Hidrología: investigaciones actuales

Availability of data on hydrologic variables such as river flow is necessary for planning and management of water resources. Many developing countries Many River basins in developing countries has no complete dataset on river flow due to degradation of gauging stations gauging stations coupled with unsatisfactory data compilation unsatisfactory data compilation and storage procedures. Different methods are available to fill missing data; however, these methods differ in performance depending on the characteristics of initial data points. The purpose of this study was to fill the missing data in the Great Ruaha River by selection of best method. In this study, simple and multiple regression analysis, and recession methods have been employed to fill the gaps of missing river flow data on ten gauging stations of Great Ruaha River catchment. Performances of these methods were assessed using Nash-Sutcliffe efficiency, Root Mean Square Error and Mean Absolute Error. The results showed that, Multiple regressions are suitable over Linear regression method for missing data during the period of high flow, however selection of either method depends on the availability of data availability on independent variable. Recession method was found to be suitable for filling missing data during the period of low flow. Though these methods were useful in filling data, the challenge was that more than one method was required to estimate all the missing data at a gauging station. This is because, missing data at a given gauging station were experienced during dry and rain seasons.

This paper presents the results of modelling study of Koshi River. The modelling approach is based on twodimensional hydrodynamic model. The simulation is carried out with model software Nays 2DH. The study analyses the inundation parameters, hazard assessment criteria, flood inundation extent delineation and identification of hazardous areas in different discharge scenarios of 25, 50 and 100 years return periods flow. Based on goodnessof- fit tests and fitting parameters, generalized extreme event (GEV) distribution method is adopted for flood frequency analysis. The model is calibrated with measured water surface elevations and simulation results. The root means square errors (RMSE) and correlation R2 between measured values and simulation results are 0.95 m and 0.98 respectively. The study results conclude that within the stretch of around 50 km from Chatara to Koshi barrage the flood will not overtop embankments and left overbank is under low danger zone. However, islands within the embankments namely Shukrabare, Rajabas, Khairatol, Shivchowk and Galphadiya are vulnerable to inundation. The modelling approach proposed in this study is an attractive option for modelling exceptional flood events when limited data and resources are available.