Showing papers in "Journal of Hydrologic Engineering in 2012"
TL;DR: In this paper, the authors presented a new methodology on the basis of subsection time series plots derived from a given time series on a Cartesian coordinate system, in which trend free time series subsections appear along the 45° straightline.
Abstract: Hydrometeorological time series include recent trends, especially over the past 30 years, as a result of climate change impact according to the Intergovernmental Panel on Climate Change (IPCC). Although there are commonly used trend identification techniques, such as Mann-Kendall (MK) and Spearman’s rho (SR) tests, their validity is possible under a set of restrictive assumptions, such as independent structure of the time series, normality of the distribution, and length of data. It is also not possible to calculate trend magnitude (slope) except through regression approach, which brings additional assumptions for the theoretical validation in practical applications. This paper presents a new methodology on the basis of subsection time series plots derived from a given time series on a Cartesian coordinate system. In such a plot, trend free time series subsections appear along the 45° straightline. Increasing (decreasing) trends occupy upper (lower) triangular areas of the square area defined by t...
483 citations
TL;DR: In this paper, the authors investigated an approach to calculate the standardized streamflow index (SSI), which allows accurate spatial and temporal comparison of the hydrological conditions of a stream or set of streams.
Abstract: In this study, the authors investigated an approach to calculate the standardized streamflow index (SSI), which allows accurate spatial and temporal comparison of the hydrological conditions of a stream or set of streams. For this purpose, the capability of six three-parameter distributions (lognormal, Pearson Type III, log-logistic, general extreme value, generalized Pareto, and Weibull) and two different approaches to select the most suitable distribution the best monthly fit (BMF) and the minimum orthogonal distance (MD), were tested by using a monthly streamflow data set for the Ebro Basin (Spain). This large Mediterranean basin is characterized by high variability in the magnitude of streamflows and in seasonal regimes. The results show that the most commonly used probability distributions for flow frequency analysis provided good fits to the streamflow series. Thus, the visual inspection of the L-moment diagrams and the results of the Kolmogorov-Smirnov test did not enable the selection of a single ...
341 citations
TL;DR: In this article, the available storage in the bioretention cell, termed as the Bioretention Abstraction Volume (BAV), is analyzed and a finite capacity to completely store all runoff from smaller events is defined by the BAV.
Abstract: The transportation and urban infrastructure relies heavily on impervious surfaces. Unmitigated rainfall runoff from impervious surfaces can lead to a myriad of environmental problems in downgradient areas. To address this issue, novel stormwater control measures (SCMs) are being emphasized and implemented widely to mitigate some of the impacts of impervious surface. Bioretention is a soil/media-based SCM that is often used for this purpose, but current design practices are highly empirical. This study compiles work from three research sites in three states to provide some fundamental underpinnings to bioretention design. Although all sites demonstrate different levels of performance, water volumetric performance trends are common to all. These trends are based on the available storage in the bioretention cell, termed herein as the Bioretention Abstraction Volume (BAV). The BAV is directly related to available media porosity and storage in the surface bowl. A finite capacity to completely store all runoff from smaller events is defined by the BAV. Normalization for this storage provides prediction for volumetric performance. Recommendations for bioretention design are provided.
138 citations
TL;DR: Wang et al. as mentioned in this paper analyzed the risk of flooding caused by such flood coincidences in consideration of flood magnitudes and the time (dates) of occurrence, and two four-dimensional copula functions were developed for the joint distribution of flood magnitude and occurrence dates.
Abstract: The coincidence of flood flows of the mainstream and its tributaries may determine flood peaks. This study analyzed the risk of flooding caused by such flood coincidences in consideration of flood magnitudes and the time (dates) of occurrence. The Pearson Type III distribution and the Log Pearson Type III are selected as the marginal distributions of flood magnitudes for annual maximum flood series, and the mixed von Mises distribution is selected as the marginal distribution of flood occurrence dates. Two four-dimensional copula functions are developed for the joint distribution of flood magnitudes and occurrence dates. The upper Yangtze River in China and the Colorado River in the U.S. are selected to evaluate the computation method for risk analysis. The coincidence probabilities of flood magnitudes and occurrence dates are calculated, and the conditional probabilities for the Three Gorges Reservoir (TGR) are analyzed. Results show that the von Mises distribution can fit the observed flood dates data well. The X-Gumbel copula is selected for risk analysis. Based on the proposed model, the coincidence and conditional probabilities for any return period can be obtained.
113 citations
TL;DR: The obtained results showed that the proposed model can monitor both short and long term patterns due to the use of multiscale time series of rainfall and runoff data as the GP inputs, and was compared favorably to ANN and GP models.
