Search

The estimation of distribution algorithm(EDA) is a newevolutionary algorithm developed as an alternative to the traditional genetic algorithm (GA). The EDA guides the search by avoiding the crossover and mutation operators of the GA in favor of building and sampling probabilistic distributions of promising candidate solutions. By increasing the probability of generating solutions with better fitness values, the EDA locates the region of the global optimum or its accurate approximation. In this study, EDAwas used to calibrate the parameters of the soil and water assessment tool hydrologic model for the Xunhe River Basin in China. The EDAwas compared with three other algorithms: (1) the Multistart Local Metric Stochastic Radial Basis Function algorithm (a surrogate optimization method), (2) the Shuffled Complex Evolution algorithm, and (3) the GA. Four metrics are presented to assess the performance of the algorithms: (1) efficiency in terms of the average best objective function value in a limited number of function evaluations, (2) variability in terms of standard deviation and the box plot, (3) reliability in terms of the empirical cumulative distribution function, and (4) accuracy in terms of the Nash–Sutcliffe efficiency coefficient and overall volume error. Results indicated that the EDA is more efficient and could provide more accurate solutions with a relatively high probability, at least for this case study. [ABSTRACT FROM AUTHOR]

A one-dimensional (1D) hydrologic-hydraulic model for simulating dual drainage in urban areas is presented. It consists of four modules: (1) rainfall-runoff transformation, (2) 1D flow routing on a street network, (3) flow interception at street inlets, and (4) flow interaction between surface water on the streets and the underground storm-water system by interfacing with the EPA-SWMM5 engine (U.S. Environmental Protection Agency-Storm Water Management Model). The hydrologic model (first module) transforms rainfall to runoff using the kinematic wave approximation and simulates the infiltration process with the Green-Ampt method. The street network model (second module) is based on a finite-volume shock-capturing scheme that solves the fully conservative Saint-Venant equations and can be used to model both subcritical and supercritical flows. The inlet model (third module) computes the amount of water intercepted by inlets. The formulation of boundary conditions at the street crossings is generalized and can be used for any number of streets, any combination of inflowing and outflowing streets, and flow regime (e.g., subcritical and supercritical flows). Flow interaction between surface water on the streets and underground storm-water system is achieved by interfacing the proposed model with EPA-SWMM5. This interaction allows flow to enter from streets to the underground storm-water system and vice versa. The proposed model has several potential applications such as the identification of critical zones for flooding (e.g., zones with high water depths and flow velocities) in urban developments and can be used to take appropriate measures for drainage control (e.g., to increase number and/or size of inlets), to determine the consequences of different degrees of inlet clogging, and to assess flooding hazards through the application of suitable hazard criteria. A summary of criteria used for storm-water hazard assessment is presented. To demonstrate the dual-drainage model's potential, an application is performed in a catchment of the metropolitan area of Chicago, Illinois. The results obtained are promising and show that the model can be a useful tool for storm-water management and flooding hazard assessment in urban areas. [ABSTRACT FROM AUTHOR]

In the interest of assessing an alternative source of precipitation input for the Chesapeake Bay Watershed model, the goal of this study is to compare the next generation weather radar (NEXRAD)-based multisensor precipitation estimator (MPE) with currently available gauge-based precipitation inputs for the model at both point-pixel and spatially aggregated scales. The comparison of gauge-based data with MPE at a point-pixel scale offers insight into how commonly known characteristics of gauge-point data are reflected in the mean areal gauge-based precipitation product. The time and effort for recalibration using MPE can be significantly reduced by strategizing the procedure according to the results of this study. Techniques and findings described here should be of general value for model users to anticipate changes in model parameters and output when moving from gauge-based to MPE precipitation in continuous simulation models. [ABSTRACT FROM AUTHOR]

Retrieving precipitation data from a rain gauge network is a classical and common practice in hydrology and climatology. These data represent the key input in hydrological modeling to reproduce, for example, the characteristics of a flood phenomenon. The accuracy of the model results is strongly dependent on the consistency of the monitoring network in terms of spatial scale, i.e., network density and location of the rain gauges and time resolution. In this context, several studies have been carried out to analyze how the rainfall sampling influences the estimation of total runoff volume. The aim of this paper is to use a physically based and distributed-parameter hydrologic model to investigate how the number and the spatial distribution of a rain gauge network influence the estimation of the hydrograph and its characteristics in conjunction with different spatial and temporal characteristics of rainfall forcing and different soil-type characteristics. The TIN-based real-time integrated basin simulator (tRIBS) hydrologic model was used to simulate hydrologic response at Baron Fork Basin, Oklahoma. Downscaled next-generation radar (NEXRAD) measurements were assumed to represent the true precipitation distribution over the basin. Additional precipitation fields have been derived from the interpolation of eight fictitious rain gauges randomly placed in the area. The hydrological response from tRIBS that is driven by these precipitation fields has been compared with the response of the model forced with NEXRAD precipitation. The analysis has been carried out assuming first simplified spatial distributions of soil characteristics and then the real soil-type distribution. Results have shown the dependence of the best rain gauges configuration for the estimation of runoff on the spatiotemporal characteristics of storm events and the soil-type distribution. [ABSTRACT FROM AUTHOR]

