Effect of Geological Feature on Regional Flood Frequency of Watersheds of North-East of Iran

آب و خاک

Article 2, Volume 31, Issue 6 - Serial Number 56, January 2018, Pages 1526-1537   

Document Type: Research Article

DOI: 10.22067/jsw.v31i6.28972

Authors

tayebe taherpour; bijan Ghahraman2^* ; kamran davary

Ferdowsi University of Mashhad

Abstract

Introduction: Finding out homogeneous watersheds based on their flood potential mechanisms, is needed for conducting regional flood frequency analysis. Similarity of watersheds based on flood potential severity depends on many factors such as physiographic and meteorological features of the watershed, geographical location and geological features. These criteria although are sound ones, they suffer from this concept that there is no attention to hydrological losses of runoff into the soil. As a result, current literature lacks for considering geological features into delineating homogeneous regions. The primary contribution of this paper is to include one geological criterion on flood regionalization. In a previous study we made a homogeneous classification for Khorasan Province of Iran without taking into consideration of infiltration features of the region. So, by taking geological features there may provide a sound comparison to regionalization issue.
Materials and Methods: To find out the effect of geological feature on delineation of homogeneous regions, 73 hydrometric stations at North-East of Iran with arid and semi-arid climate covering an average of 29 years of record length were considered. Initially, all data were normalized. Watersheds were clustered in homogeneous regions adopting Fuzzy c-mean algorithm and two different scenarios, considering and not considering a criterion for geological feature. Three validation criteria for fuzzy clustering, Kwon, Xie-Beni, and Fukuyama-Sugeno, were used to learn the optimum cluster numbers. Homogeneity approval was done based on linear moment’s algorithm for both methods. We adopted 4 common distributions of three parameter log-Normal, generalized Pareto, generalized extreme value, and generalized logistic. Index flood was correlated to physiographic and geographic data for all regions separately. To model index flood, we considered different parameters of geographical and physiological features of all watersheds. These features should be easily-determined, as far as practical issues are concerned. Cumulative distribution functions for all regions were chosen through goodness of fit tests of Z and Kolmogorov-Smirnov.
Results and Discussion: Watersheds were clustered to 6 homogenous regions adopting Fuzzy c-mean algorithm, in which fuzziness parameter was 1.9, under the two different scenarios, considering and not considering a criterion for geological feature. Homogeneity was approved based on linear moment’s algorithm for both methods, although one discordant station with the lowest data was found. For the case with inclusion of genealogic feature, 3-parameter lognormal distribution was selected for all regions, which is a highly practical result. On the other hand, for not considering this feature there were no unique distribution for all regions, which fails for practical usages. As far as index flood estimation is concerned, a logarithmic model with 4 variables of average watershed slope, average altitude, watershed area, and the longest river of the watershed was found the best predicting equation to model average flood discharge. Determination coefficient for one of the regions was low. For this region, however, we merged this region to other regions so that reasonable determination coefficient was found; the resulting equation was used only for that specific region, however. By comparing the distributions of stations and also two evaluation statistics of median relative error and predicted discharge to estimated discharge ration corresponding to 5 different return periods (5, 10, 20, 50, and 100 years). Both perspectives showed acceptable results, and including geological feature was effective for flood frequency studies. With considering the geological feature for regionalization, Besides, Log normal 3 parameters distribution was found appropriate for all of the regions. From this point of view, geological feature was useful. Median of relative error was lower for small return periods and gradually increased as return period was increased. Median of relative error was between 0.21 to 00.45 percentages for the first method, while for the second method it varied between 0.21 to 0.49 percentages. These errors are quite smaller than those reported in literature under the same climatic region of arid and semi-arid. The probable reason may due to the fact that we made a satisfactory regionalization via fuzzy logic algorithm., We considered another mathematical criterion of “predicted discharge to the observed discharge”. The optimum range for this criterion is between 0.5 and 2. While under-estimation and over-estimation are found if this criterion is lower than 0.5 and higher than 2, respectively. Based on this premise, 75 to 95 percentages of stations were categorized as good estimation under the first method of analysis. On the other hand, 78 to 97 percentages of stations were considered good for the second approach.

Keywords

Flood index; Fuzzy cluster analysis; Infiltration measure; Non-data watersheds

References

1- Alvankar S.R. 2011. Estimating maximum flood discharge by using Landsat satellite data. The 4th Conference on Iran Water Resources Management. Amirkabir Industrial University. May 2-3, 2011. Tehran. 12 pages (in Persian).

2- Ataee H., and Shiran M. 2011. Identifying homogeneous hydrologic watersheds based on geomorphologic feature effective on flood by using cluster analysi (case study: Kroon plain). Journal of Geography and environmental planning. 42, 79-98 (in Persian).

