Journal of Hydrologic Engineering

The Halphen system and the generalized extreme-value (GEV) distributions are recommended for the estimation of the hydrological extreme events. The GEV system is widely used, whereas the Halphen distributions are less known by hydrologists. The objective of this study is to compare these two systems on the basis of various criteria. A simulation study was undertaken to assess the errors related to the fit of the Halphen distributions to samples generated by the GEV distribution and vice versa. Results show that in the case of power-tail type, the Halphen type Inverse B (HIB) distribution is more adequate and safer than the Fréchet (EV2) or Gumbel (EV1) distributions especially in the case of the small sample size. For subexponential class of distributions, both Halphen Type A (HA) and Type B (HB) perform better than the Gumbel (EV1) distribution. When considering limiting distribution cases, results show a very similar behavior of EV1, gamma, and inverse gamma distribution with a small advantage to the EV1 distribution. These results as well as the theoretical statistical properties of these systems of distributions indicate that the Halphen distributions can, in some cases, perform better than the GEV system and are good candidate to fit extreme value variables.

A novel application of the Hilbert-Huang transform (HHT) method for the analysis of nonstationary and nonlinear time series is proposed in this paper. The HHT is introduced to the field of water quality analysis both to evaluate trends and to assess the cause-effects relationship between the system under study and external physical factors. The HHT combines two distinct analytical methods: the empirical mode decomposition (EMD) to decompose observed series into independent intrinsic mode functions and the Hilbert transform to transform these time-dependent functions into time-frequency functions. A practical application of the HHT method for data analysis is presented herein for four time series in the Niagara River: flow, water temperature, and incoming concentrations of two polycyclic aromatic hydrocarbons. It is shown that the EMD improves the spectral representation of the original time series, enabling a better detection of periodic trends and dominant time scales. It is concluded that the ability of the HHT of decomposing the signal based on the data and transforming it into the frequency-time domain provides insights into time series characteristics that could improve the modeling effort on the Niagara River.

This paper compares flood top width predictions generated by a one-dimensional flood model using surveyed and light detection and ranging (LiDAR)-based topographic descriptions for varying storm event return intervals. Three channel geometries are used in the analysis: (1) based entirely on survey data; (2) based entirely on LiDAR data; and (3) based on a hybrid file that merges survey-derived channel bank locations and LiDAR-derived cross sections. The study area is a 6.6-km river reach located in the Piedmont area of North Carolina. Four steady flow simulations are performed representing the 10-, 50-, 100-, and 500-year design storm events to understand the effect of storm return period on top width predictions using the three different topographic descriptions. The results from the study suggest that the LiDAR derived geometries generally predicted higher widths compared to the survey geometries, and that the magnitude of the difference is inversely related to the storm even return interval (12% average difference for a 10-year storm event to 4% for a 500-year storm event).

Climate change resulting from the enhanced greenhouse effect is expected to have significant implications for the hydrological cycle. Several studies have pointed out the importance of basin-scale investigations for determining regional impacts on water resources, including the effects of floods and droughts. In this study, a fully distributed hydrologic model is used to assess the potential impacts of climate change on water availability in a basin in Oklahoma (United States). With this aim, the hydrologic model was applied for current conditions as well as under the hypotheses of climate variations represented by scenarios consistent with a climatic trend analysis generated using a stochastic weather model. Hydrologic simulations indicate that streamflow and evapotranspiration reflect variations in precipitation differently. Positive trends in precipitation result in an increase in surface and groundwater resources, while evapotranspiration is only affected slightly due to the higher soil moisture in the basin. Sensitivity analyses of the evapotranspiration and runoff changes to precipitation variations confirm these results. Comparisons of the impacts of the precipitation trend on surface and groundwater resources showed that the increase of surface water resources is $\sim 3$ times greater, implying the groundwater system is affected less by climate change. The use of a distributed model also provided insight on the spatial variation of the water balance components. Results showed that the most significant increases of soil moisture $\left(\sim 60\%\right)$ are located along the river network and in the flat areas of the basin, characterized by a higher frequency of saturation excess runoff. In summary, climate change scenarios in this region produced an increase in water resources that can have beneficial impacts, but these positive effects are tempered by the increasing potential for flood risk. The increase of this risk has been evaluated as well in this analysis.

