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Generation of curve number using Remote Sensing and Geographic Information System
Theoretical Consideration
Methodology adopted for determination of curve number is presented in Fig (1). The individual CN were found by Table: 1 verifying the hydrological soil group by overlaying the soil and land use / land cover map. The area with a particular soil type and land use are ascribed a CN, then it is multiplied by the area it covers and its weighted value is found out. Normally the SCS model computes direct runoff with the help of following relationship (Hand book of Hydrology, 1972) S = (24500/CN) - 254 (1) Q = ((P - 0.3S)2)/(P + 0.7S) (2) Where, Q = Runoff depth, mm P = Rainfall, mm S = Maximum recharge capacity of watershed after 5 days antecedent rainfall, mm Ia = 0.3 S (Initial abstraction of rainfall by soil and vegetation, mm) CN = Curve Number, CN is found out from the table. CN = (S(Ci X Ai ))/A (3) Where, CN = weighted curve number. CNi = curve number from 1 to any no. N. Ai = area with curve number CNi A = the total area of the watershed. Table: 1 Runoff Curve Numbers for (AMC II) for the Indian Conditions  Results and Discussions The soils of the REMI watershed were grouped in different textural groups and are presented in (Fig: 2). After analysis it was found that the maximum area of Remi watershed falls under the Hydrological Soil Group ‘C’. The classified imagery of Remi watershed is shown in (Fig: 3). The areas under different land use / land cover classes are included in Table (2). Seven land use / land cover classes namely Cultivable land (20.84 %), Dense forest (26.70 %), open forest (39.54%), wasteland (scrub) (12.11%), pasture land (0.56 %) water bodies (0.25 %), habitant (0.87 %) were identified. After classification the land use / land cover map was digitized in ARC/INFO. Further land use / Land cover digital data was used for generation of CN. Table: 2 Land use /Land cover classification of the REMI watershed as derived from Image interpretation
Curve Number The weighted curve number was generated using land use / land cover map, hydrologic soil group map and standard curve number table for Indian condition. The weighted curve number for AMC II, I and III were found out as given in Table: 3. Table 3: Weighted Curve Number for Remi Watershed
There is no provision for runoff monitoring in Remi watershed, therefore the SCS CN method could be used to find out the runoff. Thus the generated Curve Numbers may be used for prediction of Runoff from an ungauged watershed. Conclusions Remote sensing and GIS technique is assumed as a viable alternative or a dependable support system to our conventional way of surveying, investigation, planning, monitoring, modelling, data storing and decision making process. The synoptic concept of satellite imagery is fairly easy for identification of the broad physical features such as stream network, land use, land cover, soils, vegetation, surface water bodies etc. Remote sensing is of immense use to develop land use / land cover map of hilly inaccessible topographic conditions such as Arunachal Pradesh. The land use / land cover is an important parameter input of SCS model. With the help of RS, GIS & SCS model it is possible to make management plans for usage and development of a watershed. Although Curve Number method is an empirical approach to determine the runoff depth from the watershed. But it can be helpful for estimating the Runoff for places, which do not have runoff records. Acknowledgement Authors are thankful to Dr. G. Ch. Chenni, Director, State Remote Sensing Centre, Itanagar (Arunacahal Pradesh) for providing laboratory facility and valuable suggestion to carry out this research work. Reference
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