Journal of Hydrologic Engineering

Urban flood risks are often determined by the frequency analysis of observed rainfall data, communicated using isohyetal maps showing rainfall totals for a range of durations and recurrence intervals. However, to assess future changes in heavy rainfall, it is necessary to study the future projected rainfall time series. Impacts of climate change are typically assessed using climate projections based on global climate model (GCM) outputs and downscaled to finer temporal and spatial scales. The projected data, however, are not generated in a format that urban planners and engineers can easily use to design for future conditions. This research presents a method to analyze and express climate data in a format that can be readily used in hydrologic models to assess the effects of future extreme rainfall events. Future conditions’ climate data were analyzed using a weighted ensemble approach, which resulted in projected rainfall frequency estimates and their confidence limits. Two multimodel data sets were selected to illustrate this approach in Cook County, Illinois, which belongs to the Chicago metropolitan area. The first data set included statistical downscaling data based on the Intergovernmental Panel for Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 3 (CMIP3) data. The weighted ensemble analysis applied to this data set produced results that indicated significant increases in projected heavy rainfall. For example, for CMIP3 Scenario A2, for the late 21st century, the 100-year, 24-h rainfall in the northern parts of the county was 29% larger than the model-generated rainfall for the present time. For the same time horizon and scenario, the confidence interval based on projected data was 87% wider compared with that of the published source (NOAA Atlas 14), calculated using the past observed data. Also, equal-weight delta-corrected IPCC CMIP5-based dynamical downscaling data were applied to the same region for the mid-21st century, producing increases in heavy rainfall fairly similar to those of CMIP3.

In a coastal aquifer, the interaction of tides and groundwater has a crucial impact on marine ecosystems. The model in this study consists of a semiconfined coastal aquifer overlying a sloping impermeable base in the presence of tidal waves. The groundwater flow was represented by a nonlinear Boussinesq equation, which was further solved analytically by converting it into a linearized form using a suitable technique. To assess the validity of the linearization technique, a numerical solution was obtained from the corresponding nonlinear equation using a fully stable alternate direction implicit scheme. The joint effects of the aquifer and tidal parameters were considered to analyze the behavior of the water table fluctuation. The accuracy between the analytical and numerical simulation results was tested by calculating the relative percentage difference. Asymptotic cases such as zero slopes and various leakage and tidal periods are discussed. Depending on the positive, negative, or zero values of flow rate and volumetric exchange, the groundwater flow can be categorized into inflow, outflow, or steady state. It was observed from the results that slope and tidal nature have a major impact on the water table fluctuation in a semiconfined coastal aquifer.

Reservoirs for flood protection are normally designed by using the annual maximum design flood (AMDF). However, management and operation of a reservoir require regulation of intrayear runoff instead of interyear runoff. The intrayear runoff varies with time within a year; hence, reservoir operation and management should deal with different aspects of river runoff. Therefore, information on inflow in the near-future time interval that may be several days long is needed. The question that has long been under discussion, especially in developing countries, such as China, is whether the use of AMDF is reasonable for reservoir operation and management. This paper presents a new kind of design water amount for reservoir operation that considers the probabilistic characteristics of the intrayear runoff that the built reservoir must cope with. Such a design amount varies with time and inflow to the reservoir and reaches the traditional annual maximum design flood when the inflow reaches a high water level. This dynamic design water amount (DDWA) is important for addressing the issue of balancing the need for flood control with storage for other purposes, and the focus on keeping free capacity can lead to water losses if the flood levels are less than expected. Thus, the DDWA is an operational design criterion that should complement the traditional design flood computation. Hence, the use of such new design water amount may make the reservoir safer in a high-water period and a higher water level, leading to more benefit in the middle- and low-water periods. The analyses for a real example reservoir (Three Gorges Reservoir, China) demonstrate the use of the new design water amount. Applying the DDWA to the Three Gorges Project reservoir may ensure safety by flood protection and increase the ensuing benefit because it is a design water amount in the near-future time interval, which may or may not be in the flood period.

