Journal of Hydrologic Engineering

A simple method, called the hydrograph-based method, is presented for estimating sediment transport during flood events in rivers. The method is characterized by the following features: (1) widely available flow hydrographs are used as primary input data; (2) the shear stress, shear velocity, and friction slope, calculated using a hydrograph, are employed to determine flow depth and velocity for a flood event; and (3) a conventional simple sediment transport formula is utilized in combination with the hydrograph-based flow parameters to estimate sediment transport during a flood event. The new hydrograph-based method is examined using a hypothetical hydrograph and demonstrated through applications to a number of flood events in two lowland rivers. The sediment concentrations predicted by the hydrograph-based method are found to be comparable or more accurate than those from the Hydrologic Engineering Center’s River Analysis System model, demonstrating the efficacy of the new method. The engineering significance of the hydrograph-based method is that it extends the range of applicability of conventional sediment transport formulas, developed for steady flows, to unsteady flood events. The data requirement is thus greatly reduced, and sediment transport computations for flood events are simplified.

The objectives of this study are to evaluate the effects of the check dam system on water redistribution in a small basin in Chinese Loess Plateau by using a practical numerical method for the water budget process. Hydrological and meteorological observations were carried out in the small dam basin ($0.43{\mathrm{km}}^2$). To quantitatively evaluate the effects of the check dam system on the water redistribution, hydrogeological modeling was applied to the check dam system in the basin. The numerical modeling was developed by kinematic wave theory for water flow. The model was verified by the numerical simulation of surface runoff discharge and groundwater level. Numerical calculation was carried out on the water budget of two cases: Case 1, no check dam (the control), and Case 2, with the check dam system in the year 2006. The model showed that the water budget of the surface runoff and groundwater flow in Case 1 was 97.3 and 2.7%, respectively. Conversely, the water budget of the runoff, evapotranspiration, infiltration, and change of water storage in Case 2 was 39.1, 49.3, 11.6, and 0%, respectively. The surface runoff in Case 2 decreased to 37.0% of that in Case 1. Compared with Case 1, two new components—evapotranspiration and infiltration—appeared in Case 2. Moreover, groundwater outflow slightly increased, and the surface runoff decreased. The study indicates that these effects of the check dam system are mainly caused by the formation of the sedimentary field. Evapotranspiration and infiltration are generated from the sedimentary field.

Long-term hydrologic prediction in ungauged basins is one of critical issues in the field of hydrologic calculations. Hydrologic analogy is an important method for long-term hydrologic prediction and hydrologic calculation in ungauged basins. The key point of hydrologic analogy is how to select a reference basin. This paper systematically discusses four popular methods to select a reference basin in China, which include grey analysis, fuzzy analysis, projection pursuit (PP), and set pair analysis. Through a case study in a small basin in Hebei Province, North China, the four methods are applied to select a reference basin. The advantages and disadvantages of these methods are explored. The results indicate that all four methods could be used in selecting a reference basin. It will depend on the familiarity and the experiences of the users with a specific method.

Various applicability criteria have been developed to determine the appropriateness of different hydraulic routing methods (e.g., kinematic, diffusive, or dynamic) a priori (i.e., before any routing is done). However, little has been done to account for hydrologic variability and basin size in these criteria. To account for hydrologic variability and basin size in this paper, an existing applicability criteria for routing methods is linked analytically to a model of peak flow, a simple stochastic representation of rainfall, and relationships of river basin geomorphology. This linkage provides a novel way for understanding and quantifying the interrelationship between basin-scale hydrologic phenomena and routing methods. Using this linkage, the aim of this paper is to explore and assess the applicability of routing methods in strongly coupled systems or headwater basins. To illustrate and assess the linkage, the Illinois River basin is used. From this assessment, the diffusive and dynamic contributions are both found to increase for the general condition of an increasing peak flow and decreasing time to peak at a given basin size. After accounting for scale dependencies, the mean diffusive and dynamic contributions are found to increase with the basin size. The rate of increase seems controlled by the interplay between the slope-area relationship and the scaling of the time to peak, which in turn depends on both the mean rainfall duration and the time of concentration in this paper. In terms of the annual maximum flood series, the dynamic and diffusive contributions show decreasing and increasing trends, respectively, as a function of the basin size for a fixed return period. The latter is found to depend on the scaling of peak floods and the slope-area relationship. For a fixed basin size, both the diffusive and dynamic contributions are found to increase with the return period. The developed framework is able to provide insight at the basin scale into the influence of hydrologic controls arising from climate, runoff, and geomorphology on the applicability of routing methods.