Abstract: In this paper, the wavelet analysis was linked to the genetic programming (GP) concept for constructing a hybrid model to detect the seasonality patterns in the rainfall–runoff time process. This approach was used to determine the dominant input variables of an artificial neural network (ANN) rainfall–runoff model via a sensitivity analysis. In this way, the main time series of two variables, rainfall and runoff, were decomposed into some multi frequency time series by the wavelet transform. Then, these decomposed time series were imposed as input data to the GP to optimize the input structure of ANN model. This methodology was utilized in daily and monthly timescale modeling for two watersheds with distinct climatologic regimes. The obtained results were compared favorably to ANN and GP models. The obtained results showed that the proposed model can monitor both short and long term patterns due to the use of multiscale time series of rainfall and runoff data as the GP inputs. Moreover, using the ...
102 citations
TL;DR: In this article, the accuracy and consistency of the curve number method were evaluated using rainfall-runoff series from 10 small forested-mountainous watersheds in the eastern United States, eight annual maximum series from New Hampshire, West Virginia, and North Carolina, and two partial duration series from Georgia.
Abstract: Engineers and hydrologists use the curve number method to estimate runoff from rainfall for different land use and soil conditions; however, large uncertainties occur for estimates from forested watersheds. This investigation evaluates the accuracy and consistency of the method using rainfall-runoff series from 10 small forested-mountainous watersheds in the eastern United States, eight annual maximum series from New Hampshire, West Virginia, and North Carolina, and two partial duration series from Georgia. These series are the basis to compare tabulated curve numbers with values estimated using five methods. For nine of 10 watersheds, tabulated curve numbers do not accurately estimate runoff. One source of the large uncertainty is a consistent decrease in storm-event curve numbers with increasing rainfall. A calibrated constant curve number is suitable for only two of 10 watersheds; the others require a variable watershed curve number associated with different magnitude rainfalls or probabilities...
98 citations
TL;DR: In this paper, a laboratory study was performed to measure clogging by sand and clay (sodium montmorillonite) in a saturated pervious concrete pavement system, and the subsequent effect of surface cleaning by pressure washing.
Abstract: From a hydrologic perspective, one limitation of pervious concrete pavement is the risk of clogging, defined as a reduction in hydraulic conductivity that reduces infiltration into the pavement or exfiltration into the subgrade. Accordingly, a laboratory study was performed to measure clogging by sand and clay (sodium montmorillonite) in a saturated pervious concrete pavement system, and the subsequent effect of surface cleaning by pressure washing. Both sand and clay caused measurable clogging that was not reversible by pressure washing. However, even after clogging, the infiltration and exfiltration rates were well above the average intensity of 66 mm/h for the 100-year 1-h design storm for Denver. This result is encouraging, but should be interpreted with caution, because in these experiments the flow-limiting layer was never the pervious concrete, but rather the subgrade, which in this case was a thin layer of sand with a large hydraulic conductivity. Accordingly, this study suggests that pervious co...
87 citations
TL;DR: In this article, the role of the flow, topography, and roughness coefficient is investigated on the output of a one-dimensional Hydrologic Engineering Center-River Analysis System (HEC-RAS) model and flood inundation map for an observed flood event on East Fork White River near Seymour, Indiana (Seymour reach) and Strouds Creek in Orange County, North Carolina.
Abstract: The process of creating flood inundation maps is affected by uncertainties in data, modeling approaches, parameters, and geoprocessing tools. Generalized likelihood uncertainty estimation (GLUE) is one of the popular techniques used to represent uncertainty in model predictions through Monte Carlo analysis coupled with Bayesian estimation. The objectives of this study are to (1) compare the uncertainty arising from multiple variables in flood inundation mapping using Monte Carlo simulations and GLUE and (2) investigate the role of subjective selection of the GLUE likelihood measure in quantifying uncertainty in flood inundation mapping. The role of the flow, topography, and roughness coefficient is investigated on the output of a one-dimensional Hydrologic Engineering Center–River Analysis System (HEC–RAS) model and flood inundation map for an observed flood event on East Fork White River near Seymour, Indiana (Seymour reach) and Strouds Creek in Orange County, North Carolina. Performance of GLUE ...
84 citations
TL;DR: In this article, the authors investigate the hydraulic performance of urban drainage systems related to changes in rainfall, and through these hydraulic parameters describe the impact of climate change on urban drainage system.
