Hydrology ofMountainous Areas (Proceedings of the Strbské Pleso Workshop, Czechoslovakia, June 1988). IAHS Publ. no. 190, 1990.

Application of adapted curve number model on the Sputka basin

P. KOVAR Department of Water Resources, University of Agriculture Prague 6 - Suchdol, Czechoslovakia

ABSTRACT The United States Soil Conservation Service Curve Number Method (CN) has been modified by incorporation of a physically bas­ed solution of the infiltration process and explicit consideration of antecendent moisture conditions. The modified model, labelled INFIL has been implemented on a 104.1 km2 river basin in Czechoslovakia with satisfactory results.

NOTATION

AVP yearly average depth of precipitation (mm) AVQ yearly average depth of runoff (mm) B auxiliary parameter (hr) C évapotranspiration constant (dimensionless) CF sum of discharge differences squared (m3/s)2

CF sum of mean discharge differences squared (m3/s)2

CN curve number (dimensionless) CN seasonal curve number (dimensionless) s

CN. actual curve number (dimensionless)

DEP daily depletion coefficient (mm/day) Hf effective capillary drive (mm)

I infiltration rate (mm/hr) la initial abstraction (usually 0.2S) (mm) J slope of basin (X) JD Julian date (dimensionless) K hydraulic conductivity (mm/hr)

1 length of main river (km) P storm rainfall depth (mm) P_ 5 day antecedent precipitation (mm)

Q direct runoff depth (mm) JT

5 day antecedent runoff (mm)

Q observed discharge ordinates (m3/s)

QC computed discharge ordinates (m3/s)

Q mean observed discharge (m3/s) rainfall rate (mm/hr)

391

P. Kovâr 392

r normalized rainfall rate (dimensionless) RE efficiency coefficient (dimensionless) S maximum storage parameter (mm) S seasonal storage (mm)

Sf storage suction factor (mm)

SP sorptivity (mm/hr) SP(9.) sorptivity when moisture content is at field

capacity (according to Green and Ampt)

S(9.) = /(2K Sf) (mm/hr)

t time (hr) t ponding time (hr)

At time step (hr) t, rainfall duration (hr)

t, lag time (hr)

U unit hydrograph ordinate (dimensionless)

¥ cumulative infiltration depth (mm) W pre-ponding cumulative infiltration depth (mm)

l initial soil moisture content, or moisture content at field capacity (dimensionless) soil moisture content at natural saturation (dimensionless)

THE CURVE NUMBER METHOD

The Curve Number method is a technique for calculating direct storm runoff developed by the U.S. Soil Conservation Service. Its variant referred to herein as NEH-4 was published in the National Engineering Handbook, Section 4 (U.S. Dept. of Agriculture, 1972).

The basic equation is

0 (P-1^)2 _ (P-0-2S)2 (1) P-Ia+S P+0.8S

which is valid for all P > 0.2S. Parameter S is then further transformed by the relationship

CN - 2 5 4 Q 0 <2) " S+254

in which CN (dimensionless) is called the "curve number". CN is 0 at S = « and 100 at S = 0. Any basin condition that can be defined by a value of S can then be described by a CN with its value

393 Application of adapted curve number model

between 0 and 100. The CN value is determined from a knowledge of soil types and land uses occurring in the basin. If more than one soil-cover complex is present in the basin a composite CN may be determined by weighting each CN by its respective fraction of the total basin area.

A naturally occurring problem in applying this method is the effect of basin wetness on CN. Values of CN are expected to vary with soil and site moisture. In NEH-4, this is handled by the introduction of three antecedent soil moisture classes I, II and III which modify CN according to the depth of antecedent 5-day rainfall. Changes in CN values should be continuous as the changes in soil moisture are continuous as well.

Another problem with the curve number method arises when the rainfall rate is variable in time. It can be shown (Aron et. al., 1977; Morel-Seytoux & Verdin, 1981) that the method implies an infiltration rate equation of the form

dW _ S2r (3) dt ~ (P-Ia+S)2

Equation (3) holds only after reaching the initial abstraction la (ponding on a surface). The evident physical shortcoming of the CN method is that increase in the rainfall rate r causes a linear increase in the infiltration rate I. A linear relationship between r and I does not reflect the physical principle of the phenomenon.

