Showing papers in "Journal of Hydrologic Engineering in 2014"

Revisiting the Concepts of Return Period and Risk for Nonstationary Hydrologic Extreme Events

Abstract: Current practice using probabilistic methods applied for designing hydraulic structures generally assume that extreme events are stationary. However, many studies in the past decades have shown that hydrological records exhibit some type of nonstationarity such as trends and shifts. Human intervention in river basins (e.g., urbanization), the effect of low-frequency climatic variability (e.g., Pacific Decadal Oscillation), and climate change due to increased greenhouse gasses in the atmosphere have been suggested to be the leading causes of changes in the hydrologic cycle of river basins in addition to changes in the magnitude and frequency of extreme floods and extreme sea levels. To tackle nonstationarity in hydrologic extremes, several approaches have been proposed in the literature such as frequency analysis, in which the parameters of a given model vary in accordance with time. The aim of this paper is to show that some basic concepts and methods used in designing flood-related hydraulic structures assuming a stationary world can be extended into a nonstationary frame- work. In particular, the concepts of return period and risk are formulated by extending the geometric distribution to allow for changing exceeding probabilities over time. Building on previous developments suggested in the statistical and climate change literature, the writers present a simple and unified framework to estimate the return period and risk for nonstationary hydrologic events along with examples and applications so that it can be accessible to a broad audience in the field. The applications demonstrate that the return period and risk estimates for nonstationary situations can be quite different than those corresponding to stationary conditions. They also suggest that the nonstationary analysis can be helpful in making an appropriate assessment of the risk of a hydraulic structure during the planned project-life. DOI: 10.1061/ (ASCE)HE.1943-5584.0000820. © 2014 American Society of Civil Engineers.

Trend Identification Simulation and Application

Abstract: Trend analysis occupy a significant role in the climate change studies for almost three decades. It is significant to try and identify monotonic trends in a given time series so as to make future predictions about the possible consequences on the urban environment, water resources, agriculture, and many other socioeconomic aspects of life. Although there are now classically accepted and frequently used trend tests in the open literature, such as Mann-Kendall trend analysis and Spearman’s rho test, they are based on some restrictive assumptions as normality, serial independence, and rather long sample sizes. Also, they search for a single monotonic trend without any specification such as low, medium, and high values, which may have different trend patterns. Many climatological records have serial dependence, and therefore, it is very helpful to provide a methodology that is not affected from such a restriction. It is the main purpose of this paper to provide simulation results and applications of a...

Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration

Abstract: Ten stochastic optimization methods—adaptive simulated annealing (ASA), covariance matrix adaptation evolution strategy (CMAES), cuckoo search (CS), dynamically dimensioned search (DDS), differential evolution (DE), genetic algorithm (GA), harmony search (HS), pattern search (PS), particle swarm optimization (PSO), and shuffled complex evolution–University of Arizona (SCE–UA)—were used to calibrate parameter sets for three hydrological models on 10 different basins. Optimization algorithm performance was compared for each of the available basin-model combinations. For each model-basin pair, 40 calibrations were run with the 10 algorithms. Results were tested for statistical significance using a multicomparison procedure based on Friedman and Kruskal-Wallis tests. A dispersion metric was used to evaluate the fitness landscape underlying the structure on each test case. The trials revealed that the dimensionality and general fitness landscape characteristics of the model calibration problem are impo...

Hydrodynamic and Hydrological Modeling of the Poyang Lake Catchment System in China

Abstract: In this paper, a distributed catchment-hydrology model and a physically based lake hydrodynamic model were used to simulate the large-scale and highly dynamic lake catchment system of Poyang Lake, in the middle reach of the Yangtze River basin, China. The simulation of the hydrodynamics of the lake is a significant extension to previous efforts to simulate Poyang Lake’s considerable variability in lake extent and flow rates. Further, the combination of the distributed catchment-hydrology model and the lake-hydrodynamic model, applied to a highly dynamic and large-scale system, is a rare attempt to develop a physically based management model of this complexity and scale. Model calibration and validation were undertaken to evaluate the model’s performance and to enhance its effectiveness in simulating catchment discharges, lake water levels, lake water surface areas, and lake flow patterns. The results showed a satisfactory agreement with field observations, with Nash-Sutcliffe efficiencies of 0.71–...

