# The Goodness of Fit between the Observed Predicted Flat Flows, the Reliability of Models for Predicting Future Flood River Flows – Statistics Project Example

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The paper “ The Goodness of Fit between the Observed Predicted Flat Flows, the Reliability of Models for Predicting Future Flood River Flows”   is an informative version of the statistics project on environmental studies. Flooding can be very devastating if the proper prediction is not carried out using modeling techniques that are accurate. The models adopted in flood prediction should be able to accurately predict the volume of water that will be flowing during heavy rainfall, determine base runoff as well as direct runoff. To begin with, determining peak runoff rates, as well as runoff volumes, are essential in the models that are used as a simulation of flooding.

This is information can also be used in designing water and soil conservation in flooding areas. The base flow and direct runoff information are usually recorded from the area of study and calculated from automatic recorders that are put there. This paper is going to study flood mathematical models that are used in flood modeling. 3.1 base flow and direct runoffUsing the data provided, the observed data event has been separated base flow and direct runoff separately.

This has been done to provide losses associated with the rainwater. In determining the base flow, it is assumed that the gradient does not affect the base of the river. This is comparable to the dry season when the flow rate is different. If we look at the initial SDM one can note that a small change leads to an increase in flow rate per second. When the base flow model is applied to simulate an event, the total flow is not known until after the base flow has been simulated and added to the direct runoff hydrograph. The rainfall/runoff model treats each catchment as a single unit, allowing some of the model parameters to be evaluated from physical catchment data.

The direct runoff model shows the SDM as well as the actual evapotranspiration and the runoff components such as interflow, base flow, and overland flow. This shown below From the figures above the discharge shows that cease to a high flow was a common occurrence period under study and not month that does not high flow rate. This is supported by the figure below for the month of January 1972. Justification of choice of method for separation and lose evaluation SDM contribution to flood flow is not at the same rate as surface runoff thus it is separately analyzed which is done as a requirement in hydrograph analysis.

The groundwater recession curve is a characteristic of the particular catchment in which it was recorded, and some part of this curve will be a constituent of the total hydrograph. If individual storm recession curves are fitted together to form a composite curve, the result is a master recession curve.

The recession curve has a continuous discharge record covering event 2291. It represents the base flow contribution after surface runoff has ceased, at as many different stages as possible.

References

Bollerslev, T 1986, Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, vol. 31, 307-27.

Bollerslev, T, Kroner, K & Chou, RY 1992, ARCH modeling in finance: a review of the theory and empirical evidence, Journal of Econometrics, vol. 52, 5–59.

Chadwick, A., Morfett, J., and Borthwick, M. 2004. Hydraulics in Civil and Environmental Engineering. London: E & F N Spon.

Clewett, J. F., N. M. Clarkson, et al. 2003. RainmanStreamFlow (version 4.3): A comprehensive climate and streamflow analysis package on CD to assess seasonal forecasts and manage climatic risk. Department of Primary Industries, Queensland.

Connell Wagner Pty Ltd, 2001. Flood investigation of the communities of Beswick, Mataranka, Djilkminggan, and Elsey – Study Report, unpublished.

Dodge, J. C. I., and O'Kane, J. P. 2003.Deterministic Methods in Systems Hydrology.Balkema, Lisse.

Dooge, J. C. I. 1977. Problems and methods of rainfall-runoff modeling. Cipriani, T.A., Maione, U. and Wallis, J.R. (Eds) Mathematical Models for Surface Water Hydrology. Proceedings of the Workshop held at the IBM Scientific Center, Pisa, Italy. Wiley, London.pp 71-108.

Engle, RF & Granger, CWJ 1987, ‘Co-integration and error correction: representation, estimation, and testing’, Econometrica vol. 55, pp. 251-76.

Gonzalo, J & Granger, CWJ 1995.‘Estimation of common long-memory components in co-integrated systems’, Journal of Business and Economic Statistics, vol. 13, no. 1, pp. 27-35.

Institute of Hydrology, 1999.Flood Estimation Handbook. Institute of Hydrology, Wallingford, UK.

Knapton, A. 2006.Regional Groundwater Modelling of the Cambrian Limestone Aquifer System of the Wiso Basin, Georgina Basin, and Daly Basin. Alice Springs, NTG Dept. Natural Resources, Environment, and The Arts.

Knapton, A. 2009.Development of a Surface Water Model of the Roper River using MIKE11.Alice Springs, NTG, Department of Natural Resources, Environment, the Arts and Sport.

Nelson, DB 1991, ‘Conditional heteroskedasticity in asset returns: a new approach’, Econometrica, vol. 59, pp. 347–370.

URS 2008.Integrated hydrologic modeling of the Daly River catchment and Development of a Water Resource Monitoring Strategy. Darwin, NT.

Wilson, E M. 1990. Engineering Hydrology. London: MacMillan.

Zakoian, JM 1994, ‘Threshold heteroskedastic models’, Journal of Economic Dynamics and Control, vol. 18, pp. 931–955.

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