Journal of Hydrologic Engineering

Reconstruction of complex braided river bathymetry is important for supporting hydrodynamic simulation and understanding river morphological processes. To our knowledge, existing methods generate and interpolate channel-fitted coordinate lines using the measured cross-sectional data to reconstruct river bathymetry. Nevertheless, in these methods, the generation of channel-fitted lines obeys the law of splines or a set of equations without considering the laws of river dynamics. Moreover, it is difficult to interpolate bathymetry using the channel-fitted lines when dealing with complex braided rivers. This paper introduced a novel method to fill the gap. Briefly, in the introduced method, an initial bathymetry is interpolated and a velocity field is calculated using a set of simplified two-dimensional (2D) shallow-water equations. Then, streamlines are generated using the velocity field data, and the elevations of the nodes on the streamlines are interpolated using the measured cross-sectional data. Finally, the bathymetry of the domain is interpolated using the streamlines and the measured elevation points or contour lines at the sandbars and floodplains. For demonstration purpose, the introduced method was applied to a $20\text{-}\mathrm{km}$ section in the middle reaches of the Yellow River (China) with many branches and sandbars. The reconstructed bathymetry of the domain was investigated in terms of their geometrical shape and hydrodynamic performance (including inundation area and water level). When compared with the measured water level, the hydrodynamic results of the reconstructed bathymetry showed acceptable accuracy. In addition, because the channel-fitted method is widely applied to interpolate the river bathymetry, we compare the channel-fitted method with the introduced method in discussion section.

Porous basins have been recommended for stormwater onsite infiltration and filtering purposes. The effectiveness of a porous basin is often evaluated by its long-term storm runoff capture rate. The key factors for estimating the runoff capture rate include local average rainfall event-depth, tributary watershed’s imperviousness, basin’s storage volume, and drain time. In this study, two sets of empirical formulas are developed from 34 long-term hourly rainfall records observed in the continental US. These empirical formulas are employed to produce the synthetic runoff volume and event capture curves, according to the basin’s drain time. All synthetic runoff capture curves exhibit similar characteristics to the exponential nonexceedance probability distribution. It is concluded that the basin’s storage volume increases as the drain time increases, and the exponential curve serves as the limiting case when the basin has a prolonged drain time due to debris clogging. It is recommended that the two sets of new empirical formulas be used to size the water quality basin if the basin’s operation is warranted; otherwise, the simplified exponential distribution should be employed as a conservative approach.

Climate change is likely to increase the frequency and intensity of flooding. Transportation infrastructure is vulnerable to extreme precipitation because it was designed using frequency, duration, and intensity relationships that do not represent future climate. Therefore, there is a need for a better understanding of how future climate can disrupt roadways and bridges. This study proposes evaluating flood impacts on the transportation infrastructure with two novel metrics: the ${\mathrm{HFR}}_R$ (roads) and ${\mathrm{HFR}}_B$ (bridges). These metrics were based on the hydrological footprint residence (HFR), which represents the dynamics of the inundation area and its duration. The Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) and the Hydrologic Engineering Center River Analysis System (HEC-RAS) one-dimensional/two-dimensional (1D/2D) models were used to calculate the HFR metrics. These metrics were estimated for the transportation infrastructure of San Antonio, Texas, for 24-h 100- and 500-year storms for representative concentration pathway (RCP) 4.5 and 8.5 scenarios. Results show that climate change will increase flood impacts in the city. The new metrics presented a larger relative increase with climate change when compared to traditional metrics, such as maximum flooded areas. This study highlights the importance of using duration components to evaluate flood impacts on transportation infrastructure.

Estimating flow resistance is essential for the hydraulic analysis of a river and the evaluation of conveyance in a specific flow condition. Under bed-load transport conditions, the resistance to the flow in an open channel is different from fixed-bed condition and requires a distinct method for its evaluation. The geometric and hydraulic parameters influence flow resistance characteristics in the mobile bed load. In the present study, a wide range of experimental flume data sets are investigated to derive the dependency of the dimensionless parameters on the flow resistance under mobile bed-load conditions. The five most important dimensionless parameters, such as relative submergence depth, bed slope, aspect ratio, Reynolds number, and Froude number, are suggested because they show a unique relationship to the dependent parameter. An artificial neural network (ANN) model to predict the flow resistance is proposed by considering these independent parameters as the input parameters. To verify the strength of the model, the performances of previous researchers’ models were also evaluated and compared with the present work by considering a wide range of data sets. It is found that the previous models can be used for a specific range of data sets only, whereas the proposed ANN-based model is capable of performing well for a wide range of geometric and hydraulic conditions of a channel.

The ideal framework for water infrastructure design in any region relies on rainfall characteristics of that region, which is defined through rainfall intensity-duration-frequency (IDF) curves. The current IDF curves are based on historical observations of precipitation. However, with the help of numerical models, more up-to-date IDF curves can be developed to reflect the current precipitation regime. Here, a weather research and forecasting (WRF) model was applied to produce the precipitation data for Arizona from 1950 to 2017. A total of 20 weather forecasting scenarios were simulated by changing the microphysics schemes to improve precipitation forecasting accuracy. The National Severe Storm Laboratory (NSSL) scheme with cloud condensation nuclei (CCN) improved the coefficient of determination by 10% and was selected as the optimum forecasting scenario. The IDF curves were then constructed based on the modeled data and annual maximum series analysis for each climate division in Arizona. The comparison between updated IDF curves and historical IDF curves showed that incorporating up-to-date precipitation data resulted in lower rainfall intensities for short durations.

Soil erosion by water is a serious and continuing environmental problem. In the present study the Water Erosion Prediction Project (WEPP) model was used in the Dal catchment to simulate sediment yield. The study was conducted with the objective of calibrating and validating WEPP at the plot scale to examine its suitability for conservation planning. The WEPP model was calibrated using modified input parameters; further, the results were validated using a different data set. The soil erodibility (${\mathrm{K}}_{\mathrm{d}}$) and critical shear stress (${\tau }_{\mathrm{c}}$) values vary from 1.70–2.7 and 0.27–0.76 Pa, respectively, for the study area. The average annual sediment yield (hillslope 1 and hillslope 2) was $6.759\mathrm{t}{\mathrm{ha}}^{-1}$ as against the measured value of $5.623\mathrm{t}{\mathrm{ha}}^{-1}$. The modeled values of sediment yield showed a close agreement with field observed values. The model also produced temporal variations of sediment yield on hillslope areas. The correlation coefficients were found to be 0.93 and 0.945, RMSE were 0.065 and $0.06\mathrm{t}{\mathrm{ha}}^{-1}$, and Nash-Sutcliffe efficiency indexes were 0.84 and 0.85 for the validation and calibration periods, respectively. The coefficient of determination (${\mathrm{R}}^2$) values were found to be 0.89 and 0.90 during calibration and validation of the model, respectively. Results indicated that the WEPP model simulated sediment yield from steep slope conditions and in forest conditions as well. The WEPP model was found to be an effective tool for simulating hydrological and soil erosion processes in watersheds having scarce data.

The conventional method of univariate flood frequency analysis based solely on peak flow ($Q$) overlooks the influence of other characteristic flood variables, such as the accumulated volume ($V$) of the flood and the duration ($D$) of flood events. A copula-based multivariate model that represents the joint behavior of these dependent flood variables could aid in computing joint return periods of flood events in tropical, seasonal rivers of India. In connection with the potential locations of high flood risk among west-flowing rivers, multivariate flood frequency analysis was performed on the Bharatapuzha, Periyar, and Chaliyar Rivers of the state of Kerala, India. A comparison of univariate return periods with multivariate return periods reveals that the intersection of flood variables corresponding to a 20-year univariate return period yields a trivariate return period of $91\mathrm{years}$ at Bharatapuzha and $144\mathrm{years}$ at Periyar and Chaliyar. The return period by the union of such flood variables is $10\mathrm{years}$. The choice of flood variables and their combination depend on the problem at hand. Additionally, basinwise confluence flood frequency models are built with the peak flow at each stream as the random variables show their spatial interdependencies using conditional probabilities and return periods. The copula-based flood coincidence risk model captures the temporal aspect of the co-occurrence of flood peaks in a basin’s streams. The co-occurrence of annual flood peaks between the stream pairs of the Bharatapuzha, Periyar, and Muvathapuzha basins is the highest toward the end of July with probabilities of approximately $2.2\times {10}^{-4}$ (at the Kumbidi and Mankara stations), $3\times {10}^{-4}$, and $1\times {10}^{-3}$, respectively. A trio of copula-based multivariate flood frequency, confluence flood frequency, and flood coincidence risk models could be used to design safe and economic hydrologic infrastructure.

