عنوان مقاله [English]
Runoff is considered to be an effective variable in the formation and intensity of floods, and water balance. It is also considered to be a very important parameter in water resources management. Surface runoff is formed due to a combination of different parameters, such as severe precipitation, a sloping ground, and saturated soils. It is especially important to predict and determine the amount of runoff produced and transferred to the basin outlet. Generally, different parts of large basins may experience a higher or lower than average precipitation, and thus different spatial distribution of precipitation. Empirical formulas may sometimes give us an inaccurate estimation of the surface runoff volume. Radar and rain gauges are the most common tool used for collecting rainfall data. Together, they can be useful for estimation of rainfall volume and its distribution in a wide area. Integrating high resolution radar based rainfall estimation with hydrological models makes a highly efficient tool for flood prediction.
Materials and Data
Baghu Basin is considered to be one of Gorgan Gulf sub basins. It covers an area of 24.4 square kilometers. Its perimeter is 23.2 kilometers. The length of its main river is 10 kilometers. The maximum altitude of the main river is 2080 m and its minimum altitude is 80 m. The river channel has an average slope of 20%. Data used in this research includes: 1-data received from east Caspian radar; 2- precipitation and daily evaporation data received from different weather stations around the basin, including Bandar Gaz, Bandar-Torkman, Hashem-Abad and Gorgan stations; 3- discharge value in previous floods of Baghu basin.
Geographic coordinates of the basin were obtained using GIS. Geographical coordinates of the basin perimeter were also extracted by radar and the basin area was defined for the radar. Then using the radar software, total amount of precipitation and runoff were measured in the basin. These were used in (1) to calculate runoff coefficient, as a percentage of rainfall:
Where C is runoff coefficient, P is precipitation elevation and R is direct runoff.
Discussion and Results
It is important to consider the effect of climatic and meteorological factors on runoff formation in basins. Severity of precipitation is the first factor. Radar based rainfall estimates indicated that increased rainfall intensity results in increased hourly runoff in the basin. The same phenomenon has been observed in some of previous floods in Baghu basin. In these cases, a sudden increase in precipitation resulted in a severe runoff increase. These floods exhibited long sharp-crested hydrographs. Spatial/temporal distribution of rainfall intensity was the second factor with a significant effect on the amount of runoff produced. Thus, the effect of rainfall distribution was also analyzed. Results indicate that rainfall distribution has affected the amount of runoff produced in different parts of the basin in different ways. Rainfall continuity was the third climatic factor with a significant role in the production of increased runoff. This factor was investigated in winter (cold seasons) floods. Apart from the intensity and volume of precipitation in these floods, precipitation continuity was the most influential factor in the production of a large amount of runoff. This shows the effect of rainfall continuity on runoff increase. Temporal distribution of rainfalls was the fourth factor influencing runoff production, and thus soil moisture. In winter, soil moisture is usually high and there is little evaporation. So soil maintains its moisture and remains wet for a longer time. In this way, a moderate and low amount of rainfall over a short period of time results in soil saturation, and runoff increase. This was investigated in Baghu basin precipitations. According to the findings of this study, increased soil moisture has resulted in runoff increase. Several climatic factors contribute to increased runoff coefficient. In high intensity floods which occur due to large volume of precipitation over a longer period of time, a huge amount of runoff would form. And if as a result of successive precipitation these factors combine with soil moisture, runoff coefficient would be even larger. In cold seasons, three factors - rainfall continuity, soil moisture and poor vegetation- results in increased runoff. However, dry soil and vegetation during warm seasons reduce the effect of intense precipitation on increasing runoff volume.
Based on the findings of the present study, it is not possible to consider a single constant runoff coefficient for the total area of a basin. Thus, it is better to determine a range of runoff coefficients for each basin. It should also be noted that each flood has its own runoff coefficient, which depends on precipitation severity, temporal/spatial distribution, rainfall duration, intensity variations during precipitation, time intervals between each rainfall occurrence and season rainfall coefficient. Respective severity or weakness of different factors, combination of various factors affecting runoff, and the amount of runoff in similar precipitation may also vary. The present study indicated that due to severe and sudden rainfalls, warm season floods had long sharp-crested hydrographs. In winter, rainfalls were continuous, but with lower intensity. Thus, their hydrograph was wider than warm season floods. In small areas with less than an hour concentration time, the effect of spatial/temporal dispersion of rainfall on the amount of runoff is important. In Baghu basin, 8 to 25 percent variation was observed in runoff coefficient of eight different floods.
