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Flood Forecasting
Published in Saeid Eslamian, Faezeh Eslamian, Flood Handbook, 2022
Priyanka Sharma, Pravin Patil, Saeid Eslamian
Flood forecasting systems for short-term flood warnings and long-term mitigation require extensive field observations. There is considerable uncertainty about the exact conditions within the basin, and although the network is improving, even the best rainfall-runoff model will have a 5% to 10% residual error. The error in the predicted peak stage depends on the slope of the rating curve at that stage, and under average conditions, the forecast for upper reaches cannot be consistently reliable within less than 30 cm, and thus lacks reliability. Hence, it is not desirable to forecast in more precise terms than justified by the conditions; consequently, forecasts should rarely be given to less than the nearest 15 cm. Nonetheless, there is an urgent need by water managers, forecasters, policy makers, and government agencies to improve the accuracy of flood forecasting and hazard mapping by using easy and process-based tools.
An object-oriented hydrologic simulator for the miomote river basin
Published in Zhao-Yin Wang, Shi-Xiong Hu, Stochastic Hydraulics 2000, 2020
The rainfall-runoff model is one of the most important components in developing river basin policy and management plans. It is difficult, however, explain to those who have little experience in hydrology how these parameters affect the design flood hydrograph and, further, the size of related projects. The simulator is particularly well suited for developing an intuitive understanding of physical meaning of these parameters. For example, the estimated value of parameter k for sub-basin 101 is 38 and the resulting peak flow from the sub-basin is 2,347m3/s. By setting k=100, one can observe that the peak flow becomes 757m3/s, but the hydrograph becomes much flatter than that in the case of k=38. This implies that k is a parameter reflecting the effect of catchment storage. Similarly, one can make an experimentation with any other parameters.
Geographical Information System for Land and Water Development in Lowland Areas
Published in Fransiscus Xaverius Suryadi, Soil and Water Management Strategies for Tidal Lowlands in Indonesia, 2020
When GIS operations are used in a correct and systematic sequence, they perform a type of computational model. For example, to model land suitability and water management zoning for a tidal lowland several variables will have to be taken into account in a systematic way. This type of activity can be defined as spatial modelling with GIS. In this spatial modelling there are three different classes of models, i.e. logical, empirical and conceptual (Heuvelink, 1993). A logical model simulates a new attribute by applying simple logical ’rules’. In this case for example modelling hydro-topographical conditions of an area is based on a logical relationship between the elevation map and the hydrometric map) Empirical models are based on an empirical understanding of the links between model input and model output. Regression and correlation techniques are often used to set up the relationship between several parameters. An example of the application of these techniques is the simulation of land subsidence of an area based on a regression function between land subsidence and soil physical characteristics. Usually a regression function is only valid for a particular area. Conceptual models are based on a fundamental understanding of the physical phenomena. Basically these models can be applied anywhere. An example of these models is a rainfall-runoff model in a watershed.
Using volunteered geographic information data for flood mapping – Wadi Deffa El Bayadh Algeria
Published in Journal of Applied Water Engineering and Research, 2022
Azzedine Otmani, Abdelkrim Hazzab, M’hamed Atallah, Ciro Apollonio, Andrea Petroselli
In order to perform the flood mapping in the Wadi Deffa watershed where the city of El Bayadh has been selected as basin outlet, a coupled modeling approach consisting of hydrological models and hydraulic models was applied. The hydrological models here tested are: (1) the rational method, (2) the HEC-HMS conceptual rainfall-runoff model and (3) the EBA4SUB conceptual rainfall-runoff model. The hydrographs derived using the three investigated hydrological models were used as input in the two 2D hydraulic models investigated here, i.e. (1) FLO2D and (2) HEC-RAS 2D. The combination of the three hydrological models and the two hydraulic models lead to six modeled flood areas, that will be compared with the observed data.