Nevertheless, in spite of these shortcomings, the CN method has been applied with satisfactory results in hydrological practice (Morel-Seytoux & Verdin, 1981; U.S. Dept. of Agriculture, 1972; Ragan & Jackson, 1980). The question is how to use hydrological information coded in CN-values for simulation of rainfall-runoff process based on physical parameters. One reasonable way is an application of the infiltration approach (Morel-Seytoux, 1978; Morel-Seytoux & Verdin, 1981) using a correspondence between CN and infiltration parameters, such as hydraulic conductivity and sorptivity.

INFILTRATION APPROACH AND ITS COMBINATION WITH THE CN-METHOD

Computation of direct runoff using the infiltration approach is based on the theory of Mein and Larson (1973) who extended the Green and Ampt approach to compute the ponding time. Furthermore, the theory of Morel-Seytoux (1978) of mutual influence of water-air movement during an infiltration process under conditions of variable rainfall has been applied.

According to the Mein and Larson theory the ponding time is given by

P. Kovâf 394

Using the rainfall rate normalized with respect to the hydraulic conductivity r = r/K , one can get

t = P r (r - 1)

(5)

For post-ponding infiltration for the case of constant rainfall, Morel-Seytoux has derived a generalized form of Philip's equation:

W = ¥ + SP(9.) — P i __x r -1

t-t + P

r

X -

Lr -1)

3 -

- / "t _P . 2

r X .

r x .,

3"

+ K (t-t ) s p'

(6)

where t is any time after ponding to the end of rainfall, ¥ is equal to rt , SP(6.) is equal to /(2K S f), the Green and Ampt sorptivity End W is the cumulative depth of infiltration at time t.

The derivation of eq. (6) is based on the fact that, for short times, infiltration capacity varies inversely with the square root of time; and also on the requirement that, at ponding time, the rainfall rate and the infiltration rate are the same.

The infiltration equations derived by Morel-Seytoux for the variable rainfall case have the following form. Ponding time is given by

t = t. . + P J-1

1 S

x r. r. J 3

f V j - 1

r (t v v v

v-1' (7)

where j is the index of the time step and v is the index over which all rainfall occurring before t. is summed. Eq. (7) must be solved iteratively. Post-ponding infiltration is computed as

¥ = W + SP(¥ , 9.) [/(t - t + B) - /B] + K (t - t ) (8) p p i p s p

where

SP(¥p, 9.) = / s f p

(9)

is the rainfall sorptivity, and

395 Application of adapted curve number model

B = _

K S. s f _P_ K (10)

where r is the rainfall intensity which produced ponding. The infiltration rate can be expressed, after differentiation of eq. (8), as

i = | sp(wp, e.) + K /(t

(11) t + B) P

From this it is evident that, in comparison with the CN-method, the infiltration approach better reflects the physical principles of the rainfall-runoff processes. On the other hand, for the infiltration approach one has to know the soil parameters, 0 , ©., K , H,, for a basin.

According to eq. (4), the storage suction factor S^ has been defined as (Morel-Seytoux & Verdin, 1981)

Sf = (Qs Q.)Hf (12)

The idea of a correspondence between CN-values and soil parameters K and Sf (when the initial water content 0. corresponds to the field capacity) has been introduced by Morel-Seytoux & Verdin (1981) who assumed the volumetric equality of infiltration losses of both methods (i.e. the CN-method and the INFIL-method) and rainfalls of constant intensity. Implementing regression analysis of a large number of rainfall-runoff events on various soil groups, relationships between CN and K , and Sf have been found. The resulting relationships which have been accepted also in our work are: Hydraulic conductivity K (mm/hr):

100-CN 12.42 if CN > 75

K = 31.39 - 0.39 CN if 36 < CN < 75 s

K = 47.07 - 0.82 CN if CN < 36 s

(13)

Sorptivity SP (mm/hr ) - for field capacity conditions (FC):

SP =

SP =

100-CN if CN > 65

• 0.146 CN if CN < 65

1.66

30.25

(14)

P. Kovâr 396

The storage suction factor Sf is then expressed using sorptivity and hydraulic conductivity values (again for FC conditions):