3- Bezdek J.C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.

4- Dalrymple T. 1960. Flood frequency analysis. US Geological Survey Water Supply Pap.er1543-A.

5- Das S., and Cunnane C. 2011. Examination of homogeneity of selected Irish pooling groups. Hydrology and Earth System Sciences, 15: 819–830.

6- Dunn J.C. 1974. A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters. Journal of Cybernetics, 3 (3): 32–57.

7- Fukuyama Y., and Sugeno M. 1989. A new method of choosingthe number of clusters for the fuzzy c-means method. Proceedings of Fifth Fuzzy Systems Symposium, pp. 247–250 (in Japanese).

8- Goel G.N. 2000. The formation of groups for regional flood frequency analysis. Hydrological Sciences-Journal-des, Sciences Hydrologiques; 45(1) 97-112.

9- Haddad K., and Rahman A. 2012. Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework: Quantile regression vs. parameter regression technique. Journal of Hydrology. 430–431, 142–161.

10- Hall M.J., and Minns A.W. 1999. The classification of hydrologically homogeneous regions. Hydrological Sciences Journal, 44 (5): 693–704.

11- Hosking J.R.M. 1990. L-Moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of Royal Statistical Society B, 52: 105-124.

12- Hosking J.R.M., and Wallis J.R. 1993. Some statistics useful in regional frequency analysis, Res. Rep. RC 17096, IBM Research Division, York town Heights, NY 10598.

13- Hosking J.R.M., and Wallis J.R. 1997. Regional Frequency Analysis (An Approach Based on Linear Moments). Cambridge University Press.

14- Jingyi Z., and Hall M.J. 2004. Regional flood frequency analysis for the Gan-Ming River basin in China. Journal of Hydrology, 296: 98–117.

15- Koorehpazan Dezfooli A. 2008. Theoretical Principles of Fuzzy sets and its Applications in Problems of Water Engineering Modeling. Amirkabir Industrial University Publication, 8737. Tehran. 261 pp (in Persian).

16- Kumar R., Chatterjee C., Kumar S., Lohani A. K., and Singh R.D. 2003. Development of regional flood frequency relationships using L-Moments for Middle Ganga Plains subzone 1(f) of India. Water Resources Management, 17: 243-257.

17- Kwon S.H. 1998. Cluster validity index for fuzzy clustering. Electronics Letters 34 (22), 2176–2177.

18- Mishra B.K., Takara K., and Tachikawa Y. 2008. Regionalization of Nepalese river basins for flood frequency analysis. Annual Journal of Hydraulic Engineering, JSCE, 52: 91-96.

19- Mooshkhian Y., Ownagh M., Bardy Shykh V., Mosaedi A., and Saad-o-ddin A. 2012. Developing regional models for estimating flood discharge in selected watersheds of Khorasan Razavi watersheds. 8th National Meeting on Science and Engineering of Watershed Management. May 1-17, 2012. Lorrestan University (in Persian)

20- Mosley M.P. 1981. Delimitation of New Zealand hydrologic regions. Journal of Hydrology, 49: 173-192.

21- Pal N.R., and Bezdek J.C. 1995. On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy systems, 3 (3): 370–379.

22- Rao A.R., and Srinivas V.V. 2006. Regionalization of watersheds by fuzzy cluster analysis. Journal of Hydrology, 318: 57-79.

23- Roa A.R., and Srinivas V.V. 2006. Regionalization of watersheds by Hybrid-Cluster analysis. Journal of Hydrology, 318: 37-56.

24- Ross T.J. 1995. Fuzzy Logic with Engineering Applications. McGraw-Hill, New York.

25- Saf B., Dikbas F., and Yasar M. 2007. Determination of regional frequency distributionsof floods in West Mediterranean river basins in Turkey. Fresenius Environmental Bulletin, 16 (10): 1300–1308.

26- Saf B. 2010. Assessment of the effects of discordant sites on regional flood frequency analysis. Journal of Hydrology, 380: 362-375.

27- Shamkooian H., Ghahraman B., Davary K., and Sarmad M. 2009. Regional flood frequency analysis by using Linear Moment theory and flood index method in Khorasan Province watersheds. Journal of Soil and Water (Agricultural Science and Industry). 23(1), 31-43 (in Persian).

28- Sveinsson O.G.B., Boes D.C., and Salas J.D. 2001. Population index flood method forregional frequency analysis. Water Resources Research, 37 (11): 2733–2748.

29- Xie X.L., and Beni G. 1991. A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 13 (8): 841–847.

30- Zaman M.A., Rahman A., and Haddad K. 2012. Regional flood frequency analysis in arid regions: A case study for Australia. Journal of Hydrology, 475: 74–83.