Showing papers in "Journal of Hydrologic Engineering in 2006"

Evaluation of the Nash-Sutcliffe Efficiency Index

Abstract: The Nash–Sutcliffe efficiency index ( Ef ) is a widely used and potentially reliable statistic for assessing the goodness of fit of hydrologic models; however, a method for estimating the statistical significance of sample values has not been documented. Also, factors that contribute to poor sample values are not well understood. This research focuses on the interpretation of sample values of Ef . Specifically, the objectives were to present an approximation of the sampling distribution of the index; provide a method for conducting hypothesis tests and computing confidence intervals for sample values; and identify the effects of factors that influence sample values of Ef including the sample size, outliers, bias in magnitude, time-offset bias of hydrograph models, and the sampling interval of hydrologic data. Actual hydrologic data and hypothetical analyses were used to show these effects. The analyses show that outliers can significantly influence sample values of Ef . Time-offset bias and bias in magnit...

Bivariate Flood Frequency Analysis Using the Copula Method

Abstract: Using the copula method, bivariate distributions of flood peak and volume, and flood volume and duration were derived. A major advantage of this method is that marginal distributions of individual variables (i.e., flood peak, volume, and duration) can be of any form and the variables can be correlated. The copula method was applied to obtain the conditional return periods that are needed for hydrologic design. The derived distributions were tested using flood data from Amite River at Denham Springs, La., and the Ashuapmushuan River at Saguenay, Quebec, Canada. The derived distributions were also compared with the Gumbel mixed and the bivariate Box–Cox transformed normal distributions. The copula-based distributions were found to be in better agreement with plotting position-based frequency estimates than were other distributions.

Application of Support Vector Machine in Lake Water Level Prediction

Abstract: This paper examines the potential of the support vector machine (SVM) in long-term prediction of lake water levels. Lake Erie mean monthly water levels from 1918 to 2001 are used to predict future water levels up to 12 months ahead. The results are compared with a widely used neural network model called a multilayer perceptron (MLP) and with a conventional multiplicative seasonal autoregressive model (SAR). Overall, the SVM showed good performance and is proved to be competitive with the MLP and SAR models. For a 3- to 12-month-ahead prediction, the SVM model outperforms the two other models based on root-mean square error and correlation coefficient performance criteria. Furthermore, the SVM exhibits inherent advantages due to its use of the structural risk minimization principle in formulating cost functions and of quadratic programming during model optimization. These advantages lead to a unique optimal and global solution compared to conventional neural network models.

Artificial Neural Network Models of Daily Pan Evaporation

Abstract: Artificial neural network (ANN) models are proposed as an alternative approach of evaporation estimation for Lake Egirdir. This study has three objectives: (1) to develop ANN models to estimate daily pan evaporation from measured meteorological data; (2) to compare the ANN models to the Penman model; and (3) to evaluate the potential of ANN models. Meteorological data from Lake Egirdir consisting of 490 daily records from 2001 to 2002 are used to develop the model for daily pan evaporation estimation. The measured meteorological variables include daily observations of air and water temperature, sunshine hours, solar radiation, air pressure, relative humidity, and wind speed. The results of the Penman method and ANN models are compared to pan evaporation values. The comparison shows that there is better agreement between the ANN estimations and measurements of daily pan evaporation than for other model.

Closure of "Watershed-Scale Evaluation of a System of Storm Water Detention Basins"

Abstract: The effectiveness of an existing system of storm water detention basins operating at the watershed scale is evaluated. Data utilized in the study were collected from Valley Creek watershed in Chester County, Pa., which has undergone rapid development from the westward spread of suburban Philadelphia. Since the late 1970s, more than 100 storm water detention basins have been constructed in this 62 km2 (24 mi2 ) watershed, each designed on a site-by-site basis. The design objective of these detention basins is to limit a site’s postconstruction peak flow rate to or below its predevelopment level for 2- through 100-year storms. To evaluate the watershed-wide effectiveness of the network of detention basins, all basins were surveyed and included in a hydrologic model of the watershed. The U.S. Army Corps of Engineers Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was calibrated by using measured rainfall and observed streamflow from a U.S. Geological Survey (USGS) stream gauge. Res...

