRFC, USGS, and USACE. The physical scope of the model, which is unique in real-time hydrologic forecasting, includes 23 locks and dams on the Ohio River and tributaries, with numerous bridges, off-channel storage areas, and lateral structures, such as levees. The model comprises over 3,100 cross sections, spanning approximately 3,200 km of continuous modeled reach. HEC-RAS model–based hydrologic forecasts show improvement in terms of reduced peak stage bias, mean absolute error, and root mean square error when compared to observed USGS river stages over lumped-parameter hydrologic routing methods that are currently used operationally. Overall, verification of forecast river stages, spanning a broad range of river levels, including the timing of hydrograph peaks, is comparable between the HEC-RAS and legacy model–based routings. Validation analyses indicate improvement over a previous model implementation following the inclusion of several additional tributary inflows within the 1D hydrodynamic model framework and recalibration. Operational complications are also discussed, such as problems related to handling real-time gate operations on the Ohio River mainstem, major tributary inflows that are insufficiently modeled, and use of lateral inflows from uncalibrated Sacramento Soil Moisture Accounting (SAC-SMA) hydrologic model subbasins that flow directly into the Ohio River mainstem.

The kinematic wave theory has been a widely accepted technique for modeling overland flow. Although it is recognized that overland flow is nonlinear, linear approximations have nonetheless been applied and found to be satisfactory on small impermeable areas such as those found in the urban environment as well as on steep slopes. Linear approximation also holds for floods, which may explain the popularity of the unit hydrograph method. This study derives analytical solutions of one-dimensional (1D) kinematic wave equations for overland flow on a plane subjected to moving storms using the method of characteristics. Two cases of storm movement are considered: a storm moving downstream and a storm moving upstream. The 1D solutions are compared with other analytical solutions and with the laboratory simulation. The proposed analytical solution, despite the simplifications embedded therein, suggests that it can be useful for engineering purposes. It clearly shows the flow characteristics during the rising limb, at equilibrium (constant discharge achieved), and during the recession limb of the hydrograph. Also, it can be used to quantify the error resulting from the assumption of a stationary storm covering the entire watershed, which seldom happens.

The groundwater flow velocity might be temporally variable instead of being a constant as most analytical solutions of solute transport in the subsurface commonly assume. This study investigates the impact of transient flow on solute transport in the subsurface with time-dependent groundwater flow velocity. This study is based on the analysis of breakthrough and leaching processes of solute transport in a one-dimensional (1D) setting. As an example, the flow velocity is assumed to follow an exponential function of time and eventually approaches its steady-state value. Analytical solutions of such models are obtained using the Laplace transform assuming a homogeneous media and Fickian type of dispersion, and the impacts of different parameters of the temporally exponential function of the groundwater flow velocity on solute transport are thoroughly analyzed. The results indicate that a larger power index in the temporally and exponentially decreasing velocity equation results in a faster solute transport process. A sensitivity analysis of parameters shows that the solute transport is most sensitive to the initial flow velocity for the case with exponentially increasing velocity, whereas it is most sensitive to the final steady-state velocity for the case with exponentially decreasing velocity. The general conclusion is that groundwater flow transiency usually has significant impacts on the solute transport process and should not be overlooked.

Flood impacts threaten the socioeconomic conditions of peoples’ lives in the Mekong River Basin. In this study, the rainfall-runoff-inundation (RRI) model, capable of simulating rainfall runoff and flood inundation simultaneously, was used to enhance understanding of flooding characteristics in this region by using grid-satellite-based rainfall. Input data for the simulation included HydroSHEDS topographic data, APHRODITE precipitation data, MODIS land use data, and river cross sections. Moreover, the shuffled complex evolution developed at The University of Arizona (SCE-UA) global optimization method was integrated with the RRI model to calibrate sensitive parameters. In this study, the flood event in 2000 has been selected in the Mekong River Basin. The simulation results were compared with observed discharges at monitoring stations along the river and an inundation map from Landsat 7 satellite imagery and the Mekong River Commission (MRC) data. The results indicated good agreement between the observed and simulated discharges, for example, with Nash-Sutcliffe efficiency $(\mathrm{NSE})=0.86$ at the Stung Treng Station. The model predicted inundation extent with a success rate $(\mathrm{SR})=67.50\%$ and modified success rate $(\mathrm{MSR})=74.53\%$ compared with satellite Landsat 7, and $\mathrm{SR}=68.27\%$ and $\mathrm{MSR}=75.11\%$ compared with MRC data. Therefore, the RRI model was successfully used to simulate a large-scale inundation flood event in 2000 using a grid precipitation data set in the Mekong River Basin. However, the underestimation might be due to the uncertainties of input data, river geometry, the large scale of the basin, coarse resolution of topographic data, and error in remote sensing image in detecting the flood extent.