Abstract: Changes in climate were a growing concern during the last decade and will be even greater in the coming years. When investigating the impact from changes in the climate on urban drainage systems, two challenges are (1) what type of input rainfall data to use and (2) what parameters to use to measure the impacts. The overall objective of this study is to investigate the hydraulic performance of urban drainage systems related to changes in rainfall, and through these hydraulic parameters describe the impact of climate change. Input rainfall data represent today’s climate and three future time periods (2011–2040, 2041–2070, and 2071–2100). The hydraulic parameters used were water levels in nodes (e.g., as the number of floods, and frequency and duration of floods) and pipe flow ratio. For the study area, the number of flooded nodes and the geographical distribution of floods will increase in the future, as will both the flooding frequency and the duration of floods.
84 citations
TL;DR: In this article, the authors investigated the effect of dense reservoir networks on water availability and/or sustainability of mesoscale or large-scale basins, including the Upper Jaguaribe Basin in Brazil.
Abstract: Dense reservoir networks, with thousand of small dams, can be increasingly found throughout the world, especially in water-scarce environments, such as the Brazilian northeastern region. Although the effect of individual small dams might be negligible, their joint effect has proved to be relevant on water and sediment connectivity. Literature, however, is scarce concerning the effect of such networks on water availability and/or sustainability of mesoscale or large-scale basins. This research intended both to assess the effect of the dense reservoir network of the semiarid Upper Jaguaribe Basin (UJB; 24,200 km2 , in Brazil) for a 45-year period (1961–2005) and to investigate a network arrangement that maximized its hydrologic sustainability. Imagery of the years 1970 and 2002 was analyzed to assess temporal evolution of the network. The Water Availability in Semiarid Environments (WASA) model, which proved valid for the Upper Jaguaribe Basin, was used to assess its sustainability for almost 100 different...
82 citations
TL;DR: The differential evolution (DE) algorithm is studied for estimation of Muskingum model parameter and a case study with actual data showed an excellent performance in its optimization result and performance analysis and demonstrates that DE is an alternative technique to estimate the parameters of the Muskedum model.
Abstract: The accurate estimation of Muskingum model parameter is essential to give flood routing for flood control in water resources management. The Muskingum model continues to be a popular method for flood routing, and its parameter estimation is a global optimization problem with the main objective to find a set of optimal model parameter values that attains a best fit between observed and computed flow. Although some techniques have been employed to estimate the parameters for Muskingum model, an efficient method for parameter estimation in Muskingum model is still required to improve the computational precision. Therefore, in this paper, the differential evolution (DE) algorithm is studied for estimation of Muskingum model parameter. A case study with actual data from previous literature, the experimental results showed an excellent performance in its optimization result and performance analysis and demonstrates that DE is an alternative technique to estimate the parameters of the Muskingum model.
TL;DR: In this article, the curve number (CN) method is used as a technique for estimating surface runoff depth from rainstorms in the Sicilian watershed, and three different methods are evaluated at the basin scale from rainfall-runoff multiday events, in the observation period 1940-1997 (recorded length mean equal to 20 years).
Abstract: The curve number (CN) method is widely used as a technique for estimating surface runoff depth from rainstorms. This simply lumped method is based on the main parameter CN, which represents the lumped expression of basin absorption, and on a parameter that represents interception, infiltration during the early part of a storm, and surface depression storage, called initial abstraction. In this paper, CN is evaluated at the basin scale from rainfall-runoff multiday events, in the observation period 1940–1997 (recorded length mean equal to 20 years) for 61 Sicilian basins with three different methods: NEH4 method, asymptotic fitting method, and a least-squares method. A first analysis of Sicilian watershed behavior indicates a major occurrence of standard CN response (43 basins), as opposed to a complacent response (10 basins), and a few cases of violent behavior (3 basins). For basins with complacent behavior a modified formula of a runoff CN equation is proposed. The original assumption of the ini...
TL;DR: The development of models using Artificial Neural Network with back propagation and Levenberg-Maquardt algorithms, radial basis function, Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir in northern India are presented.