Identification of groundwater pollution sources using GA-based linked simulation optimization model

Abstract: The genetic algorithm (GA)–based simulation optimization approach is used for optimal identification of unknown groundwater pollution sources Simple as well as complex scenarios of multiple unknown groundwater pollution sources are considered A flow and transport simulation model is externally linked to the GA-based optimization model to simulate the physical processes involved The simulation model uses potential pollution source characteristics that are evolved by the GA and simulates the resulting concentration measurement values at observation locations These simulated spatial and temporal pollutant concentration measurement values are used to evaluate the fitness function value of the GA The main advantage of the proposed methodology is the external linking of the numerical simulation model with the optimization model This approach makes it feasible to solve the source-identification problems for complex aquifer study areas with multiple unknown pollution sources The performance of the developed methodology is evaluated for combinations of source characteristics (locations, magnitudes, and release periods), data availability conditions, and concentration measurement error levels

Sediment Transport Model for Seepage Erosion of Streambank Sediment

Abstract: Erosion by lateral, subsurface flow is known to erode streambank sediment in numerous geographical locations; however, the role of seepage erosion on mass failure of streambanks is not well understood. In the absence of an established sediment transport model for seepage erosion, the objectives of this research were to investigate the mechanisms of erosion due to concentrated, lateral subsurface flow and develop an empirical sediment transport model for seepage erosion of noncohesive sediment on near-vertical streambanks. Laboratory experiments were performed using a two-dimensional soil lysimeter of a reconstructed streambank profile packed with three different soil layers to mimic seepage erosion occurring at Little Topashaw Creek (LTC) in northern Mississippi. Soil samples from LTC streambanks indicated considerable hydraulic conductivity contrast between an overlying silt loam layer (SiL), highly permeable loamy sand, and confining clay loam layer. Lysimeter experiments were conducted with various ups...

Modified K-NN Model for Stochastic Streamflow Simulation

Abstract: This paper presents a lag-1 modified K-nearest neighbor K-NN approach for stochastic streamflow simulation. The simula- tion at any time t given the value at the time t1 involves two steps: 1 obtaining the conditional mean from a local polynomial fitted to the historical values of time t and t1, and 2 then resampling i.e., bootstrapping a residual at one of the historical observations and adding it to the conditional mean. The residuals are resampled using a probability metric that gives more weight to the nearest neighbor and less to the farthest. The "residual resampling" step is the modification to the traditional K-NN time-series bootstrap approach, which enables the generation of values not seen in the historical record. This model is applied to monthly streamflow at the Lees Ferry stream gauge on the Colorado River and is compared to both a parametric periodic autoregressive and a nonparametric index sequential method for streamflow generation, each widely used in practice. The modified K-NN approach is found to exhibit better performance in terms of capturing the features present in the data. The need to identify alternatives that will improve upon the ISM motivated the research presented in this paper. The ISM is a "nonparametric" method in that it makes no assumption of the functional form of the underlying model; instead, the method is data-driven. Keeping with the "nonparametric" spirit of ISM, we developed the proposed modified K-nearest neighbor K-NN method. The proposed approach retains all the aspects of the traditional K-NN time series bootstrap technique developed by Lall and Sharma 1996, but the "modification" enables simu- lating values not seen in the historical record. We evaluate the performance of our proposed approach by applying it to the monthly streamflow data from U.S. Geological Survey USGS stream gauge 09380000 located on the Colorado River at Lees Ferry, Arizona. We also compare the modified K-NN method with the ISM and a first-order periodic autoregressive model PAR1, each widely used in practice. The paper is organized as follows: a brief background on stochastic streamflow modeling including a description of the ISM and the PAR models is first presented, for the benefit of readers. Our proposed approach is then presented. A description of the results and summary conclude the presentation.

Runoff and Sediment Yield Modeling using Artificial Neural Networks: Upper Siwane River, India

Abstract: Accurate estimation of both runoff and sediment yield is required for proper watershed management. Artificial neural network (ANN) models were developed, to predict both runoff and sediment yield on a daily and weekly basis, for a small agricultural watershed. A total of five models were developed for predicting runoff and sediment yield, of which three models were based on a daily interval and the other two were based on a weekly interval. All five models were developed both with one and two hidden layers. Each model was developed with five different network architectures by selecting a different number of hidden neurons. The models were trained using monsoon season (June to October) data of five years (1991–1995) for different sizes of architecture, and then tested with respective rainfall and temperature data of monsoon season (June to October) of two years (1996–1997). Training was conducted using the Levenberg–Marquardt backpropagation where the input and output were presented to the neural network a...