The Lavras Simulation of Hydrology (LASH) hydrological model is classified as a deterministic, semiconceptual, and spatially distributed model. This model was developed to make available a tool for hydrological analysis in watersheds with a limited database, and it has presented good streamflow predictions. The aim of this study was to calibrate and validate the LASH model for hydrological simulation of the following watersheds which make up the headwaters of the Grande River basin (southern Minas Gerais state, Brazil): the Aiuruoca, Grande, Sapucaí, and Verde river basins. The LASH model simulated adequately the hydrological regimes of the previously mentioned watersheds, presenting satisfactory performances for all the watersheds, which can be emphasized by the Nash-Sutclifffe coefficient () values greater than 0.70. The analysis of the main hydrological processes simulated by the LASH model confirmed that the results were consistent with the hydrological characteristics observed on the respective watersheds. The simulated daily flow-duration curves demonstrated good agreement in relation to the observed curves, indicating only a few errors in estimation of extreme streamflow events. It was found that the LASH model can be applied for hydrological simulation of headwater watersheds situated in the Grande River basin under land-use scenarios and meteorological and environmental conditions other than the ones used in this study. Therefore, it can be considered an important tool for planning and management of water resources in this region. [ABSTRACT FROM AUTHOR]

This paper focuses on calibration and validation of a hydrologic model of the Rio Conchos Basin, a main Mexican tributary of the binational Rio Grande Basin. The Rio Conchos provides approximately 55% of the water deliveries to the United States under the 1944 water treaty signed between both countries. The hydrologic modeling has been developed using the one-dimensional, two-layer soil moisture accounting scheme embedded in the Water Evaluation and Planning (WEAP) model. A 10-year period was used to calibrate the model, which was achieved by a trial-and-error method for the adjustment of the model parameters. The results show a Nash-Sutcliffe coefficient of 0.84 at Ojinaga station (mouth of the basin) and 0.81 at La Boquilla, indicating good model performance. In general the model predicts well the monthly, annual, and maximum flows; but there are significant differences between the model values and undeveloped (naturalized) flows for low flow periods, especially at La Boquilla station. For model validation, a special period of 10 years was used, corresponding to drought conditions in the basin. Results show a Nash-Sutcliffe coefficient of 0.88 at Ojinaga station, with error in annual volume less than 1% on average. [ABSTRACT FROM AUTHOR]

In this paper, a distributed numerical model is proposed on the basis of (1) a hydrologic model for the water exchange laws; (2) a surface runoff model based on a hybrid two-dimensional (2D) diffusive/kinematic wave approximation able to calculate flow over all kinds of wet/dry ground surface slopes; (3) a groundwater flow model based on the 2D Darcy law for both saturated and partly saturated zones; (4) a 2D hillslope erosion model for the sediment transport; and (5) a explicit finite-volume discretization with specific schemes according to the characteristics of the flow equations, upwind for the hyperbolic equations and centered for the parabolic equations. The resulting model offers a variable time step, ensuring numerical stability with the time step size sensitive to the grid cell size in the diffusive wave case and an entropy correction of the upwind fluxes to ensure conservative solutions near local maxima in the slopes controlling the water movement. The validation and practical application of the model is presented in Paper II, in which the potential usefulness of the proposed model is demonstrated. [ABSTRACT FROM AUTHOR]

The development of a distributed two-dimensional (2D) hydrologic-hydraulic simulation model was presented in Paper I. The simulation model combined overland flow (kinematic/diffusive wave models), hillslope sediment transport, and groundwater flow apart from the water exchange mechanisms between zones. Particular attention was paid to the upwind discretization of the surface flow equations. In this paper, the proposed model is validated by using four test cases with exact solutions, one academic test case, and two laboratory test cases. The model adequately reproduced front advance over dry beds of any slope and water table evolution in simple cases. As practical application of the model, the simulation of real events in two experimental basins is also presented. The work is focused on the influence of the choice of the empirical parameters on the model results concerning solid and liquid discharges. Also, because of the lack of information referring the boundary and initial conditions of the groundwater flow in real basins, it is difficult to evaluate the accuracy of the complete model. [ABSTRACT FROM AUTHOR]