The goals of storm-water management have shifted from a flood control to a holistic and sustainable strategy, emphasizing the relationship between rainfall event size and pollutant loadings. The first flush concept is the first part of a rainfall event that contains the largest pollutant loading. Questions have been raised on the concept’s validity for storms across different land uses and pollutant types. The existence and magnitude of the first flush impacts sizing of best management practices used to meet pollutant reduction goals, assessment sampling methodologies, and state storm-water management strategies. Current concepts support the use of distributed control measures focused on smaller storms off impervious surfaces. Runoff from a small impervious parking area was sampled incrementally during multiple storm events to measure pollutant concentration with respect to storm depth. This sampling routine established the existence of a first flush for a single use paved parking area. Total suspended solids, nitrate, chloride, dissolved copper, and dissolved cadmium exhibited a first flush up to a rainfall depth of 25.4 mm; total dissolved solids, total nitrogen, total phosphorus, nitrite, phosphate, and dissolved chromium did not exhibit a first flush.

Droughts are an inevitable consequence of meteorological variability, and the design of water resource infrastructure and management strategies to mitigate their effects requires assessment of the risk. Crucial characteristics of droughts are related to their peak intensities, durations, and severities. These variables are typically correlated and copulas provide a versatile means to model their dependence structure. In Australia, for example, drought severity is associated with the El-Niño Southern Oscillation. Data from two rainfall districts in New South Wales, one on the east and the other on the west of the Great Dividing Range, are considered. These rainfall data are categorized into three states, El-Niño, Neutral, and La-Niña, according to the prevailing Southern Oscillation Index. Gumbel-Hougaard copulas and $t$ -copulas are fitted to the droughts in the three states. The copula parameters are estimated separately for each state, and the differences are analyzed. The goodness-of-fit of the Gumbel-Hougaard and $t$ -copulas are compared, and the limitations of the two copula models are discussed. The times between drought events are also analyzed according to the El-Niño Southern Oscillation state they occur in. The fitted copulas are used to estimate annual recurrence intervals of at least one of the three variables, and of all three variables, exceeding critical values taking into account of the mixture of states.

Adaptive cluster sampling represents a design whereby more samples during high storm hydrographs can be obtained in the field compared to the commonly used calendar-based. To compare the effect of these sampling designs on accuracy of sediment rating curve estimation, we performed a study for Gorgan-Rood River, Iran by synthesizing sample sets from daily records. The load estimates obtained by conventional, two bias-corrected, and logged mean load within discharge classes (LMLWDCs) rating curves were statistically evaluated. All rating curves derived from calendar-based sample sets–except those with a nonparametric correction factor–underestimated the average load from 25 to 76%. Rating curves derived from adaptive sample sets, however, increased the sediment load to as much as 30%. Among this group of rating curves, LMLWDC produced the most accurate results with only 3% overestimation and a coefficient of variation in the order of 14% when the sampling frequency was semiweekly. The more accurate estimates from adaptive sample sets are likely due to the inclusion of more samples from high load periods.

This paper presents a one-dimensional hydrodynamic modeling of a large-scale river network and floodplains. The study site comprises the Upper Paraguay River and its main tributaries (a total of 4,800 km of river reaches) in South American central area, including a complex river network flowing along the Pantanal wetland. The main issues are related to preparing input data for the hydraulic model in a consistent and georeferenced database and to representing different flow regimes. Geographic information systems-based automatic procedures were developed in order to produce cross-sectional profiles that encompass the large floodplains and to link hydraulic data and spatial location. The marked seasonal flow regime and relative smooth hydrographs of Paraguay River were quite well reproduced by the hydraulic model. For the tributaries, it must be mentioned the model’s ability to simulate both cases when the hydrograph does not present a marked peak flow, due to water loss for the floodplain, and when the hydrograph presents a more common shape, with recession and peak flows well defined.