In order to eliminate the wavelet boundary effect caused by the limitation of data length, a univariate predictive extension method based on phase space reconstruction and least squares support vector machine (LSSVM) is proposed. Compared with the traditional extension methods, whether in large scale or small scale, the predictive extension method can effectively eliminate the boundary effect of the wavelet transform. The predictive extension method is used to extend the average annual flow of Zamashik, Qilian, Yingluoxia, and Zhengyixia hydrological stations in the Hei River. Then, wavelet transform multiscale analysis is performed on the extended time series. The wavelet variance curve of the runoff time series after extending is obviously smoother than the unprocessed wavelet variance curve, and to a certain extent, the pseudo period caused by the wavelet boundary effect is eliminated. Analysis results show there are 10-year and 36-year cycles at Zamashik station, 10-year and 25-year cycles at Qilian station, 16-year and 38-year cycles at Yingluoxia station, and 9-year, 21-year, and 30-year cycles at Zhengyixia station.

Continental and global dynamic hydrological models have emerged recently as tools for large-scale analyses. One such tool is a dynamic process-based rainfall-runoff and water quality model called Hydrological Predictions for Environment (HYPE). This study presents and compares historical simulations of runoff and sediment concentrations for three nested-model domains using global, continental (Europe), and national (Sweden) catchment-based HYPE applications. Future impacts on runoff, soil moisture, and aridity from changing climate were assessed using the global and continental HYPE applications with three coupled model intercomparison project phase 5 (CMIP5) global climate models (GCMs). Simulated sediment concentrations varied considerably among the nested models in spatial patterns due to different data sources, whereas runoff values were more similar. Regardless of the variation, the global model was able to provide information on climate change impacts comparable to those from the continental and national models for hydrological indicators. Global hydrological models are thus valuable tools for, e.g., first screenings of climate change effects and detection of spatial patterns. Comparison across nested domains demonstrates the significance of scale that needs to be considered when interpreting the impacts alongside with model performance.

Lakes are sensitive to environmental changes, and an example of this change is the decreased water level in the Yamzho Yumco Lake (YYL, in southern Tibetan Plateau), which is opposite of the reported expansion in most other lakes of the Tibetan Plateau. In this study, we report a high-resolution dataset of daily monitored water levels from 1974 to 2010 in the YYL, which was used to elucidate annual and seasonal variations of the lake water level. These data are coupled to the stable isotope signals in the lake water and to a water balance model to provide an overall picture of factors and processes affecting the lake. The data revealed an annual average rate of 0.12 m per year lowering of the lake water level, but there was a relative increase in the summer and autumn seasons. It was found that a large amount of precipitation and low evaporation were primary reasons for increasing periods of the lake water level. The extensive glacier melting process driven by a sharp rise in temperature is another key factor for the increasing period between 1997 and 2004. The annual general water level decline before 1996 is attributed to the slow glacier melting rate and reduced precipitation, while a drastic decline of the water level after 2005 could be related to water leakage at the lake bottom, enhanced by a thawing of the permafrost. This process is driven by increasing soil temperatures and human activity. Finding out the causes of the YYL shrinkage trend provides vital implications for the management of water resources in the Tibetan plateau cold regions.