Journal of Hydrologic Engineering

Evaluation of the uncertainty effect of input parameters on model outputs is presented. HYDROL-INF, an infiltration-runoff model for layered soils, is used for simulating infiltration and surface runoff. The predictive uncertainty related to the modeling is evaluated. Specifically, a three-step procedure is implemented for sensitivity analysis of the model. The first step involves application of the local sensitivity analysis to gain a qualitative ranking of the whole set of input parameters for different model outputs with a relatively low computational cost. In the second step, the first-order second moment (FOSM) method is used to obtain the most sensitive parameters to the output from the parameters identified by the local sensitivity analysis. Third, the robust and computationally efficient Fourier amplitude sensitivity test (FAST) is conducted to overcome the nonlinearity problem for estimating the uncertainty of the model. Furthermore, the proposed methodology is applied to a three-layer soil system with varying permeability under unsteady rainfall. The soil system consists of loamy sand, sandy loam, and clay loam. The model outputs considered herein include cumulative infiltration and runoff. Dissimilar lists of crucial parameters are identified by using the FOSM and FAST methods. It is found that the combination of different soil layers and rainfall conditions have significant influences on the variance contribution of individual parameters to the total variance of the model.

Floods are often generated by more than one physical mechanism, e.g., rainfall and snowmelt. Consequently, traditional flood frequency methods that use a single distribution may not adequately describe the observed flood variability. Mixed distribution models have been proposed but they have two major drawbacks when applied to observed data: (1) determining the appropriate number of components or flood mechanisms and (2) identifying the probability distribution to be used for each component. Further, available flood data are often not sufficient for detecting mixture populations. As a result, mixed-distribution models can be difficult to apply in practice. In this paper we present a nonparametric approach based on local polynomial regression for estimating a flood quantile function that is data driven, flexible, and can capture any arbitrary features present in the data, alleviating the drawbacks of the traditional methods. We applied the proposed method to a suite of synthetic data from mixture of conventional distributions and to flood records that exhibit mixed population characteristics from Gila River basin of southeast and central Arizona. It is found that the proposed method provides a better fit to both the synthetic and historical data. Although the proposed method is presented in the context of mixed population flood frequency estimation, the data-driven nature of the method lends itself as a simple, robust, and attractive alternative to traditional flood frequency estimation.

Within three decades of European-descended settlers arriving in Iowa, much of the land cover across the state was transformed from prairie and forest to farmland, patches of forest, and urbanized areas. Between 1832 and 1859, the General Land Office surveyed the state of Iowa to aid in the disbursement of land. In 1875, an illustrated atlas of the State of Iowa was published. Using these two data resources for classifying land cover, the hydrologic impact of the land cover change resulting from the first three decades of settlement is presented in terms of the effect on the area-weighted average curve number, a term commonly used to predict runoff from rainstorms. In the four watersheds studied, the area-weighted average curve number increased by a mean of 16.4 from 61.4 to 77.8 with the greatest magnitude of change occurring in the two western Iowa watersheds as opposed to the two more heavily forested eastern Iowa watersheds.

The objective of this study was to conduct an accuracy and cost/effectiveness analysis of various reference evapotranspiration $\left({\text{ET}}_o\right)$ estimation equations that rely solely upon the collection of meteorological data. A meteorological station was established in an open grassland near Ft. Meade, Florida. The ASCE Penman-Montieth (PM) equation (full ASCE-PM equation) was set as the standard to which nine variants of five equations were compared. The number of parameters that had to be measured for each equation ranged from five (full ASCE-PM equation) to one (Hargreaves equation). ${\text{ET}}_o$ was calculated on daily time steps. A variant of the ASCE-PM equation with solar radiation, wind speed, relative humidity, and temperature measured and the Simple equation with only solar radiation measured were most accurate and cost/effective. The most accurate and cost/effective alternative equations were those in which some of the less-important energy and mass-transfer terms were omitted and/or calculated from less-expensive instrumentation. The Simple equation instrumentation required only 33% of the cost of the full ASCE-PM, while only reducing the effectiveness by $\sim 10\mathrm{\%}$ . With the Simple equation’s minimal data requirements and superior accuracy and cost/effectiveness, the Simple equation could be a viable tool for the estimation of ${\text{ET}}_o$ in situations where funding is limited.

