1,721,069 research outputs found
Inverse modelling of the rainfall-runoff relation: A multi-objective model calibration approach
No full textCivil Engineering and Geoscience
Rainfall-Runoff modelling using artificial neural networks
No full textHydrologyWater ManagementCivil Engineering and Geoscience
Geostatistics and Hydrology: Part 3: Hydro-Meteorological Network Design
No full textAt present, the collection of environmental information is increasing in importance. Environmental modelling and defining measures relating to environmental protection policies are usually taken on the basis of the collected information. Especially in the more developed countries, hydrologists and meteorologists are in this respect privileged since often long records of hydrological information exist. Hydrological information like precipitation data, runoff data, groundwater table data, piezometric data, etc., is gathered and stored throughout the years by monitoring gauging stations of a (well designed) network. In developing countries however gauging stations are often badly spatially distributed, not well managed and often not included in a network. This causes regions sometimes to be 'over-gauged' by independent (interrelated) agencies or at the other hand regions sometimes to be undergauged or not monitored at all. Many water resources management projects are (still) designed with inadequate and incorrect data or even with virtually no data at all. As a consequence of this, it is likely that wrong water management decisions will be taken, that wrong design criteria will be applied, and that inappropriate and uneconomic designs will be developed and operated. The designing of efficient and economic networks is therefore an important issue for hydrologists and civil engineers. The system or network of hydrological gauging stations provides the necessary information to be able to understand and to describe the hydrological phenomena and processes under study.Water ManagementCivil Engineering and Geoscience
Multiobjective training of artificial neural networks for rainfall-runoff modeling
No full textThis paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for this purpose. The performances of the multiobjective algorithms Multi Objective Shuffled Complex Evolution Metropolis–University of Arizona (MOSCEM-UA) and Nondominated Sorting Genetic Algorithm II (NSGA-II) have been compared to the single-objective Levenberg-Marquardt and Genetic Algorithm for training of these models. Performance has been evaluated by means of a number of commonly applied objective functions and also by investigating the internal weights of the networks. Additionally, the effectiveness of a new objective function called mean squared derivative error, which penalizes models for timing errors and noisy signals, has been explored. The results show that the multiobjective algorithms give competitive results compared to the single-objective ones. Performance measures and posterior weight distributions of the various algorithms suggest that multiobjective algorithms are more consistent in finding good optima than are single-objective algorithms. However, results also show that it is difficult to conclude if any of the algorithms is superior in terms of accuracy, consistency, and reliability. Besides the training algorithm, network performance is also shown to be sensitive to the choice of objective function(s), and including more than one objective function proves to be helpful in constraining the neural network training.Water ManagementCivil Engineering and Geoscience
Raingauge Network Optimization and Gis: A Case Study of the Mananga Basin
No full textThe study area of this case study is the river basin of the Mananga river, one of the main rivers on Cebu-island, the Philippines. Today the Mananga is used as discharge river, in the near future this river may be used for drinking water purposes of the region around Cebucity, some 25 kilometres east of the Mananga catchment. This catchment area comprises about 102 square kilometres, and is surrounded by a chain of sharp-edged mountains which reach to a maximum height of 700 meter. The total length of the Mananga-river is 24.5 kilometres, the drainage area upstream Camp-4 is about 64 km2 (see the front page figure). According to the Köppen classification the elimate of Cebu is classified as TC; tropical characterized by high temperatures and humidity, and heavy rainfall. There are, however, pronounced regional and, in most parts, seasonal variations. Average annual rainfall ranges from 1300 to 1700 mm; average annual temperatures ranges from 26°C to 29°C.Water ManagementCivil Engineering and Geoscience
Network optimization: A simple approach applying GIS and MLR