Abstract: The prediction of the sediment loading generated within a watershed is an important input in the design and management of water resources projects. High variability of hydro-climatic factors with sediment generation makes the modelling of the sediment process cum- bersome and tedious. The methods for the estimation of sediment concentration based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment concentration. The focus of this paper is to present the development of models using Artificial Neural Network (ANN) with back propagation and Levenberg-Maquardt algorithms, radial basis function (RBF), Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir, located in the Sutlej basin in northern India. The input vector to the various models using different algorithms was derived con- sidering the statistical properties such as auto-correlation function, partial auto-correlation, and cross-correlation function of the time series. It was found that the M5 model performed well compared to other soft computing techniques such as ANN, fuzzy logic, radial basis function, and REPTree investigated in this study, and results of the M5 model indicate that all ranges of sediment concentration values were simulated fairly well. This study also suggests that M5 model trees, which are analogous to piecewise linear functions, have certain advantages over other soft computing techniques because they offer more insight into the generated model, are acceptable to decision makers, and always converge. Further, the M5 model tree offers explicit expressions for use by field engineers. DOI: 10.1061/(ASCE)HE.1943-5584.0000445. © 2012 American Society of Civil Engineers. CE Database subject headings: Suspended sediment; Neural networks; Fuzzy sets; Reservoirs. Author keywords: Suspended sediment concentration; Neural networks; Fuzzy Logic; M5; REPTree; Bhakra reservoir.
TL;DR: In this paper, a comparative case study between SWMM and a presently developed fuzzy logic model for the predictions of total runoff within the watershed of Cascina Scala, Pavia in Italy is presented.
Abstract: A comprehensive hydrological model, like the storm water management model (SWMM), has been widely used for rainfall-runoff simulation. In recent years, simple and effective modern modeling techniques have also brought great attention to the prediction of runoff with rainfall input. A comparative case study between SWMM and a presently developed fuzzy logic model for the predictions of total runoff within the watershed of Cascina Scala, Pavia in Italy is presented. A data set of 23 events from 2000 to 2003 including with the total rainfall and total runoff are adopted to train fuzzy logic parameters. Other data (1990–1995) with detailed time variations of rainfall and runoff are available for the setup and calibration of SWMM for runoff modeling. Among the 1990–1995 data, 35 independent rainfall events are selected to test the prediction performance of the SWMM and fuzzy logic models by comparing the predicted total runoffs with measured data. Comparisons and performance analyses in terms of the root-mean-squared error and coefficient of efficiency are made between the SWMM and the fuzzy logic model. The predicted total runoffs from either the SWMM or the fuzzy logic model are found to agree reasonably well with the measured data. For large rainfall events, the fuzzy logic model generally outperforms the SWMM unless the modification of the impervious ratio is applied to improve the SWMM results. However, the SWMM can produce the time varying hydrograph whereas fuzzy logic is subject to limitation of the methodology and is unable to generate such an output.
TL;DR: In this paper, a detailed modeling of rainfall runoff processes and flow routing along a complex large-scale region, the Upper Paraguay River Basin (UPRB), encompassing a drainage area of approximately 600,000 km2, which extends over Brazil, Paraguay, and Bolivia.
Abstract: This paper presents a detailed modeling of rainfall-runoff processes and flow routing along a complex large-scale region, the Upper Paraguay River Basin (UPRB), encompassing a drainage area of approximately 600,000 km2, which extends over Brazil, Paraguay, and Bolivia. Within the UPRB lies the Pantanal, the world’s largest wetland, with extraordinary biodiversity and great ecologic value, but which currently is threatened by anthropogenic activities. A conceptual model was applied with two main components: (1) simulation of the basin and part of the Paraguay River tributaries by means of the distributed large-scale hydrological model MGB-IPH using simpler flow routing methods; and (2) simulation of the main drainage network, approximately 4,800 km of river reaches, with a one-dimensional hydrodynamic model. Despite the data scarcity, complexity, and the intricate river drainage network of the region, the coupled model was able to represent the hydrological regime of the basin. Comparisons between...
TL;DR: In this article, the authors examined sensitivity of model performance to the objective function used during automated calibrations, and found that the model performance is sensitive to both the absolute deviations and residuals.
Abstract: Previous studies have reported limitations of the efficiency criteria commonly used in hydrology to describe goodness of model simulations This study examined sensitivity of model performance to the objective function used during automated calibrations Nine widely used efficiency criteria were evaluated for their effectiveness as objective function, and goodness of the model predictions were examined using 13 criteria Two cases (Case I: Using observed streamflow data and Case II: Using simulated streamflow) were considered to accomplish objectives of the study using a widely used watershed model (SWAT) and good-quality field data from a well-monitored experimental watershed Major findings of the study include (1) automated calibration results are sensitive to the objective function group—group that work based on minimization of the absolute deviations (Group I), group that work based on minimization of square of the residuals (Group II), and groups that use log of the observed and simulated st
TL;DR: In this article, a nonlinear aggregated drought index (NADI) was developed first to classify the drought condition of a catchment considering all significant hydrometeorological variables that have effects on droughts.
