Generation of curve number using Remote Sensing and Geographic Information System

Generation of curve number using Remote Sensing and Geographic Information System

SHARE

Ashish Pandey and A. K. Sahu
Department of Agricultural Engineering
North Eastern Regional Institute of Science and Technology
Nirjuli, Arunachal Pradesh India-791 109
[email protected]

India supports 16 percent of world population on 2.42 percent of global land area (Manorama Year book 2000). An estimated 175 Million Hectares (M ha) of land constituting about 66 percent ( Motsara, 1998) of total geographical area suffers from deleterious effect of soil erosion and land degradation. Active erosion caused by water and wind alone accounts for 150 M ha of land, which accounts to a soil loss of about 5300 million tones of top soil. In addition 25 M ha have degraded due to ravine and gullies, shifting cultivation, salinity / alkalinity, water logging etc. In the wet tropical and subtropical climate, which prevails in most parts of Arunachal Pradesh, there is a usual phenomenon of erosion by degradation by the action of water. It is estimated that in Arunachal Pradesh about 669.35 million tones of soil is eroded annually with the rate of 90.7 ton/ha/yr (Singh, 1999).

Watershed, a geographically dynamic unit area that contributes runoff to a common point has been accepted as a basic unit for planning and implementation of the protective, curative and amelioration programmes. An accurate understanding of the hydrological behaviour of a watershed is important for effective management. RS and GIS technique can be used effectively to generate the land use/land cover and change detection map for evaluating the changes in an area. Land cover / Land use is perhaps the category in which remote sensing has made its largest impact and comes closest to maximizing the capabilities of remote sensing. For hilly and inaccessible terrain such as Arunachal Pradesh, it is often impractical to conduct even a limited ground survey, this has become very effective tools.

GIS, which has been designed to restore, manipulate, retrieve and display spatial and non-spatial data, is an important tool in analysis of parameters such as land use/ land cover, soils, topographical and hydrological conditions. To carry out resource monitoring and assessment of area of interest, information derived through remote sensing data has to be merged or integrated with database in GIS. Thus the remote sensing along with GIS application aid to collect, analyze and interpret the data rapidly on large scale intermittently and is very much helpful for watershed planning.

For ungauged watersheds accurate prediction of the quantity of runoff from land surface into rivers and streams requires much effort and time. But this information is essential in dealing with watershed development and management problems. Conventional methods of runoff measurements are not easy for inaccessible terrain of Arunachal Pradesh. Remote sensing technology can augment the conventional method to a great extent in rainfall-runoff studies. Many researchers (Ragan and Jackson, 1980; Slack and Welch, 1980, Tiwari et al., 1991) have been utilized the satellite data to estimate the USDA soil conservation Services (SCS) Runoff Curve Number (CN). In this study SCS Curve Number technique modified for Indian condition has been used for generation of CN for Remi Watershed.

Materials and Methods

Study Area
The area selected for the present study is the REMI watershed, which is located in the East Siang district of Arunachal Pradesh under Pasighat circle. The area of watershed is 210.00 Km2. The watershed area lies in the Survey of India (SOI) topo-sheet No. 82 P/4, 82P/8, 83M/1 and 83M/5 .It is located between 27o 50’to 28o 05′ N latitude and 950 05′ to 950 25′ E longitude.

The topo-sheet of the watershed in the scale of 1:50000, soil texture, soil series map and soil resources data were collected from State Remote Sensing and Application Centre, Itanagar. The topography of the watershed is undulating and the drainage is medium to excessive. The average slope of the watershed is very gentle to moderate ranging from 1- 20 % .The elevation of the watershed ranges from 140 to 1500 m above mean sea level. The climate of the area is humid sub-tropic. The region receives a high rainfall during monsoon and it turns sporadic during pre and post monsoon. The annual average rainfall is about 2000 mm and a mean annual temperature of 20o C. The surface texture of the soil is Clay Loam. The problem of erosion hazard is moderate to severe.

Satellite Data
The IRS-1B geocoded imagery was collected from State Remote Sensing and Application Center, Itanagar. The path 13 and 14, row 48 scenes of IRS-1B (LISS II) satellite with date of pass December 16, 1996 were used to prepare the land use/ land cover maps of the Remi watershed.

Hardware and software used for data analysis
Workstations available at State Remote Sensing and Application Centre, Itanagar equipped with GIS facilities were used. The ARC INFO software packages were used for generation of vector layers. Then it was transferred to Arc-view for taking the output.

The visual interpretation of the satellite imagery was done and the study area has been classified into seven classes, namely

  1. Pasture Land
  2. Habitant
  3. Cropped area
  4. dense forest,
  5. Open forest
  6. Waste land (scrubs)
  7. Water body.

After classification the land use / land cover map was digitized in vector form in polygon mode. In vector form one can assign values for different class polygons, which help in the calculation of curve numbers.

The soil map (Fig:2), was also collected from State Remote Sensing and Application Centre, Itanagar and it was digitized in polygon mode. For different soils (i.e., polygons) different values were assigned. Different values of soils (Polygons) were latter used for determination of SCS curve numbers.

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

Sl. No. Land use Area (km2) % of total Area
1 Cultivated land (good crop)  43.790 20.84
2 Forest Dense forest Open forest  56.0883.04 26.7039.54
3 Waste land (Scrub)  25.45 12.11
4 Water bodies  0.25 0.12
5 Habitant (Township and villages)  0.87 0.42
6 Pasture land  0.56 0.27
 Total area  210.00km2 100

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

AMC  CURVE NUMBER
I 49
II 68
III 83

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

  • Handbook of Hydrology (1972). Soil Conservation Department, Ministry of Agriculture, New Delhi
  • Manorama Year Book (1999 ) Malayalam Manorama Press, Kottyam
  • Motsara, M.R. (1998) Need for the Right Mix. The Hindu Survey of Indian Agricultural 1999. Kasture and sons, National Press, Chennai: 169-174
  • Ragan, R M and T J Jackson (1980). Runoff Synthesis Using Landsat and SCS Model, J. of Hydrology, Divn., ASCE, Vol. 106 (HYS5): 667-678
  • Singh, S (1999) Resource atlas of Arunachal Pradesh, Government of Arunachal Pradesh
  • Slack R B and Jackson T J (1980) Runoff synthesis using Landsat and SCs Model, Journal of the Hydraulics Division, ASCE, 106 (HY 5): 667-668
  • Tiwari, K.N. P. Kumar, M. Sibastian and K. Paul (1991). Hydrological Modelling for Runoff Determination: Remote Sensing Technique, J. Water Resources Planning and Management 7(3): 178-184