Effect of Short-Term and Long-Term Persistence on Identification of Temporal Trends

Abstract: In this study, the trends in precipitation in the northwest (NW) of Iran were identified using the four different versions of the Mann-Kendall method, i.e., the conventional Mann-Kendall method (MK1); the Mann-Kendall method following the removal of the effect of significant lag-1 autocorrelation (MK2); the Mann-Kendall method after the removal of the effect of all significant autocorrelation coefficients (MK3); and the Mann-Kendall method by considering the Hurst coefficient (MK4). Identification of trends was carried out on different time scales (monthly, seasonal, and annual) using the precipitation data of 50 years from 1955 to 2004 of the sixteen stations selected from the NW region of Iran. The Theil-Sen method was used to estimate the slopes of trend lines of precipitation series. Results showed that: (1) on a monthly time scale, the statistically significant Z-statistics were negative for all but one (July) month; and the strongest negative (positive) precipitation trend-line slope among all the negative (positive) cases was found to be −0.89ð0.38Þ mm=year at Bijar (Kermanshah) station in NW Iran; (2) on a seasonal time scale, the median of trend-line slopes was found to be negative in all four seasons; the winter and spring season's precipitation series witnessed negative trends for almost all the stations using all four different versions of the MK test; and in the summer and autumn seasons, both upward and downward trends were observed for most of the sites of NW Iran; (3) in an annual time scale, all stations had witnessed negative trends using both the MK1 and the MK4 tests. However, application of the MK4 instead of the MK1 reduced the absolute value of the Z-statistic for most of the time series. The strongest negative annual trend-line slope was −4.04 mm=year at Bijar station. Therefore, the observed decreases in precipitation in NW Iran in the recent half of the past century may have serious implications for water resources management under the warming climate with probably a higher rate of the population growth and the higher consumption of freshwater as a result of the rise in standards of living of the population of NW Iran. DOI: 10.1061/(ASCE)HE.1943-5584.0000819. © 2014 American Society of Civil Engineers.

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

Abstract: Nonstationary behavior of recent climate increases concerns among hydrologists about the currently used design rainfall estimates. Therefore, it is necessary to perform an analysis to confirm stationarity or detect nonstationarity of extreme rainfall data to derive accurate design rainfall estimates for infrastructure projects and flood mitigation works. An extreme rainfall nonstationarity analysis of the storm durations from 6 min to 72 h was conducted in this study using data from the Melbourne Regional Office station in Melbourne, Australia, for the period of 1925–2010. Stationary generalized extreme value (GEV) models were constructed to obtain intensity–frequency–duration relationships for these storm durations using data from two time periods, 1925–1966 and 1967–2010, after identifying the year 1967 as the change-point year. Design rainfall estimates of the stationary models for the two periods were compared to identify the possible changes. Nonstationary GEV models, which were developed for...

Quantifying the Uncertainty of Design Floods under Nonstationary Conditions

Abstract: Estimating design quantiles for extreme floods in river basins under nonstationary conditions is an emerging field. Nonstationarities could arise from a variety of human and natural factors such as urbanization and climate change. Concepts of return period, design quantile (return level), and risk have already been developed for situations in which increasing or decreasing trends and abrupt shifts in extreme events are present. Because of limited data records, sampling variability, model errors, and the errors in projections into the future, significant uncertainties in the estimates of design floods of future projects will arise. To address the issue of uncertainty resulting from limited sample size of the observations, three methods have been developed for computing confidence intervals for the design quantile corresponding to a desired return period under a nonstationary framework, including (a) delta, (b) bootstrap, and (c) profile likelihood methods. These methods have been developed assuming...

