Home Articles Estimation of runoff through Remote Sensing

Estimation of runoff through Remote Sensing

Indu Jaiswal, R.Mani Murali, V. K. Panchal
Defence Terrain Research Laboratory,
Metcalfe House, Delhi-110054.

Abstract
Most environmental process show complicated interrelations, both time and space, leading to numerical models with a complex mathematical structure. Also environmental models require huge amount of data often coming from many sources like Remote sensing. Knowledge of Runoff that depends upon many factors like precipitation, recharge of the basin, type of soil etc is one such important parameter.

In this study an attempt is made to compute Runoff estimation from Remote sensing data so as to provide a quick result for decision-makers, before any experimentation of quantification is taken up. This is a less time consuming method and also gives more and more reliable results as the imperviousness of the drainage area increases and the value of Runoff coefficient tends to approach unity. This method can be used effectively in the design of storm water drains and small water control projects.

Introduction
Throughout the world the need for hydrological studies results from engineering problems encountered by man, such as flooding, design of bridges and dams, soil erosion, design of storm water drains. Most studies focused on stream flow.

The lack of adequate data and the extreme variability in space and time of all factors that control the runoff process causes the validity of this methodology.

Principle
The principle behind this methodology is that, the depth of excess precipitation or direct runoff is always less than or equal to the depth of precipitation, likewise after runoff begins the additional depth of water retained in the watershed is less than or equal to some potential maximum retention. There is some amount of rainfall Ia for which no runoff will occur, so the potential runoff is (P – Ia ). According to the SOIL CONSERVATION SERVICE (1972) hypothesis the ratios of the two actual to the two potential quantities are equal that is

( Fa / S) = (Pe / (P – Ia )) …… (1) From continuity principle

P = Pe + Ia + Fa ……..(2)

combining equation (1) and (2)

Pe = ((P – Ia )2 ) / (P – Ia + S) ………(3)

This is the basic equation for computing the depth of excess rainfall or direct runoff from a storm .

By study of results from many small experimental water sheds, an empirical relation was developed that is

Ia = 0.2S …………….(4)
then Pe = ((P – 0.2S )2 ) / (P +0.8 S) ……………..(5)

Plotting the data for P and Pe from many watersheds the SCS found curves of the type shown in fig.

To standardize these curves, a dimensionless curve number CN is defined such that 0 £ CN £ 100. For impervious surface and water surface CN = 100; for natural surfaces CN 100. The curve number and S are related by

S =( ( 1000 / CN ) – 10) ………………….(6)

Where S is in inches. The curve is for normal antecedent moisture conditions( AMC II ) . For dry condition (AMC I) or wet condition (AMC III) equivalent curve numbers can be calculated as follow.

CN( I) = (4.2 CN(II) ) / ( 10 – 0.058 CN(II)) ……….(7)

CN(III) = (23 CN(II)) / ( 10+ 0.13 CN (II)) …………..(8)

Satellite view of the study area

These Curve Number are varying as soil type and land use, For this soil is classified into four groups.

Group A : Deep sand, Deep loess, Aggregated silts
Group B : Shallow loess, sandy loam.
Group C : Clay loams, shallow sandy loam, soils low in organic content and soils usually high in clay
Group D : Soils that swell significantly when wet, heavy plastic clays, and certain saline soils.

Landuse / landcover of surrounding area of Delhi

The value of CN for various land uses on these soil types are given in TABLE 1

click here to view large size table

Methodology
First of all overlays for landuse and soil is generated from RS data of the given area. Then CN value is obtained from the Table 1. This CN value is used in equation (6) and value of S is obtained .By using this value of S in equation (5) direct runoff is calculated in terms of precipitation.

Experiment
In this study the satellite LISS III imagery of surrounding area of Delhi like Gajiabad is interpreted for generating Landuse / landcover map, Geomorphological map, and Soil map. The Soil type of study area comes under Group C. Now according to landuse weighted CN value is calculated as given in table 2.

LANDUSE SOIL GROUP – C
1.Agricultural Land Percentage CN Value Product
75% 78 5850
2.Residential (65% impervious) 15% 90 1350
3.Forest 5% 77 385
4.Water bodies 3% 100 300
5.Roads with curbs and storm sewer 2% 98 196
Total = 8081

Thus weighted CN value = 8081/ 100 = 80.81
S = ((1000 / CN) – 10 ) = 12.37 inches .
substituting the value of S in equation (6)
Pe = ((P – (0.2*12.37) )2 ) / (P +(0.8*12.37))

Pe = ( P – 2.474 )2 / ( P + 9.896)
Assume collective precipitation is 5 inches.
Then Pe = 0.428 inches

Conclusion
However this approach does not give aquarate result, because the runoff not only depends upon the precipitation but also upon the recharge of the basin, but this gives more and more reliable result as the imperviousness of the drainage area increases the value of CN increases upto 100. This is a less time consuming approach. This can be used in the design of storm water drains design of dam, flood-forecasting etc.

References

  1. Applied Hydrology by Ven Te Chow , David R. Maidment and Larry W. Mays.
  2. Hydrology and water resources engineering by S. K. Garg.
  3. ASCE , Journal of Hydrology JAN 1999
  4. Remote sensing and image Interpretation By Lillisand and Keifer.
  5. Proceedings of ISRS Symposium 1999