Hydrologic Modeling of an Eastern Pennsylvania Watershed with NEXRAD and Rain Gauge Data

Abstract: This paper applies the Soil Water Assessment Tool (SWAT) to model the hydrology in the Pocono Creek watershed located in Monroe County, Pa. The calibrated model will be used in a subsequent study to examine the impact of population growth and rapid urbanization in the watershed on the base flow and peak runoff. Of particular interest in this paper is the exploration of potential use of Next Generation Weather Radar (NEXRAD) technology as an alternative source of precipitation data to the conventional surface rain gauges. NEXRAD estimated areal average precipitations are shown to compare well with the gauge measured ones at two climate stations in the study area. Investigation of the spatially distributed NEXRAD precipitation estimates revealed that average annual precipitation can vary spatially as much as 12% in the Pocono Creek watershed. The SWAT model is calibrated and validated for monthly stream flow, base flow, and surface runoff. Hydrographs generated from both gauge and NEXRAD driven model simula...

Nonparametric Approach for Bivariate Drought Characterization Using Palmer Drought Index

Abstract: A drought is usually represented by duration and severity, and may last several months or years. Multidimensional characteristics of a drought make univariate analysis unable to reveal the significant relationship among drought properties. Furthermore, historical records tend to be too short to fully evaluate drought characteristics. A practical method was proposed in this study to estimate the bivariate return period of droughts based on the use of synthetic data to overcome the above considerations. The bivariate return period of droughts is dependent on the drought interarrival time and the joint distribution of drought properties. A nonparametric method was employed in this study to estimate the joint distribution of drought properties. The historical droughts in the Conchos River Basin, Mexico were evaluated based on their return period estimated by the proposed method. The proposed method allowed a better understanding of the joint probabilistic behavior of droughts beyond the limitation of the univ...

Updating Rainfall IDF Relationships to Maintain Urban Drainage Design Standards

Abstract: The hydrologic design standards for urban drainage systems are commonly based on the frequency of occurrence of heavy rainfall events. Observations of recent climate history indicate that the frequency of occurrence of heavy rainfall events is increasing. This increasing trend will likely continue in the future due to global warming. In this study, extending from previous analysis results for Chicago, the rainfall intensity–duration–frequency (IDF) relationships were determined to represent the climate conditions of the first and second halves of the last century. Using these IDF relationships, the impact of the observed increase in heavy rainfall events on the design and performance of urban drainage systems were quantified. This quantification demonstrated the need for updating rainfall IDF relationships to reflect changing climate conditions. In the design of new and retrofitting or replacement of old urban drainage systems, up to date IDF relationships need to be used to maintain design standards.

Behavior of Unsaturated Layered Soil Columns during Infiltration

Abstract: This paper presents the laboratory test results of vertical infiltration on two soil columns of finer over coarser soils subject to simulated rainfalls under conditions of no-ponding at the surface and constant head at the bottom. The main objectives were to evaluate the effect of rainfall intensity and duration; and to provide experimental evidence for soil water redistribution and hysteresis. The results show that rainfall intensity had a major effect on infiltration in the finer layer but had limited effect in the coarser layer due to the large difference of saturated permeability between the two layers. A relatively short rainfall duration resulted in a delayed response of pore pressure and water content to the rainfall after its cessation, while a relatively long duration did not result in such a delayed response. The delayed response indicated the redistribution of soil water in infiltration. Different paths of water content versus matric suction were followed during the tests indicating the apparent hysteretic behavior of soil water. In addition, the coarser layer restricted the increase of pore pressure in the finer layer. A minor variation of saturated soil permeability had minimal effect on infiltration.

Generalized Fick's Law and Fractional ADE for Pollution Transport in a River: Detailed Derivation

Abstract: The fractional advection–dispersion equation (ADE) is a generalization of the classical ADE in which the second-order derivative is replaced with a fractional-order derivative. While the fractional ADE is analyzed as a stochastic process in the Fourier and Laplace space so far, in this study a fractional ADE for describing solute transport in rivers is derived in detail with a finite difference scheme in the real space. In contrast to the classical ADE, the fractional ADE is expected to be able to provide solutions that resemble the highly skewed and heavy-tailed time–concentration distribution curves of water pollutants observed in rivers.

Enhanced Runoff Curve Number Model Incorporating Storm Duration and a Nonlinear I~a-S Relation

Abstract: Incorporating the storm duration and a nonlinear relation for initial abstraction ( Ia ) , this paper presents an enhanced version of the Soil Conservation Service curve number-based Mishra–Singh model. The proposed version is compared with existing formulations using a large set of storm rainfall-runoff events derived from the water database of the United States. The results of the paired t -test of related samples and standard error of estimate ( Se ) statistic comparison of split sampling-based calibration and validation and simulation on full data set show that the proposed version performs far better than all other existing versions.