Impact of Climate Change on Flood Frequency Using Different Climate Models and Downscaling Approaches

Abstract: The analysis of the climate change impact on flood frequency represents an important issue for water resources management and flood risk mitigation. However, for small/medium catchments ( 40,000 km2) and downscaling procedures are required. In this paper, two different GCMs selected among the Coupled Model Intercomparison Project 3 models, the Hadley Center Coupled Model, the Parallel Climate Model, and two statistical downscaling approaches, (1) delta change, and (2) quantile mapping, are compared. For the generation of long hourly time series of rainfall, temperature, and discharge, stochastic weather generators coupled with a continuous rainfall-runoff model are employed. Therefore, the frequency of annual maxima rainfall and discharge is projected for the future period 2070–2099 over three small subcatchments in the Upper Tiber River Basin, central Italy. Results reveal that both the GCMs...

Trend, Independence, Stationarity, and Homogeneity Tests on Maximum Rainfall Series of Standard Durations Recorded in Turkey

Abstract: Data of 14 standard duration annual maximum rainfall series with durations of 5 min to 24 h and lengths between 30 and 73 years up to 2010 that were recorded in Turkey are used. Mann-Kendall and linear regression trend, von Neumann independence, Wald-Wolfowitz stationarity, and Mann-Whitney homogeneity tests are applied on 155 complete series with a 24-h duration and 23 complete series with 14 standard durations. Next, only the linear regression test is applied on 174 incomplete series of 14 standard durations. The results of these tests indicate that almost 90% of all annual maximum rainfall series are trend free, independent, stationary, and homogeneous at a critical probability of 5%. Finally, the newly proposed Sen’s 1∶1 trend line method is applied to some series and its results are observed to be in agreement with both the Mann-Kendall and linear regression tests. It is concluded that standard duration annual maximum rainfall series in Turkey can be generally treated as independent and ident...

Genetic Programming in Groundwater Modeling

Abstract: Determination of water-table elevation corresponding to aquifer recharge or discharge is an important issue in sustainable groundwater management. This approach necessitates the application of numerical simulation models that may require substantial amounts of input data related to aquifer parameters and specifications, even for modeling only part of the aquifer, which makes the calculations expensive. Moreover, comprehensive aquifer modeling is a time-consuming and computationally intensive process. Artificial intelligence tools can replace simulation models and decrease computational efforts by using input and output data sets without considering complex relations of the system to be modeled. This paper employs an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as artificial intelligence tools to extract governing groundwater flow equations in Ghaen and Karaj aquifers in Iran. For both aquifers, several input-output data sets, for both training and testing data sets, ...

Impact of SWMM Catchment Discretization: Case Study in Syracuse, New York

Abstract: This study examined how the level of catchment discretization influenced the model parameterization and output uncertainty of the Storm Water Management Model (SWMM) 5.0. Two catchment delineations for a highly urbanized sewershed in Syracuse, New York were developed: (1) the macroscale model containing a minimum required number of subcatchments to retain the original sewer network properties; and (2) the microscale model in which each subcatchment was defined for a unique soil and land-use combination. For both scales, the model parameters were calibrated and the uncertainty of model outputs was quantified using the generalized likelihood uncertainty estimation (GLUE) methodology. Then, calibrated posterior parameter sets were applied at micro- and macroscales individually to a second sewershed, which was also delineated at both micro- and macroscales, to test observed versus simulated flows. The results indicated that the catchment disaggregation level had a great impact on both parameterization...

Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting

Abstract: The present study demonstrates the capability of two preprocessing techniques such as wavelets and moving average (MA) methods in combination with feed-forward neural networks—namely, back propagation (BP) and radial basis (RB) and multiple linear regression (MLR) models—in the prediction of the daily inflow values of the Malaprabha reservoir in Belgaum, India. Daily data on 11 years of rainfall, inflow, and streamflow at an upstream gauging station have been used. The observed inputs are decomposed into subseries using discrete wavelet transform with different mother wavelet functions, and then the appropriate subseries is used as input to the neural networks for forecasting reservoir inflow. Model parameters are calibrated using 7 years of data, and the remaining data are used for model validation. More statistical indices have been used to determine the optimal models. Optimum architectures of the wavelet neural network (WNN) models are selected according to the obtained evaluation criteria in ...

Modeling of Event and Continuous Flow Hydrographs with HEC–HMS: Case Study in the Kelani River Basin, Sri Lanka

Abstract: This paper describes a case study of event and continuous hydrologic modeling in the Kelani River basin in Sri Lanka using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). An extremely high rainfall event in November 2005 was used to calibrate model parameters, and extremely high rainfall events in April–May 2008, May–June 2008, and May 2010 were used to validate the event model. The calibrated, direct runoff and base flow parameters were then used in the continuous hydrologic model. The Green and Ampt infiltration loss method was used to account for infiltration loss in event-based modeling and a five-layer soil moisture accounting loss method was employed in continuous modeling. The Clark unit hydrograph method and the recession base flow method were used to simulate direct runoff and base flow, respectively. The results depict the capability of HEC–HMS to reproduce streamflows in the basin to a high accuracy with averaged computed Nash–Sutcliffe efficiencies of 0.91 for e...

Flooding hazard mapping in floodplain areas affected by piping breaches in the Po River, Italy

Abstract: In recent years, flood-related risk has been increasing worldwide, being inundations among the natural disasters which induce the maximum damage in terms of economic losses. In the research reported in this paper, a methodology to map the flooding residual hazard due to levee failure events induced by piping in embankments protecting flood-prone areas is proposed. Ensemble simulations are used to account for uncertainties in location, geometry, and time-evolution of the levee breaches. Probabilistic flooding-hazard maps are generated combining the results of 192 inundation scenarios, simulated by using one-dimensional (1D) and two-dimensional (2D) hydrodynamic models. The methodology is applied considering 96 different locations and sizes of breaches occurred along a 23-km reach protected by the right levee of the Po River, the right levee of the Taro River, and the left levee of the Parma River, which delimit a 100-km2 study area. The influence of obstacles to the flood propagation and consequent...

Impact of climate variability on runoff in the north-central United States

Abstract: Large changes in runoff in the north-central United States have occurred during the past century, with larger floods and increases in runoff tending to occur from the 1970s to the present. The attribution of these changes is a subject of much interest. Long-term precipitation, temperature, and streamflow records were used to compare changes in precipitation and potential evapotranspiration (PET) to changes in runoff within 25 stream basins. The basins studied were organized into four groups, each one representing basins similar in topography, climate, and historic patterns of runoff. Precipitation, PET, and runoff data were adjusted for near-decadal scale variability to examine longer-term changes. A nonlinear water-balance analysis shows that changes in precipitation and PET explain the majority of multidecadal spatial/temporal variability of runoff and flood magnitudes, with precipitation being the dominant driver. Historical changes in climate and runoff in the region appear to be more consiste...

Curve Number: Empirical Evaluation and Comparison with Curve Number Handbook Tables in Sicily

Abstract: The curve number (CN) method is widely used for estimating direct runoff depth from rainstorms. The procedure is on the basis of the parameter CN, a lumped expression of basin absorption and runoff potential, and a second parameter, initial abstraction (IA), which represents the interception, infiltration, and surface depression during the early part of a storm. The evaluation of CN in Sicily at a basin scale from rainfall-runoff multiday events is done using rainfall-runoff observations during the period 1940–1997 (mean record length of 20 years) in 61 Sicilian watersheds using three different methods: (1) the national engineering handbook, section 4 hydrology (NEH4) method (NEH4M) (the median CN for the annual flood events); (2) asymptotic fitting of ordered and natural data; and (3) least-squares method using rainfall-runoff using both ordered and natural data. Asymptotic fitting showed a major occurrence of the standard CN response (43 basins), with a lesser complacent response (10 basins), an...

