Journal of Hydrologic Engineering

To assess the impacts of rainfall and land-use changes on soil erosion, the Liusha River watershed in China was chosen as a case study for its representative topography and climate. Investigations were conducted using the soil and water assessment tool (SWAT) model and maps of land-use and soil types, together with meteorological data from six gauging stations. The Nash-Sutcliffe efficiency $E_{ns}$ (0.84 and 0.75), correlation coefficient $R^2$ (0.93 and 0.90), and percent bias (PBIAS) ($+13.36$ and $-34.88\%$) values attained during the calibration (1985–1994) and validation periods (1995–2006), respectively, indicate that the SWAT can be used in this area to simulate the average annual soil erosion under different land-use scenarios. Two scenarios of land-use change, namely the conversion of farmland to forest and the conversion of grassland to farmland, were used to estimate runoff and sediment yield under typical rainfall conditions. In both land-use change scenarios, the value of sediment-yield change caused by land-use change increased with rain intensity, while the rate of sediment-yield change remained unchanged. Of the different soil types investigated, the greatest variation in sediment yield caused by land-use change occurred where loess is the underlying surface. Red soil shows the next greatest variation, followed by black and finally purple soil. The sensitivity of the four soils to the rate of sediment-yield variations is the opposite, with purple soil being the most sensitive, followed by red then black soils, and loess displaying the least sensitivity. The results of this study provide a reference that can assist in the evaluation of benefits of soil conservation projects in different regions and inform investment decisions.

The spatial variability of soil hydraulic properties has to be considered to provide realistic predictions of unsaturated flow and transport at field scale. Steady unsaturated flow through randomly heterogeneous soils is analyzed and upscaled in this study. Numerical experiments of unsaturated flow have been performed on single realizations of unsaturated soil parameters generated by using three-dimensional turning band random generator. In the first part of the study a linear relation for unsaturated hydraulic conductivity as a function of soil suction ($\psi$) given by $K=K_s{e}^{\beta \psi }$ is used, with constant β and saturated hydraulic conductivity ($K_s$) as perfectly correlated random space functions. In the first part of the study, linear conductivity relation given by Gardner is used, with Gardner’s constant ($\beta$) and saturated conductivity ($K_s$) as perfectly correlated random space functions. The steady flow fields generated are analyzed to explore the effect of variability of the product ($\beta$): the pore-size distribution parameter whose reciprocal represents the characteristic capillary length ($h_{\text{cap}}$) and the correlation length (${\lambda }_Z$) of the generated soil properties. A set of one-dimensional simulations are performed, with various values of the product $\beta {\lambda }_Z$ ratio of correlation length to capillary dispersion length (${\lambda }_z/h_{\text{cap}}=1$, $»1$ and $«1$) used to compare the moisture distribution tendency. The resulting steady pressure fields are compared and analyzed. In some cases, the soil behaves as homogeneous, and in other cases as stratified. The second part of the study seeks to identify simple upscaling laws for block-scale nonlinear constitutive relationships under high tension, with the parameters of their measurement scale counterparts represented by the van Genuchten model. Random fields are generated to represent layering of perfect and imperfectly stratified soils, and numerical simulations are performed in flows that are perpendicular and parallel to stratification. Upscaling of hydraulic conductivity curve $K(\psi )$ and moisture retention curve $\theta (\psi )$ is performed for perfectly stratified and imperfectly stratified soils, and anisotropy is also studied for both types of layering considered. It is demonstrated that the upscaled nonlinear curves can be represented by simple exponential relations and/or by linear relations obtained by piecewise linearization. The linear relations obtained can be expressed in equivalent terms that can be used in analytical solutions to directly obtain effective hydraulic conductivity values.

A critical need for farmers, particularly those in arid and semiarid areas is to have a reliable, accurate and reasonably accessible means of estimating the evapotranspiration rates of their crops to optimize their irrigation requirements. Evapotranspiration is a crucial process because of its influence on the precipitation that is returned to the atmosphere. The calculation of this variable often starts from the estimation of reference evapotranspiration, for which a variety of methods have been developed. However, these methods are very complex either theoretically and/or because of the large amount of parameters on which they are based, which makes the development of a simple and reliable methodology for the prediction of this variable important. This research combined three concepts such as cluster analysis, multiple linear regression (MLR), and Voronoi diagrams to achieve that end. Cluster analysis divided the study area into groups based on its weather characteristics, whose locations were then delimited by drawing the Voronoi regions associated with them. Regression equations were built to predict daily reference evapotranspiration in each cluster using basic climate variables produced in forecasts made by meteorological agencies. Finally, the Voronoi diagrams were used again to regionalize the crop coefficients and calculate evapotranspiration from the values of reference evapotranspiration derived from the regression models. These operations were applied to the Valencian region (Spain), a Mediterranean area which is partly semiarid and for which evapotranspiration is a critical issue. The results demonstrated the usefulness and accuracy of the methodology to predict the water demands of crops and hence enable farmers to plan their irrigation needs.