This paper presents a comprehensive review of a fundamental and challenging issue in hydrology: the regionalization of streamflow and its advances over the last two decades, specifically 1990–2011. This includes a discussion of developments in continuous streamflow regionalization, model parameter optimization methods, the application of uncertainty analysis in regionalization procedures, limitations and challenges, and future research directions. Here, regionalization refers to a process of transferring hydrological information from gauged to ungauged or poorly gauged basins to estimate the streamflow. Huge efforts have been devoted to regionalization of flood peaks, low flow, and flow duration curves (FDCs) in the literature, while continuous streamflow regionalization is helpful in deriving each of these variables. Continuous streamflow regionalization can be conducted through rainfall-runoff models or hydrologic model–independent methods. In the former case, model parameters are used as instruments to transfer hydrological information from gauged to ungauged basins, whereas the latter case transfers streamflow directly through data-driven methods. According to the reviewed regionalization studies, streamflow regionalization has been done mostly through hydrologic models, whereas the focus of these studies is on identifying the best methods to transfer the model parameters. Conceptual rainfall-runoff models, such as Hydrologiska Byråns Vattenbalansavdelning (HBV) and Identification of Unit Hydrographs and Component Flows from Rainfall, Evaporation and Streamflow Data (IHACRES) have emerged as the most frequently used models in this category. Physiographic attributes (e.g., catchment area, elevation, and slope of basins or channels) and meteorological information (e.g., daily time series of rainfall and temperature) are the most commonly used in the regionalization studies. Diversity in catchment physical attributes and climatic variability produces different performances for each regionalization method’s application in various regions. However, overall, spatial proximity and physical similarity have shown satisfactory performance in arid to warm temperate climate (e.g., Australia) and regression-based methods have been preferred in warm temperate regions (e.g., most European countries). Similarly, in cold and snowy regions (e.g., Canada) spatial proximity and physical similarity approaches seemed to be good options among the hydrologic model–dependent methods. Hydrologic model–independent methods have been applied only in few cases, and the results have indicated that in warm temperate regions linear and nonlinear regression methods perform well.

A direct analytical approach is proposed to study the turbulent surface water flow on a slope. Both unplanted ground and grassed ground are investigated by dividing the flow field into two layers—homogenous water layer/vegetation layer and permeable soil layer, respectively. The soil and vegetation layers are regarded as porous media, and Biot’s poroelastic theory is applied. The effect of vegetation on flow velocity is also analyzed. As the Reynolds stresses become larger, the turbulent strength increases, and the velocity distributions become more uniform. The average velocity is compared with that calculated by Manning’s formula and the kinematic-wave equation. The result shows that the average velocity using the kinematic-wave equation is the highest, and the present solution is the smallest for both cases.

Watershed modeling requires accurate estimates of precipitation; however, in some cases it is necessary to simulate streamflow in a watershed for which there is no precipitation gauge records within close proximity to the watershed. For such cases, we propose an approach to estimating watershed-scale precipitation by combining (or fusing) gauge-based precipitation time series with radar-based precipitation time series in a way that seeks to match input precipitation for the watershed model with observed streamflow at the watershed outlet. We test the proposed data fusion approach through a case study where the Soil and Water Assessment Tool (SWAT) model is used to simulate streamflow for a portion of the Eno River Watershed located in Orange County, North Carolina. Results of this case study show that the proposed approach improved model accuracy ($E=0.60$; $R^2=0.74$; $\mathrm{PB}=-10.2$) when compared to a model driven by gauge data only ($E=0.50$; $R^2=0.54$; $\mathrm{PB}=-25.5$) or radar data only ($E=0.33$; $R^2=0.61$; $\mathrm{PB}=-13.7$). While this result is limited to a single watershed case study, it suggests that the proposed approach could be a useful tool for hydrologic engineers in need of retrospective precipitation estimates for watersheds that suffer from inadequate gauge coverage.

The influence of urban development on the volume of storm water runoff and on water temperature, and the associated impact on streams in urban watersheds, has generated a need for tools to predict the temperature of urban storm water runoff, particularly for the assessment of future urban development. To address this need, the Minnesota Urban Heat Export Tool (MINUHET) model was developed to simulate the flow of heat and storm water through a small urban watershed for a rainfall-runoff event. The tool includes process-based hydrological and thermal models for runoff from developed, undeveloped, or vegetated land uses, for various components of drainage networks, and for some best management practices such as detention and infiltration ponds, storm water vaults, and rock cribs. The primary output of MINUHET is a time series of the flow rate and temperature of runoff, which can be used to determine the heat content of the runoff at the outlet of the modeled watershed and aid in the assessment of the thermal impact on receiving waters. This paper provides an overview of the hydrologic and heat transfer processes simulated by MINUHET and presents a case study in which MINUHET is applied to a 5.06-hectare (12.5-acre) residential neighborhood in Plymouth, Minnesota for which runoff flow and temperature data were available for a series of rainfall events. The model was manually calibrated to several parameters, including saturated hydraulic conductivity, Manning’s roughness, and thermal diffusivity and heat capacity of asphalt. Runoff flow rate, runoff temperature, and heat export (heat content of the runoff relative to a specified reference temperature) were accurately simulated for a total of four rainfall events using the calibrated parameters. A sensitivity analysis of the model revealed that simulations were especially sensitive to dew point (rainfall) temperature, rainfall rate and saturated hydraulic conductivity, and to thermal properties and thickness of asphalt. These results illustrate the importance of both data quality and a truthful representation of the modeled watershed in producing accurate simulations with MINUHET.

