Journal of Hydrologic Engineering

Deep saline aquifers have the large required capacity to dispose of or store fluids in the subsurface. The consequent overpressure may cause undesirable brine leakage through possible interlayer pathways. Faults can accommodate fluid leakage to shallower formations. Pressure transient analysis can be used for early detection of leakage through faults. This study developed an analytical model for pressure transient behavior in response to leakage through a fault to an overlying formation. A fault structure characteristically includes a core zone surrounded by damaged zones. In a departure from previous models and in order to honor the general fault configuration, we considered the anisotropic flow in three dimensions inside the fault zone. The solution was derived by a system of diffusivity equations for the reservoir layers as well as the fault zone in the Laplace–Fourier domain. The analytical solution was validated against the numerical simulation results. Applying the pressure derivative, we showed that the solution can differentiate between up-, across-, and along-fault fluid migration. The fault characterization method was provided based on the pressure derivative dependency on the fault conductivities, which illustrates the ability to identify up-fault leakage while differentiating it from partial communication across the fault. Pressure derivative curves were grouped based on the values of fault conductivities for fault leakage characterization. In addition, the effect of altered regions’ properties was investigated for the other side of the fault and the overlying formation. An example problem was presented to show the potential of the analytical approach and its application for fault leakage characterization.

A multitude of studies have derived one-dimensional velocity distributions, assuming open channels to be rectangular, and have extended their applications to rivers, canals, and other open channels. A cumulative distribution function (CDF) of velocity distribution is proposed based on a channel’s cross-sectional geometry. This paper derives a one-dimensional velocity distribution for wide rigid trapezoidal channels using the maximization of Shannon entropy and proposed CDF. Two new parameters, geometric parameter ($a$), and linear weightage factor ($k$), are introduced and the dependency of CDF on these two parameters is discussed. These two parameters take into account the cross-sectional geometry of the section and the location of flow velocity measured along the depth, respectively, and, hence, their contributions to velocity distribution, which were not considered when formulating Chiu velocity distribution. The proposed velocity distribution has been found to work significantly well, especially in the middle zone of channel depth, when compared to Chiu’s velocity distribution. This has been verified using experimental as well as river field data (assuming the river’s cross-section channel to be trapezoidal). The paper also explains the limitation of Chiu’s velocity distribution when applied to obtain the average flow velocity in trapezoidal channels. Further, the shear stress distribution (in vertical direction along the depth of channel perpendicular to flow direction) is also derived by entropy maximization based on channel geometry, and the relation of shear stress at the channel bed with bed width and cross-sectional area is established. Finally, the CDF formulation is also proposed for velocity distribution in exponential channels based on channel geometry.

Urban stormwater management is turning towards use of infiltration facilities, directing water that would have previously flowed overland into subsurface flow paths. Stormwater infiltration alters soil moisture, groundwater, and streamflow regimes, but it has been unclear how the spatial arrangement of infiltration-focused facilities affects catchment-scale water balances. This project used a physically based numerical model to investigate how spatial arrangements of infiltration facilities and subsurface media affects partitioning of water between unsaturated and saturated zones and baseflow duration and timing. More spatially distributed infiltration facilities, as compared to spatially clustered facilities, produced greater unsaturated zone storage, less saturated zone storage, and more total subsurface storage in scenarios where surface ponding was not severe. In silt, widespread surface ponding was observed. In sand, baseflow response to precipitation was delayed with clustered infiltration compared to distributed infiltration. These results can be used to guide decisions about how the spatial arrangement of stormwater infiltration facilities can affect urban catchment management goals, such as increasing plant available water and aquifer recharge or producing desired baseflow timing.

Finding the precise value of pixel-scale evapotranspiration (ET) for an entire basin is a major challenge to hydrologists. Many efforts have been made to conquer this challenge, among which remote sensing methods are the most promising ones. The surface energy balance algorithm for land (SEBAL) is one of the several established remote sensing methods to estimate ET. The anchor pixel selection process (e.g., hot and cold pixels) is one of the critical steps in the SEBAL model that also determines the accuracy of the model outputs. Several researchers have improved anchor pixel selection by an automated fashion. In the current study, a new simple method has been proposed to seek the best anchor pixels. Then, the daily ET outputs were assessed using observed data from the Eddy covariance (EC) tower data at Santa Cruz River Watershed for 2014–2015. The results showed that the daily ET with measured data at the study site confirmed our automated anchor pixel selection method to be reliable in terms of selecting appropriate hot and cold pixels under dry conditions by producing ET maps with reasonable accuracies (${\mathrm{R}}^2=0.78$ and $\mathrm{RMSE}=0.46\mathrm{mm}{\mathrm{day}}^{-1}$). Our study suggests that considerations of simple image-derived parameters, such as temperature difference between hot and cold pixels, distance from a representative station, and elevation differences could improve the automatic selection of anchor pixels and the implementation of the SEBAL model under dry conditions. The machine learning technique could be combined with the proposed automated algorithm to map ET in different climates (not only arid and semiarid) faster and with more accuracy.

