Journal of Hydrologic Engineering

When fitting hydraulic models of groundwater flow to pumping test data, Bayesian inference provides a framework for quantifying the posterior uncertainty of aquifer parameters estimated from data and the most likely range of parameters that are consistent with the data. In this study, noise-perturbed drawdown data is measured. For clarity, groundwater models with few parameters are considered and Markov chain Monte Carlo is used to quantify uncertainty of transmissivity, storativity, and leakage parameters. These models exhibit many of the features typically encountered in much higher dimensional computational groundwater models like multimodality, failure of least squares algorithms, and poorly determined parameters. For comparison, Bayesian inference is contrasted with least squares model fitting.

The Saint Venant equations are the equations governing open channel flow when a flood propagates. To obtain an analytical solution, the Saint Venant equations are approximately transformed to a nonlinear diffusion equation. The numerical result of the nonlinear diffusion equation explains the nonlinearity of the open channel flow and overland flow and the resulting asymmetry of the water surface profile. When the Saint Venant equations are numerically applied to flood routing in a finite region and finite time, a downstream boundary condition is necessary. Analytical solution of the appropriate nonlinear diffusion equation does not require the downstream boundary condition. The numerical result of the analytical solution of the approximate nonlinear diffusion equation is found to be suitable for simulation of the original nonlinear differential equation and the Saint Venant equations. The linearized diffusion model is found to be restricted in practical applications, and it cannot express nonlinearity. Attenuation of flood peak is also approximately derived and compared with numerical computations for the analytical solution of the approximate nonlinear diffusion equation, the nonlinear diffusion equation, the Saint Venant equations, and the linearized diffusion equation.

Many cities are located at or near the confluence of streams where availability of water resources may be enhanced to sustain user needs while also posing an increased flooding risk from multiple tributaries. An accurate flood-frequency estimator that models the joint flood potential at a basin confluence is needed. Given that long-term flow observations are often unavailable, estimating flood-frequency at ungauged basin confluences proves challenging. Through the use of copulas, this case study demonstrates how an improved flood-frequency analysis can be performed for stream confluences at Nashville, Tennessee. The approach involves four major steps: initial data quality control, fitting of marginal distributions of tributary peak flows, construction of a suitable copula dependence structure, and identification of flood-frequency at the confluence point based on synthesized peak flows. This case study may help researchers and practitioners develop a better understanding of joint flood-frequency with consideration of upstream dam regulation among several contributing watersheds.

Runoff changes in response to land use and land cover changes in the Huaihe River were drastic in the last few decades and are poorly understood because results from those studies are often equivocal. Hitherto, the methodology to quantify the effects of land use and land cover changes on the runoff response has been mainly the paired catchment approach, which is a blackbox, and usually restricted to small headwater basins where a control can be established. A model-based change-detection approach is developed in this study as an alternative to paired catchment methods. This approach is particularly suited to evaluating effects of land use and land cover changes on the hydrologic response in large to mesoscale watersheds in which suitable control is not possible. The Xinanjiang model was used to evaluate the newly implemented approach in the Dapoling watershed (with an area of $1,640{\mathrm{km}}^2$) in the upper Huaihe river basin. Three schemes were used to examine changes in the data series: (1) Calibration for a period before (or after) changes and simulations of runoff that would have been observed without land use and land cover changes (reconstruction of runoff series); (2) comparison of calibrated parameter values for periods before and after the land use and land cover change; and (3) comparison of runoffs simulated with parameter sets calibrated for periods before and after the landcover change. The results show that, since 1976, the medium-coverage and high-coverage natural forest area has decreased, and the corresponding runoff has declined by nearly 25% from 1976 to 2005 attributable to the continuous expansion of tea gardens and human development. Model parameters, for example, the evapotranspiration coefficient KC, varied considerably from 0.64 to 0.94 attributable to the land use and land cover change within the watershed. This study demonstrates that the modeling approach may be a useful alternative to the paired watershed approach for examining land use and land cover changes and their impact on the runoff.

It is a challenging task for working hydrologists of Myanmar to get information from all gauge and discharge sites during a flood to model the forecast properly. In such a case, the concept of this work is very useful for real-time flood forecasting, particularly when data of all the gauge sites are not available regularly or timely. In that context, one has to rely on some accessible sites to get a workable forecast. Additionally, the best combination of the available data can be selected for making the flood forecast. The study is done for the establishment of a flood forecasting model with maximum efficiency using very little information. Three upstream sites named as Sagaing, Monywa, and Chauk of the Ayeyarwady river are selected as the base station and the downstream Pyay as the forecasting station in this study. The artificial neural network (ANN) multilayered feed forward (MLFF) network along with the Takagi-Sugeno (TS) fuzzy inference model are applied in this paper. The developed model is used to forecast the stage from 1 to 4 days in advance. The values of three performance evaluation criteria, namely the efficiency, the root-mean-square error (RMSE), and the coefficient of correlation, were found to be very good and consistent. The results of ANN and fuzzy models remain at par, but the fuzzy model remains somewhat better than the ANN model. It is determined that for stage forecasting at Pyay, preferably the stage at Sagaing-Monywa-Chauk, Sagaing-Monywa, or Sagaing-Chauk is necessary on a priority basis. Regarding the influence of base stations on forecasting, Chauk remains the best, followed by Sagaing and Monywa. The fuzzy model performs better than the ANN model when the case of peak modeling comes. The study provides a best combination of available data for workable flood forecasting with sufficient lead time for planning and operating relief measures.

Closed basins dotting the landscape of south and central Florida have aroused the interest of numerous hydrologists in the past. Lacking surface water drainage features such as creeks and natural streams, closed basins drain internally through the subsurface during storm events. Little is known, however, about the magnitude and role of subsurface drainage fluxes to deep groundwater aquifers. In this study, instruments were deployed to monitor the surficial and deep aquifer heads at groundwater discharge points in two urbanized closed basins. The purpose of the study was to develop and apply field methods to estimate rapid subsurface runoff following rainfall events and evaluate the effectiveness of internal drainage in urban drainage planning. The two sites were in Hillsborough County, Florida, and one location was a 6-m-deep sinkhole that receives urban runoff from the rest of the basin. The data captured rapid subsurface drainage exceeding $25\mathrm{cm}/\mathrm{day}$ at these discharge locations following rainfall. The two locations collected subsurface runoff rapidly and released the runoff gradually into the deep aquifer system. Results have practical implications for urban drainage in closed basins.

The relationship between air-water interfacial area and capillary pressure under higher water-content conditions is investigated for four natural porous media. The results show that the magnitude of the air-water interfacial area increases with increasing capillary pressure, consistent with the decrease in water saturation. The maximum observed air-water interfacial areas are dependent upon the magnitude of residual water saturation, which itself is condition-dependent. The more well-sorted porous-medium exhibited a greater rate of change of air-water interfacial area with capillary pressure than the more poorly sorted porous media. The observed relationship between air-water interfacial area and capillary pressure was quantified by coupling an empirical equation describing the air-water interfacial area versus water saturation relationship with the van Genuchten equation relating water saturation and capillary pressure. This equation produced reasonable simulations of the measured data.