S SP2 (15)

f " 2 K s

APPLICATION OF THE INFIL MODEL ON THE SPUTKA BASIN

The infiltration approach described above was programmed in FORTRAN IV as the INFIL model with the hydraulic soil parameters K , and SP derived from CN values. Before the computational procedure based on the equations (5) to (12) is implemented, the CN value and consequently the S value have to be found using the conventional Soil Conservation Service method but respecting an areal variability of the basin (Hawkins, 1978). The seasonal variability of the basin storage S has been considered using a water balance analysis (Kovâï, 1980) and lysimeter data. The seasonal storage S can be computed as

(sin(JD - 5 + 180°) + 1) Ss + 1.3 i i + 0.2 S (16)

where JD is the Julian date:

JD = 30 (MONTH - 1) + DAY (17)

Then the seasonal curve number CN is calculated as s

CN = 2 5 4 0 0

s S + 254 (18) s '

The last step in the determination of the actual value of the curve number CN. has been a modification of methods due to Hawkins (1978) and Williams & LaSeur (1976), which yields

30480 CNA = (19)

30480 (API. - 5DEP - Q_) CN v 5 ^T

where

397 Application of adapted curve number model

A P I . = Z <P_ CT 1 ) T=l

(20)

C is the évapotranspiration constant (C=0.93) DEP is the daily depletion coefficient given by

DEP AVP - AVQ

365 (21)

QT = 0 if P_ < 0.2 S 1 s

(P - 0.2S)2

QT = — if PT > 0.2 S^ P + 0.8S

(22)

From the actual CN. value the have been obtained from eqs. (13

The INFIL model has been firs Experimental Runoff Areas (ERA) Brno. For this reason, the data and uniform slope J = 12 X, time ERA-C1 has been maintained with variable rain was simulated by s determined as CN = 90.0 and then The results, summarized in Table when realizing that they pertain

corresponding values of K and SP ) to (15). s

t tested with the data from of the Water Research Institute in from ERA-C1 with an area of 20 m2

step At = 2.0 min have been used. a completely bare surface. The prinklers. The CN value has been adapted using eqs (16) to (22). 1, are acceptable, particularly to an extremely small runoff area.

Table 1 Results from Experimental Runoff Area CI

Date

7/8/1973

20/8/1973

20/8/1974

Rainfall (simulated)

Depth

mm

13.9

14.2

6.2

Duration

min

26

36

21

Maximum intensity

mm/min

1.2

1.2

1.9

Direct runoff

Observed

Q

mm

2.1

7.7

2.1

Computed

by CN method

mm

1.7

9.6

3

by INFIL method

mm

2.3

8.2

2.1

P. Kovâf 398

In a second test, the INFIL model has been implemented on a mountainous stream Sputka (Czechoslovakia) whose basin area is 104.1 km2. Soils in the basin vary from the mountainous sandy loam to silt loam and from brown soils to the podzol soil types, mostly depending on the altitude. The basin area is 39 % forest, 48 X pasture, 12 % arable land and only 1 % is urbanized. The composite curve number has been found by weighting each curve number by the corresponding area. Considering about 85 % of the soil to be in group B and the rest in group C, one obtains the following:

Forest (B) Pastures (B)

(C) Arable land (B) Impervious

66 x 0.39 79 x 0.45 = 86 x 0.03 = 88 x 0.12 = 98 x 0.01

25.7 35.6 2.6 10.6 1.0

The composite CN = 76.0

There were 22 rainfall-runoff events tested by the INFIL model with the time step At = 30 min. The INFIL model has been used not only for the computation of direct runoff but also in conjunction with the unit hydrograph technique. The resulting hydrographs at the basin outlet have been satisfactory. Alternatively, a transformation process using the Soil Conservation Service method of convoluting the net rainfall with the normalized unit hydrograph has been used and produced similar results. For the second way of computation the lag time has been computed using the following formula

(3.28 l ) 0 - 8 (0.04 S + l ) 0 , 7

t = (23) 1900 J V'D

Convolution of net rainfall with the unit hydrograph has been calculated applying the formula

QC = Z. . P. U . . (24) n j=l 2 n-j+1 v '

Performance of the model has been evaluated by computing the volumetric error (%), the peak error (%) and the efficiency coefficient RE as follows

RE= l ~ CF~ (25)

399 Application of adapted curve number model

I P4

s~?

r

-21.

CM

-54.

CM

+16.

CM

-31.

-*

-12.

CO

^

o

^

<-H

+32.