Flood Estimation by GIUH-Based Clark and Nash Models

Abstract: With the recent and drastic decline of hydrological in situ networks, flood estimation from ungauged basins is an imperative research area in scientific hydrology. In this regard, geomorphologic instantaneous unit hydrograph (GIUH) based on models by Clark in 1945 and Nash in 1957 are developed and applied to the Ajay River Basin at Jamtara in northern India. The geomorphologic parameters of the basin were estimated using ERDAS Imagine 8.5 image processing and geographic information system (GIS) software. The direct surface runoff (DSRO) hydrographs derived by the GIUH-based models without using historical runoff data, the conventional Clark IUH model option of the HEC-1 package, and the Nash IUH model, were compared with observed DSRO hydrographs employing four performance criteria. The DSRO hydrographs are computed with reasonable accuracy by the GIUH-based Clark and Nash models, when compared to the Clark IUH model option of the HEC-1 package and the Nash IUH model. It is observed that the GIS supporte...

Watershed Environmental Hydrology Model: Environmental Module and Its Application to a California Watershed

Abstract: A newly developed watershed environmental hydrology (WEHY) model is presented as a state-of-the-art nonpoint source (NPS) model. The model consists of hydrologic and environmental modules, and describes environmentally relevant hydrologic processes based upon physically based governing equations to model the fate of pollutants such as sediment and phosphorus in the watershed. Unlike other physically based NPS models, the WEHY model is unique in its upscaling approach to the governing equations of hydrologic and environmental processes, which results in the governing equations that are compatible with the computational grid resolution while accounting for subgrid heterogeneities through upscaled model parameters. Upscaling was performed by means of a technique called ensemble averaging. The model was tested at the Ward Creek Watershed in Lake Tahoe Basin for its performance in a subalpine watershed setting. Comparisons of predicted and observed values were in good agreement and showed good promise of the a...

Combining Rainfall-Runoff Model Outputs for Improving Ensemble Streamflow Prediction

Abstract: This study reviewed various combining methods that have been commonly used in economic forecasting, and examined their applicability in hydrologic forecasting. The following combining methods were investigated: The simple average, constant coefficient regression, switching regression, sum of squared error, and artificial neural network combining methods. Each method combines ensemble streamflow prediction (ESP) scenarios of the existing rainfall-runoff model, TANK, those of the new rainfall-runoff model that has been developed using an ensemble neural network for forecasting the monthly inflow to the Daecheong multipurpose dam in Korea. In addition to the combining, the ESP scenarios were adjusted using correction methods, such as optimal linear and artificial neural network correction methods. Among the tested combining methods, sum of squared error (SSE), a combining method using time-varying weights, performed best with respect to the root mean square error. When SSE was coupled with optimal linear cor...

Methods for Developing Naturalized Monthly Flows at Gaged and Ungaged Sites

Abstract: The state of Texas recently implemented a water availability modeling (WAM) system to support water management activities in its 23 river basins. Hydrology is represented in the modeling system by sequences of naturalized streamflows at all pertinent locations for each month of a several decade long period of analysis. Flows at stream gaging stations are adjusted to remove the effects of historical water resources development and use. The resulting naturalized flows are distributed to numerous ungaged sites of interest in modeling water management. Methods are incorporated into the WAM system for converting gaged flows to naturalized flows and transferring the flows from gaged to ungaged locations. Flow naturalization adjustments consist primarily of removing the effects of historical reservoir storage and evaporation, water supply diversions, and return flows from surface and groundwater supplies, and in some cases other considerations. The WAM system includes several alternative methods for distributing sequences of monthly naturalized flows from gaged to ungaged locations. The option most often used is based on the Natural Resource Conservation Service curve-number-based rainfall-runoff relationship. The methodologies for developing naturalized flows at gaged and ungaged sites incorporated in the Texas WAM system are generally applicable for similar modeling applications in other places.

Reservoir Storage Curve Estimation Based on Remote Sensing Data

Abstract: A reservoir is one of the most efficient measures for integrated water resources development and management. The reservoir storage curve is a vital parameter for multipurpose reservoir operation and its precision is a key issue for water balance and strategic risk management. Compared with the traditional approach, the method based on remote sensing RS data provides better information, which can be helpful in reservoir operation and management. Fengman Reservoir was chosen as a case study to obtain the new storage curve that is based on LandSat data. The inflows of the reservoir were calculated in dry seasons December, January, and February from 1958 to 1986 on the basis of the designed storage curve and the new estimated curve, respectively. Compared with the observation data, the average relative error of inflow using the new estimated curve is much less than one using the designed curve. The results showed that reservoir storage curve estimation based on RS data is reasonable and has relatively high precision.