Hydrologic Performance of Three Partial-Infiltration Permeable Pavements in a Cold Climate over Low Permeability Soil

Abstract: The hydrologic performance of three partial-infiltration permeable pavement (PP) systems was evaluated at the Kortright Centre for Conservation in Vaughan, Ontario, Canada over 22 months. P...

Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method

Abstract: Artificial neural networks (ANNs) have proved to be an efficient alternative to traditional methods for hydrological modeling. One of the most important steps in the ANN development is the determination of significant input variables. This study proposes a new method based on the copula-entropy (CE) theory to identify the inputs of an ANN model. The CE theory permits to calculate mutual information (MI) and partial mutual information (PMI), which characterizes the dependence between potential model input and output variables directly instead of calculating the marginal and joint probability distributions. Two tests were carried out for verifying the accuracy and performance of the CE method. The CE theory-based input determination methodology was applied to identify suitable inputs for a flood forecasting model for a real-world case study involving the three gorges reservoir (TGR) in China. Test results of application of the flood forecasting model to the upper Yangtze River indicates that the pro...

Effects of Climate Variations and Human Activities on Runoff in the Zoige Alpine Wetland in the Eastern Edge of the Tibetan Plateau

Abstract: The Zoige alpine wetland on the eastern edge of the Tibetan Plateau is an important headwater area for the Yellow River Basin. The White and Black Rivers are two major tributaries in the Zoige Basin. However, the alpine wetland has experienced a rapid degradation due to human and other recent environmental changes in the region. The effects of climate variations and human activities on runoff in this region are still unclear. In this study, those changes in runoff were quantified and categorized using the hydrologic sensitivity analysis method and a monthly water balance model. The temperature index-based snow melting submodel was integrated into a monthly water balance model to account for the considerable snow meltwater from the wetland in the summer season. The nonparametric Mann-Kendall test was used to analyze the annual and seasonal climatic trends in the Zoige Basin. Results suggest that during the past 55 years (1957–2011), annual precipitation was significantly decreasing at a rate of −0....

Quantile-Based Downscaling of Precipitation Using Genetic Programming: Application to IDF Curves in Saskatoon

Abstract: Intensity-duration-frequency (IDF) curves are commonly used in engineering planning and design. Considering the possible effects of climate change on extreme precipitation, it is crucial to analyze potential variations in IDF curves. This paper presents a quantile-based downscaling framework to update IDF curves using the projections of future precipitation obtained from general circulation models (GCMs). Genetic programming is applied to extract duration-variant and duration-invariant mathematical equations to map from daily extreme rainfall quantiles at the GCM scale to corresponding daily and subdaily extreme rainfall quantiles at the local scale. The proposed approach is applied to extract downscaling relationships and to investigate possible changes in the IDF curves for the City of Saskatoon, Canada. The results show that genetic programming is a promising tool for extracting mathematical mappings between extreme rainfall quantiles at the GCM and local scales. The duration-variant mappings w...

Optimal Management of a Freshwater Lens in a Small Island Using Surrogate Models and Evolutionary Algorithms