.*•«

r $

•3 ex,

a

a

13

ê

r~-co

4.2

\D

CD

CM

S

11.9

^O

Q

CO

a

6.1

o r

rH

S

21.5

r

*ô

9 N

CO

01

1 l i s SI

t--

CO

VD p-.

CO

CO r~-

CM

•* i-H

co

r

-* r

CO

in CO

iH

in r-

CM

o t-5 r-~

m

m S

VD

r--rH

8

o co CO

o-> <H m

CO

o CO

M 2

o

CM

i

a

9 s .

4.5

$

r~~ i-5 m

S i/7-3

1/7

5.5

5

in <"3 in

v£>

J/8-

23/8

2.

8

ON

m

CO

8

p:

5.3

CO-

r~

76-2

0/6

>7

9

«

P. Kovâr 400

AO-

55-

30-

25-

20-

15 •

10-

5-

0 -

L

// / /

" / /

/ h-

A II M,

! i

— i —

V — i — i — i — i —

31/7 -- 4/8/1977

RE - 0,95 VOL- ERROR — 7 , B % PEAK ERROR - 8/1 %

— I — 1 1 — — f —^ 10 20 30 40 SO 60 70 60 90 100 110 120 t C hr 3

17/6 - 20/6 / l 979

R E - 0,91

VOL. ERROR-8,4% PEAK ERROR-7/0 %

10 20 30 40 SO 60 70 8 0 t l * r l

Fig. 1 Comparison of discharges for the Sputka basin (solid line-observed; dashed line- computed by INFIL model)

where

CF = Z(Qn - QCn):

CFQ = E(Qn - Q)2

Q = mean observed discharge (in one event)

The comparison of some tested rainfall-runoff events is given in Table 2 and Fig. 1 (Kovaf, 1987).

401 Application of adapted curve number model

CONCLUSIONS

The results so far obtained by the INFIL model are encouraging. The use of CN values in which important hydrological information is encoded and which are related to the theoretical infiltration pro­cess seems to be an efficient method for design flood estimation on ungauged basins. The relationship between CN values, hydraulic conductivity and sorptivity has proved to be applicable in our con­ditions in Czechoslovakia.

REFERENCES

Aron, G., Miller, A.C., & Lakatos, D.F. (1977) Infiltration formula based on SCS curve number. J. Irrig. Drain. Div. ASCE

Hawkins, R.H. (1978) Runoff curve numbers with varying site Moisture. J. Irrig. Drain. Div. ASCE

Kovâr, P. (1987) Determination of direct runoff by the adapted CN method. Transactions of the Agricultural University, Prague

Kovâf, P. (1980) The Conceptual Water Balance Model. PhD Thesis, Agricultural University, Prague

Mein, R.G., & Larson, C.L. (1973) Modeling infiltration during a steady rain. Wat. Resour. Res., 9 (2)

Morel-Seytoux, H.J. (1978) Derivation of equations for variable rainfall infiltration. Wat. Resour. Res., 14 (4)

Morel-Seytoux, H.J. & Verdin, J.P. (1981) Extension of the SCS rainfall-runoff methodology for ungauged watersheds. Colorado

U.S. Dept. of Agriculture (1972) National Engineering Handbook, Sectin 4, Soil Conservation Service, Washington, D.C.

Ragan, R.M. & Jackson, T.J. (1980) Runoff synthesis using landsat and SCS model. J. Hydraul. Div. ASCE

Williams, J.R. & LaSeur, W.V. (1976) Water yield model using CSC curve numbers. J. Hydraul. Div. ASCE

Hydrology ofMountainous Areas (Proceedings of the Strbské Pleso Workshop, Czechoslovakia, June 1988). IAHS Publ. no. 190, 1990.

Some aspects of hydrodynamic models used in mountainous areas

E. ZEMAN, R. ZIZKA Division of Hydraulics & Hydrology, Technical University Thâkurova 7 Prague, Czechoslovakia

ABSTRACT A hydrodynamic model has been used as a basis for a computer program which makes it possible to compute water surface profiles under both subcritical and supercritical conditions. Re­sults of computations for different types of rivers are presented and limits of applicability of the model are discussed.