Few river systems remain unaltered by engineered water management structures. Yet research investigating the interdependence between natural and engineered components of a hydrologic system within a modeling framework is limited. Most current models either focus on the natural system, incorporating only a portion of the engineered structures and often excluding elements like reservoir operations, or focus on the engineered system, simplifying the temporal and spatial variations of the surface water/groundwater flow system. The objective of this work is to link an object-oriented model of surface water operations to a physically based, fully integrated surface/subsurface hydrologic model to capture the effects of water management decisions on the groundwater and surface water flow systems. The capabilities of the new linked modeling framework are demonstrated in the heavily managed Lower Republican River Basin (LRRB) in portions of Nebraska and Kansas in the central United States. This area of the basin contains two storage reservoirs and a network of surface water canals from seven irrigation districts, in addition to thousands of diversions from groundwater and surface water irrigators. The linked model was able to reasonably represent the surface and groundwater flow conditions and demonstrated the interdependence between the surface water operations and the groundwater/surface water flow system. The temporal variability of groundwater/surface water interactions has a significant impact on reservoir operations and streamflow. This work demonstrated how integrating surface water operations and groundwater/surface water flow components into a model improves the representativeness of the simulated results and better captures the temporal and spatial variations in hydrologic processes occurring within the domain.

It is well known that multimodel averaging can considerably improve hydrological model simulation skill. However, the need to set up and run different models can be a time-consuming task. This work expands on the classic multimodel averaging approach by feeding models with different climate datasets and treating each version as a unique model in the ensemble. Three hydrological models and four climate datasets were combined to produce multimodel/multi-input, multimodel/monoinput and monomodel/multi-input combined flows using a weighting scheme that minimizes the root mean square error (RMSE) between the combined and observed hydrographs. The results show that model averaging improves performance significantly and that the proposed multi-input averaging provides higher skill than multimodel averaging. A combination of all models run with all datasets (12 members in total) produced the best results with the averaged hydrograph being more accurate than any single member on 70% of the catchments. The median Nash-Sutcliffe Efficiency (NSE) metric value increase was 0.07 overall in validation under the multimodel/multi-input framework, all while providing improved simulation reliability.

The study discusses future runoff projection and uncertainty arising from different choices of global circulation models (GCMs), different scenarios of future emission, and different statistical downscaling methods for two watersheds in south Arkansas and north Louisiana. Three ensemble averaging methods, simple model averaging (SMA), reliability ensemble averaging (REA), and hierarchical Bayesian model averaging (HBMA), are used to compare the projected ensemble average and variance of future runoff derived from hydroclimate models. Contributions of individual sources of uncertainty are quantified by the analysis of variance (ANOVA) method and the HBMA method. An ensemble of 78 climate change projections, derived from 13 GCMs from the coupled model intercomparison project phase 5 (CMIP5), two representative concentration pathways (RCPs), and three statistical downscaling methods, are used as the forcing input to the hydrologic evaluation of landfill performance model version 3 (HELP3) to project future runoff. The result shows that HBMA performs slightly better than SMA and REA in reproducing the historical mean annual cycle of runoff. Fall runoff would increase and winter and spring runoff would decrease toward the late century. Uncertainty analysis by both ANOVA and HBMA concludes that GCMs are the major source of uncertainty, followed by the downscaling methods and then the emission scenarios. GCM uncertainty is more significant in spring and summer than other seasons. Downscaling method uncertainty shows increases in fall and winter. Emission scenario uncertainty is shown to be significant only in winter and spring for the late century.

The objective of this research is to reduce scale effects on surface runoff routing processes in coarse-resolution hydrologic models to produce event hydrograph characteristics (peak and timing) similar to those obtained from fine-resolution models. Herein, this concept is referred to as upscaling. The developed upscaling approach combines statistical and physics-based techniques and is applied to the Ohio River Basin using a synthetic 2-year, 24-h runoff experiment. Cumulative distribution functions (CDFs) for surface flow path travel times based on 90-m-resolution digital elevation model (DEM) data and conceptualized model units representing individual catchments in a model are matched by adjusting surface roughness along simulated hillslopes. The travel time CDF for individual catchments based on 90-m DEM data are approximated using the beta distribution to facilitate applications in large watersheds. Nine model resolutions are considered: 1 (reference model resolution), 3.2, 10, 32, 100, 320, 1,000, 3,200, and $10{,000\mathrm{km}}^2$, where model resolution corresponds to the threshold area used to define the underlying river network and catchment boundaries. Simulated hydrographs at the outlet for the eight coarser model resolutions have peak discharges and times to peak that are similar to those obtained from the reference model resolution. To match hydrograph characteristics, surface roughness along the hillslope flow paths are adjusted by, on average, $-85$ to $+94\%$, where the positive values are for the $3.2\text{-}{\mathrm{km}}^2$ resolution and the largest reductions are for the ${\mathrm{10,000}\text{-}\mathrm{km}}^2$ resolution.