Abstract: This paper examines a linked simulation-optimization procedure based on the combined application of an artificial neural network (ANN) and genetic algorithm (GA) with the aim of developing an efficient model for the multiobjective management of groundwater lenses in small islands. The simulation-optimization methodology is applied to a real aquifer in Kish Island of the Persian Gulf to determine the optimal groundwater-extraction while protecting the freshwater lens from seawater intrusion. The initial simulations are based on the application of SUTRA, a variable-density groundwater numerical model. The numerical model parameters are calibrated through automated parameter estimation. To make the optimization process computationally feasible, the numerical model is subsequently replaced by a trained ANN model as an approximate simulator. Even with a moderate number of input data sets based on the numerical simulations, the ANN metamodel can be efficiently trained. The ANN model is subsequently linked with GA to identify the nondominated or Pareto-optimal solutions. To provide flexibility in the implementation of the management plan, the model is built upon optimizing extraction from a number of zones instead of point-well locations. Two issues are of particular interest to the research reported in this paper are: (1) how the general idea of minimizing seawater intrusion can be effectively represented by objective functions within the framework of the simulation-optimization paradigm, and (2) the implications of applying the methodology to a real-world small-island groundwater lens. Four different models have been compared within the framework of multiobjective optimization, including (1) minimization of maximum salinity at observation wells, (2) minimization of the root mean square (RMS) change in concentrations over the planning period, (3) minimization of the arithmetic mean, and (4) minimization of the trimmed arithmetic mean of concentration in the observation wells. The latter model can provide a more effective framework to incorporate the general objective of minimizing seawater intrusion. This paper shows that integration of the latest innovative tools can provide the ability to solve complex real-world optimization problems in an effective way. DOI: 10.1061/(ASCE)HE.1943-5584 .0000809. © 2014 American Society of Civil Engineers.

Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation

Abstract: This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in AI model outputs stems from errors in model input and nonuniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. The BAIMA employs a Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. The BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights are determined using the Bayesian information criterion (BIC) that follows the parsimony principle. The BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty because of model no...

Comparison between Parametric and Nonparametric Approaches for the Calculation of Two Drought Indices: SPI and SSI

Abstract: A comparison is provided between parametric and nonparametric approaches for the calculation of two drought indices: the standardized precipitation index (SPI) and the standardized streamflow index (SSI). The parametric approach has been implemented considering two possibilities: (1) a unique probability distribution for the variable of interest with parameters depending on the month of the year, and (2) the probability distribution depending on the month of the year. The parameters’ estimation is made using the maximum likelihood method, while the distribution selection is operated using the Kolmogorov-Smirnov goodness-of-fit test. For the nonparametric approach, the Weibull plotting position has been used to calculate the cumulative frequency. For both indices, the 1-month, 3-month, and 12-month time scales have been considered. As a case study, two 80-year-long monthly time series in Italy are considered: Roma Collegio Romano for precipitation, and Tevere River Basin at Ripetta for streamflow, ...

Optimization of Water Diversion Based on Reservoir Operating Rules: Analysis of the Biliu River Reservoir, China

Abstract: Interbasin water-transfer projects are constructed to solve regional water-shortage problems. This study attempts to develop a set of water-diversion strategies to assist in the decision-making process of effectively diverting the water. The reservoir operation rule curves are improved by considering water-diversion rule curves that consist of hydrological-stage and water-level factors. A set of water-diversion strategies consisting of diversion rule curves, diversion flows, and supply rules is then developed to guide the actual water diversion. To minimize total water supply shortages of the urban, agricultural, and ecological water demands, the optimization model is constructed and the variables of diversion strategy are optimized by genetic algorithm. Furthermore, three scenarios including scenarios of no water diversion, water diversion with constant flow, and diversion with strategy are used to better understand the advantages of applying diversion strategies. The authors present a case study...

Improved Particle Swarm Optimization–Based Artificial Neural Network for Rainfall-Runoff Modeling

Abstract: This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui Watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate gradient, gradient descent, and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from the LM-NN, and these results were then compared with those from PSO-based ANNs, including the conventional PSO neural network (CPSONN) and the improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. The results show that the PSO-based ANNs performed better than the LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing data set for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 ...

