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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130569Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 945
 Irene, B. G., V. O. Consuelo, and R. P. David, Integrated assessment of policy interventions for promoting sustainable irrigation in semi-arid environments: A hydro-economic modeling approach; Journal of Environmental Management 2013. 128: p. 144 -160.
 Milly, P. C., K. A. Dunne, and A. V. Vecchia, Global pattern of trends in streamflow and water availability in a changing climate. Nature 2005. 438(17): p. 347–350.
 Bawden, A. J., et al., A spatiotemporal analysis of hydrological trends and variability in the Athabasca River region, Canada. Journal of Hydrology, 2014. 509: p. 333-342.
 Li, H., et al., Evaluating runoff simulations from the Community Land Model 4.0 using observations from flux towers and a mountainous watershed. Journal of Geophysical Research: Atmospheres, 2011. 116(D24).
 Patil, S. and M. Stieglitz, Controls on hydrologic similarity: role of nearby gauged catchments for prediction at an ungauged catchment. Hydrol. Earth Syst. Sci, 2012. 16(2): p. 551–562.
 Sivapalan, M., Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrol. Process, 2003. 17(15): p. 3163–3170.
 Sivapalan, M., et al., Water cycle dynamics in a changing environment: Improving predictability through synthesis. Water Resources, 2011. 47(10).
 Wang, D. and M. Hejazi, Quantifying the relative contribution of the climate and direct human impacts on mean annual streamflow in the contiguous United States. Water Resour. Res., 2011. 47(10,W00J12).
 Xu, X., et al., Relative importance of climate and land surface changes on hydrologic changes in the US Midwest since the 1930s: implications for biofuel production. Journal Hydrology, 2013. 497(8): p. 110–120.
 Fetter, C. W., Applied Hydrogeology. 1988, New Jersey: Prentice Hall, Upper Saddle River.
 Ward, F. A. and T. P. Lynch, Integrated River Basin Optimization: Modeling Economic And Hydrologic Interdependence1. 1996, Wiley Online Library.
 Karamouz, M., F. Szidarovszky, and B. Zahraie, Water Resources Systems Analysis. 2003: Lewis Publishers. USA.
 Olomoda, I.A. Impact of climatic change on river Niger hydrology. in International Workshop on Managing Shared Aquifers Resources in Africa. 2004. Tripoli Libya.
 FMAWRRD, (Federal Ministry of Agriculture Water Resources and Rural Development), Planning Report for Gurara Interbasin Water Transfer Scheme. 1986, University of llorin, Nigeria: University of llorin Consultancy Services.
 Droogers, P. and J. Bouma, Simulation modelling for water governance in basins. International Journal of Water Resources Development, 2014. 30(3): p. 475-494.
 Mirchi, A., D. Watkins Jr, and K. Madani, Modeling for watershed planning, management, and decision making. Watersheds: management, restoration, and environmental impact. New York: Nova Science Publishers, 2010.
 Alexander, Y. S., W. Dingbao, and X. Xianli, Monthly streamflow forecasting using Gaussian Process Regression. Journal of Hydrology, 2014. 511(2014): p. 72-81.
 Bourdin, D. R., S. W. Fleming, and R. B. Stull, Streamflow modelling: a primer on Applications: Approaches and challenges. Atmos. Ocean, 2012. 50(4): p. 504-536.
 Beven, K. and J. Freer, Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J. Hydrology, 2001. 249(1): p. 11-29.
 Kirchner, J. W., Getting the right answers for the right reasons: linking measurements, analyses, and models to advance the science of hydrology. Water Resour. Res., 2006. 42(W03S04).
 McDonnell, J. J., Moving beyond heterogeneity and process complexity: a new vision for watershed hydrology. Water Resour.Res, 2007. 43(W07301).
 Hsu, K., H. Gupta, and S. Sorooshian, Artificial neural-network modeling of the rainfall-runoff process. Water Resour. Res., 1995. 31(10): p. 2517–2530.
 Rudraiah, M., S. Govindaiah, and S. Vittala, Morphometry using Remote Sensing and GIS Techniques in the Sub-Basins of Kagna River Basin, Gulburga District, Karnataka, India. J. Indian Soc. Remote Sens., 2008. 36: p. 351–360.
 Vogel, R. M., I. Wilson, and C. Daly, Regional regression models of annual streamflow for the United States. J. Irrig. Drain. Eng, 1999. 125(3): p. 148–157.
 Zealand, C. M., D. H. Burn, and S. P. Simonovic, Short term streamflow forecasting using artificial neural networks. J. Hydrol, 1999. 214(1): p. 32–48.
 Chang, F.-J. and Y.-C. Chen, A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of hydrology, 2001. 245(1): p. 153-164.
