Journal of Hydrologic Engineering

Estimation of spatial distribution of rainfall at different time scales is one of the basic steps in water resource studies. Problems in selection of proper methods for mapping rainfall can cause uncertainty in water budget studies and simulation of hydrologic models. Application of interpolation techniques is more problematic in mountainous areas where the number and spatial distribution of rain gauges is usually inadequate. There are numerous methods for estimation of spatial distribution of rainfall based on observed point data. Geostatistical methods are among interpolation techniques that are favored in many engineering studies since they consider spatial structure of data points. In this study, a number of methods were evaluated to estimate spatial distribution of annual rainfall in the southwest of Iran. These methods consist of thin plate smoothing splines (TPSS), weighted moving average, ordinary kriging, and cokriging. The cross-validation technique was applied in conjunction with analysis of cumulative distribution function and trends with elevation to evaluate the performance of different methods. While semivariogram analysis confirmed the existence of nonstationarity in the study area, division of the region based on cluster analysis allowed application of ordinary kriging. Moreover, although the TPSS method with an order of two was marginally the most accurate method based on the cross-validation results, the cokriging method showed better consistency with the topography of the area. As extrapolation is always required in mountainous regions, none of the methods produced acceptable results in extrapolation of rainfall point data to higher elevation areas.

Base-flow separation has long been an important research topic in hydrology. In the Yellow River Basin in China, base flow is of special concern in the headwater catchments because of its importance for integrated water resources management in the basin as a whole. To quantify base flow in the headwater catchments of the Yellow River Basin for 1956–2000, the two-parameter Kalinin method has been proposed with improved implementation procedures and parametrization techniques. Results show that base flow contributes a larger proportion to streamflow in the headwater catchments than that of surface flow, but with a rather low variation. The results are consistent with that of the other two digital filter methods. Sensitivity analysis of parameter performance in the Kalinin method shows, in detail, that the hydrograph of separated base flow is sensitive to a recession constant, and the other parameter $\left(\beta \right)$ is highly related to the base-flow index and the ratio of base flow against surface flow $\left(\delta \right)$ .

This paper presents an evaluation of next generation weather radar (NEXRAD) Stage III and MPE precipitation estimates for the Missouri River Basin River Forecast Center (MBRFC) using daily gauge data from the National Weather Service cooperative network. This gauge network is independent of data used to develop the NEXRAD operational products, and, thus, provides an independent assessment of NEXRAD error characteristics. NEXRAD bias, correlation, and percent detection are mapped across the MBRFC to demonstrate the spatial distribution of NEXRAD errors. In general terms, the NEXRAD estimates perform much better in the southeastern portion of the basin than they do in the central and mountainous areas. Although the overall NEXRAD-gauge statistics appear to be worse than those reported in other published studies, the results of this study show marked improvement in NEXRAD estimates of warm season precipitation over the eight-year study period.

Time series models provide a valuable tool for simulation and forecasting hydrologic variables. However, time series models require fitting long series of records. This study explores the applicability of soil water assessment tool (SWAT), a deterministic hydrologic model, to generate long data series to fit autoregressive and autoregressive moving average models, in order to perform short-term forecasting of monthly streamflow and groundwater table depth in areas that lack long historical records. SWAT performed well in reproducing the statistical structure of the variables making it possible to fit time series models to simulated series. Time series models fitted to SWAT simulated data and to historical records showed a similar but poor performance to forecast monthly streamflow in all watersheds. However, time series fitted to SWAT data for groundwater table depth showed good performance for forecasting this variable with correlation coefficients between 0.58 to 0.70 and Nash-Sutcliffe model efficiencies from 0.22 to 0.46 in the validation period.

Understanding overland flow behavior on a natural hillslope and its accurate modeling is essential in hilly watersheds to address many water resource related issues, such as flash flood, soil erosion, and agroforestry. This problem is complicated because the hydrologic influences of microtopography, vegetation characteristics, slope, and preferential infiltration on overland flow are highly interdependent and nonlinear. This study aims at characterizing the overland flow behavior on a natural hillslope by carrying out about 35 in situ experiments with various rainfall intensities and degrees of vegetation. Further, in order to compute an average Manning’s roughness coefficient, one dimension shallow water equations were solved using a finite volume method. The relationship of rainfall intensity and preferential infiltration rate, obtained from in situ experiments, was coupled with the overland flow governing equations. Apart from replicating field observations accurately, the model results established relationships among rainfall intensity, preferential infiltration rate, Manning’s roughness coefficient and time of concentration, and also quantified the influence of degree of vegetation and preferential infiltration rate on overland flow for the study plot.

