Journal of Hydrologic Engineering

Few studies have focused on the multivariate joint design of drought properties. Using daily streamflow data, this paper analyzes the impacts of pooling and exclusion of drought events in determining hydrological drought properties. The bivariate joint distributions of droughts were determined by using copulas. A novel method for bivariate design, the most-likely weight function, was applied to select design pairs for drought properties. The confidence interval of design curves of a given joint return period was limited by the measure of the joint probability density function due to the uncertainty of the estimated copula parameter. By comparing subseries separated by the low-flow change point, drought properties and their joint distributions, and designs were investigated under changing environment. Results showed that it was appropriate to set the pooling and excluding ratios as 0.2 and 0.41, respectively. The dependences of drought properties were positive and Archimedean copulas fitted well the bivariate joint distribution of droughts. The uncertainty of design duration–peak was remarkably greater than that of duration–severity and severity–peak. The design duration–severity was considerably stationary. Under changing environment, drought duration, severity, and peak remarkably decreased.

A simplified infiltration model for highly permeable porous media was introduced, assuming the matric potential gradient as negligible compared to the gravitational gradient. This model enabled us to determine the delay time, i.e., the time that the water front takes (from the beginning of rainfall) to reach the bottom of the highly permeable layer. Posidonia oceanica (Linnaeus) Delile residues were used as a porous media, in order to study the infiltration process that provides salt leaching under natural rainfall when these residues are arranged in a storage area, before reusing. By using a laboratory rainfall simulator, delay times were measured to verify the applicability of the aforementioned infiltration model. Application of the infiltration model revealed that compaction and subsidence phenomena occurred in such highly permeable porous media. Derivation of an extended formulation was then required to take into account two calibration constants with a clear physical meaning, evidencing the occurrence of a minimum delay time. The calibration procedure enabled a good fit between observed and estimated delay times.

Synthetic unit hydrographs are used to predict the response of a watershed to a precipitation event. They utilize the physical characteristics of a watershed for their development. Although a variety of synthetic unit hydrograph methods are available in the profession, history shows that the Natural Resources Conservation Service’s (NRCS) Dimensionless Unit Hydrograph is the most commonly used method. Within the NRCS’s peak flow equation, the peak rate factor (PRF) is a required user input parameter; however, little guidance is given throughout the literature for selecting an appropriate value. The goal of this case study was to back-calculate site-specific PRFs for various watersheds throughout New Jersey as a result of rainfall attributed to Hurricane Irene in August 2011. A total of 26 watersheds were investigated with results showing PRFs ranging between 170 and 975. Regional trends were sought but found to be inconclusive. Additionally, statistical analysis showed no correlation between the PRF and various watershed characteristics such as longest hydraulic length, slope, area, percent impervious, among others.

The assessment of water availability using hydrological models is subject to modeling uncertainties. Model performance evaluation based on conventional likelihood measures on an overall time-series simulation has shortcomings. To overcome this, a model diagnostic evaluation is carried out. The main objectives of the study are to (a) demonstrate the strength of the model diagnostic assessment, (b) formulate an improved likelihood measure through a flow duration curve (FDC)–based flow portioning to enhance model performance, and (c) correlate the model simulation to basin hydroclimatic features. The objectives are achieved through the use of a conceptual rainfall-runoff model within a generalized likelihood uncertainty estimation (GLUE) framework applied on 17 basins in the Southeastern United States. The results indicate that the diagnostic assessment is crucial in model evaluation. The two-phase model assessment helped to improve model performance by 6% in terms of Nash–Sutcliffe efficiency for overall flow time series, 32% in terms of average volume efficiency, and 24% in terms of the ratio of the root-mean square error to the standard deviation of observation for the low flows.

The Clean Water Act and USEPA’s total maximum daily load (TMDL) program established requirements to determine the amount of a pollutant that a waterbody can assimilate and still attain water-quality standards. A variety of methods have been used in order to determine the TMDL, from complex hydrodynamic and water-quality modeling to simpler analytical techniques. This paper explores TMDL modeling from the perspective of the agencies tasked with implementation to reduce pollutant loads from within their jurisdictions. The paper discusses the difference in emphasis between modeling for TMDL development and implementation. The discussion includes the need for modeling by stakeholders and agencies implementing TMDLs, criteria for selecting models, and a short summary of several that have proven useful. Finally, recommendations are offered for TMDL developers and implementers to improve the modeling effort that leads to successful attainment of water quality.

A large city surrounded by mountains like Hong Kong is susceptible to both flash floods in midmountain areas and local flooding in low-lying areas when a large rainstorm occurs. Severe rainstorms tend to be more frequent under a changing climate. Hence, it is essential to simulate rainfall-runoff processes in the urban area and evaluate flood coping strategies. This paper presents a model that is capable of simulating the overland flow process and underground drainage hydraulics simultaneously, based on detailed topographic, rainfall, and drainage network information about the northern Hong Kong Island. The model was calibrated with a flood-extent map and further validated with a flood event in June 2008. Using the model, the flood hazards associated with flood coping projects in three phases were simulated. Although these flooding control measures were intended to meet a major societal need for alleviating flooding problems and are shown to be effective in alleviating local flooding, the densely urbanized area may still suffer flooding when severe storm events occur. Hence, new strategies are needed to cope with the urban flood risk under more critical storms.

Geospatial transposition of observed storms can be useful for examining potential rainfall and flood hazards, and several recent software packages help facilitate transposition of remote sensing–based rainfall observations from radar or satellites. Two unanswered questions persist, however, in regions that exhibit heterogeneity in extreme rainfall properties: is transposition reasonable, and, if so, how should it be performed? This note posits that the answer to the first question depends on the degree of heterogeneity, and it attempts to answer the second question via “rescaling” transposed rainfall according to the degree of heterogeneity between two locations. General considerations for this rescaling are discussed, and two rescaling methods are introduced. Both methods are illustrated using gauge-corrected radar rainfall data from three locations in the Front Range of the Rocky Mountains in Colorado that exhibit moderate heterogeneity in an extreme rainfall hydroclimate. The second of these methods, termed stochastic distribution ratio rescaling, is presented here for the first time and has the advantage of straightforward uncertainty estimation.