Probable Maximum Precipitation in a Changing Climate: Implications for Dam Design

Abstract: Modern dams are overwhelmingly designed under the assumption of climatic stationarity by using a static design value known as probable maximum precipitation (PMP). Therefore, it is worthwhile to explore the impact of relaxing the assumption of stationarity and recalculating design PMP values by using currently practiced procedures enhanced by numerical modeling or observational climate trends. This study reports the findings of nonstationary PMP recalculations at three large dam sites in the United States (South Holston Dam in Tennessee, Folsom Dam in California, and Owyhee Dam in Oregon). The results indicate that currently accepted PMP values are significantly increased when future changes in dew points from observational trends or numerical models are taken into account. It is plausible that such future changes in these meteorological thresholds, had they been known among the engineering community when PMPs were designed, would have received the necessary attention regarding the future uncertai...

Critical Examination of Area Reduction Factors

Abstract: Area reduction factors (ARFs), which are used to convert estimates of extreme point rainfall to estimates of extreme area-averaged rainfall, are central to conventional flood risk assessment. Errors in the estimation of ARFs can result in large errors in subsequent estimates of design rainfall and discharge. This paper presents a critical examination of commonly used ARFs, particularly those from the U.S. Weather Bureau TP-29, demonstrating that they do not adequately represent the true properties of extreme rainfall. This lack of representativeness is due mainly to formulations that mix rainfall observations from different storms and different storm types. Storm catalogs developed from a 10-year high-resolution radar rainfall data are used set to estimate storm-centered ARFs for Charlotte, North Carolina. Storms are classified as either tropical or nontropical to demonstrate that storm type strongly influences spatial rainfall structure. While there appears to be some relationship between ARF str...

Optimizing the Use of Land and Water Resources for Maximizing Farm Income by Mitigating the Hydrological Imbalances

Abstract: This study presents the formulation and application of a linear programming model for the maximization of net annual farm income from an area located in the Rohtak district of Haryana, India. A groundwater balance constraint was imposed on the model, which mitigates the waterlogging problem of the area, while making an optimal allocation of land and water resources. The model results showed a reduction in rice, gram, barley, and mustard areas against an increase in wheat, cotton, and sugarcane under optimal conditions. Under the optimal land and water allocation, groundwater use is increased while canal allocation is decreased. The net annual farm income from the command area has increased by about 26% under optimal allocations. The sensitivity analysis of the model parameters showed that a better price of crops is the most sensitive parameter, followed by the crop area and cost of cultivation. State agencies and farmers involved in the actual agricultural production process are advised to practic...

Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset

Abstract: Missing values in in situ monitoring data is a problem often encountered in hydrologic research and applications. Values in a data set may be missing because of sensor error or failure of data recording devices. Whereas various imputation techniques have focused on hydrometeorological data, very few studies have investigated gap-filling methods for soil moisture data. This paper aims to fill that gap by investigating well-established statistical and data-driven methods for infilling missing values in a high resolution, soil moisture time series. Since 2006, the authors collected hourly soil moisture data in the Hamilton-Halton Watershed, Southern Ontario, Canada at four research sites. Each site contained nine stations with time domain reflectometry (TDR) soil sensors at six soil depths. From these distributed data sets, the authors removed values randomly (∼5%) and systematically (∼20%) from the data to evaluate the effectiveness of the monthly average replacement (MAR), soil layer relative diffe...

Algorithm for Delineating and Extracting Hillslopes and Hillslope Width Functions from Gridded Elevation Data

Abstract: The subdivision of catchments into appropriate topography-based hydrologic units is an essential step in rainfall-runoff modeling, with the hillslope serving as a common fundamental unit for this purpose. Hillslope-based modeling approaches can utilize, for instance, the hillslope width function as a one-dimensional representation of three-dimensional landscapes by introducing profile curvatures and plan shapes. In this work, an algorithm was developed to delineate and extract hillslopes and hillslope width functions based on a new approach to calculate average profile curvatures and plan shapes from digital terrain data. The proposed method uses fuzzy logic rules and provides a quick and reliable assessment of hillslope characteristics, classifying hillslopes according to nine elementary landscapes (the so-called Dikau shapes). The algorithm was first tested on two contrasting (flat and steep) catchments in Quebec, Canada. The hillslope width functions obtained with the proposed method were able ...