Prediction of direct runoff using geomorphologic instantaneous unit hydrograph (GIUH) for a fourth-order hilly subwatershed of the Ramganga River Catchment in Uttarakhand (India) is presented. Based on kinematic-wave theory, the GIUHs were derived using two approaches: (1) geomorphologic parameters (GIUH-I) and (2) stream-order-law ratios (GIUH-II). The travel times for the overland-flow and the stream-flow in Horton–Strahler stream-ordering system of the watershed were determined analytically and probabilistically. The statistical analysis showed better overall correlation between predicted and observed direct runoff hydrographs for GIUH-II approach as compared to GIUH-I; however GIUH-I gave marginally better prediction of peak rate and volume of runoff. The time to peak was consistently underpredicted by half hour for all the storm events. The results in general indicate reasonably good applicability of these approaches to estimate direct runoff from ungauged hilly watersheds for better water management planning in the region.

A relatively straightforward approach has been used to obtain solutions for spring depletion and drawdowns when water is abstracted from a well near a spring. The aquifer system, which consists of a semiconfined pumped aquifer overlain by an aquitard containing a free surface, is identical with the aquifer system used in a solution obtained previously by Hunt and was first used by Boulton to obtain his well known solution for delayed yield flow to a well. The derived solution uses the Stehfest algorithm for the numerical inversion of a Laplace transform and is more complete than the solution obtained earlier by Hunt, since it gives drawdown solutions in addition to a solution for spring depletion. Calculations are carried out to show the influence of parameters in the problem before using the solution to fit calculated curves to field measurements that were made for an MS study by Smith. Good agreement is obtained between measurements and the calculated curves, which suggests that the mathematical solution gives an accurate description of the physical model.

A hybrid time series-nonparametric sampling approach, referred to herein as semiparametric, is presented for the estimation of model predictive uncertainty. The methodology is a two-step procedure whereby a distributed hydrologic model is first calibrated, then followed by brute force application of time series analysis with nonparametric random generation to synthesize serially correlated model residual errors. The methodology is applied to estimate uncertainties in simulated streamflows and related flow attributes upstream from the mouth of a rapidly urbanizing watershed. Two procedures for the estimation of model output uncertainty are compared: the Gaussian-based l-step forecast and the semiparametric ensemble forecast. Results show that although both methods yielded comparable uncertainty bands, the Gaussian l-step forecast underestimated the width of the uncertainty band when compared to the semiparametric method. An ensemble of streamflows generated through Latin-hypercube Monte Carlo simulations showed relatively larger values of the coefficient of variation for long-term average annual maximum daily flows than for long-term daily, monthly maximum daily, and monthly median of daily flows. Ensemble of flow duration curves is generated from the error-adjusted simulated flows. The computed low flows displayed greater values of the coefficient of variation than flows in the medium and high range. The ensemble flow durations allow for the estimation of daily flow range upstream from the outlet with 95% confidence for a specified design recurrence period. The computed uncertainties of the predicted watershed response and associated flow attributes provide the basis for communicating the risk to stakeholders and decision makers who are involved in the future development of the watershed.

Accurate parameter estimation is important for reliable rainfall-runoff modeling. Previous studies emphasize that a sufficient length of continuous events is required for model calibration to overcome the effect of initial conditions. This paper investigates the feasibility of calibrating rainfall-runoff models over a number of limited storm flow events. For a subcatchment having a moderate influence from initial soil moisture conditions, this study shows that rainfall-runoff models could still be calibrated reliably over a set of representative events provided that the events cover a wide range of peak flow, total runoff volume, and initial soil moisture conditions. This approach could provide an alternative calibration strategy for a small watershed that has a limited data length but consists of runoff events with a wide range of magnitudes. Compared to continuous-event calibration, event-based calibration appears to perform better in simulating the overall shape of hydrograph, peak flow and time to peak. However, continuous-event calibration was found to be more reliable in providing runoff volume, suggesting that continuous-event calibration should still be used when runoff volume is the main concern of a study.