Abstract: Drought forecasting plays an important role in the planning and management of water resources systems, especially during dry climatic periods. In this study, a nonlinear aggregated drought index (NADI) was developed first to classify the drought condition of a catchment considering all significant hydrometeorological variables that have effects on droughts. An artificial neural network (ANN)—based drought forecasting approach was then developed by using the time series of the NADI to forecast NADI values. In forecasting future drought conditions, the NADI produces the overall dryness within the system as compared to the traditional forecasting of rainfall deficiency, which considers only the meteorological droughts. Two ANN forecasting models, namely a recursive multistep neural network (RMSNN) and a direct multistep neural network (DMSNN), were developed in this study. Overall, these models were capable of forecasting drought conditions well for up to 6 months of future forecasts, which were stat...
TL;DR: The Natural Resources Conservation Service (NRCS) developed the runoff curve-number (CN) method for estimating direct runoff from storm rainfall as mentioned in this paper, which was used for designing structures and for evaluating their effectiveness Structural design is usually based on a single event of a certain probability of occurrence.
Abstract: The Natural Resources Conservation Service (NRCS) [previously Soil Conservation Service (SCS)] developed the SCS runoff curve-number (CN) method for estimating direct runoff from storm rainfall The NRCS uses the CN method for designing structures and for evaluating their effectiveness Structural design is usually based on a single event of a certain probability of occurrence During the years when many floodwater-retarding watershed projects were planned and constructed (1950–1980), the CN equation was used in a continuous mode to evaluate the projects To operate CN in a continuous mode, runoff was estimated from a daily rainfall record of approximately 30 years For each day of recorded rainfall, the five-day antecedent rainfall was used to assign a CN1 (dry condition), CN2 (average condition), or CN3 (wet condition), and runoff was estimated with the appropriate CN With the development of continuous hydrologic simulation models, CN was related directly to soil water content or estimated usin
TL;DR: In this paper, the average of the maximum floods (Q¯ ) to the drainage area with an exponent of 0.8Q¯ ¼ CA was derived, assuming in the absence of sufficient data the relationship between discharge and area was parabolic.
Abstract: = maximum discharge; A = drainage area; andC = coefficient related to the region. O’Connell chose a valueof 0.5 for the exponent, assuming in the absence of sufficient datathat the relationship between discharge and area was parabolic(Dooge 1986).With nearly 50 years of additional data, Fuller (1914) analyzedlong records of daily flows and peak flows from around the world,but particularly from the United States. He related the average ofthe maximum floods (Q¯ ) to the drainage area with an exponentof 0.8Q¯ ¼ CA
TL;DR: In this paper, the authors investigated the effects of spatial resolution on model predictions in an urban catchment, and to understand the mechanism(s) responsible for the scale effect, and developed models at various spatial resolutions, perform simulations, and compare the predictions of total outflow volume and peak flow.
Abstract: Model subdivision is used to capture spatial heterogeneity in input parameters and it is well-established that spatial resolution (i.e., degree of aggregation) affects model output. However, a general consensus about the effect does not exist. The objective of this study was to investigate the effects of spatial resolution on model predictions in an urban catchment, and to understand the mechanism(s) responsible for the scale effect. The general approach is to develop models at various spatial resolutions, perform simulations, and compare the predictions of total outflow volume and peak flow. Models were developed on the basis of actual drainage networks, and artificial ones generated on the basis of a fractal algorithm by using the Artificial Network Generator (ANGel). Simulations were performed by using the EPA Storm Water Management Model (SWMM), and model output was compared for 50 storms. There was very little difference in the total annual outflow volumes predicted by the different resolutions. Howe...
TL;DR: In this article, the authors compared the performance of GEP, neuro-fuzzy (NF), and artificial neural network (ANN) techniques to estimate suspended sediment loads by using recorded daily river discharge and sediment load data and found that the GEP model performed better than the NF and ANN models.
Abstract: Accurate estimation of sediment loads is important for the management and construction of water resources projects. In the first part of this study, the convenient gene expression programming (GEP), neuro-fuzzy (NF), and artificial neural network (ANN) techniques were applied to estimate suspended sediment loads by using recorded daily river discharge and sediment load data. These models were compared with one another in terms of the coefficient of determination, root mean square error, mean absolute error, variance accounted for, and Nash-Sutcliffe statistic criteria. It was found that the GEP model performed better than the NF and ANN models. In the second part of this study, the discrete wavelet conjunction models with convenient GEP, NF, and ANN techniques were constructed and compared with one another. Comparison results indicated that the wavelet conjunction models significantly increased the accuracy of single GEP, NF, and ANN models in suspended sediment estimation. The wavelet-GEP model p...
