Journal of Hydrologic Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Most roadway construction projects disturb existing vegetation exposing bare soil to environmental variables (i.e., precipitation, wind, and freeze and thaw) that cause erosion. Soil eroded during storm events is conveyed by storm water runoff and may become deposited in receiving waterways. Inlet protection practices (IPPs) are temporary erosion and sediment controls commonly used around inlet drainage structures to prevent erosion while also retaining sediment on-site. Effective IPPs impound storm water, prevent upstream channel erosion, and promote settling along the perimeter of a device. Stringent effluent regulations coupled with greater public awareness with regards to surface water pollution have created the need for understanding the performance of commonly used erosion and sediment control practices. This study developed a methodology and testing apparatus for large-scale reproducible performance-based testing of standard IPPs at the Auburn University Erosion and Sediment Control Testing Facility. A two-phased testing protocol comprised of installation improvements and performance evaluations was developed. A test channel, 13.42 m (19 ft) in length, 5.80 m (19 ft) in width, sloped at 5% towards a 1.22-m (4 ft) inlet was constructed to test IPPs. Using the Alabama rainfall and erosion characteristics, water and sediment delivery mechanisms were designed and developed for implementing experimental flows and sediment introduction rates of $0.035{\mathrm{m}}^3/\mathrm{s}$ ($1.25{\mathrm{ft}}^3/\mathrm{s}$) and $21.2\mathrm{kg}/\min$ ($46.7\mathrm{lb}/\min$), respectively. Data collection includes pretest and posttest channel surveys, flow-through rates, total suspended solids, and turbidity. Finally, a case study using a typical wattle barrier IPP demonstrates the developed testing methodologies, installation improvements, and performance measures.

A procedure for the objective reduction of a rain gauge network is developed and applied for the Susquehanna River Basin (SRB) in the United States. The procedure utilizes evaluation of the theoretical error variance associated with precipitation analysis using a variant of the National Weather Service’s (NWS) Multisensor Precipitation Estimator (MPE). The uncertainty analysis is carried out for 16 different combinations of the precipitation accumulation period, season, magnitude, and areal extent, and use or nonuse of the Flash Flood Potential Index (FFPI), which is used as a proxy for the spatially varying runoff ratio. To estimate the statistical parameters of the procedure, the historical archive of the MPE products operationally produced by the Middle Atlantic River Forecast Center (MARFC) was used. The marginal value of each rain gauge in the Susquehanna Flood Forecasting and Warning System (SFFWS) network to the parent network is assessed by calculating the increase in the theoretical error variance in radar-gauge precipitation analysis over the SRB following hypothetical removal of the gauge. The parent network is made of 73 gauges in the SFFWS network plus 120 high-quality hourly and subhourly rain gauges within and in the vicinity of the SRB. The results show that significant variability exists in the rankings of the marginal value of the SFFWS gauges among the 16 cases considered. The most significant source of this variability is the seasonal variation in the spatial structure of precipitation. The second most significant source is the use or nonuse of the FFPI. Given the significant sensitivity to these and possibly other factors, one may not expect a unique solution for optimal network reduction. For robust decision making, an ensemble of solutions should be considered that reflects the range of variability in such factors.

In this study, statistical downscaling of large-scale general circulation model (GCM) simulations to monthly precipitation of Kemer Dam, in Turkey, has been performed through relevance vector machines (RVMs). All possible regression methods along with statistical measures have been used to select potential predictors through reanalysis data providing air850, hgt850, and prate variables as the optimal. The determined explanatory variables are then used for training RVM-based statistical downscaling model. A least-squares support vector machine (LSSVM)-based downscaling model is also constructed to compare the downscaling performance of RVM through some performance evaluation measures such as $R^2$, $\mathrm{Adj}{R}^2$ and RMS error (RMSE). Because RVM is able to obtain the better modeling accuracy in terms of all performance measures during the testing period, third-generation coupled climate model (CGCM3) simulations run through the trained RVM to obtain future scenario results. The effectiveness of the RVM model is illustrated through its integration to climate scenarios (20C3M and A2). The statistical significance of the probable changes obtained with used methods is examined by Mann-Whitney U (M-W) and t-tests considering scenario forecasts. According to pessimistic A2 scenario results, statistically significant decreasing trends are foreseen for both seasonal and annual precipitation in the study basin.

