Journal of Hydrologic Engineering

Energy loss in a steep open channel due to randomly spaced spherically shaped macroroughness elements such as boulders was investigated using a three-dimensional fluid dynamics solver. First, a relationship for energy loss at large Froude numbers due to a single boulder was derived as a function of flow rate, flow depth, and boulder diameter. Nondimensional energy loss increases with Froude number and decreases with the relative submergence. However, the exponents in the power law relationship are different for three different ranges of submergence ratio: $<0.5$, 0.5–1.0, and $>1.0$. The energy loss attributable to a cluster of boulders depends on cluster density, Froude number, and submergence ratio. For the same number of boulders, energy loss decreases as cluster density increases. However, variation in the pattern of boulder arrangement has only a marginal effect ($<4\%$) when the submergence ratio is more than 0.5. The simple procedure proposed for estimating energy loss due to a cluster of randomly distributed boulders of equal size predicts energy loss within 10% accuracy.

In this study, the annual and seasonal changes in extreme precipitation indices were investigated in Northern Cyprus by using the non-parametric Mann–Kendal trend test and Sen’s slope estimator. To this end, quality control of data procedure including estimation of missing data, detection of unreasonable values, and outliers were applied to 36 daily precipitation series to identify different errors. In order to test the homogeneity of the daily series, a two-step approach with four homogeneity tests and overall classification were used. The investigation of trends for extreme precipitation indices indicated that the trends were mostly nonsignificant and increasing in scale annually. On seasonal scale, the percentage of stations with increasing trend was almost the same with the percentage of stations with decreasing trend in autumn and spring. Besides, the upward trend was observed at more than 77% of the stations in winter with 11% having significantly increasing trends. Generally, there was no spatial coherence as well as a mixed pattern of trends changing from station to station.

Particle-size distribution (PSD), especially clay content, is an important physical property of soil because of its large influence on soil hydrological characteristics, salinity, fertility, erodibility, nutrient content, swelling/shrinking, and degradation. The authors present a case study of soil PSD and its relationship with soil-water content (SWC) in a gravel-mulched field in an arid area of northwestern China using a statistical regression model and Pearson correlation coefficients. The SWC was correlated with clay content, and both increased with soil depth, with correlation coefficients ranging from 0.901 to 0.972. The regression analysis of clay content and SWC from various depths used the least-squares method, and the predictive accuracies of the regression models were evaluated with the data for measured clay content. Both the mean error and root mean square error were low and the prediction accuracy was high. The SWCs were significantly positively correlated between soil layers, with all correlation coefficients greater than 0.989, indicating that the SWC in a specific layer could be well estimated by the SWC in the adjacent subsoil. Clay content can thus be predicted from measured SWC, and can be further used to evaluate soil quality and productivity. This study also provides a theoretical reference for the prevention of salinization, the adjustment of land use and agricultural production, and the conservation of soil and water.

The Yellow River is the largest sandy river in the world with an annual transport capacity of approximately 1.6 billion tons. There is growing concern of water availability and soil erosion in this basin. Understanding the runoff regime and sediment transport in the upper Yellow River basin (UYRB), especially in the context of climate change, is crucial for sustainable water resource management and soil–water conservation. Herein, the authors attempt to quantify the response of water and sediment discharge to climate change in the UYRB. The authors employed a distributed hydrological model, i.e., the Soil and Water Assessment Tool (SWAT), to simulate the runoff and sediment load under different scenarios, including climate change and detrended climate conditions. To predict the future trend, the authors designed scenarios based on downscaled forcing data from three global climate models (GCMs) which are with respect to the representative concentration pathway 8.5 (RCP8.5) of the Coupled Model Intercomparison Project Phase 5 (CMIP5). In response to the decrease in precipitation and increase in temperature from 1966 to 2009, annual runoff and sediment load have significantly decreased at a rate of $-11.6\mathrm{mm}/\mathrm{decade}$ and $-1.3\mathrm{Mt}/\mathrm{decade}$, respectively. Precipitation plays a dominant role in reshaping these trends, with a contribution more than four times greater than that of temperature. Moreover, sediment yields may decline in the near future (2049–2064), especially during late summer and early fall. Runoff change holds substantial uncertainty owing to the different projection based on the GCMs.