Permeable pavements benefit urban hydrology through detention of stormwater in the aggregate base and subsequent exfiltration to the underlying soil. The majority of previous research has focused on permeable pavements constructed in sandy soils and/or treating only direct rainfall. Four permeable pavements employing internal water storage (IWS) zones and situated over low-permeability soils were intensively monitored for their hydrologic performance in northern Ohio. Volume reduction varied from 16 to 53% for permeable pavements with low drawdown rates ($<0.35\mathrm{mm}/\mathrm{h}$) and loading ratios $\left(\frac{\mathrm{Watershed}\mathrm{Area}+\mathrm{Permeable}\mathrm{Pavement}\mathrm{Area}}{\mathrm{Permeable}\mathrm{Pavement}\mathrm{Area}}\right)$ exceeding $5:1$. Postconstruction drawdown rates were similar to saturated hydraulic conductivity ($K_{\mathrm{sat}}$) measured during construction, suggesting that lateral exfiltration and evaporation were relatively minor contributors to volume reduction. Stormwater was completely captured (i.e., no discharge from the permeable pavements) during 4–80% of observed storm events. Average depth of abstraction ranged from 3.0 mm (site with highest loading ratio) to 25.2 mm (site treating only direct rainfall). Substantial peak flow mitigation was observed for all rainfall events not producing surface runoff (i.e., untreated bypass). Under these conditions, peak flow was diminished by more than 80% for seven events exceeding the 1-year, 5-min design rainfall intensity for Cleveland, Ohio. Lower loading ratios, reduced surface runoff, an IWS zone, and higher underlying soil $K_{\text{sat}}$ directly impacted volume reduction and peak flow mitigation. Overall, permeable pavement mitigated negative hydrologic impacts of impervious surfaces even when sited over low-conductivity clay soils.

Stormwater reservoirs are used to control the runoff volume and to attenuate the peak flow rate from urban catchments. The required capacity of a stormwater reservoir and its dimensions are calculated with the help of rainfall-runoff models, using a time series of observed rainfall or a design rainfall. However, many studies have revealed that the reliability of models of urbanized basins is far from perfect. This study presents an optimization of the capacity of a stormwater reservoir in a small urbanized basin, taking into account the predictive uncertainty of the rainfall-runoff model. An analysis of the model uncertainty using the generalized likelihood uncertainty estimation (GLUE) framework allowed for an assessment of the optimal capacity of the stormwater reservoir required to ensure a given safety level. Such a probabilistic approach is compared with a deterministic approach, which is commonly used in practice, where the effect of uncertainty of the rainfall-runoff model is usually neglected. The numerical experiment showed that stochastic and deterministic approaches are not equivalent as the calculated capacities of the reservoirs were different. The deterministic solution leads to an overestimation of the reservoir capacity from 20 to 150%, compared to the mean capacity obtained using a stochastic formulation. The results indicate that the uncertainty of the runoff model might potentially be a significant factor in stormwater reservoir design.

This paper presents an analytical solution of the advection-dispersion equation (ADE) with the dispersion coefficient and velocity being directly proportional to the spatial linear nonhomogeneous function. It is one of the solutions obtained in two particular cases in which the dispersion coefficient and velocity are (1) spatially dependent and (2) temporally dependent. These analytical solutions, particularly the one addressed here, which are of great importance in the existing hydrological theories, had been elusive. These theories assert the spatio-temporal dependence of the transport coefficients of the ADE to frame pollutant transport in aquifers in real situations. This study is carried out in an infinite medium for instantaneous and continuous point sources. The source of the pollutant’s solute mass is defined through a nonhomogeneous production term in the ADE. Darcy velocity is considered to be spatio-temporally dependent in nondegenerate form. According to hydrodynamic dispersion theory, the dispersion coefficient is proportional to the $n$th power of the velocity, where $n$ varies from 1 to 2. This paper compares solute transport problems for $n=1$ and $n=2$ and obtains expected results. Green’s function method (GFM) is used to obtain an analytical solution in general form, from which those for instantaneous and continuous sources in different combinations of spatial and temporal dependence are derived. The spatial dependence is considered linear, whereas temporal dependence is considered asymptotic, exponential, and sinusoidal. To use GFM, a moving coordinate transformation equation is developed which reduces the ADE into a solvable form. The analytical solutions of known dispersion problems are derived as particular cases. The effect of spatial and temporal dependence of the transport parameters on the solute transport is shown through illustrations.