Maximum precipitation during a historical period is estimated by means of a physically based regional atmospheric model over three watersheds in Northern California: the American River watershed (ARW), the Yuba River watershed (YRW), and the Upper Feather River watershed (UFRW). In Northern California, severe storm events are mostly caused by a high-moisture atmospheric flow from the Pacific Ocean, referred to as atmospheric river (AR). Therefore, a method to maximize the contribution of an AR on precipitation over each of the targeted watersheds is proposed. The method shifts the atmospheric boundary conditions of the regional atmospheric model in space with latitude and longitude so that the AR strikes each of the targeted watersheds in an optimal direction and location to maximize the precipitation over these watersheds. For this purpose, the fifth generation Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) is used as the regional atmospheric model, and the NCAR/National Centers for Environmental Prediction (NCEP) Reanalysis I data set is used as the input data for the MM5. The 72-h basin-average maximized precipitation depths (MPs) are 600, 632, and 448 mm, respectively, over the ARW, YRW, and UFRW watersheds. They are 42, 38, and 18% larger, respectively, than the largest values obtained from the reconstruction of the historical storm events over the target watersheds by means of the MM5 model, which used the NCAR/NCEP historical reanalysis data for its synoptic initial and boundary atmospheric conditions. This methodology can be applied not only to MP estimation during the historical period, but also to the ones in the future if the future projection data by the global circulation model (GCM) are used as input to the MM5.

A hydropedological study was conducted to investigate the impact of the construction of Al-Khoud recharge dam on soil development in a dry region of Oman. The study involved detailed descriptions of pedons, surface and subsurface soil textural analyses, and double-ring and tension infiltrometer tests in areas inside and adjacent to the dam. The reservoir area of this 25-year-old hydraulic structure has rapidly changed due to spatiotemporally variable deposition of sediments from the dammed water and intensive scraping of the silt cake. This resulted in the formation of multiple micro depressions and a nonflat shape of the reservoir bed. The subsurface soils of the bed showed heterogeneity and complex patterns of sediment deposition as a response to the human-induced changes in the soil development, geomorphology, and hydrological properties of the dam area. In most pedons, a sequence of Stokes’ law–generated porous layers indicates ponded conditions of flood events and reservoir filling correlated with the hydrographs of the gaging station that feeds the reservoir. Textural analysis indicates the difference of ponding depth durations and sediment load is highly variable for pedons inside the reservoir and in the off-dam adjacent zones, both upstream and downstream of the embankment. The formation of a unique three-dimensional cascade of silt blocks, sandwiched by sand-filled horizontal and vertical fractures, was discovered. The propagation of silt entrained by infiltrated water into an originally coarse parent alluvium and the ensuing reduction of permeability were detected. Cumulative infiltration and infiltration rate curves and saturated hydraulic conductivities are related to the soil hydraulic-capillary properties.

Investigating the impact of climate change at regional scales is of importance for adaption purposes. In this study, a regional climate model PRECIS is for the first time run for East China on a spatial resolution of 25 km using two global climate models (GCMs) ECHAM5 and HadCM3 as boundary data under A1B emission scenario. The baseline period is 1961–1990. An upgraded quantile mapping method is used to correct the bias of PRECIS outputs for providing reliable future climate change projections (2011–2040). The regional climate model is then coupled with a mesoscale distributed hydrology soil vegetation model (DHSVM), to investigate the impact of climate change on future water resources in the Jinhua River Basin, East China. The hydrological model is calibrated and validated on a daily basis and show reasonable performance. The results show that under both GCMs, statistically, no significant changes in future annual runoff are projected. However, large changes in seasonal and monthly runoff are projected. ECHAM5 projects large decreases in summer ($-11.8\%$) and autumn ($-54.7\%$) and slight increases in spring and winter whereas HadCM3 projects large decreases in autumn ($-65\%$) and large increases in spring (28.7%) and winter (60%). The seasonal changes in precipitation in this region are the main driving force for the changes in future runoff.

Because the nonlinear Muskingum model continues to be popular, the need for precise calibration of the model parameters provides an active area of research in hydrologic engineering. Although various techniques have been proposed to estimate the three parameters in the nonlinear Muskingum model, a more accurate method with the fast convergence rate is still required to improve the computational precision. In this research, the modified honey bee mating optimization (MHBMO) algorithm with the modified routing procedure is used for the estimation of Muskingum parameters using two different objective functions. The proposed algorithm is applied to two case studies from previous literature and the results are compared with those of other methods. The results demonstrate that the MHBMO algorithm not only converges fast but it also captures the best optimal parameter values. The sensitivity analyses, carried out on the MHBMO algorithm control parameters, show that for each parameter, the results are stable for a wide range. Based on the results, it is concluded that the MHBMO algorithm can be used confidently to estimate the parameters of the nonlinear Muskingum model.