Evaluation of loss models and effect of LU/LC changes on surface runoff in Subarnarekha river basin
Published in ISH Journal of Hydraulic Engineering, 2021
Asit Kumar Dandapat, Sanat Nalini Sahoo
Conservation and circulation of rainwater as it rotates from the land to the sky and comes back again is called the ‘water cycle’ or ‘hydrological cycle.’ The water cycle is a never-ending cycle. This cycle is made up of a few main parts: precipitation, infiltration, runoff, transpiration, and evaporation. Runoff is the portion of precipitation that neither evaporates and transpires nor penetrates the surface to become groundwater (Subramanian 2013). Excess runoff can lead to flooding, which occurs when there is too much precipitation. The hydrological cycle can be disturbed due to changes in land use by the altering the base flow and annual mean discharge of the basin. For simulation processes of watershed runoff, spatial and temporal data are required. The current paper describes the role of different loss models for runoff simulation in Subarnarekha River Basin by SWAT model. Effective estimation of runoff values and groundwater recharges from a rainfall event helps in the development of all water resources (Tripathi et al. 2003; Khan Mujiburr Fehman 2014; Jianzhu Li et al. 2015). Nowadays, the hydrologic response of catchment systems is changing due to a rapid increase in urbanization and industrial growth including deforestation, land cover, and land use pattern modifications. Along with climate modification, soil heterogeneity has also put great emphasis on the flow of many rivers all around the world. Therefore, to evaluate the impact of these modifications, hydrological models have been developed across the world to study the hydrologic behavior of a catchment system. Rainfall-runoff model helps to compute loss rate, peak runoff rate, runoff volume, and base flow. It is employed for flood protection, forecasting of the real-time flood, water demand forecasting, water resources management and to assess the modification in stream flow. For a particular input parameter, hydrologic models predict the behavior of basin parameter. These models catch one-time series information as input data and create some other time series information as output. Basic steps involved in hydrologic modeling are to delineate watershed, to obtain hydrologic and geographic data, to select modeling approach, to calibrate/verify model, and to use the model for assessment or prediction or design.
Application of adaptive grey method for rainfall forecasting in a watershed
Published in ISH Journal of Hydraulic Engineering, 2021
P. Shirisha, K. Venkata Reddy, Deva Pratap
Rainfall is a key component in hydrological process. It exhibits temporal and spatial variability. Management of water resources is possible with the knowledge of rainfall. Forecasting is the process of collecting data at present time and determining the condition at future time. Rainfall forecasting is essential for a runoff model, to predict flood with sufficient lead time in a watershed. Rainfall forecasting is not a new research area, but the forecasting is improved and made easy with the advancement of new techniques and methods from time to time (Burlando, et al. 1993; Lardet and Obled 1994). In recent years, soft computing techniques are also used to forecast rainfall (Yu et al. 2004; Gholam et al. 2009; Lin and Wu 2009; Nhita and Adiwijaya 2013). Among various theories and techniques available for forecasting, grey theory is one of the widely used concepts. The grey model is proposed by Deng (1989). Since then, grey model [GM (1,1) – first-order single variable prediction] has been extensively applied in different fields of research (Yu et al. 2000; Hui et al. 2009; Kang et al. 2009; Li 2012; Chang et al., 2013b; Chen et al. 2017; Hsu et al. 2018; Yang et al. 2018) including hydrology and weather forecast. Grey model is advantageous as it is efficient in forecasting the data with a minimum of four data records. Grey model better handles the disordered raw data by converting into ordered data using differential equations (Deng 1989). Grey series forecasting using GM (1,1) model is used to forecast the rainfall. Grey system bridges the gap between theoretical and practical values. A detailed description of the grey model is given in Deng (1989). Grey theory combined with fuzzy is applied to forecast rainfall with a lead of 1–3 h by Yu et al. (2000). Wang (2002) used fuzzy grey prediction model to predict the stock price instantaneously at a specified time. Huang et al. (2016) developed a real-time flood forecasting system for a watershed in Taiwan, where rainfall is forecasted by grey model. A real-time flood forecasting system, with flow updating algorithm developed by Ho and Lee (2015), used grey theory to forecast the rainfall. Trend and Potency Tracking Method (TPTM) was proposed by Li and Yeh (2008) to examine the data behaviour. GM (1,1) is modified to Adaptive Grey Model [AGM (1,1)] by introducing the TPTM to understand the trend of the data by Li et al. in 2009. Chang et al. (2013a) employed rolling framework to AGM (1,1). In rolling framework, the data at current time step are included in the raw data series and the data at future time step are forecasted. Each iteration has only four data records. The old data record is replaced with new observed data record. Current data are used for each set of prediction. The procedure is repeated until the rainfall stops.