In this study, an experimental 12-h lead-time flood forecasting methodology that combines the fifth generation mesoscale model (MM5) of the U.S. National Center of Atmospheric Research with the physically based, spatially distributed watershed environmental hydrology (WEHY) model is described and applied to the Shiobara Dam watershed in Japan in order to explore its utility. The Shiobara-Dam watershed is a mountainous steep-sloped watershed that has an area of 123 km2. Meanwhile, the routine atmospheric assimilation data that are provided by the Japan Meteorological Agency (JMA) over Japan, have spatial resolution of 20 km and are at 12-h time intervals. In order to utilize the JMA’s atmospheric data at 12-h intervals as initial and boundary conditions for 12-h lead-time hourly precipitation forecast inputs to the WEHY model of Shiobara-Dam watershed for runoff forecasts, the MM5 nonhydrostatic atmospheric forecast model was chosen and nested inside the JMA’s data domain. The JMA’s atmospheric data were used as initial and boundary conditions for MM5 which then produced detailed forecasted precipitation fields for the WEHY model of Shiobara-Dam watershed for its hourly runoff forecasts for a 12-h lead time. The results of an application of this forecasting scheme to a 48-h rainfall-runoff event over Shiobara-Dam watershed are presented and discussed. [ABSTRACT FROM AUTHOR]

Spatially distributed on-site devices such as detention tanks and bioretention are becoming more common as a means of controlling urban storm-water quantity and quality. One approach to modeling the cumulative catchment-scale effects of such devices is to resolve the catchment down to the scale of a land parcel or finer, and then to model each device separately. This involves computational and input data demands that may be impracticable, especially in planning or preliminary design stages of storm-water system design. To reduce these demands, the spatial resolution can be coarsened by aggregating land parcels and devices, but this may compromise model accuracy. The focus of this study was examination of the effects of aggregation on predictions of water quantity and quality (for a representative contaminant, total suspended solids) for detention, infiltration, and bioretention devices. A detailed model for urban storm water improvement conceptualization simulation was set up for a 0.83 km2 catchment with 810 source areas and associated devices, and the model was then reduced to three aggregation levels (55 devices, seven devices, and one device). The influence of aggregation was assessed by comparing the predictions of the aggregated models against the predictions of the detailed model. Aggregation had little effect on the predictions of maximum concentration (<2% difference compared with the detailed model), load (<4%), and baseflow (<5%) when the devices were sized in proportion to the impervious area and when there was high soil permeability. Aggregation to a single device increased peak flow compared with the detailed model, by up to 38.1% for bioretention and less for other devices. The peak flow increase was a consequence of reducing the range of travel times in the aggregated drainage network. Aggregation to seven devices had considerably less effect on peak flow (up to 8.7% increase). Addition of variability to the size of the devices introduced further aggregation effects. Methods to extend the aggregation approach to cater for variability in device sizing are proposed in the paper. The results of the study suggest that aggregation can be used to reduce computational and input data demands, with little penalty in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

The potential of ensemble meteorological forecasts in improving ensemble spring peak flow prediction up to 14 days ahead is investigated for the Saguenay-Lac-Saint Jean watershed located in northeastern Canada. Large-scale ensemble meteorological forecasts (precipitation and temperature) generated by the National Center for Environmental Prediction's (NCEP) Global Forecast System (GFS) are bias corrected for two meteorological stations in the watershed. The bias corrected NCEP ensemble meteorological forecast data are used as input in the hydrological model Hydrologiska Byråns Vattenbalan-avdelning (HBV) to simulate ensemble reservoir inflows and Serpent River flows up to 14 days ahead. The ensemble inflow and flow forecasts are compared with climatology as well as with the case in which only observed historical data are used for spring peak flow forecasting. The study results show that there is a significant improvement for the longer forecast range in the model forecast performance when bias-corrected NCEP forecast data are used. The improvement for forecasts for the spring season as well as for the entire year is revealed by Brier and rank probability skill score (BSS and RPSS, respectively) for reservoir inflow and Serpent River flow forecasts. Visual inspection of scatter plots between observed and simulated flows, and hydrographs of ensemble mean and ensemble members also reveal the potential of NCEP meteorological forecasts for improving spring flow forecasting. [ABSTRACT FROM AUTHOR]