rainfall modeling Articles
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Rainfall-runoff model parameter conditioning on regional hydrological signatures: application to ungauged basins in southern Italy
Parameter estimation for rainfall-runoff models in ungauged basins is a key aspect for a wide range of applications where streamflow predictions from a hydrological model can be used. The need for more reliable estimation of flow in data scarcity conditions is, in fact, thoroughly related to the necessity of reducing uncertainty associated with parameter estimation. This study extends the ...
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A conceptual grey rainfall-runoff model for simulation with uncertainty
This paper presents an approach based on grey numbers to represent the total uncertainty of a conceptual rainfall-runoff model. Using this approach, once the grey numbers representing the model parameters have been properly defined, it is possible to obtain simulated discharges in the form of intervals (grey numbers) whose envelope defines a band which represents the total model uncertainty. ...
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Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China
Rainfall is a key part of the hydrological cycle, and correct forecasting of rainfall is vital in the planning and management of water resources. Generalized regression neural network (GRNN) and support vector regression (SVR) were both applied to forecast monthly rainfall, and the conventional autoregressive model was built for comparison. Furthermore, Akaike Information Criteria were used to ...
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Assessment of the suitability of rainfall–runoff models by coupling performance statistics and sensitivity analysis
Conceptual rainfall–runoff models are widely used to understand the hydrologic responses of catchments of interest. Modellers calculate the model performance statistics for the calibration and validation periods to investigate whether these models serve as satisfactory representations of the natural hydrologic phenomenon. Another useful method to investigate model suitability is sensitivity ...
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Effects of data time-step on the accuracy of calibrated rainfall–streamflow model parameters: practical aspects of uncertainty reduction
The effects of data time-step on the accuracy of calibrated parameters in a discrete-time conceptual rainfall–streamflow model are reviewed and further investigated. A quick-flow decay time constant of 19.9 hr, calibrated for the 10.6 km2 Wye at Cefn Brwyn using daily data, massively overestimates a reference value of 3.76 hr calibrated using hourly data (an inaccuracy of 16.1 hr or ...
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A new hybrid algorithm for rainfall–runoff process modeling based on the wavelet transform and genetic fuzzy system
In this paper, two hybrid artificial intelligence (AI) based models were introduced for rainfall–runoff modeling. In the first model, a genetic fuzzy system (GFS) was developed and evolved for the prediction of watersheds' runoff one time step ahead. In the second model, the wavelet-GFS (WGFS) model, wavelet transform was also used as a data pre-processing method prior to GFS ...
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Rainfall–runoff modelling using genetic programming
This paper presents the application of genetic programming to the generation of models to assess the total runoff of a basin starting from the total rainfall in it and using data recorded in a sub-basin at the valley of Mexico (the Mixcoac sub-basin to the west of Mexico City). The modelling process is developed contrasting two types of models with different complexity degree: (1) a nonlinear ...
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Statistical characteristics of rainfall in the Onkaparinga catchment in South Australia
The main objective of this study was to investigate the statistical characteristics of point rainfall and the novelty of the work was the development of a hybrid probability distribution that can model the full spectrum of daily rainfall in the Onkaparinga catchment in South Australia. Daily rainfall data from 1960 to 2010 at 13 rainfall stations were considered. Spatial dependency among the ...
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Characterization of dynamic evolution of the spatio-temporal variation of rain-field in Hong Kong
A significant part of Hong Kong has hilly terrain with relatively short flow concentration time and, hence, is susceptible to the threat of flash floods and landslides during intense convective thunderstorms and tropical cyclones. For places like Hong Kong, a rainfall model that could adequately capture small-scale temporal and spatial variations would be highly desirable. The main challenge ...
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Simulation of Climatic Conditions in Centrifuge Model Tests
A review is presented of the need for simulation of effects associated with tide, rainfall, temperature variations, etc., in geotechnical centrifuge models. Interest in the overall theme was generated by a perceived need to model rainfall in long-term testing of models of an embankment which was otherwise found to dry out. The system used to overcome this particular problem is outlined, and ...
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A non-linear rainfall-runoff model applied to Amazon small catchments with limited data to simulate the flow duration curves
In Amazon, hydroelectric power production excludes ungauged small catchments. Thus, the main objective of this paper is the implementation of a non-linear rainfall-runoff model on small catchments in the Amazon region to simulate flow duration curves for the hydropower production. Because the rainfall-runoff process is non-linear, modification is applied to a simple linear model based on the ...
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Sampling rainfall events: a novel approach to generate large correlated samples
It is essential that the correlation between variables is considered properly when using sampling-based methods. Modeling rainfall events is of great interest because the rainfall is usually the major driving force of hydrosystems. A novel method for generating correlated samples is introduced providing that the marginal distributions of variables as well as their correlations between them are ...
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Multivariate models for rainfall based on Markov modulated Poisson processes
Point process models for rainfall are constructed generally based on Poisson cluster processes. Most commonly used point process models in the literature were constructed either based on Bartlett–Lewis or Neyman–Scott cluster processes. In this paper, we utilize a class of Cox process models, termed the Markov modulated Poisson process (MMPP), to model rainfall intensity. We use ...
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Hybrid wavelet-support vector machine approach for modelling rainfall–runoff process
Because of the importance of water resources management, the need for accurate modeling of the rainfall–runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall–runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network ...
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Quantitative analysis of runoff reduction in the Laohahe basin
In order to determine the reason for runoff reduction, daily natural runoff series were restored using a conceptual rainfall–runoff model. The period of 1970–1979 was regarded as a base period with little human activity; model parameters for each subcatchment within the Laohahe basin were calibrated for this period. The effects of human activity and climate change on runoff were quantified by ...
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Development of method for X-band weather radar calibration
Calibration of the X-band LAWR (Local Area Weather Radar) is traditionally based on an assumed linear relation between the LAWR radar output and the rainfall intensity. However, closer inspection of the data reveal that the validity of this linear assumption is doubtful. Previous studies of this type of weather radar have also illustrated that the radar commonly has difficulties in estimating ...
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Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modeling
In rainfall–runoff modeling, the wavelet-ANN model, which includes a wavelet transform to capture multi-scale features of the process, as well as an artificial neural network (ANN) to predict the runoff discharge, is a beneficial approach. One of the essential steps in any ANN-based development process is determination of dominant input variables. This paper presents a two-stage ...
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Hydrologic forecasting using artificial neural networks: a Bayesian sequential Monte Carlo approach
Sequential Monte Carlo (SMC) methods are known to be very effective for the state and parameter estimation of nonlinear and non-Gaussian systems. In this study, SMC is applied to the parameter estimation of an artificial neural network (ANN) model for streamflow prediction of a watershed. Through SMC simulation, the probability distribution of model parameters and streamflow estimation is ...
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Effects of rainfall data resolution on watershed-scale model performance in predicting runoff
The hydrologic simulation program-FORTRAN (HSPF) model is widely used to develop management strategies for water resources, but its effectiveness is limited by predictive uncertainties associated with model input data. This study evaluated the effect of rainfall data resolution on the model performance when predicting runoff. We examined hourly, 3-hourly, 12-hourly, and 24-hourly temporal ...
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An updated hydrological review on recent advancements in soil conservation service-curve number technique
Although many hydrologic models are available for the estimation of direct runoff from storm rainfall, most models are limited because of their intensive input data and calibration requirements. The Soil Conservation Service-Curve Number (SCS-CN) technique has been applied successfully throughout the entire spectrum of hydrology and water resources, even though originally it was not intended to ...
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