TL;DR: In this paper, a five parameter daily vegetated roof water balance model (VR-WBM) was developed, calibrated, and validated by using experimental Vegetated Roof data from the Seacoast, New Hampshire region.
Abstract: A five parameter, daily vegetated roof water balance model (VR-WBM) was developed, calibrated, and validated by using experimental vegetated roof data from the Seacoast, New Hampshire region. The lysimeter experiment on a sedum canopy characterized water storage with a 0.051 mm resolution. Overall, the results show that the average stormwater runoff reduction was 32% for the study period, and an average reduction per storm was 57%. Average daily evapotranspiration (ET) rates were 1.24 mm/day during the warmest month and 0.52 mm/day during the coolest month. For well-watered conditions, the ET losses were well-modeled by using a grass reference evapotranspiration (ET) value with a crop coefficient of 0.53 for the study’s sedum canopy in which the onset of stomatal closure occurs when the soil moisture is 0.11 m3/m3. When soil moisture content values are lower than 0.11 m3/m3, evapotranspiration rates decrease linearly with declining soil wetness. The VR-WBM does an excellent job predicting runo...
TL;DR: In the United States, the Wolf Creek Dam, the largest artificial reservoir east of the Mississippi River, has periodically undergone grouting of seage holes throughout 2010 as mentioned in this paper, and this grouting may not take long.
Abstract: As the world’s population increases, the rising demand for water will be compounded further by the need to sustain economic growth (Vorosmarty et al. 2000). According to one report by the United Nations Environment Program (UNEP), the stress on freshwater resources is expected to significantly magnify and spread to other regions of the world by 2025 (see Fig. 1; UNEP 2002). Historically, one of the common engineering solutions to guarantee a steady water supply against a rising demand has been to construct surface water impoundments on rivers. Such large-scale infrastructure, commonly known as dams and artificial reservoirs, trap a sufficiently large amount of water from the local hydrologic cycle to make up for a shortfall when demand exceeds the variable supply from nature. In other words, dams can be regarded as a strategic (long-term) solution to resolve the tactical (short-term) challenges of balancing the water deficit compounded by population growth and economic activity. In the United States, statistics suggest that building dams is outdated and considered a twentieth-century construct by the civil engineering profession (Fig. 2) Graf et al. 2010; Graf 1999). However, for vast regions of the underdeveloped or developing world, large dam-construction projects are being implemented in increasing numbers for tackling the rising water deficit in emerging economies (Fig. 3). Examples of such large dam projects are the Southeast Anatolia Project, or GAP (Turkish acronym) project, in Turkey, comprising 22 dams on the Tigris and Euphrates rivers (Unver 1997), the Three Gorges Dam (TGD) in China (Shen and Xie 2004), Itaipu Dam in Brazil (Pierce 1995), and the proposed Indian River Linking Project (Misra et al. 2007). From a global perspective, dam operations and water management in impounded basins remain relevant worldwide, while dam design and building are pertinent mostly to the developing world, comprising Africa, South America, and Asia, where most of the rivers remain unregulated. The heritage of modern dam building is nearly a century old. For example, the construction of the oldest dam in the Tennessee River Valley, called the Wilson Dam in Alabama, began in 1918 (Gebregiorgis and Hossain 2012). With a long heritage built on knowledge gained from previous failures and success stories, the civil engineering profession has made tremendous progress in dam safety against hazards of earthquakes (e.g., Marcuson et al. 1996), piping/seepage (e.g., Casagrande 1961; Sherard 1987), structural instability (e.g., Terzaghi and LaCroix 1964; Vick and Bromwell 1989), and optimization of dam operations to serve multiple, but competing, applications (Dai and Labadie 2001; Datta and Burges 1984). Similarly, much is now known about the management of postdam effects on aquatic ecology (e.g., Ligon et al. 1995; Richter et al. 1996), riparian vegetation (e.g., Merritt and Cooper 2000), geomorphology (e.g., Graf 2006), and dam removal as a result of sedimentation (Morris and Fan 1998; Graf et al. 2010). In general, the aspects of dam design and operations that have improved during the last century are those that are directly visible or have instantaneous impact on the land surface. This is not surprising, as the essence of engineering is hands-on in nature. What can be touched, sensed, and immediately visualized in the real world can be accounted for in the design and operation of an infrastructure. For example, the importance of fish ladders to minimize the disturbance to predam fish-migration paths was quickly appreciated by the engineering community during the early history of dam building. Now fish ladders are a common provision during the planning of a dam along a river. Similarly, when the Teton Dam