The availability of complete, continuous, and reliable groundwater data is essential for preparing a regional-scale groundwater model. Poor sampling of groundwater level is often encountered. Satellite-derived data can provide possible solution to such a problem. Better coverage and timely sampled data can be easily obtained from satellite-derived data. However, getting direct and exact groundwater-level measurements from satellite data is not possible. Satellite data, hence, are used as latent variables (or secondary attributes) to predict groundwater level. A geostatistical approach (cokriging) was brought into use to predict groundwater level by using the available secondary attributes. The study was carried out in the Indo-Gangetic Basin by using Gravity Recovery and Climate Experiment (GRACE) anomaly data and Tropical Rainfall Measuring Mission (TRMM) precipitation data as secondary attributes. The study was carried out at the finest resolution available. Groundwater-level measurements measurements are available at seasonal scale at point support, and the analyses were performed for the premonsoon season of 2005 and 2008. Four performance indicators—mean absolute error (MAE), bias, root-mean-square error (RMSE), and coefficient of variation of root-mean-square variation [CV(RMSE)]—determined the best result obtained from the analyses. The cokriging analyses were carried out for different neighborhood search radii. Four different neighborhood search radii were chosen for the present study on the basis of range of variogram of the primary attribute: (1) less than the range of variogram; (2) range of variogram; (3) more than the range of variogram; and (4) infinite radius. Different neighborhood radii were chosen to visualize any changes in predictability of the different models prepared. The best prediction was obtained for the premonsoon of 2005 with very low error values at infinite neighborhood search radius [$\mathrm{bias}=1.61\mathrm{m}$; $\mathrm{MAE}=1.77\mathrm{m}$; $\mathrm{RMSE}=2.35\mathrm{m}$; and $\mathrm{CV}(\mathrm{RMSE})=0.32$]. With a neighborhood search radius of less than the range of semivariogram, missing values were obtained for the premonsoon of both 2005 and 2008. The overall prediction was found to be satisfactory, suggesting very good predictability of the geostatistical approach.

Sediment load in the Ganga river is among the highest in the world. Effective discharge for suspended sediment transport at 15 stations on the Ganga river has been computed using a mean and rating curve approach in the present study. Discharge and sediment data were procured from Central Water Commission, India. The present study shows that discharges of moderate magnitude and frequency are responsible for maximum sediment transport in the Ganga river. About 90% of the total sediment is transported by discharges with a return period of less than three years on annual maximum series at most of the stations. Bankfull discharge is manifold the effective discharge at most of the stations, except Gandhighat and Hathidah. The ratio of bankfull discharge and effective discharge varied in a wide range of 1.13–3.02. However, effective discharge at these stations had a strong correlation with a discharge of 1.5-year return interval. Application of the rating curve approach led to a systematic underestimation or overestimation of total sediment load, which may result in overestimation of effective discharge. For large rivers, the frequency of data collection, i.e., daily or 10-daily, does not affect the effective discharge and has been found to differ only by 0.5%.

A double constraint method is proposed to analyze pumping tests in a confined aquifer. An expression for estimation of aquifer transmissivity is derived by minimizing the sum of squared errors in the Darcy equation and a similar expression for estimation of aquifer storativity by minimizing the sum of squared errors in the water balance equation. The feasibility of the method is illustrated by two case studies and the results are compared with classical methods for pumping test analysis. The results indicate that the proposed methodology is accurate, robust, easy to implement and execute, and simpler and less time consuming than classical optimization methods.