NOTATION

A cross-sectional area (total) A. subsectional area (at subsection j) C Chezy coefficient Fr* Froude number ÛH. difference between assumed and computed total energy

heads (at cross section i) K. subsection conveyance L^2 river reach length between two adjacent cross-

sections designated 1 and 2 P wetted perimeter Q water discharge R hydraulic radius Z 1 9 total energy head loss between cross-sections

1 and 2 b„ top width d water depth d, hydraulic depth d water depth of nonuniform flow d normal depth of uniform flow g gravitational acceleration h water surface elevation Ah. difference between assumed and computed water

surface elevations (at cross-section i) h velocity head v i energy gradient i bed slope n Manning roughness k Strickler roughness t time u mean velocity in vertical profile u. mean subsectional velocity v mean cross-section velocity

403

E. Zeman & R. Zizka 404

distance along channel bed elevation at cross-section i Coriolis number Coriolis number (estimated) Coriolis number (at cross-section i) Coriolis number (at subsection j) Coriolis number (measured) Coriolis number (calculated from stripes) Coriolis number (calculated from subsections) Boussinesq number head loss coefficient due to channel expansion and/or contraction angle of inclination of channel bed

INTRODUCTION

Real time forecasting and simulation is based on a hydrodynamic model with hydrological inputs from river reaches with gauging stations.

Most computer programs for water surface profile computations are based on the application of the energy equation for gradually varied open-channel steady flows. The same hydrodynamic approach has been used for a program recently designed by the authors.

The program should enable water surface profile computations under both subcritical and supercritical conditions. It should make it possible to estimate the velocity distribution coefficients a and/or $ (known as Coriolis and Boussinesq numbers). This paper provides a general description of the methods and procedures util­ized in the program and presents experiments on hydrodynamic models applied to various types of rivers.

Roughly, one can divide rivers according to their bed slope into the following categories: mountainous rivers, rivers located in the low lying flatlands and rivers belonging to neither one of these two.

Mountainous rivers are characterized by steep slopes, coarse material and low depths. Under such conditions boulders often pro­trude into the flow and bed roughness is very large. In this case, usually either diffusive or kinematic models are applied for water surface profile computations.

Rivers located in the low lying flatlands have totally different properties in comparison with the former type. The bed slope is very mild and bed material is fine. Also, the width of the river tends to be rather significant. Most numerical experiments with hydrodynamic models based on full dynamic equations have been re­cently carried out for rivers of this type.

However, there exists a rather wide-spread group of rivers with hydrodynamic parameters which correspond neither to the first group nor to the second one. Unfortunately, the occurrence of the third type is also common in some mountainous basins. The question of the type and limits of the hydrodynamic model to be applied is rather important under such conditions.

Our attention has been concentrated on the limits of application of a hydrodynamic model. Conclusions and examples of results are presented which should help users of programs of a similar type.

z. l

a «E a. l

a. o? m a s a gss

405 Some aspects ofhydrodynamic models

BASIC EQUATIONS

Two basic concepts are generally utilized in the water surface pro­file computations when a hydraulic routing procedure is employed, i.e. unsteady and steady approach.

For ID unsteady flow (e.g. Cunge, et al. 1981) the equation of continuity and the full dynamic equation can be written as follows

b 3h + AQ _ n (1) 9t 3x

S-iKWë-O-W-o For steady nonuniform simulations (e.g. HEC - 2 Water Surface

Profile Computer Program) the energy concept is usually applied. The basic equation for this concept is

V2 V2

Zl + dl + al 2g~ = Z2 + d2 + a2 if" + Z12 ( 3 )

The aim of modelling irregular cross-sections of natural water­courses has led to the introduction of correction coefficients, the Boussinesq number g and the Coriolis number a in eq. (2) and eq. (3), in order not to be forced to use the assumption of a uniform velocity distribution in the cross-section. The most common exam­ple of a situation in which this correction seems to be necessary is the flow in the cross-section if it either includes overbank areas or if it is highly irregular. The coefficients in eq. (2) and eq. (3) appear as the result of an assumption of non-uniform velocity distribution in the cross-section. An approach based on Schonefelds' assumptions (Henderson, 1966) concerning the computa­tion of the depth-averaged velocity at every point in the cross-section is common in hydrodynamic models. Comparing the expres­sions for P and a in the well known forms

(4) J

1

_ 2

u V 2

_ 3

u

dA A

dA (5)

It is easy to note that eq. (5) is more sensitive to the non-uniformity of the velocity profile than eq. (4). It can be ex­pressed in the form of an unequality

1 < 3 < « (6)

E. Zeman & R. Zizka 406

This reasoning has led to the conclusion that the whole invest­igation of the steady nonuniform flow computations is to be based on the energy concept, in which the Coriolis number plays an im­portant role.