No full textThe collection of information (data) of a specific kind, in space and/or time is of major importance to the hydrologist. On regional scale, measurements of precipitation at several locations provide information of the input of a river catchment system. The study area with dimensions of 60 * 55.4 km2 , comprises 16 precipitation stations establishing a network. In general one can say that many experiments have been performed on gauged regions and many suggestions have been made relating the desired accuracy with the distance between the stations and the density of the network with the relief (WMO) , the local climatic conditions and the estimation of the study flood for a desired return period. Also it is proved that the increase of the network density is not always the best solution (even though one might not be so interested in the increase of costs). So the real problem is not only quantitative but qualitative as weIl and therefore more complex. This study dealt with the optimization of the existing network applying a mathematical- and a digital technique in combination with operational aspects. Although the influence of the individual approaches is difficult to quantify, conclusions which can be drawn are very satisfying; 2 stations can be abolished, 2 stations need to be re-equipped, for 3 stations the observer needs to be reconsidered. Also possible locations for new stations are distinguished. For composing the optimum network and rainfall-runoff relation ships additional research with more sophisticated digital techniques are requested.Water ManagementCivil Engineering and Geoscience
Closing horizontal groundwater fluxes with pipe network analysis : an application of the REW approach to an aquifer
No full textApplication of satellite products and hydrological modelling for flood early warning
Get PDFFloods have caused devastating impacts to the environment and society in Awash River Basin, Ethiopia. Since flooding events are frequent, this marks the need to develop tools for flood early warning. In this study, we propose a satellite based flood index to identify the runoff source areas that largely contribute to extreme runoff production and floods in the basin. Satellite based products used for development of the flood index are CMORPH (Climate Prediction Center MORPHing technique: 0.25 by 0.25, daily) product for calculation of the Standard Precipitation Index (SPI) and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for calculation of the Topographic Wetness Index (TWI). Other satellite products used in this study are for rainfall-runoff modelling to represent rainfall, potential evapotranspiration, vegetation cover and topography. Results of the study show that assessment of spatial and temporal rainfall variability by satellite products may well serve in flood early warning. Preliminary findings on effectiveness of the flood index developed in this study indicate that the index is well suited for flood early warning. The index combines SPI and TWI, and preliminary results illustrate the spatial distribution of likely runoff source areas that cause floods in flood prone areas
Diagnostic assessment on urban floods using satellite data and hydrologic models in Kigali, Rwanda
Get PDFThe Nyabugogo commercial center of Kigali city is geographically located between the Mont Kigali and Jali forming a bottleneck. Frequent flooding, of which no records are available, have characterized the area for the past few years. Additionally, the area hydrological behavior knowledge was lacking. This study provides an assessment of the frequent flooding in the area using hydrologic models and satellite data. CMORPH 8Km 30min, time variably bias corrected, is used to assess the rainfall events likely to cause flooding with their patterns. The HEC-HMS Curve Number (CN), Muskingum routing and base flow recession sub models are used to estimate the upstream area runoff later serving as upstream boundary conditions to the 1D2D flood model of the area set up with the national 10X10 m DTM and measured river cross sections developed in SOBEK 1D2D. Local regionalization of optimum parameters from gauged sub catchments rainfall-runoff models, using area and main channel length conversion with their proximity factors, is applied to model the runoff from ungauged systems. The Peak weighted root mean square error (PWRMSE), relative volumetric error (RVE) and Nash-Sutcliff (NS) are used as objective functions for the rainfall-runoff model calibration. A PWRMSE of 3.4, RVE of -4.9 and NS of 0.6 were obtained after calibration. 4 extreme rainfall events were detected with a spatial-temporal pattern exhibiting a horizontal movement from east to west with higher amounts observed during March-April-May and October-November-December. Results indicated flash floods in the area with 7 sub catchments found to contribute much runoff in the Nyabugogo River during flooding. Also, the flood extent, depth and velocity were affected by the DEM resolution, building representation and significantly by the surface roughness. Strong backwater effects at the confluence points of the Nyabugogo River and its tributaries Yanze and Mpazi act as triggers to flooding during heavy rainfall
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