Comparison of Three Alternative ANN Designs for Monthly Rainfall-Runoff Simulation

Abstract: The performance of three artificial neural network (ANN) designs that account differently for the effects of seasonal rainfall and runoff variations were investigated for monthly rainfall-runoff simulation on an 815 km2 watershed in central Oklahoma. The ANN design that accounted explicitly for seasonal variations of rainfall and runoff performed best by all performance measures. Explicit representation of seasonal variations was achieved by use of a separate ANN for each calendar month. For the three ANN designs tested, a regression of simulated versus measured runoff displayed a slope slightly under 1 and positive intercept, pointing to a tendency of the ANN to underpredict high and overpredict low runoff values.

Validation of the Hydrological Processes in a Hydrological Model

Abstract: Hydrological models are often required to model watersheds where the conditions change over time. Calibration and validation of these models is a difficult process that requires validation of each of the major hydrological processes within the model. This paper presents the calibration, validation, and sensitivity analysis of the WATFLOOD hydrological model. The calibration process is usually based on streamflow and may involve an implicit validation of the hydrological processes when the internal state variables are monitored to ensure that the model operates realistically. This paper presents explicit validations of several internal state variables (soil moisture, evaporation, snow accumulation and snowmelt, and groundwater flow) and the statistical characteristics of the streamflow. The WATFLOOD model is shown to track each of these variables with sufficient accuracy for operational use of the model. In addition, several behavioral sensitivity checks are presented to show that the model behaves in a re...

Artificial Neural Network Modeling of Dissolved Oxygen in a Wetland Pond: The Case of Hovi, Finland

Abstract: Artificial neural networks (ANNs) are flexible tools from neuroinformatics that have performed well in a number of hydrologic applications so far They tend to be particularly useful when applied to complex processes, the details of which are not well understood The dissolved oxygen regime in constructed wetland ponds is, in turn, such a complex process, governed by a considerable number of hydrologic, hydrodynamic, and ecological controls which operate at a wide range of spatiotemporal scales This paper reports on the results from a study conducted to test the adequacy of artificial neural networks in modeling near-bottom concentrations of dissolved oxygen in the Finnish free water surface wetland at Hovi Various different networks of the multilayer perceptron (MLP) type of ANN were developed The application proved successful, and in particular it was observed that MLPs were able to “learn” the mechanism of convective oxygen transport quite well The ANN was also used to determine the relative influe

Combined Hydraulic and Black-Box Models for Flood Forecasting in Urban Drainage Systems

Abstract: Rapid urbanization and its implications for both water quality issues and floods have increased the need for modeling of urban drainage systems. Many operational models are based on deterministic solutions of hydraulic equations. Improving such models by adding a “black-box” component to deal with any systematic structure in the residuals is proposed. In this study, a conventional deterministic stormwater drainage network model is first developed for a rapidly developing catchment using the HYDROWORKS (now called Infoworks) package, from Wallingford Software in the United Kingdom. However, despite the generally satisfactory results, the HYDROWORKS model tended to underestimate the flow volume. In this paper, a black-box or “systems” model is fitted to the hydraulic urban drainage model in order to improve its overall efficiency. A study was conducted of suitable black-box models, which included the nonlinear artificial neural network model (ANN), and the linear time series models of Box and Jenkins in 197...

From a NEXRAD Rainfall Map to a Flood Inundation Map

Abstract: An application integrating the Hydrologic Engineering Center's (HEC)-Hydrologic Modeling System hydrologic simulation model and the HEC-River Analysis System hydraulic simulation model into a seamless floodplain mapping application is presented. The application is implemented with an ArcGIS 9 workflow model called Map to Map, which converts a map of rainfall data to a flood inundation map. The simulation models are integrated into the application by establishing information exchange points at which time series of information are passed to a model or returned from a model. Communication between simulation models and the Geographic Information System (GIS) is made possible by interface data models, which provide a one-to-one mapping between data structures within the simulation model and the GIS. A case study is presented for Rosillo Creek in Texas, in which the Map-to-Map model computes flood inundation polygons from rainfall data. Map to Map gives the user a powerful floodplain mapping and real-time flood...

Quantitative Estimation of Reservoir Sedimentation from Three Typhoon Events

Abstract: Study of soil erosion in the reservoir watershed, the main source of reservoir sedimentation that affects the reservoir's lifespan and capacity, is of vital importance for watershed management. Due mainly to the lack of data, empirical formulas are commonly used to estimate reservoir sedimentation. However, these estimations are far from accurate. Field measurements data of discharge and suspended sediment were collected during three typhoon events in Shihmen Reservoir watershed, Taiwan. Temporal variations of water surface elevation, discharge, and concentration of suspended sediment were measured. A numerical model, Hydrological Simulation Program Fortran HSPF, developed by the USEPA was adopted to simulate the sediment yield. However, as calibration and verification data are not always available and the parameter-calibration process is complicated and tedious for novice users of the model, an artificial neural network ANN model was proposed. Significant amount of the synthetic data from the calibrated HSPF model were first generated to train the ANN model, which in turn was used to estimate the sediment yield. Comparisons of the sediment yield using both the HSPF and ANN model give correlation coefficients of 0.96 for training and 0.93 for validation. Without the complicated parameter calibration process, the ANN model was faster and easier to use than the HSPF model.

Numerical Modeling and Analysis of Solute Velocity and Macrodispersion for Linearly and Nonlinearly Sorbing Solutes in a Single Fracture with Matrix Diffusion

Abstract: The behavior of the solute velocity and effective macrodispersivity of solute front in the fracture for the transport in a single fracture in the presence of rock matrix diffusion is analyzed using numerical modeling. The study is limited to a constant continuous solute source boundary condition in a single fracture with constant aperture. Analysis is made for linear and nonlinear sorption cases. Expressions are provided for solute velocity and effective macrodispersivity of the solute fronts during asymptotic and preasymptotic stages using spatial moment analyses. The effective retardation factor and dispersivity of solutes in the fracture are found to follow an exponential function with an exponent related to fracture-matrix parameters.

Evaluation of Traditional and Nontraditional Optimization Techniques for Determining Well Parameters from Step-Drawdown Test Data

Abstract: Adequate knowledge of the hydraulic characteristics of production wells is indispensable for the proper development and management of wells and for the selection of suitable pumps In this study, the characteristic hydraulic parameters of production wells were determined by the widely used graphical analysis of step-drawdown pumping test data as well as the two traditional gradient-based nonlinear optimization techniques (viz, Levenberg–Marquardt and Gauss–Newton) and the nontraditional optimization technique, genetic algorithm Three stand-alone and interactive computer programs were developed to optimize the hydraulic parameters of production wells by these numerical techniques The efficacy and robustness of the developed computer codes were examined using eleven sets of step-drawdown data from diverse hydrogeologic conditions The results of this study revealed that all the three numerical techniques yielded superior well parameters with lower values of root-mean-square errors (RMSE) for all the elev

Hybrid, Markov Chain-Based Model for Daily Streamflow Generation at Multiple Catchment Sites

Abstract: A hybrid, seasonal, Markov chain-based model is formulated for daily streamflow generation at multiple sites of a watershed. Diurnal increments of the rising limb of the main channel hydrograph were stochastically generated using fitted, seasonally varying distributions in combination with an additive noise term, the standard deviation of which depended linearly on the actual value of the generated increment. Increments of the ascension hydrograph values at the tributary sites were related by third- or second-order polynomials to the main channel ones, together with an additive noise term, the standard deviation of which depended nonlinearly on the main channel’s actual increment value. The recession flow rates of the tributaries, as well as of the main channel, were allowed to decay deterministically in a nonlinear way. The model-generated daily values retain the short-term characteristics of the original measured time series (i.e., the general shape of the hydrograph) as well as the probability distribu...

Predictability of Annual Sediment Loads based on Flood Events

Abstract: Water resources managers always are searching for cost-effective monitoring programs that provide maximum information for minimum cost. Monitoring of sediment discharges from streams and rivers is one of the expensive efforts that is always reduced or cut when financial resources are limited. This has resulted in a very limited number of long-term sediment monitoring sites in the United States. Instead of long-term continuous monitoring programs, alternative monitoring approaches could potentially provide reliable estimates of sediment loads at reduced cost. One of those approaches is monitoring sediment loads during flood events and then using that information to estimate annual sediment loads. To test this approach, annual suspended sediment loads calculated based on continuous sediment monitoring were compared with the loads calculated based on monitoring the major floods only. Streams transport large percentages of the annual sediment loads from a watershed during a small number of floods that occur o...