scholarly journals KOHONEN NEURAL NETWORKS FOR RAINFALL-RUNOFF MODELING: CASE STUDY OF PIANCÓ RIVER BASIN

2013 ◽  
pp. 176-182 ◽  
Author(s):  
Camilo Allyson Simoes de Farias ◽  
Celso A. G. Santos ◽  
Artur M. G. Lourenço ◽  
Tatiane C. Carneiro
Keyword(s):  
Neural Networks ◽  
River Basin ◽  
Input Data ◽  
Missing Values ◽  
Rainfall Runoff ◽  
Data Set ◽  
Runoff Modeling ◽  
Proposed Model ◽  
Kohonen Neural Networks ◽  
Streamflow Data

The existence of long and reliable streamflow data records is essential to establishing strategies for the operation of water resources systems. In areas where streamflow data records are limited or present missing values, rainfall-runoff models are typically used for reconstruction and/or extension of river flow series. The main objective of this paper is to verify the application of Kohonen Neural Networks (KNN) for estimating streamflows in Piancó River. The Piancó River basin is located in the Brazilian semiarid region, an area devoid of hydrometeorological data and characterized by recurrent periods of water scarcity. The KNN are unsupervised neural networks that cluster data into groups according to their similarities. Such models are able to classify data vectors even when there are missing values in some of its components, a very common situation in rainfall-runoff modeling. Twenty two years of rainfall and streamflow monthly data were used in order to calibrate and test the proposed model. Statistical indexes were chose as criteria for evaluating the performance of the KNN model under four different scenarios of input data. The results show that the proposed model was able to provide reliable estimations even when there were missing values in the input data set.

scholarly journals Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

2005 ◽  
Vol 2 (3) ◽  
pp. 639-690 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Overland Flow ◽  
Model Performance ◽  
Source Area ◽  
Subsurface Water ◽  
Watershed Hydrology ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based ◽  
Infiltration Excess

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. Through flux exchanges among the different spatial domains of the REW, surface and subsurface water interactions are fully coupled. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work is the first full application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

scholarly journals Picturing and modeling catchments by representative hillslopes

2017 ◽  
Vol 21 (2) ◽  
pp. 1225-1249 ◽  
Author(s):  
Ralf Loritz ◽  
Sibylle K. Hassler ◽  
Conrad Jackisch ◽  
Niklas Allroggen ◽  
Loes van Schaik ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Water Dynamics ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based Model ◽  
Optimal Representation ◽  
Physically Based ◽  
Physically Based Models ◽  
Streamflow Data ◽  
Runoff Events

Abstract. This study explores the suitability of a single hillslope as a parsimonious representation of a catchment in a physically based model. We test this hypothesis by picturing two distinctly different catchments in perceptual models and translating these pictures into parametric setups of 2-D physically based hillslope models. The model parametrizations are based on a comprehensive field data set, expert knowledge and process-based reasoning. Evaluation against streamflow data highlights that both models predicted the annual pattern of streamflow generation as well as the hydrographs acceptably. However, a look beyond performance measures revealed deficiencies in streamflow simulations during the summer season and during individual rainfall–runoff events as well as a mismatch between observed and simulated soil water dynamics. Some of these shortcomings can be related to our perception of the systems and to the chosen hydrological model, while others point to limitations of the representative hillslope concept itself. Nevertheless, our results confirm that representative hillslope models are a suitable tool to assess the importance of different data sources as well as to challenge our perception of the dominant hydrological processes we want to represent therein. Consequently, these models are a promising step forward in the search for the optimal representation of catchments in physically based models.

scholarly journals Evaluation of upper Uruguay river basin (Brazil) operational flood forecasts

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Fernando Mainardi Fan ◽  
Paulo Rógenes Monteiro Pontes ◽  
Diogo Costa Buarque ◽  
Walter Collischonn
Keyword(s):  
River Basin ◽  
Power Plants ◽  
Performance Metrics ◽  
Hydroelectric Power ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Forecasting System ◽  
The Difference ◽  
And Performance ◽  
Uruguay River

ABSTRACT System for hydrological forecasting and alert running in an operational way are important tools for floods impacts reduction. The present study describes the development and results evaluation of an operational discharge forecasting system of the upper Uruguay River basin, sited in Southern Brazil. Developed system was operated every day to provide experimental forecasts with special interest for Barra Grande and Campos Novos hydroelectric power plants reservoirs inflow, with 10 days in advance. We present results of inflow forecasted for floods occurred between July 2013 to July 2016, the period which the system was operated. Forecasts results by visual and performance metrics analysis showed a good fit with observations in most cases, with possibility of floods occurrence being well predicted with antecedence of 2 to 3 days. Comparing the locations, it was noted that the sub-basin of Campos Novos, being slower in rainfall-runoff transformation, is easier forecasted. The difference in predictability between the two basins can be observed by the coefficient of persistence, which is positive from 12h in Barra Grande and from 24h to Campos Novos. These coefficient values also show the value of the rainfall-runoff modeling for forecast horizons of more than one day in the basins.

Large-scale Groundwater Simulation using Artificial Neural Networks in the Danube River Basin

2020 ◽  
Author(s):  
Illias Landros ◽  
Ioannis Trichakis ◽  
Emmanouil Varouchakis ◽  
George P. Karatzas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Basin ◽  
Danube River ◽  
Bayesian Regularization ◽  
Data Set ◽  
The Danube River ◽  
Precipitation Temperature ◽  
Artificial Neural ◽  
Danube River Basin

<p>In recent years, Artificial Neural Networks (ANNs) have proven their merit in being able to simulate the changes in groundwater levels, using as inputs other parameters of the water budget, e.g. precipitation, temperature, etc.. In this study, ANNs have been used to simulate hydraulic head in a large number of wells throughout the Danube River Basin, taking as inputs, precipitation, temperature, and evapotranspiration data in the region. Different ANN architectures have been examined, to minimize the simulation error of the testing data-set. Among the different training algorithms, Levenberg-Marquardt and Bayesian Regularization are used to train the ANNs, while the different activation functions of the neurons that were deployed include tangent sigmoid, logarithmic sigmoid and linear. The initial application comprised of data from 128 wells between 1 January 2000 and 31 October 2014. The best performance was achieved by the algorithm Bayesian Regularization with a error of the order  based on all observation wells. A second application, compared the results of the first one, with the results of an ANN used to simulate a single well. The pros and cons of the two approaches, and the synergies of using both of them is further discussed in order to distinguish the differences, and guide researchers in the field for further applications.</p>

Rainfall-runoff modeling considering soil moisture accounting algorithm, case study: Karoon III River basin

2016 ◽  
Vol 43 (4) ◽  
pp. 699-710 ◽  
Author(s):  
Homa Razmkhah ◽  
Bahram Saghafian ◽  
Ali-Mohammad Akhound Ali ◽  
Fereydoun Radmanesh
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Rainfall Runoff ◽  
Soil Moisture Accounting ◽  
Runoff Modeling
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