The main objectives are to show how correct the Schonefelds' assumptions concerning the procedure for calculations of both co­efficients P and a should be if the river is rather narrow under conditions of a higher nonuniformity where the influences of a higher velocity and both the bank and bottom roughnesses are con­siderable.

Fig. 1 shows the definition of the energy concept according to eq. (3). The standard procedure (Henderson, 1966; French, 1985) has been used for water surface profile computations. The method is based on the trial-and-error procedure. Required parameters valid for eq. (3) are found after a few iteration steps. The pro­cedure, after the initial parameters having been estimated, leads to the computation of the water surface elevation

Ah„ AH,

1 - Pr*(l ± 0.5 Ç) ±

(7) 3 *e L12

2 R

where Froude number Fr* for irregular channels is given by

Q2 Fr* = (8)

A2(g dh cos9)

| IL

Fig. 1 Notation and definition for the energy concept

tùOti 5"g

407 Some aspects of hydrodynamic models

The total energy head at cross section is 2

a. v. H = - ï — ^ - + d. + z (9)

2 g

Eq. (7) properly used can significantly reduce the number of trial values needed to estimate the water surface elevation correctly. With regard to this trial-and-error step method of calculating gradually varied flow profiles, it should be noted that the cal­culations are sequential, i.e. each step is dependent on the pre­ceding one. At the close vicinity of the critical value of Froude number Fr* = 1 the above mentioned property could cause oscilla­tion and breakdown of the system itself, since the transition from subcritical to supercritical conditions has not been included in the presented program.

In most natural channels, the possibility that the channel is composed of a main section and one or more overbank sections has to be considered. In such a situation the depth of flow in the over-bank area differs from the depth of flow in the main channel. Roughness of the overbank section may be significantly different from the one of the main section.

Because of a variability in the velocity distribution along the cross-section, the Coriolis number a has to be used to define the velocity head h correctly. For a nonuniform cross-section one can rewrite eq. (5) as

.3

a. l

Viu «j Aj <10> A v3

The average velocity in each subsection can be estimated by

K. i * u. = - J — — (11)

3 A. 3

After rearranging terms the well-known formula valid for the estimate of Coriolis number a. is obtained

33 3

(E. , A . ) 2 jj ( K.Ï 1 JJ J=1

<^_i M 3

2

I A J j=l Jj' v 3

(12)

Provided that the subsections at a given cross sectional profile are small enough, the velocity distribution effect can be neglected and thus a. can be assumed to equal unity.

The total head loss Z.,, from eq. (3) is the sum of friction losses and local head losses in the given reach. The sum can be written as

E.Zeman&R.Zizka 408

2 2

|v„ - v_I Zno = i L10 + £.

(13) 12 ~ e 12 T.2 2g

Friction loss in a reach is a product of energy gradient i and length of the reach L-„. There exist many ways how to express an average energy gradient in the particular reach of the channel. Energy gradient i can be determined for every subsection (Kolâr, et al., 1983; Macln, et al., 1985) within the reach under consid­eration. This approach gives good results for natural channels with a low degree of irregularity.

The program presented has been developed for highly irregular channels and that is why the approach of French (1985) has been applied using only one value of average energy gradient i for a given reach, in particular

l e

Q 33

3=1 3

(14)

In the program, four equations have been used for computation of an average energy gradient. In accordance with the type of water surface profile, the recommended formula of Reed and Wolfhill (1976) has been applied.

The Chezy coefficient C plays an important role in the above mentioned calculations. Four different concepts have been used for computation of the C value; in particular Manning, Pavlovskiy, Strickler, and one based on a logarithmic velocity profile. The possibility of a selection of an appropriate expression which can be used for complicated cross-sectional geometry or various types of surfaces and bed roughnesses, seems to be advantageous.

The logarithmic formula is considered to be very useful from the point of view of future extension of the present program by a sub­routine which could calculate sediment transport and estimate the influence of alluvial roughness on flow parameters. Procedures of Ackers and White (Breusers, 1986) based on the logarithmic velocity distribution has recently given promising results.