 Tokar, A. S. and M. Markus, Precipitation-runoff modeling using artificial neural networks and conceptual models. J. Hydrol. Eng, 2000. 5(2): p. 156–161.
 Hsieh, W. W. and B. Tang, Applying neural network models to prediction and data analysis in meteorology and oceanography. Br. Am. Meteorol. Soc, 1998. 79: p. 1855–1870.
 Vapnik, V., The Nature of Statistical Learning Theory. 1995, New York, NY: Springer Verlag.
 Sun, A. Y., Predicting groundwater level changes using GRACE data. Water Resour. Res, 2013. 49(9): p. 5900–5912.
 Zhou, Z.-H., Ensemble Methods: Foundations and Algorithms. 2012: CRC Press.
 Ghosh, S. and P. Mujumdar, Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv. Water Resour., 2008. 31(1): p. 132–146.
 Rasmussen, C.E. and C.K. Williams, Gaussian processes for machine learning. Adaptive Computation and Machine Learning. Vol. vol. xviii. 2006, Cambridge, Mass: MIT Press. 48.
 Girard, A., et al., Gaussian Process priors with uncertain inputs – application to multiple-step ahead time series forecasting, in Advances in Neural Information Processing System S.e.a. E. Becker, Editor. 2003, MIT Press: Cambridge, Mass. p. 529–536.
 Quiñonero-Candela, J. and C.E. Rasmussen, A unifying view of sparse approximate Gaussian process regression. J. Mach. Learn. Res, 2005. 6: p. 1939–1959.
 Rasmussen, C. E. and H. Nickisch, Gaussian processes for machine learning (GPML) toolbox. J. Mach. Learn. Res., 2010. 11: p. 3011–3015.
 Reis, D. S., J. R. Stedinger, and E. S. Martins, Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation. Water Resour. Res, 2005. 41(W10419): p. 10.
 Robertson, D. E. and Q. J. Wang, A Bayesian approach to predictor selection for seasonal streamflow forecasting. J. Hydrometeorol., 2012. 13(1): p. 155–171.
 Wang, Q., D. Robertson, and F. Chiew, A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res, 2009. 45(5): p. W05407.
 Ritter, D. F., R. C. Kochel, and J. R. Miller, “Process Geomorphology 3rdEd”. 1995, W.C. Brown Publishers: Dubuque. 539.
 Kuczera, G., et al., Modelling yield changes following strip thinning in a mountain ash catchment: an exercise in catchment model validation. Journal of Hydrology, 1993. 150: p. 433–457.
 Strahler, A.N., Quantitative Geomophology of Drainage Basins and Channel Networks, in Handbook of Applied Hydrology, E. Chow VT, Editor. 1964, McGraw Hill Book Company: New York. p. 4-11.
 Ibrahim, H.M. and E.A. Isiguzo, Flood frequency analysis of Gurara River catchment at Jere,Kaduna State, Nigeria. Sci. Res. Essay, 2009. 4(6): p. 636-646.
 Jimoh, O. D. and O. S. Ayodeji. Impact of the Gurara River (Nigeria) interbasin water transfer scheme on the Kaduna River at the Shiroro Dam. in Water Resources System-Hydrological Risk, Management and Development 2003. Sapporo: IAHS.
 Jimoh, O. D. and B. F. Sule, Hydrological regime of the Gurara river., in 5th Annual Conference of National Association of Hydrogeologist 1992: Minna.
 Raper, J. and M. Bundock, Development of a generic spatial language interface for GIS. P. Mather, Geographical information handling-research and applications. Chichester, UK: John Wiley & Sons, 1993.
 Arun, D., Runoff Estimation for Darewadi Watershed using RS and GIS. International Journal of Recent Technology and Engineering (IJRTE), 2013. 1(6).
 Chow, V. T., D. R. Maidment, and L. W. Mays, Applied Hydrology. 1988, New York: McGraw-Hill.
 US Department of Agriculture, National Engineering Handbook, Part 630 Hydrology, in Chapter 7 Hydrologic Soil Group. January 2009. 2009.
 Waikar, M. L. and P. N. Aditya, Morphometric Analysis of a Drainage Basin Using Geographical Information System: A Case study. International Journal of Multidisciplinary and Current Research, 2014: p. 179- 184.
 Soil Conservation Service (SCS), Design of hydrograph. 2002: U.S Department of Agriculture, Washington, DC.
 Salami, A., et al., Morphometrical Analysis and Peak Runoff Estimation for the Sub-Lower Niger River Basin, Nigeria. Slovak Journal of Civil Engineering, 2016. 24(1): p. 6-16.