N. K. Goel

DERIVING STAGE-DISCHARGE-SEDIMENT CONCENTRATION RELATIONSHIPS USING FUZZY LOGIC

Abstract Many practical problems in water resources require knowledge of the sediment load carried by the rivers, or of the load the rivers can carry without danger of aggragadation or degradation. Hence, the measurement of sediments being transported by a river is of vital interest for planning and designing of various water resources projects. The conventional methods available for sediment load estimation are largely empirical, with sediment rating curves being the most widely used. The rating relationships based on regression techniques are generally not adequate in view of the inherent complexity of the problem. In this study, a fuzzy logic technique is applied to model the stage–discharge–sediment concentration relationship. The technique has been applied to two gauging sites in the Narmada basin in India. Performance of the conventional sediment rating curves, neural networks and fuzzy rule-based models was evaluated using the coefficient of correlation, root mean square error and pooled average relative (underestimation and overestimation) errors (PARE) of sediment concentration. Comparison of results showed that the fuzzy rule-based model could be successfully applied for sediment concentration prediction as it significantly improves the magnitude of prediction accuracy.

Temporal and spatial variability of annual and seasonal rainfall over Ethiopia

Abstract Characterization of the seasonal and inter-annual spatial and temporal variability of rainfall in a changing climate is vital to assess climate-induced changes and suggest adequate future water resources management strategies. Trends in annual, seasonal and maximum 30-day extreme rainfall over Ethiopia are investigated using 0.5° latitude × 0.5° longitude gridded monthly precipitation data. The spatial coherence of annual rainfall among contiguous rainfall grid points is also assessed for possible spatial similarity across the country. The correlation between temporally coinciding North Atlantic Multidecadal Oscillation (AMO) index and annual rainfall variability is examined to understand the underlying coherence. In total 381 precipitation grid points covering the whole of Ethiopia with five decades (1951–2000) of precipitation data are analysed using the Mann-Kendall test and Moran spatial autocorrelation method. Summer (July–September) seasonal and annual rainfall data exhibit significant decreasing trends in northern, northwestern and western parts of the country, whereas a few grid points in eastern areas show increasing annual rainfall trends. Most other parts of the country exhibit statistically insignificant trends. Regions with high annual and seasonal rainfall distribution exhibit high temporal and spatial correlation indices. Finally, the country is sub-divided into four zones based on annual rainfall similarity. The association of the AMO index with annual rainfall is modestly good for northern and northeastern parts of the country; however, it is weak over the southern region. Editor Z.W. Kundzewicz; Associate editor S. Uhlenbrook Citation Wagesho, N., Goel, N.K., and Jain, M.K. 2013. Temporal and spatial variability of annual and seasonal rainfall over Ethiopia. Hydrological Sciences Journal, 58 (2), 354–373.

Application of Clustering Techniques Using Prioritized Variables in Regional Flood Frequency Analysis—Case Study of Mahanadi Basin

The selection of suitable site characteristics and the number of clusters play an important role for finding homogeneous regions in regional flood frequency analysis. The present study investigates the partition of the Mahanadi basin into homogeneous regions by applying different clustering techniques by using fewer but influential variables. As such, the entire basin is not hydrometeorologically homogeneous. Principal component analysis has been initiated in finding appropriate site characteristics (variables) as per priority. Out of seven variables, four variables are selected on priority. Possible numbers of cluster are found by applying the Kohonen self-organization map and Andrews plot. Other clustering techniques, such as hierarchical clustering fuzzy C-mean (FCM) and K -mean, are applied on prioritized variables to verify the result of clustering. The intercomparison of clustering techniques gives the optimum number of sites to be placed in a particular cluster. The sites clustered as per FCM give ...

Soft Computing–Based Workable Flood Forecasting Model for Ayeyarwady River Basin of Myanmar

AbstractIt is a challenging task for working hydrologists of Myanmar to get information from all gauge and discharge sites during a flood to model the forecast properly. In such a case, the concept of this work is very useful for real-time flood forecasting, particularly when data of all the gauge sites are not available regularly or timely. In that context, one has to rely on some accessible sites to get a workable forecast. Additionally, the best combination of the available data can be selected for making the flood forecast. The study is done for the establishment of a flood forecasting model with maximum efficiency using very little information. Three upstream sites named as Sagaing, Monywa, and Chauk of the Ayeyarwady river are selected as the base station and the downstream Pyay as the forecasting station in this study. The artificial neural network (ANN) multilayered feed forward (MLFF) network along with the Takagi-Sugeno (TS) fuzzy inference model are applied in this paper. The developed model is...

Stochastic modelling of the sediment load of the upper Yangtze River (China)

Abstract The temporal and spatial sediment load characteristics of Asias longest river, the Yangtze, have been examined. Annual and monthly sediment load characteristics in the temporal domain were modelled. The annual sediment load data from 1950 to 1990 and monthly sediment load data from 1950 to 1969 were used. Statistical tests such as the turning point test, Kendalls rank correlation test and Andersons correlogram test were applied for randomness and trend identification. The periodicity in the sediment load data was analysed by harmonic analysis and stochastic component was modelled by auto-regressive model. The results indicate that the annual sediment load series is trend free at 5% significance level and the monthly means and standard deviations of sediment load show periodicity. The month-to-month correlation structure is nonstationary. Using the AR(1) model for the dependent stochastic component, 100 years of monthly sediment data were generated. The monthly means of observed and generated data match well.

Effect of Climate Change on Runoff Generation: Application to Rift Valley Lakes Basin of Ethiopia

AbstractIn this paper, an attempt has been made to investigate the potential impact of climate change on runoff generation at two agricultural watersheds. Climate change and key future signals of its variability were assessed using general circulation models (GCMs). Given that GCMs are operating at coarser resolution, the statistical downscaling model was applied to reduce large-scale atmospheric variables into localized weather variables from the Bjerknes Center for Climate Research–Bergen Climate Model 2.0 and Commonwealth Scientific and Industrial Research Organization (CSIRO) Mark (MK) 3.0 GCM outputs. As precipitation variables are composed of biases, both linear and power transformation bias correction methods were applied to obtain bias-corrected daily precipitation. Bias-corrected daily precipitation and temperature variables were used to simulate runoff for current and future climate scenarios using the Soil and Water Assessment Tool (SWAT) model. The statistical downscaling model, followed by bi...

Development of a Fuzzy Flood Forecasting Model for Downstream of Hirakud Reservoir of Mahanadi Basin, India

Floods occurring at delta of Mahanadi are mostly due to contribution of downstream catchment of Hirakud reservoir of Mahanadi basin. Controlling flood through other structural measures is inadequate and difficult. The downstream part is devoid of a sound flood forecasting method. In order to protect the life and property, a nonstructural measure like a workable flood forecasting model is needed to mitigate the destruction at delta by enhancing appropriate and timely relief measures. Establishment of a physical base model requires a lot of meteorological and physical information of the catchment. When such detailed information or data are not available, an alternative method based on soft computing technique may play an important role. Therefore, in this paper, a soft computing-based flood forecasting model using fuzzy inference system is attempted at Mundali station in Mahanadi river and the results compared with observed values. The input and output peaks are grouped into low, medium, high, medium high, and very high categories and operated by nine fuzzy rules.