In this paper, a hydrometeorological approach to the probabilistic simulation of soil moisture is proposed. The time series of in situ soil moisture and meteorological variables at a monthly scale from different monitoring stations across India are utilized. Preliminary investigation with both precipitation and near-surface air temperature as meteorological variables reveals that the strength of association between soil moisture and precipitation is more significant compared to that between soil moisture and temperature. Precipitation-based probabilistic estimation of soil moisture using the proposed hydrometeorological approach is tested with in situ observed soil moisture data and with soil moisture data of the Climate Change Initiative project, funded by the European Space Agency. The results are found to be promising. In addition, the proposed hydrometeorological approach is able to provide the information on uncertainty associated with the estimation. It is also shown that the parameter of the developed model is linked to the predominant soil textural class. Higher values of the model parameter [dependence parameter ($\theta$) for the selected copula] correspond to clay loam, medium values correspond to loamy sand or loam, and low values correspond to sandy loam to sandy soil. Three stations outside India, which are widely separated across the globe with known soil textural class, are also considered, and the performance of the proposed model is tested by estimation of the model parameter depending on the soil textural class. The results are found to be reasonably good, indicating the spatial transferability of the proposed approach.

Wetlands are the transitional zones between uplands and downstream flooded systems; they play an important role in controlling storm water peak flow and downstream water quality. Since surface water/subsurface water interactions affect solute transport within wetlands significantly, contaminant transport models incorporating these interactions need to be investigated for wetland areas. Wetland solute transport dynamics (WETSAND) is a comprehensive wetland model, which has both surface flow and solute transport components. In WETSAND, water quality components are solved by advection-dispersion-reaction equations, which incorporate surface water/groundwater interactions by including the incoming/outgoing mass due to the groundwater recharge/discharge. In this study, analytical solutions for the contaminant transport equations of WETSAND are developed and compared to the numerical solutions obtained by WETSAND. These analytical solutions provide a physical insight to wetland water quality. Results show that analytical solutions are in good agreement with the numerical solutions. Moreover, the effect of interactions on wetland water quality is discussed.

The natural resources conservation service (NRCS) curve number method is widely used to estimate runoff from rainfall events for four hydrological soil groups (A, B, C, and D). However, the NRCS soil groups do not yet include peat soils. Therefore, this study analyzed 59 rainfall-runoff events from two peat-dominated watersheds in Finland (Marjasuo, Röyvänsuo) and one in Norway (Grualia). The analysis used the United States Army Corps of Engineers Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to calibrate curve numbers and initial abstraction ratios ($I_a/S$). This gave a mean $I_a/S$ of 0.036, with a mean standard error of 0.003 and 95% confidence interval of 0.030–0.042, which is significantly lower than the value of 0.2 recommended in the NRCS curve number method. The mean curve number was 61, with a mean standard error of 0.9 and 95% confidence interval of 59–63 for the study watersheds. This study confirmed that application of the NRCS curve number method in HEC-HMS required separation of base flow from runoff. Calibration by HEC-HMS is an equivalent method to determine curve number from rainfall-runoff data.

The rainfall threshold (RT) method is a nonstructural flood mitigation approach that is emerging as an effective flood forecasting tool. A critical RT value is the minimum cumulative rainfall depth necessary to cause critical water level or discharge at a cross section of a river. The major drawback of the RT approach is associated with the offline methods used for extracting critical RT values based on some fixed watershed characteristics and rainfall conditions. In this paper, a novel methodology is presented for real-time updating of RT curves for flood forecasting using a rainfall-runoff model and an artificial neural network. In this method, in addition to the rainfall depth, observed discharges are also used to update the rainfall threshold curves for real-time soil moisture and rainfall temporal and spatial patterns. The method was tested on the Walnut Gulch watershed with a 50-min time of concentration for selected historical rainfall events. It was shown that applying the proposed updating method can prevent the issuance of false warning, e.g., for the flood of August 2006, and in some cases increase the lead time of flood forecasting, e.g., 20-min increase in lead time for the flood of June 2008. Using data for 14 major historical rain events, it was shown that by applying the updating method, the hit rate is increased by an average of 28% and the false rate is decreased by an average of 51%.