Analysis of daily streamflow is of interest to river restoration and conservation efforts in many regions in the world. However, the paucity of stream-gauging stations presents significant challenges in making flow predictions. In this study, two process-based rainfall-runoff models that differ in complexity were used to estimate a reliable simulation of the daily streamflow hydrograph using 15 subbasins in the Deerfield River Basin, a major tributary to the Connecticut River Watershed. Catchment characteristics were employed across the study area, and regional regression equations were developed that correlate these physical and climate characteristics with the parameters of the rainfall-runoff models. The regression-based regionalization approach had a higher degree of accuracy when compared with simpler regionalization approaches, with an average normalized RMS error (NRMSE) value of 0.26 compared with 0.42 and 0.32 for a spatial proximity method and a naïve-mean method, respectively. In addition, the more complex rainfall-runoff model performed better than the less complex model with Kling-Gupta efficiency (KGE) values of 0.78 and 0.68, respectively, suggesting that model discretization may play a significant role in hydrologic model accuracy. These findings support a viable framework for addressing water resource management at a small-catchment level. In addition, this study may contribute to regionalization of rainfall-runoff model parameters for ungauged basins in the northeastern U.S. region.

Seasonal design floods reflecting seasonal variation information are very important for reservoir operation and management. The seasonal design flood estimation method currently used in China is based on univariate frequency analysis and assumes that the seasonal and annual design frequencies are equal, which neither satisfies flood prevention standards nor considers the interdependence between different seasonal floods. The Danjiangkou reservoir located in the Han River basin, the first pilot basin of most regulated water resources management policy in China, was selected as a case study. After dividing the entire flood season into subseasons, a dependent structure of summer and autumn floods was established by copula functions. Three bivariate flood quantile selection methods, namely the equivalent frequency combination (EFC) method, conditional expectation combination (CEC) method, and conditional most likely combination (CMLC) method, were performed to estimate unique seasonal design floods to meet the needs in engineering and compared with the univariate design values. The evaluation criteria and a boundary identification method were used to assess the rationality of these combination methods. Compared with the CEC and EFC methods, the CMLC method has smaller root-mean square error (RMSE) and bias values by 12.9–34.8% and 22.6–36.4%, respectively. The CMLC and EFC methods are within the feasible regions, whereas the CEC estimators are beyond the feasible range. The results of economic analysis show that the CMLC method can enhance the floodwater use rate from 79.7 to 82.8% for the wet year and from 91.8 to 93.7% for the dry year in comparison with the EFC method. The CMLC method is more rational in physical realism and recommended for estimating seasonal design floods in the Danjiangkou reservoir, which would provide rich information as references for flood risk assessment, reservoir scheduling, and management.

This work has two purposes: (1) starting from the classic hydraulic geometry formulation, present a mathematically parsimonious, two-parameter, characterization of channel shape and size; and (2) analytically quantify cross-sectional area, top width, average depth, critical energy, and bank-full discharge at critical conditions as a function of these two parameters. The matter of channel asymmetry is examined, and a metric for quantifying asymmetry is presented. The robustness of this characterization is demonstrated in an application to several observed channels in Maryland and Delaware across three physiographic regions: Allegheny plateau, Piedmont, and coastal plain. Values of the shape parameter, $u$, ranged from 1.6 to 4.3, and asymmetry measures ranged from 0.09 to 0.51 for the demonstration cross sections. The channel characterization and set of channel property relationships have value for both analysis and design applications related to stream restoration, in which the consistency of a designed channel with existing field conditions is imperative for channel stability.