failed (Sherard 1987), the importance of design provisions to minimize seepage, particularly in karstic geology, has now become a standard engineering practice. The Wolf Creek Dam, the largest artificial reservoir east of the Mississippi River, has periodically undergone grouting of seepage holes throughout its existence (Boynton and Hossain 2010). With increased fluctuation of flows downstream of dams, it did not take long for the concept of environmental flow (Tharme 2003) and indicators of hydrologic alteration (IHA) (Richter et al. 1996) to be devised for better ecosystem-centric dam operations in impounded basins. When more residential and commercial development is planned in an impounded river basin, it is intuitive to the engineer that the increase in imperviousness of the land surface may require larger detention basins at select locations to account for the increased runoff and erosion from excess rainfall. The climatic impacts (i.e., feedbacks) of dams, however, are unique areas that have received little consideration by the engineering profession for dam building and operations. Climate, by virtue of its definition, represents anything but a hands-on phenomenon. Unlike weather, climate impacts are not measured instantaneously. Given the current breadth of engineering curricula that exclude atmospheric and climate-science subjects as prerequisites at the freshmen and sophomore levels, a large artificial lake having an
TL;DR: In this article, the authors used Pettitt-Mann-Whitney change-point statistical analysis to identify the hydrologic change points caused by human activities and to quantify hydrological alterations in the system.
Abstract: The Illinois River is a tributary of the Mississippi River that connects Lake Michigan and the Mississippi River. Starting in 1848 when the Illinois and Michigan Canal began to open, the Illinois River has experienced some major human activities such as the Lake Michigan flow diversion, creation of levee and drainage districts on floodplains, and construction of locks and dams on the river. This paper uses Pettitt-Mann-Whitney change-point statistical analysis to identify the hydrologic change points caused by human activities and to quantify hydrologic alterations in the system. Observed stage data from 12 U.S. Army Corps of Engineers gauges and observed flows from three U.S. Geological Survey gauges were used to analyze human effects on hydrologic and hydraulic conditions in the Illinois River. The year 1938 was identified as the change point for low flows and low stages and 1972 as the change point for high flows and high stages. The low flow and stage condition changes were due to a combination of added flow from Lake Michigan, levee and drainage district construction, and construction of locks and dams, whereas the high flow and stage condition changes were due to hydroclimatic change within the Illinois River basin. Analyses based on the Indicators of Hydrologic Alteration (IHA) have shown that the magnitudes, frequency, duration, and number of reversals during low flood conditions were greatly modified by: (1) the construction of locks and dams on the Illinois River that were completed in 1938, (2) the reduction of flow diversion from Lake Michigan, and (3) the hydroclimatic condition change around 1972. The latter change probably contributed to the loss of both soil-moist plants and submerged aquatic plants that once provided several important ecosystem services in the system. The analyses described in this paper, coupled with hydraulic and ecological models, can help with site selection and management plans for the ecosystem restoration of floodplains in regulated rivers. DOI: 10.1061/(ASCE)HE .1943-5584.0000465. © 2012 American Society of Civil Engineers.
TL;DR: In this article, the authors evaluated four curve number (CN) determination methods from 16 watersheds in the southwestern U.S. using 1,284 events that satisfy rainfall and runoff criteria.
Abstract: Four curve number (CN) determination methods are evaluated from 16 watersheds in the southwestern U.S. using 1,284 events that satisfy rainfall and runoff criteria. The use of ordered pairs versus natural pairs of rainfall and runoff data has a larger effect on the CN, whereas the difference from using a partial duration series versus an annual series was not significant. The best-available USDA soil series data were obtained for 20 Arizona watersheds and 10 groups of New Mexico natural runoff plots. The hydrologic soil groups (HSG) were determined from either direct USDA assignment or textural properties and compared with the HSGs required by the CNs and the cover condition. This study showed a standard error of about one HSG, resulting in an error in CN of approximately seven units when using the best-available data. Compared with USDA handbook table values, the CNs found from rainfall and runoff data were higher for 21 of the 30 semiarid watersheds.
TL;DR: In this paper, a global sensitivity analysis (SA) model has been used to investigate seasonal sensitivity of streamflow parameters of a watershed simulation model on the headwaters of the Little River Watershed, one of the United States Department of Agriculture's experimental watersheds.
