Developing Guidelines for Two-Dimensional Model Review and Acceptance

Two independent modelers ran two hydraulic models, SRH-2D and HEC-RAS 2D. The models were applied to the Lakina River (MP 44 McCarthy Road) and to Quartz Creek (MP 0.7 Quartz Creek Road), which approximately represent straight and bend flow conditions, respectively. We compared the results, including water depth, depth averaged velocity, and bed shear stress, from the two models for both modelers. We found that the extent and density of survey data were insufficient for Quartz Creek. Neither model was calibrated due to the lack of basic field data (i.e., discharge, water surface elevation, and sediment characteristics). Consequently, we were unable to draw any conclusion about the accuracy of the models. Concerning the time step and the equations used (simplified or full) to solve the momentum equation in the HEC-RAS 2D model, we found that the minimum time step allowed by the model must be used if the diffusion wave equation is used in the simulations. A greater time step can be used if the full momentum equation is used in the simulations. We developed a set of guidelines for reviewing model results, and developed and provided a two-day training workshop on the two models for ADOT&PF hydraulic engineers

Evaluating river cross section geometry for a hydraulic river routing model : Guadalupe and San Antonio river basins

textA new methodology is presented to construct reliable river channel cross section approximations. These approximations are based on the idea of downstream hydraulic geometry as well as supported by the information collected by the USGS streamflow measurement stations across the study area. A hydraulic river routing model (SPRNT) is run with the newly constructed cross section approximations. Initial conditions for the simulation are estimated based on the steady state solution for the model. Boundary conditions or lateral inflows for the river network are estimated based on the outputs of a Land Surface model: Noah, which provides surface and sub-surface runoff for every catchment area in the San Antonio and Guadalupe river basins. Simulations are compared with observed measurements from the USGS stations.Civil, Architectural, and Environmental Engineerin

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins

Impacts of trends and uncertainties in river flooding due to climate change

Projected climate changes will have an effect on frequencies and duration of\ud river flooding and therefore on design criteria for dikes or on risk assessment. In\ud addition to existing sources of uncertainty, extremes and variability of climatological\ud input will change. To deal with this problem the purpose of this project can be split into\ud two main parts. First, to identify possible effects of climate changes on extreme\ud discharges of rivers and particularly the uncertainty involved. Second, to determine the\ud appropriate level of modelling needed to predict such effects taking into account the\ud uncertainties. The major subsystems are climate, catchment and river. Important aspects\ud are the additional uncertainty introduced by each subsystem and the appropriate level of\ud modelling a subsystem. In this paper some preliminary excersises to address these\ud questions with respect to catchment and river are shown, based on very schematic\ud models not representing any particular catchment

Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic