Predict River Discharge Research Articles

Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.

STREAMFLOW PREDICTION IN UNGAUGED CATCHMENTS IN THE EAST COAST OF PENINSULAR MALAYSIA USING MULTIVARIATE STATISTICAL TECHNIQUES

The east coast of Peninsular Malaysia is one of the most vulnerable regions of Malaysia to hydrological disasters, which is believed to become more vulnerable due to climate change. Studies to have better understandings of the hydrological processes in the region are therefore, of paramount importance for disaster risk mitigation. However, unavailability of long-term river discharge data is one of the major constraints of hydrologic studies in the area. The major objective of this study is to predict river discharge in ungauged river basins in the study area. For this purpose, a set of multiple linear regression equations and exponential functions have been developed, which are expressed in the forms of multivariate equations. Available streamflow data along with other catchment characteristics from gauged catchments were used to develop the equations and were subsequently applied to the poorly gauged or ungauged catchments within the study area for prediction of streamflow. In this present study, 4 to 7 explanatory variables were selected as the input variables, which comprise of climatic, geomorphologic, geographic characteristics, soil properties, land use pattern and land cover of the area. Ten flow metrics as maximum, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95, mean and minimum were therefore predicted. Thus, the results of the developed multivariate equations revealed the model to be capable of predicting the desired flow metrics at ungauged catchments in the area under consideration with reasonable accuracy.

New fuzzy neural network–Markov model and application in mid- to long-term runoff forecast

ABSTRACTIn this paper, a mid- to long-term runoff forecast model is developed using an ideal point fuzzy neural network–Markov (NFNN-MKV) hybrid algorithm to improve the forecasting precision. Combining the advantages of the new fuzzy neural network and the Markov prediction model, this model can solve the problem of stationary or volatile strong random processes. Defined error statistics algorithms are used to evaluate the performance of models. A runoff prediction for the Si Quan Reservoir is made by utilizing the modelling method and the historical runoff data, with a comprehensive consideration of various runoff-impacting factors such as rainfall. Compared with the traditional fuzzy neural networks and Markov prediction models, the results show that the NFNN-MKV hybrid algorithm has good performance in faster convergence, better forecasting accuracy and significant improvement of neural network generalization. The absolute percentage error of the NFNN-MKV hybrid algorithm is less than 7.0%, MSE is less than 3.9, and qualification rate reaches 100%. For further comparison of the proposed model, the NFNN-MKV model is employed to estimate (training and testing for 120-month-ahead prediction) and predict river discharge for 156 months at Weijiabao on the Weihe River in China. Comparisons among the results of the NFNN-MKV model, the WNN model and the SVR model indicate that the NFNN-MKV model is able to significantly increase prediction accuracy.Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei

Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain.

Transit data analysis and artificial neural networks (ANNs) have proven to be a useful tool for characterizing and modelling non-linear hydrological processes. In this paper, these methods have been used to characterize and to predict the discharge of Lor River (North Western Spain), 1, 2 and 3 days ahead. Transit data analyses show a coefficient of correlation of 0.53 for a lag between precipitation and discharge of 1 day. On the other hand, temperature and discharge has a negative coefficient of correlation (-0.43) for a delay of 19 days. The ANNs developed provide a good result for the validation period, with R(2) between 0.92 and 0.80. Furthermore, these prediction models have been tested with discharge data from a period 16 years later. Results of this testing period also show a good correlation, with R(2) between 0.91 and 0.64. Overall, results indicate that ANNs are a good tool to predict river discharge with a small number of input variables.

Testing the applicability of a kinematic wave-based distributed hydrological model in two climatically contrasting catchments

Steep mountainous areas account for 70% of all river catchments in Japan. To predict river discharge for the mountainous catchments, many studies have applied distributed hydrological models based on a kinematic wave approximation with surface and subsurface flow components (DHM-KWSS). These models reproduce observed river discharge of catchments in Japan well; however, the applicability of a DHM-KWSS to catchments with different geographical and climatic conditions has not been sufficiently examined. This research applied a DHM-KWSS to two river basins that have different climatic conditions from basins in Japan to examine the transferability of the DHM-KWSS model structure. Our results show that the DHM-KWSS model structure explained flow regimes for a wet river basin as well as a large flood event in an arid basin; however, it was unable to explain long-term flow regimes for the arid basin case study.

Discharge Assessment by Using Integrated Hydrologic Model for Environmental Technology Development

River water quality degradation is one of the most significant environmental challenges. Over the years, many models have been used to investigate the current state of Malaysian rivers and its effects to the environment. River discharge is an important factor in water quality investigation. An integrative computational model, GIS coupled with SWAT model was being used to predict river discharge of this research. The simulation results in the period 1999 to 2010 represented fluctuation of discharge relatively well with both R2 and NSI values were above 0.6. The results proved that the development of integrative GIS technology coupled with SWAT model is a good tool for environmental technology development in terms of investigating the current state of Langat river water quality as well as the capability of simulating the river discharge in the river basin. This shows that GIS-SWAT interface can be a reliable tool for water quality modeling in Malaysia in the future and further development on the software technology is a benefit for the water resources and environmental studies.

Simulating and predicting river discharge time series using a wavelet‐neural network hybrid modelling approach

AbstractAccurate simulation and prediction of the dynamic behaviour of a river discharge over any time interval is essential for good watershed management. It is difficult to capture the high‐frequency characteristics of a river discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet‐neural network (WNN) hybrid modelling approach for the predication of river discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of river discharge into sub‐series with low (approximation) and high (details) frequency, and these sub‐series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict river discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.

Propagation of uncertainties in coupled hydro-meteorological forecasting systems: A stochastic approach for the assessment of the total predictive uncertainty

The pressure on the scientific community to provide medium term flood forecasts with associated meaningful predictive uncertainty estimations has increased in recent years. A technique for assessing this uncertainty in hydro-meteorological forecasting systems is presented. In those, the uncertainties generally propagate from an atmospheric model through a rainfall-runoff model. Consequently, it appears to be difficult to isolate the errors that stem from the individual model components. In this study, the integrated flood forecasting system uses the 7-day rainfall and temperature forecast of the American atmospheric GFS model (deterministic run) as forcing data in a conceptual hydrologic model (deterministic run) coupled with a linear error model in order to predict river discharge. The linear error model is added to the hydrologic model run, in order to take advantage of the correlation in time between forecasting errors, thereby reducing errors that arise from hydrologic simulations. To assess the predictive uncertainty (total uncertainty) of the coupled models, the method makes use of a bivariate meta-gaussian probability density function. The latter allows estimating the probability distribution of the integrated model errors conditioned by the predicted river discharge values. The proposed methodology is applied to the case study of the Alzette river located in the Grand Duchy of Luxembourg. Confidence limits are computed for various lead times of prediction and compared with observations of river discharge.

Regionalizing mean annual flow and daily flow variability for basin‐scale sediment and nutrient modelling

AbstractRiver discharges vary strongly through time and space, and quantifying this variability is fundamental to understanding and modelling river processes. The river basin is increasingly being used as the unit for natural resource planning and management; to facilitate this, basin‐scale models of material supply and transport are being developed. For many basin‐scale planning activities, detailed rainfall‐runoff modelling is neither necessary nor tractable, and models that capture spatial patterns of material supply and transport averaged over decades are sufficient. Nevertheless, the data to describe the spatial variability of river discharge across large basins for use in such models are often limited, and hence models to predict river discharge at the basin scale are required. We describe models for predicting mean annual flow and a non‐dimensional measure of daily flow variability for every river reach within a drainage network. The models use sparse river gauging data, modelled grid surfaces of mean annual rainfall and mean annual potential evapotranspiration, and a network accumulation algorithm. We demonstrate the parameterization and application of the models using data for the Murrumbidgee basin, in southeast Australia, and describe the use of these predictions in modelling sediment transport through the river network. The regionalizations described contain less uncertainty, and are more sensitive to observed spatial variations in runoff, than regionalizations based on catchment area and rainfall alone. Copyright © 2006 John Wiley & Sons, Ltd.

Prediction of river discharge and surface water quality using an integrated geographical information system approach

Abstract A methodology is developed for the prediction of river discharge and surface water quality (indexed by nitrogen loading) of a predominantly rural catchment using simple models in an integrated Geographical Information System (GIS). River discharge is predicted using the Soil Conservation Service (SCS) runoff Curve Number model, and surface water quality by the export coefficient model. Main input variable to these models is information on land-use along with ancillary information such as soils. Land-use is an important parameter that affects both discharge and water quality, and it can be derived from classification of remotely sensed images. Unlike conventional models, the models employed here do not require large amounts of data on several hydro-meteorological variables. The models are applied to a rural catchment in eastern England where major land-use changes have occurred in the recent past. Historical land-use data are derived from a variety of sources including maps, aerial photographs and remotely sensed satellite images for various dates ranging from 1931 to 1989. A GIS is a valuable means to enable large amounts of spatial data to be integrated, and to facilitate data manipulation for the specific application of the models. Results are validated using observed runoff and water quality records, and it is shown that the model predictions are of acceptable accuracy. This study demonstrated an application of a GIS to employ simple models to predict river discharge and water quality.