STORM WATER MANAGEMENT IN URBAN AREA USING REMOTE SENSING AND MODELLING USING SWMM TECHNOLOGY

Abstract

In recent times the frequency of floods has increased and has caused huge economic damages to the country and have even resulted in loss of life of people. Due to rapid urbanization the percentage of imperviousness has increased and as a result availability of natural land has decreased. Major percentage of rainfall is converted to surface runoff due to less availability of pervious surfaces and hence as a result instead of ground water recharge majority of rain water reaches to storm drainages. Hence, if the capacity of drainage is not enough to carry the rain water then it leads to accumulation of water and if the same condition remains for longer period of time then flooding situation may occur. So, it becomes necessary to evaluate such regions where flooding condition can occur in future and necessary action can be taken. In flood modelling the major problem is of data scarcity, under such conditions the challenge is to identify the flood hotspots. Hence a comprehensive study was done to identify the flood prone region. The input parameters considered were percent imperviousness, percentage slope, area of sub catchment, manning’s roughness coefficient for drainage pipe, length of conduit, shape of conduit, depth of invert elevation, percentage of ponded area, surcharge depth, initial depth of water at junction and rainfall values. The study area selected is Dehradun district, due to less availability of drainage network data few assumptions based on logical considerations were done for the construction of SWMM model and accordingly the output has been evaluated. Hence 2 experiments were considered for comprehensive analysis, experiment 1 included the road network of national highway and state highway and experiment 2 included the road network of national highway, state highway and major roads of Dehradun district and it is assumed that the drainage network lies below the road network in each of the two experiments. Return period considered is 2 years and the rainfall event time duration considered is 24 hours. Rainfall values of past 30 years is considered for the analysis. The results of both the experiments were analyzed and found that majority of junction nodes starts flooding after 1:15 hours of start of rainfall event and the location of flooded nodes lies in the built-up area mostly. Also, approximately 69 % of rainfall over study area gets converted to surface runoff. Finally, with the help of ArcGIS and SWMM software flood hotspot map indicating the region that are flood hotspot if a rainfall of 2-year return period with 24 hours duration would occur over study area was prepared and flood prone regions were identified. The output derived from SWMM software can be helpful for town planner and long-term flood management in the Dehradun district.