When a large reservoir is fed by many tributaries, the measurement of reservoir inflow is a difficult task. For a hydroelectric reservoir, the estimation of natural inflow is required to optimize hydropower production without compromising environmental and infrastructure safety. This can be accomplished using the water balance equation (WBE). An important input of this equation is water levels measured from the reservoir of interest. However, the water-level measurements obtained from limnimetric gauges are affected by wind. Water-surface fluctuations caused by this meteorological forcing are carried over in the WBE. Consequently, natural inflow’s signal may become noisy. On an hourly or daily basis, the wind-induced errors in natural inflow may become significant enough to make a reasonable estimation of real-time values impossible. This paper uses unsteady- and steady-state hydrodynamic modeling to estimate the WBE short-term errors originating from wind effect in the natural inflow of the reservoir. A hydrodynamic model calibrated with in situ observations has been used for this purpose in the Gouin Reservoir case study in Quebec, Canada. These results show that using a steady-state approximation can underestimate the effect of wind on daily estimates of the natural inflow because of the neglected seiches and inertial forces related to wind effect.

A case study examining Bayesian techniques for assessing parameter and predictive uncertainty of semiarid flash flood events is presented here. The focus is on testing a fully distributed rainfall-runoff model (i.e., AFFDEF) linked with Markov chain Monte Carlo (MCMC) samplers to simulate four semiarid flash flood events with varying rainfall durations ($<24\mathrm{h}$) and amounts ($>20\mathrm{mm}$). MCMC samplers showed consistent behaviors with the a priori assumption and successfully improved performances on complex and multivariate search problems of semiarid flood simulation over the Abol-Abbas watershed, Iran. Analysis suggests that parameters associated with infiltration and interception capacity along with the contributing area threshold for the digital river network were the key model parameters and were more influential on the shape and volume of the flood hydrograph. Model predictive uncertainty was heavily dominated by error and bias in the soil water storage capacity, which reflects inadequate representation of the upper soil zone processes in the AFFDEF distributed model. Overall, the modeling results revealed that a fat-tailed Gaussian distribution using the standard least-squares (SLS) error assumption yielded improved estimates of parameter and predictive uncertainty for the semiarid flood events. This case study emphasizes the importance of proper statistical representation of the residual error distribution as a basis to improve parameter and predictive uncertainty.

The Hurst–Kolmogorov (HK) behavior in geophysical series is the product of fluctuations occurring simultaneously at several time scales. In streamflow time series, the clustering of similar events characterizes this behavior and brings forth the peculiarly persistent structure of such series. Because the Hurst exponent expresses the intensity of the HK behavior in time series, it is essential to preserve it in modeling hydrological series. This paper addresses an approach for multisite monthly streamflow generation in a HK framework. A deseasonalized symmetric moving average (SMAD) model is proposed, in which the seasonal components are removed from the streamflow series prior to their submission to the model. A case study applied at six gauging stations in the Iguazu River Basin, southern Brazil, is presented. SMAD synthetic series are compared with a multisite contemporaneous autoregressive integrated moving average (CARIMA) model. The results show that both models exhibited similar performance regarding marginal distribution statistics. However, the SMAD model provided a more precise reproduction of the individual persistence structures and cross-correlations among sites. It also generated estimates with higher uncertainty.

Improved understanding of linkages of hydrogeological systems with human interactions is required for sustainable development of groundwater resources in arid and semiarid regions. In this paper, an integrated approach coupling geographical information system (GIS) and a finite-difference method–based numerical model was used to carry out groundwater flow modeling of the Aynalem well field aquifer system in Ethiopia to determine impacts of external hydraulic stresses on the groundwater regime and to investigate the long-term effects of current pumping practices for municipal water supply to the city of Mekelle, Ethiopia. It incorporates stream–aquifer interaction, water budgeting, and long-term groundwater level prediction. Results show good agreement between simulated and observed hydraulic heads with a correlation coefficient of 0.97. The well yield potential would decrease because of the alarming rate ($1.9\mathrm{m}/\text{year}$) of decline in groundwater level. An approximately 38-m decline in groundwater level is expected by the end of year 2022, and thus no additional wells are recommended for groundwater sustainability. Results reveal the need of regulation on groundwater draft, determination of optimized pumping rates, and groundwater recharge measures for the sustainability of groundwater resources. The sensitivity analysis reveals that the recharge is more sensitive compared to hydraulic conductivity and streambed conductance.

Traditional hydrologic models employ a rainfall-runoff modeling approach in which the accuracy of the results is largely dependent upon the ability to minimize the temporal and spatial errors associated with the rainfall data. The current paper examines the feasibility of using an alternative approach in which observed streamflow time series are disaggregated into baseflow, point source, and nonpoint source time series, which are then used as forcing functions to generate point and nonpoint source pollution load predictions. The potential utility of the approach is examined by comparing the results of four different watershed simulations against results obtained from the traditional approach using an Loading Simulation Program C (LSPC) model with standard statistical tests for both calibration and validation analyses. The disaggregation approach was found to yield superior results to that obtained using the traditional approach.