This paper aims to define trends and dominant modes in annual instantaneous maximum flows (AIMF) covering the period 1961–2014 from 10 gauge stations located in different river basins in Turkey. To achieve this aim, discrete wavelet transform (DWT) and additive wavelet transform (AWT) in conjunction with the Mann-Kendall (MK) test are used and compared for the first time. Moreover, global wavelet spectrum (GWS) is employed to test the significance of the most effective periodic components. The sequential MK test is also used to determine the start or change points of trend in AIMF series. From the MK trend results, five stations showed a statistically significant (at a 5% level) negative trend for AIMF series and short-term periodic components (2 and $4\mathrm{years}$) were found to be the most effective components, which are responsible for producing a real trend founded on the data series. The GWS analysis indicated that the most dominant components identified are significant. In addition, the MK-$z$ values of the most effective periods derived from AWT generally showed a better agreement with MK-$z$ value of original time series with higher correlation coefficient compared to those of DWT. The sequential MK graphs of the AWT-based time series also produced a better harmony with the sequential MK of the original data. Finally, the results showed AWT coupled with the MK provided a very efficient and accurate analysis for defining the most effective modes in the AIMF series and can be successfully used in any hydrological time series.

Strong winds ($W$), storm tides ($Z$), and heavy rains ($R$) are the main destructive factors related to typhoons that affect the coastal regions of Jiangsu, where the “triad” phenomenon is obvious. Statistical results from 1960 to 2014 during typhoon events with a “triad” feature indicate that the mean high tide level, mean wind speed, and mean 24 h rainfall in three coastal cities (Lianyungang, Yancheng and Nantong) are approximately 2.96 m, $12.5\mathrm{m}/\mathrm{s}$, and 74.8 mm, respectively. Copulas and the particle swarm optimization (PSO) method are applied, indicating that the Gumbel copula and Weibull distribution are appropriate theoretical models for joint and marginal distributions of these destructive factors, respectively. However, a reanalysis of the parameters of the Weibull function and Gumbel copula is necessary, given that a strong typhoon may appear in the future. Typhoon-induced rains and tides are the main disaster factors of flooding in the coastal regions of Jiangsu. This paper proposes the cost-oriented method, the safety-oriented method, and a compromise between cost reduction and safety insurance to optimize the combination of rains and tides.

People often need precipitation data when building hydraulic engineering, especially the maximum daily precipitation data, so it is very important to study the maximum daily precipitation for hydraulic engineering. The spatiotemporal distribution characteristics of annual and seasonal maximum daily precipitation in the Hengduan Mountains were analyzed based on the maximum daily precipitation data collected at 30 meteorological stations between 1971 and 2013. The following results were obtained. The annual and seasonal maximum daily precipitation gradually increase from north to south; in comparison, the temporal variations in the annual and seasonal maximum daily precipitation are not significant. Except for winter, the standard deviations and coefficient of variation values of maximum daily precipitation in the other time scales are negatively correlated with both elevation and latitude, and most of those correlations are significant. Shape parameters of maximum daily precipitation have no significant correlation with either elevation or latitude, whereas the scale and location parameters have significant correlation with both elevation and latitude at different confidence levels. In addition, except for summer and autumn, there are significant negative correlations between maximum daily precipitation and elevation and latitude at different confidence levels during different return periods in the other time scales.

The present study focuses on the assessment of groundwater level monitoring networks (GWLMN) by examining the statistical relationship between groundwater level (GWL) uncertainty and sustainable groundwater indicators (SGI). In this study, four SGIs are considered that are the key groundwater dynamics drivers, which include aquifer properties, a command area, a noncommand area, and the cropping season. The entropy-based theory has been innovatively used to estimate the uncertainty in random variables with SGI and existing observation wells (OWs). Entropy measures estimated in the analysis are marginal entropy (ME), joint entropy (JE), mutual entropy or transinformation (TE), and conditional entropy (CE). The applicability of the entropy-based theory to complex aquifer systems with varying hydrological characteristics is analyzed, estimating the optimum distance between two OWs, temporal frequencies, and priority zones for monitoring. The proposed framework has been used to analyze data from 30 OWs obtained from the Wainganga subbasin with an area of ${\mathrm{3,320}\mathrm{km}}^2$ in India. Criteria considered for selecting suitable OWs for monitoring are (1) the lowest uncertainty (ME), and (2) the representative monitoring location based on the SGI. OWs are considered significant for monitoring if they fail to meet Criterion 1 or 2. The results show that out of the 30 OWs in the existing network, 28 OWs are found significant for monitoring purposes. The outcome of priority zoning was compared and examined using groundwater recharge and irrigation wells density. The results of this study will provide useful insights for computing the spatial and temporal uncertainty in GWLMNs and can help to assess the suitability of the network.

Developing intensity, duration, and frequency (IDF) of rainfall with the reflection of future climate change is required for designing and managing water infrastructures. Hence, this study was aimed to predict future changes in IDF information for seven stations of the Tigray Region. Observed hourly and spatiotemporally downscaled Hadley Center Climate Model version 3 (HadCM3) model output data sets via Statistical Downscaling Model (SDSM) 4.2.9 and Hyetos models were used. Gumbel [Extreme Value Type I (EVI)] distribution and MIDUSS IDF Curve Fit tool were employed for frequency analysis and estimation of IDF equation parameters. Finally, general trends for three future climate timelines were established. The result revealed that the rainfall intensity is expected to decrease by 15.9%, 14.7%, and 20.3% for short storms and increase by 21.3%, 26.9%, and 18.9% for longer storms in the 2020s, 2050s, and 2080s, respectively, with frequent intensive rainfall occurrences by the 2020s and 2050s under the A2 emission scenario (high emission scenario). These have a great message to which the existing and future water management facilities are designed, functioned, and sustained. Thus, design facilities need to be updated as per the reflection of climate change impacts.