A basic problem in evaluating the accuracy of an annual maximum series is knowing the sample size that will provide a specific level of relative accuracy. Current methods are inadequate and do not consider all of the necessary variables needed to describe the relationship between return period, record length, skew, and accuracy. The objective of this work was to provide a graphical approach that will enable a user to assess the expected accuracy in 2-, 10-, and 100-year magnitudes expected from a log-Pearson Type III analysis. A Monte Carlo simulation was used to determine the relative errors for the upper 80, 90, and 95% levels of confidence for the 2-, 10-, and 100-year floods. Analyses of the graphs show that accuracy decreases with increases in return period and skew. The results show good agreement to an example problem from Bulletin 17B.

Vegetation on banks of natural channels exercises a significant influence on the flow structure and in turn on the velocity distribution as well as the distribution of Reynolds stress (the random turbulent fluctuations in fluid momentum). This study experimentally investigated the effect of vegetation on banks on accelerating flow in a gravel-bed flume. The results showed that in the presence of vegetation the Reynolds stress distribution became nonlinear and the velocity distribution took on a deformed S shape near the vegetated banks. In addition, vegetation on banks had more influence on the location of maximum velocity than had the aspect ratio. The log law fitted the data near the bed up to $y/h=0.2$ ( $h=\text{flow}$ depth and $y=\text{distance}$ from the bed), however, very near the bed the scatter in data was significant. Finally, the Coles law cannot be applied for accelerating flow when the vegetation was present on banks.

The universal soil loss equation (USLE) and its main derivate, revised universal soil loss equation (RUSLE), have been used all over the world to estimate the soil loss. Specific effects of topography over soil erosion are estimated through the $LS$ factor as the product of slope length factor $L$ and slope steepness factor $S$ . New procedures, based on geographic information systems (GIS), have been developed to estimate the slope length $\left(\lambda \right)$ in hydrographic basins. Slope length $\left(\lambda \right)$ is defined as the distance from the point of origin of overland flow to the point where either the slope gradient decreases enough that deposition begins, or runoff water enters a well-defined channel. The purpose of this research was to compare two methodologies based on GIS, Hickey and Sextante, for obtaining the slope length value $\left(\lambda \right)$ . Both methodologies make a valid slope length $\left(\lambda \right)$ concept interpretation; nevertheless, the Hickey method was specifically made for slope length calculation, as it incorporates a variable cut-off slope angle that improves the detection of the beginning and the end of each slope length $\left(\lambda \right)$ .

This study has experimentally and theoretically investigated the downstream boundary condition of unconfined groundwater flow through the trapezoidal aquifers of which upstream end is vertical and downstream seepage face is sloping. Flow through the trapezoidal aquifers is experimentally simulated by a Hele-Shaw model. The upstream end is impermeable and the downstream boundary forms a drawdown flow on a sloping seepage face. The drawdown flow is formed on a seepage face when hydraulic gradient at a seepage point is less than the seepage face slope. Discharge through the sloping seepage face is found to be proportional to fluid depth (thickness of the fluid layer) at the seepage point, where a phreatic surface crosses the seepage face. When the angle of the seepage face to the horizontal is between 45 and 90°, the hydraulic gradient at the seepage point is found to be 1/2. This is independent of the seepage face slope. When the angle of the seepage face to the horizontal is less than 45°, the phreatic surface crosses the seepage face in the hydraulic gradient of $\text{tan}\left(\beta +\theta \right)/\left[1+\text{tan}\left(\beta +\theta \right)\right]$ , in which $\beta$ is the angle of the seepage face to the channel bottom and $\theta$ is the angle of the bottom slope. The theoretical result of the downstream boundary condition is found to be in good agreement with the experimental data. The phreatic surface slope or the hydraulic gradient at the seepage point forms the downstream boundary condition for the Boussinesq equation when the downstream seepage face is sloping between 0 and 90°.