This paper presents key issues associated with uncertainty in flood inundation mapping. Currently flood inundation extent is represented as a deterministic map without consideration to the inherent uncertainties associated with various uncertain variables (precipitation, stream flow, topographic representation, modeling parameters and techniques, and geospatial operations) that are used to produce it. Therefore, it is unknown how the uncertainties associated with topographic representation, flow prediction, hydraulic model, and inundation mapping techniques are transferred to the flood inundation map. In addition, the propagation of these individual uncertainties and how they affect the overall uncertainty in the final flood inundation map is not well understood. By using a sample data set for Strouds Creek, N.C., this paper highlights key uncertainties associated with flood inundation mapping. In addition, the idea of a probabilistic flood inundation map is articulated, and an integrated framework approach that will connect data, models, and uncertainty analysis techniques in producing probabilistic flood inundation maps is presented.

The suitability of applying the NRCS curve number (CN) to continuous runoff prediction is examined using a new technique of back-calculating CN values from the output of a complex nonlinear hydrologic model. The continuous watershed model Hydrologic Simulation Program-FORTRAN (HSPF) is employed as a simple theoretical watershed in two numerical procedures designed to link CNs with model parameters. The influence of soil type, soil depth, storm depth, storm distribution, and initial abstraction ratio value $\left(\lambda \right)$ on the calculated CN value is investigated. For the first procedure, a soil physics model was developed to calculate the soil moisture content equivalent to the NRCS antecedent runoff condition (ARC) II based on soil type and soil depth. Additionally, the model provides estimates for HSPF soil moisture and infiltration parameters with the remaining parameters adopted from previous calibrations on real watersheds. Curve numbers are back-calculated from HSPF parameter sets using the direct runoff produced from a cyclic synthetic storm event time series input to HSPF. The second numerical method is based on an established asymptotic curve fitting approach to determining CNs from data using real time series rainfall records and simulated runoff events. It was determined that the calculated CN values demonstrated a nonlinear dependence on the computational variables listed above. While significant error was found to be associated with the CNs resulting from the first numerical procedure, the second method suggests some promise for the use of CN values as surrogates for HSPF parameter sets for long term hydrologic analysis.

An optimization method is proposed for estimating transmissivity, storage coefficient, and effective well radius from drawdowns in large-diameter wells. Unsteady pumping is considered; any variation in pumping discharge can be accounted for. Pumping and residual drawdowns can be analyzed simultaneously using the new method. The application of the method is illustrated through the examples of prior studies. The new method reliably estimates the parameters. For the examples considered, the estimates of transmissivity and effective radius of well are more accurate than the estimate of storage coefficient. The proposed method is superior to the prior method because it is objective, more quantitative, and more general.

The existence of extreme events in data sets can distort the accuracy of computed statistics. Some favor censoring outliers while others oppose censoring measured values. Detection methods are available for use in the univariate case, but methods for identifying outliers in multivariate data are limited. Past research has provided detection methods, but they are limited by the number of outliers, the number of predictor variables, or limited levels of significance. A method for detecting outliers in multivariate data that is valid for as many as five outliers is presented. Critical values for levels of significance for sample sizes from 10 to 100 are presented. The method is tested on actual hydrologic data. The method is compared with Rosner’s univariate outlier test. The differences between the two methods, the effectiveness of the two methods, and their limitations are examined.

The design flood can be determined by transposing the design storm observed in the region surrounding the project and converting the storm to flow by use of a simple rainfall-runoff relation or a watershed runoff model. In this study, a design hyetograph in a double-triangular shape of $48\mathrm{h}$ duration was derived from rainfall intensity–duration–frequency curve to represent the time distribution of severe typhoon rainstorms. The design floods generated by performing a rainfall-runoff model using design hyetographs as inputs were verified using the flow records from 17 watersheds in Taiwan. The result indicated that the generated design discharge using the proposed hyetograph for specified return periods were close to the discharge obtained from frequency analysis using flow records. The proposed design hyetograph can be applied to areas that are usually threatened by typhoon rainstorms.