12. Ahm. M, Thorndahl. S, Rasmussen. M. R and Bass. L., 2013, Estimating sub catchment runoff coefficients using weather radar and a downstream runoff sensor; water science & technology ;1293-1299echnology.
13. Anquetin. S, Braud. I, Vannier. O., Viallet. P. Boudevillain. B., Creutin. J.D Manus.C,2010; Sensitivity of the hydrological response to the variability of rainfall fields and soils for the Gard 2002 flash-flood event, Journal of Hydrology 394 (2010) 134–147.
14. Borga. M, Anagnostou. E.N, Frank. E, 2000, On the use of real-time radar rainfall estimates for flood prediction in mountainous basins, Journal of Geophysical Research, Vol. 105, NO. D2,2269-2280.
15. Byung. S, Kim., Jun. B, Hong, Hung. S, Kim, Seok. Y, Yoon, 2007, Combining radar and rain gauge rainfall estimates for flood forecasting using conditional merging method. World Environmental and Water Resources Congress 2007.
16. Cranston. M. D and Black. A.R, 2006; Flood warning and the use of weather radar in Scotland: a study of flood events in the Ruchill Water catchment, Meteorology. Appl. 13, 43–52.
17. Dong-Sin, S. Ming-Hsu, L. Ray-Shyan, W. (2008), Distributed Flood Simulations with Coupling Gauge Observations and Radar-rainfall Estimates, Water Resources Manage, Vol 22:843–859.
18. Giannoni. A, Smith. A. S, Zhang.Y, Roth.G ,2003; Hydrologic modeling of extreme floods using radar rainfall estimates, Advances in Water Resources 26. 195–203.
19. Looper J.P, Vieux. B.E,2012; An assessment of distributed flash flood forecasting accuracy using radar and rain gauge input for a physics-based distributed hydrologic model; Journal of Hydrology 412–413 ,114–132.
20. Mapiam. P.P., Sharma. A, Chumchean. S and Sriwongsitanon.N,2009; Runoff estimation using radar and rain gage data, 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009; 3719-3725.
21. Marra. F , E.I. Nikolopoulos. J. D. Creutin. M. Borga.; 2014; Radar rainfall estimation for the identification of debris-flow occurrence thresholds; Journal of Hydrology 519; 1607–1619.
22. Nicholas. Kouwen.; 1998, Water flood, Micro-Computer Based Flood Forecasting System Based on Real-Time Weather Radar; Canadian Water Resources Journal; 62-77.
23. Overeem, A. Buishand. T.A. and Holleman, I. , 2009, Extreme rainfall analysis and estimation Of depth-duration-frequency curves using weather radar, Weter Resources Reserch, Vol. 45: 1-15.
24. RB5-Manuals- Rainbow Training Manual, 2012
25. RB5-Manuals-Ranbow*. Products & Algorithms Release 5.37.0 -2013-04-01 .
26. Sami, E. Alin. C. Khalidou. B. 2010, Validation and use of rainfall radar data to simulate water flows in the Rio Escondido basin, Stoch Environ Res Risk Assess, Vol.24:559–565.
27. Seed. A, Siriwardena. L, X. Sun, P. Jordan, J. Elliott; 2002; On the Calibration of Australian Weather Radars; Cooperative Research Centre for Catchment Hydrology, (Technical Report 02/7,2002).
28. Versini P.A, 2012; Use of radar rainfall estimates and forecasts to prevent flash flood in real time by using a road inundation warning system, Journal of Hydrology 416–417 (2012) 157–170.
29. Wang. p, Smeaton. A. Lao. S, Connor. E. Ling. Y. Connor.N ,2009, Short-Term Rainfall Nowcasting: Using Rainfall Radar Imaging Eurographics Ireland.
30. Xin He,2011. Weather radar based quantitative precipitation estimation in modeling of catchment hydrology, PhD thesis, University of Copenhagen.
31. Yilmaz K.K, Hogue. S, Gupta. H.V, Wagener. T; 2005; Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting; Journal of Hydrometeorology.; Vol 6; 497-517.