This paper assesses the risk of increase in water demand for a wide range of irrigated crops in an irrigation network located downstream of the Aidoghmoush Dam in East Azerbaijan by considering climate change conditions for the period 2026–2039. Atmosphere-ocean global circulation models (AOGCMs) are used to simulate climatic variables such as temperature and precipitation. The Bayesian approach is used to consider uncertainties of AOGCMs. Climate change scenarios of climatic variables are first weighted by using the mean observed temperature-precipitation (MOTP) method, and related probability distribution functions are produced. Outputs of AOGCMs are used as input to water requirement models. Then, produced by using the Monte Carlo method, 200 samples (discrete values) from the probability distribution functions of monthly downscaled temperature and precipitation in the study area are extracted by using a software for sensitivity and uncertainty analysis. Time series of climatic variables in future periods are then generated (temperature variable to calculate potential evapotranspiration and rainfall variable to calculate effective rainfall). To estimate crop water requirements, crop evapotranspiration (from the product of potential evapotranspiration in the previous step and coefficient of crop computed) and effective precipitation (from time series of the previous step) are calculated. The Food and Agricultural Organization of the United Nations (FAO) methods, FAO-24 and Penman-Monteith, were used to compute crop and potential evapotranspiration, respectively. Because of lack of required data, potential evapotranspiration in future periods is computed through the relationship of temperature and potential evapotranspiration in the baseline period; the same procedure is conducted for temperature. Net water requirement (NWR) and the risk of changes in water demand volume of crops (e.g., wheat, barley, alfalfa, soybean, feed corn, forage, potato, and walnut orchards) are computed by entering 200 monthly time series of downscaled temperature and precipitation in future periods. The results indicate that risk of changes in crop water requirements increases by approximately 3% for a 25% risk, approximately 17% for a 50% risk, and approximately 33% for a 75% risk. Also, based on the current cultivated area, on average, the volume of water demand only for the aforementioned crops will be approximately $2.5({10}^6{\mathrm{m}}^3/\mathrm{year})$ with a risk of 25%, approximately $16({10}^6{\mathrm{m}}^3/\mathrm{year})$ with a risk of 50%, and approximately $31({10}^6{\mathrm{m}}^3/\mathrm{year})$ with a risk of 75%. Wheat and barley are more resistant and less sensitive to climate change than other crops considered.

In this paper, copula functions are used to model the dependence structure of monthly mean solar radiation $R$ and sunshine duration hours $D$. The efficiency of five well-known bivariate parametric copula functions, including (1) normal, (2) student’s $t$, (3) Clayton, (4) Frank, and (5) Gumbel, are evaluated in a seasonal basis for nine radiometric stations in Iran. First, the most appropriate marginal probability distributions for $R$ and $D$ were individually selected from 16 univariate distributions; then, performance of the parametric copulas for modeling the dependence structure of $R\text{-}D$ joint empirical probability distribution was assessed using two criteria. Finally, based on appropriate parametric copulas, the joint simulation of marginal variables was accomplished using the conditional sampling technique. The results show that the best marginal distribution fitted on the original data $D$ and $R$ is normal distribution when transformed with Johnson function (in more than a half of cases). Because of high (low) correlation of $R$ and $D$ in the left (right) tail of scatter diagram, the Clayton model had better fitting on the empirical copula than other models. The joint simulation using appropriate parametric copula functions indicated that the Clayton yield a better performance in terms of the slope of relation between $R$ and $D$. Besides, this model does not introduce unreasonable data. Therefore, the Clayton model is proposed as an appropriate copula model for simulating $R$ and $D$ data.

Three Archimedean copulas were employed to model annual maximum flood peak data of different lengths. Estimation methods based on ranks were employed for parameter estimation. Marginals were modeled with the generalized extreme value (GEV) distribution. Then, uncertainty in modeling results was investigated with the change in data length. The joint and conditional return periods were also analyzed with the selected copula model to see how it varied with data length. Results showed that the accuracy of modeling deteriorated with the decrease in data length and that the best-fitting copula model depended on the data length. The uncertainty of modeling results may be due to the uncertainty of the flow itself when the data length is shortened. The data length has a negative effect not only on copula modeling but may also have an adverse effect on the marginal, which is an important factor when using a copula model to do bivariate analysis.

Although they are widely in use and in many cases are mandated in Europe and North America, the installation of green roofs has been relatively limited in Melbourne and Australia as a whole. A case study is presented that investigates the implementation of green roofs across the Melbourne central business district (CBD) in Australia. The study aims to give an immediately possible and best-case scenario for the benefits achievable depending on the proportion of Melbourne’s roof area covered with green roofs. The following environmental benefits (EBs) of the retrofit are assessed for both scenarios for a city block: (EB No. 1) reduction in urban heat island (UHI) effect (EBUHI), (EB No. 2) reduction in runoff volume (EBR), (EB No. 3) improved water quality (EBL), (EB No. 4) reduction in storm water runoff peak flow (EBPF), and (EB No. 5) greenhouse gas (GHG) emission reduction (EBGHG). These benefits are then scaled up, assuming that the retrofit of green roofs is expanded to cover the entire CBD roof area in the same proportions. This demonstrates the range and scale of benefits that could be achieved under widespread implementation, such as temperature reductions, significantly reducing the UHI effect, and estimated reductions of storm water runoff peak flow, which may mitigate or reduce the frequency and severity of flash flooding.