Trend analysis and artificial neural networks forecasting for rainfall prediction

The growing severe damage and sustained nature of the recent drought in some parts of the globe have resulted in the need to conduct studies relating to rainfall forecasting and effective integrated water resources management. This research examines and analyzes the use and ability of artificial neural networks (ANNs) in forecasting future trends of rainfall indices for Mkomazi Basin, South Africa. The approach used the theory of back propagation neural networks, after which a model was developed to predict the future rainfall occurrence using an environmental fed variable for closing up. Once this was accomplished, the ANNs’ accuracy was compared against a traditional forecasting method called multiple linear regression. The probability of an accurate forecast was calculated using conditional probabilities for the two models. Given the accuracy of the forecast, the benefits of the ANNs as a vital tool for decision makers in mitigating drought related concerns was enunciated. Keywords: artificial neural networks, drought, rainfall case forecast, multiple linear regression. JEL Classification: C53, C4

MODELLING THE IMPACTS OF SELECTED WATERSHED MANAGEMENT STRATEGIES ON SEDIMENT REDUCTION UPSTREAM OF SHIRORO DAM, NIGERIA

This research utilized a regulated hydrological model, Soil and Water Assessment Tool (SWAT) interfaced with Geographical Information System (GIS), in studying the effectiveness of the chosen watershed management strategies on sediment reduction upstream of Shiroro dam, Nigeria. Selected management approaches were modelled while calibration and validation of the model were achieved using observed streamflow data. Findings indicated a good correlation during calibration and validation period. Application of reforestation, vegetative filter strips and stone bunds in the watershed reduces sediment production by 27 %, 39 %, and 15 % respectively. Thus, the sediment management scenarios depicted within this research are considerably sustainable and effective

MODELLING THE IMPACTS OF SELECTED WATERSHED MANAGEMENT STRATEGIES ON SEDIMENT REDUCTION UPSTREAM OF SHIRORO DAM, NIGERIA

This research utilized a regulated hydrological model, Soil and Water Assessment Tool (SWAT) interfaced with Geographical Information System (GIS), in studying the effectiveness of the chosen watershed management strategies on sediment reduction upstream of Shiroro dam, Nigeria. Selected management approaches were modelled while calibration and validation of the model were achieved using observed streamflow data. Findings indicated a good correlation during calibration and validation period. Application of reforestation, vegetative filter strips and stone bunds in the watershed reduces sediment production by 27 %, 39 %, and 15 % respectively. Thus, the sediment management scenarios depicted within this research are considerably sustainable and effective

Hydrological Characterization of a Watershed for Streamflow Prediction

In this paper, we extend the versatility and usefulness of GIS as a methodology for any river basin hydrologic characteristics analysis (HCA). The Gurara River basin located in North-Central Nigeria is presented in this study. It is an on-going research using spatial Digital Elevation Model (DEM) and Arc-Hydro tools to take inventory of the basin characteristics in order to predict water abstraction quantification on streamflow regime. One of the main concerns of hydrological modelling is the quantification of runoff from rainstorm events. In practice, the soil conservation service curve (SCS) method and the Conventional procedure called rational technique are still generally used these traditional hydrological lumped models convert statistical properties of rainfall in river basin to observed runoff and hydrograph. However, the models give little or no information about spatially dispersed information on rainfall and basin physical characteristics. Therefore, this paper synthesizes morphometric parameters in generating runoff. The expected results of the basin characteristics such as size, area, shape, slope of the watershed and stream distribution network analysis could be useful in estimating streamflow discharge. Water resources managers and irrigation farmers could utilize the tool for determining net return from available scarce water resources, where past data records are sparse for the aspect of land and climate