The northeast and southwest monsoons control the climate of the city of Chennai. Rainfall analysis with an emphasis on drought using the precipitation data of 115 years (1901–2015) was done to quantify the trends. The trend is analyzed annually and seasonally, which revealed a nonsignificant increasing trend in annual rainfall and a significant decreasing trend in winter. The rainfall is irregular and is confined to the latter half of the year, with the maximum contribution occurring in just three months. Several indexes and drought indicators were used for the temporal analysis and for the quantification of the severity of the rainfall and drought events. The precipitation concentration index (PCI) values point to irregular rainfall distribution in almost 84% and a large monthly variability in 38% of the years studied. The magnitudes of the northeast and southwest monsoons are in opposition. The values of the Seasonality Index (SI) signify that most of the rainfall is concentrated in few seasonal months with a prolonged dry season. The rainfall concentration is greatly correlated with the drought pattern. The Rainfall Anomaly Index (RAI) values indicate that out of 115 years, there were five extremely dry and four extremely wet years.

Studying the effect of cloud cover on the temporal upscaling of instantaneous evapotranspiration (ET) is significant in the effort toward a more accurate and widely applied upscaling method to obtain the exact ET on a daily or longer time scale, thereby benefiting the practical applications. In this article, the authors concentrated on the effects of cloud cover in different amounts and for varying time durations, with three commonly used upscaling approaches including the constant evaporative fraction (EF) method, the constant reference evaporative fraction (${\mathrm{EF}}_r$) method, and the constant global solar radiation ($R_g$) method. Transient cloud cover and persistent cloud cover were defined according to the occurrence time, namely, the cloud that appeared 1 h before or after the upscaling moment and the cloud lasting the whole day except during the upscaling time, respectively. The different cloud cover amounts were indicated by the different losses of downwelling shortwave irradiance. The instantaneous fluxes were simulated from the atmosphere-land exchange (ALEX) model, which was driven by the meteorology measurements at the Yucheng station in China. The results showed that (1) the cloud caused the deterioration of the underestimation or overestimation of the daily ET upscaling in comparison with the results of clear days. Specifically, the persistent cloud cover had a more significant effect on the three upscaling methods; for the transient cloud cover, the upscaling results had larger deviations when the cloud appeared before the upscaling moments than when it appeared after them; (2) the effects on the upscaling factors and the upscaling results both increased proportionally with the growth of the cloud cover; and (3) the constant EF_r method performed best for both clear and cloudy situations, with a minimal bias less than $4.7\mathrm{W}/{\mathrm{m}}^2$ (5.5%) and a root-mean-square error (RMSE) less than $8.9\mathrm{W}/{\mathrm{m}}^2$ (20.6%); the EF method was most severely affected, with a bias up to $24.1\mathrm{W}/{\mathrm{m}}^2$ (28.3%) and an RMSE up to $24.9\mathrm{W}/{\mathrm{m}}^2$ (57.7%); the $R_g$ method had an intermediate accuracy with a bias less than $20.9\mathrm{W}/{\mathrm{m}}^2$ (24.6%) and an RMSE less than $20.3\mathrm{W}/{\mathrm{m}}^2$ (47.1%); and (4) all three approaches were influenced most significantly around noontime.

Real-time availability of satellite-based precipitation products gives the hydrologic prediction community an opportunity to enhance hydrologic prediction for a watershed or river basin. In the present study, the latest tropical rainfall-measuring mission (TRMM) multisatellite precipitation analysis (TMPA) research product, 3B42V7, was assessed against a gauge-based Indian Meteorological Department (IMD) gauge-based gridded data set using statistical and contingency table methods for an agricultural watershed in India. A comparative analysis of the TRMM and IMD data sets was carried out on daily, monthly, seasonal, and yearly bases for 16 years (1998–2013). The analysis revealed that the TRMM data set performed reasonably well but showed significant biases. Although it underestimated watershed-averaged daily precipitation ($\text{bias}=-28.65\%$, correlation $\text{coefficient}=0.42$), its results improved for monthly precipitation ($\text{bias}=-26.17\%$, correlation $\text{coefficient}=0.86$). Moreover, its rainfall detection capability was determined to be better during the monsoon season than during the nonmonsoon season. In all timescales, however, it usually underestimated heavy rains. The utility of the TRMM precipitation data set in hydrologic modeling was evaluated via the semidistributed soil and water assessment tool (SWAT) hydrologic model. Using the IMD gauged data–calibrated SWAT model, TRMM data set–based simulation showed limited hydrologic prediction on a daily scale, whereas its prediction capability was fairly good on a monthly scale. The SWAT model exhibited remarkable improvement in both daily and monthly simulation when recalibrated with TRMM precipitation data. However, when driven by IMD data, the model always performed better than its TRMM-driven counterpart. The analysis indicated that the use of the TRMM precipitation data set can be a compensating approach after suitable bias correction and that it has the potential for hydrologic prediction in data-sparse regions.