Abstract: Computer models have become vital decision-making tools in many areas of science and engineering including water resources. However, models should be properly evaluated before use to improve the likelihood of making sound decisions based on their results. The model evaluation technique practiced today in hydrology assumes that model parameters are season insensitive and attempts to identify “optimal” values that would describe watershed behavior during dry and wet seasons. This assumption could compromise accuracy of model predictions. This study demonstrates performance improvement that would be achieved when a season-based model evaluation approach is pursued. A global sensitivity analysis (SA) model has been used to investigate seasonal sensitivity of streamflow parameters of a watershed simulation model on the headwaters of the Little River Watershed, one of the United States Department of Agriculture’s experimental watersheds. Two separate analyses have been performed: the conventional approach in wh...
TL;DR: In this article, a Bayesian, inference-based, Markov chain Monte Carlo (MCMC) method coupled with an autoregressive moving average (ARMA) error model framework was used to assess the uncertainty of the HYDROTEL when simulating daily streamflows.
Abstract: In this study, a Bayesian, inference-based, Markov chain Monte Carlo (MCMC) method coupled with an autoregressive moving average (ARMA) error model framework was used to assess the uncertainty of the process-based, continuous, distributed hydrological model HYDROTEL when simulating daily streamflows. The uncertainty analysis was performed, as a case study, in two distinct watersheds (Montmorency, Quebec, Canada, and Sassandra, Ivory Coast, West Africa). The MCMC uncertainty analysis showed to be effective, primarily with respect to the fulfillment of the statistical assumptions of the error model. The results of the uncertainty analyses demonstrated that almost 95% of the observed daily outlet flows were bracketed by the 95% prediction uncertainty bands. This indicates that the parameter uncertainty associated with the ARMA error model could reach the prediction uncertainty. It was possible to mimic the prediction uncertainty using only the most sensitive model parameters for the Montmorency and S...
TL;DR: In this paper, two artificial neural network (ANN) models were developed for semidistributed modeling of the suspended sediment load process of the Eel River watershed located in California.
Abstract: The sediment load transported in a river is the most complex hydrological phenomenon due to a large number of obscure parameters and the existence of both spatial variability of the basin characteristics and temporal climatic patterns. In this paper two artificial neural network (ANN) models were developed for semidistributed modeling of the suspended sediment load process of the Eel River watershed located in California. The first model was an integrated ANN model trained by the data of multiple stations inside the watershed. In the second model, a geomorphology-based ANN model, space-dependent geomorphologic parameters of the subbasins, extracted by geographic information system tools, accompanied by time-dependent meteorological data, were imposed on the network. In both models, three-layer perceptron neural networks were trained considering various combinations of input and hidden layers’ neurons, and the optimum architectures of the models were selected according to the computed evaluation cr...
TL;DR: In this article, the authors investigated the role of bioretention in reducing peak flows in an urbanized watershed in Blacksburg, Virginia by using two modeled scenarios: one where runoff from many land uses was routed through the practice and another in which only runoff from large impervious areas was routed.
Abstract: Although many studies have evaluated the hydrologic effects of bioretention at the site-level, few have investigated the role bioretention plays when distributed throughout a watershed. This study aims to assess bioretention’s effects on an urbanized watershed in Blacksburg, Virginia by using two modeled scenarios: one where runoff from many land uses was routed through the practice, and another in which only runoff from large impervious areas was routed. Peak flows, volumes, and lag times from these models were compared to the watershed’s current and predeveloped conditions. Both scenarios provided reductions in peak flows with respect to existing conditions for modeled storm events, sometimes to levels below the predeveloped condition. Neither case was able to reduce volumes to predevelopment levels; the option to treat impervious areas had a negligible effect on runoff volume. Both cases were able to extend lag times from the existing development condition. On the basis of these results, bioretention a...
TL;DR: In this article, the state-of-the-art nonparametric method K-Nearest Neighbor (K-NN) approach was used for downscaling models with an emphasis on optimal choice in selection of nearest neighbors.
Abstract: The climate impact studies in hydrology often rely on climate change information at fine spatial resolution. Because general circulation models (GCMs) operate on a coarse scale, the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, downscaling models are developed using the state-of-the-art nonparametric method K-Nearest Neighbor (K-NN) approach, with an emphasis on optimal choice in selection of nearest neighbors for obtaining simultaneous projections of mean monthly maximum and minimum temperatures (Tmax and Tmin) as well as monthly precipitation and pan evaporation to lake-basin scale in a semiarid region that is considered to be a climatically sensitive region in India. The performance of the K-NN approach was evaluated based on several statistical performance indicators. A comparison of K-NN has been made with a linear multiple regression (LMR)-based downscaling model. Also, the prevailing view in the literature regarding optimal...