Rainfall Flow Data Research Articles

Modelagem chuva-vazão via redes neurais artificiais para simulação de vazões de uma bacia hidrográfica da Amazônia

The techniques of artificial neural networks (ANNs) have been used in the prediction of hydrological variables due to the ability to generalize information, which makes the implementation of models less costly and more agile. In this study, the phenomenon of converting rainfall into streamflows of a Guama River Hydrographic Sub-basin (GRHS) in the State of Para, Amazon, was simulated. The models are based on MLP (Multilayer Perceptron) and NARX (Nonlinear Autoregressive with Exogenous Inputs) ANNs, with Hyperbolic Tangent activation function in hidden layer neurons, being trained by the supervised training algorithm Levenberg-Marquardt. Comparing the proposed ANNs, the NARX-ANN models presented better performances compared to the MLP-ANN model. The best of the NARX-ANN models presented, on average, for the training, validation and test phases, R² equal to 0.9901, RMSE equal to 11.73 m3s-1 and MAPE equal to 5.94%. These results show the possibility of simulating the streamflow of small and medium hydrographic basins in the Amazon through the combination of NARX-ANNs, mainly those basins with no or limited rainfall-flow data.

Data-based mechanistic rainfall-runoff continuous-time modelling in urban context

This paper presents a data-based mechanistic modelling (DBM) approach to rainfall-runoff modelling based on the direct identification and estimation of continuous-time models from discrete-time series. It is argued that many mechanistic model parameters are more naturally defined in the context of continuous-time, differential equation models. As a results, there are advantages if such models are identified directly in this continuous-time form rather than being formulated and identified as discrete-time models. An illustrative example based on the analysis of rainfall-flow data from the Kerinou catchment in Brest demonstrates the relevance of the proposed modelling approach.

Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology

Abstract. In this paper, we discuss a joint approach to calibration and uncertainty estimation for hydrologic systems that combines a top-down, data-based mechanistic (DBM) modelling methodology; and a bottom-up, reductionist modelling methodology. The combined approach is applied to the modelling of the River Hodder catchment in North-West England. The top-down DBM model provides a well identified, statistically sound yet physically meaningful description of the rainfall-flow data, revealing important characteristics of the catchment-scale response, such as the nature of the effective rainfall nonlinearity and the partitioning of the effective rainfall into different flow pathways. These characteristics are defined inductively from the data without prior assumptions about the model structure, other than it is within the generic class of nonlinear differential-delay equations. The bottom-up modelling is developed using the TOPMODEL, whose structure is assumed a priori and is evaluated by global sensitivity analysis (GSA) in order to specify the most sensitive and important parameters. The subsequent exercises in calibration and validation, performed with Generalized Likelihood Uncertainty Estimation (GLUE), are carried out in the light of the GSA and DBM analyses. This allows for the pre-calibration of the the priors used for GLUE, in order to eliminate dynamical features of the TOPMODEL that have little effect on the model output and would be rejected at the structure identification phase of the DBM modelling analysis. In this way, the elements of meaningful subjectivity in the GLUE approach, which allow the modeler to interact in the modelling process by constraining the model to have a specific form prior to calibration, are combined with other more objective, data-based benchmarks for the final uncertainty estimation. GSA plays a major role in building a bridge between the hypothetico-deductive (bottom-up) and inductive (top-down) approaches and helps to improve the calibration of mechanistic hydrological models, making their properties more transparent. It also helps to highlight possible mis-specification problems, if these are identified. The results of the exercise show that the two modelling methodologies have good synergy; combining well to produce a complete joint modelling approach that has the kinds of checks-and-balances required in practical data-based modelling of rainfall-flow systems. Such a combined approach also produces models that are suitable for different kinds of application. As such, the DBM model considered in the paper is developed specifically as a vehicle for flow and flood forecasting (although the generality of DBM modelling means that a simulation version of the model could be developed if required); while TOPMODEL, suitably calibrated (and perhaps modified) in the light of the DBM and GSA results, immediately provides a simulation model with a variety of potential applications, in areas such as catchment management and planning.

A disaggregating approach to describe overland flow occurrence within a catchment

A parametrically parsimonious, data-based model was built on observations at hillslope and catchment scale to simulate the distribution of overland flow within a small East African Highlands catchment (Kwalei, Tanzania). A rainfall-flow Data Based Mechanistic model identified catchment effective rainfall and separated the discharge quick flow, interpreted as the combination of overland flow plus reinfiltration along the slopes, and the slow flow, interpreted as ground water displacement. Observations of overland flow occurrence along the slopes were used to derive probability distribution functions (pdfs) of overland flow in relation to effective rainfall for two pre-defined hydrologic response units (HRUs): perennial (HRU_1) versus other crops (HRU_2). At low effective rainfall, overland flow was more frequent in HRU_2, while at high effective rainfall overland flow in the two HRUs was similar. The pdfs were employed to disaggregate the quick flow into HRU overland flow depth. Reinfiltration was accounted for by assuming that only the overland flow generated in the lower part of the field would drain downslope. Effective reinfiltration length was about 4 m. Comparison of model simulations versus Gerlach trough measurements indicated that rainfall intensity was not accounted for sufficiently. The use of smaller time steps or, alternatively, of a rainfall intensity threshold could improve model performance. However, given the high variability of overland flow observed along the slopes and the limited dataset, model simulations were considered satisfactory. Though the model needs further testing on other datasets, the disaggregating approach represents an inductive alternative to the use of infiltration equations to model overland flow within a catchment.

Advances in real-time flood forecasting.

This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the inherent uncertainty that characterizes river-catchment dynamics and the problems of model over-parametrization. The advantages of alternative, efficiently parametrized data-based mechanistic models, identified and estimated using statistical methods, are discussed. It is shown that such models are in an ideal form for incorporation in a real-time, adaptive forecasting system based on recursive state-space estimation (an adaptive version of the stochastic Kalman filter algorithm). An illustrative example, based on the analysis of a limited set of hourly rainfall-flow data from the River Hodder in northwest England, demonstrates the utility of this methodology in difficult circumstances and illustrates the advantages of incorporating real-time state and parameter adaption.