In many watersheds around the world where the importance of assessing a watershed as a whole is overlooked, new water resources projects are designed solely based on historic flow data. Such projects might fail to operate satisfactorily when designed without considering the uncertainties associated with future hydrologic changes. In this study, a system dynamics model (SDM) is developed to quantify the potential hydrologic impacts of future developments in parts of the Karkheh River basin, Iran, and assess their effects on hydroelectricity generation of existing and projected hydropower plants. Results indicate that upstream development could reduce future annual energy production by 254 GWh. Interbasin water transfer from the nearby Sirvan River basin to the Karkheh River basin was also investigated as a viable option to increase future energy production. Simulation results revealed that an average $88\mathrm{GWh}/\mathrm{year}$ increase in electricity production can be achieved per ${100\times 10}^6{\mathrm{m}}^3$ of annual environmental flow release out of transferred water from Sirvan to Karkheh River basin.

Flood events consist of flood peaks and flood volumes that are mutually correlated and need to be described by multivariate analysis methods, of which the copula functions are most desirable. Until now, the multivariate flood frequency analysis methods based on copulas does not consider the historical floods or paleological information. This may underestimate or overestimate the flood quantiles or conditional probabilities corresponding to high return periods, especially when the length of gauged record data series is relatively short. In this paper, a modified inference functions for margins (MIFM) method is proposed and used to estimate the parameters of both marginal distribution and joint distribution with incorporation of historical information. The conditional probabilities of flood volumes given that the peak discharge exceeding various values were derived. The Three Gorges reservoir (TGR) in China was selected as a case study. The bivariate flood quantiles were obtained based on bivariate return period and compared with current univariate design values. It is shown that the proposed method provides an alternative way for multivariate frequency analysis with historical information.

Increasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e., Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Niño–southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April–October) and the water year, which is from October of the previous year to September of the current year for a period from 1955–2006. A data-driven model, least-square support vector machine (LSSVM), was developed that incorporated oceanic atmospheric oscillations to increase the streamflow lead time. Based on performance measures, predicted streamflow volumes are in agreement with the measured volumes. Sensitivity analyses, performed to evaluate the effect of individual and coupled oscillations, revealed a stronger presence of coupled PDO, NAO, and ENSO indices within the basin. The AMO index shows a pronounced effect when individually compared with the other oscillation modes. Additionally, model-forecasted streamflow is better than that for climatology. Overall, very good streamflow predictions are obtained using the SVM modeling approach. Furthermore, the LSSVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back-propagation models, artificial neural network, and multiple linear regression. The current paper contributes in improving the streamflow forecast lead time, and identified a coupled climate signal within the basin. The increased lead time can provide useful information to water managers in improving the planning and management of water resources within the Kaidu River Basin.

Risk assessment of lake flooding is studied through uncertainty propagation of the rainstorm control model. The failure risks of insufficient drainage capacity are assessed using the classic reliability approach and time-dependent reliability approach separately. The rainstorm control model consists of three submodels: a design rainstorm model to determine design storm hyetograph, a rainfall-runoff model to generate inflow hydrograph of the lake, and a lake operation model to simulate the storage volume of lake. Monte Carlo simulation with stochastic rainfall is implemented to provide random input for the combined model. The effect of uncertainty propagation on the three submodels is examined. The developed methodology is demonstrated with a case study, and results reveal that it offers an appropriate technique to assess flooding risks and consider the effect of uncertainty propagation on urban storm water modeling in designing a new drainage plan.

In this paper, an attempt has been made to investigate the potential impact of climate change on runoff generation at two agricultural watersheds. Climate change and key future signals of its variability were assessed using general circulation models (GCMs). Given that GCMs are operating at coarser resolution, the statistical downscaling model was applied to reduce large-scale atmospheric variables into localized weather variables from the Bjerknes Center for Climate Research–Bergen Climate Model 2.0 and Commonwealth Scientific and Industrial Research Organization (CSIRO) Mark (MK) 3.0 GCM outputs. As precipitation variables are composed of biases, both linear and power transformation bias correction methods were applied to obtain bias-corrected daily precipitation. Bias-corrected daily precipitation and temperature variables were used to simulate runoff for current and future climate scenarios using the Soil and Water Assessment Tool (SWAT) model. The statistical downscaling model, followed by bias correction, effectively reproduced the current weather variables. Increased extreme daily precipitation and temperature events prevailed for future scenarios. Dry spell length increased during the driest months and remained stable during wet seasons. There was no defined future precipitation change pattern. At two watersheds in the Rift Valley Lakes Basin in Ethiopia, the simulated runoff varied from $-4$ to 18% and $-4$ and 14%, respectively. Simulated average annual runoff showed slight variation between the GCMs at both watersheds.