DESIGN OF "SNF" PROGRAM

The SNF (Steady Nonuniform Flow) program has been developed to calculate water surface profiles for steady, gradually varied non­uniform flow in both prismatic and non prismatic channels. Both supercritical and subcritical profiles can be calculated. The effects of various obstacles such as bridges, weirs and other hydrotechnical structures can be introduced only through boundary conditions. Since the governing equations do not contain any time-dependent terms, the flow considered has to be steady. The flow must be varied gradually, since the above mentioned equations assume a hydrostatic pressure distribution. According to eq. (3), the flow is considered to be one-dimensional.

The computational program has been processed in such a way that all data for a given reach which are necessary for the run have

409 Some aspects of hydrodynamic models

been created by a program unit called "CRT.BAS". This segment has been designed in a fully interactive form using the advantage of a "menu" driven run. The main part of the program has been created as an independent program unit "SNF.BAS".

All data created by the unit "CRT.BAS" are available for the main part in a non-interactive way. Outputs can be arranged in accordance with the user's wishes.

The second program unit is essentially an automated, iterative step-wise computational scheme as shown in the previous chapter.

To give an idea of the calculation, a run is briefly described: providing that computations are proceeding upstream (Fr < 1.0), it is assumed that data are available to calculate all variables in eq. (3) at a section with known parameters. Water surface eleva­tion at another section is then assumed from which other hydraulic parameters,such as a corresponding partial and/or total conveyance K and velocity head h are determined. Together with values ob­tained from the previous step, average energy gradient i is deter­mined by eq. (14). Finally, using eq. (13) the total head loss between two subsequent profiles is calculated. The value of an assumed total head H is compared with the value of computed total head H using known values at section 1. The value and sign of the total Read difference AH. governs the iteration.

A special attention has been paid to the first trial value of water surface elevation h„ at section 2. Because of some difficul­ties during calibration runs it was decided to set up elevation h„ by a direct-input procedure.

Since the water surface profile in a compound channel should be determined by the program as well, the mutual impact of energy in­terchange between the main channel flow and overbank sections above the bankful stage should be taken into account. Thus the channel is divided into subsections. The subsection consists of a finite number of strips of a constant width DB. The geometric variables are computed for each strip and subsection separately. Later on, from these values, the total area A, wetted perimeter P and the top width b„ are determined as well as the hydraulic radius R and the hydraulic depth d, of the cross-section.

An example of the description of the cross-section geometry required, together with complete roughness data, is given in Fig. 2 (Cross-sectional geometry with roughness data divided into DB strips is above and a measured velocity profile from the Morava river at RaSkov below).

Two approaches were applied for estimating the velocity distri­bution coefficient a„.

The first method is based on the concept of stripe geometry and provides a , while for a (second method) the geometrical variables from subsections are used. The choice is left to the user.

A more sophisticated method for calculation of the Froude number was presented by Blalock and Sturm (1981). It has not been used because the concept is rather complicated.

CALIBRATION, VERIFICATION AND APPLICATION OF SNF PROGRAM

The designed model has been applied to several water courses in Czechoslovakia.

E. Zeman & R. Zizka 410

Fig. 2 Cross-section of the Morava River (RaSkov)

A complete set of velocity profiles, roughness and geometrical description was necessary for the calibration procedure. Finally, after an analysis, two sets of data had been selected for calibra­tion purposes.

A reach (of the second type) of the Vltava river in Prague between Troja and Stvanice weirs has been considered. The reach had been studied by means of a different computational program designed by Gabriel et al. (1982). Therefore, the same reach was chosen at the beginning for debugging the designed program.

Some ten years ago, deformations due to extreme discharges as well as various other hydraulic and hydrological phenomena had been investigated in a reach of the Blanice river which can be charac­terized, according to the classification introduced earlier, as the third type.

The study based on extensive field experiments consists of a complete set of data (water level profiles, velocity fields, bed elevation and roughness) which fulfils the common requirements for calibration. More than 20 cross-sections within the experimental reach have been examined. Six profiles from the upper part of the experimental reach were finally chosen for calibration purposes (Fig. 3, Fig. A - velocity profiles and general layout of the Blanice river at the Pra2ak location). Since the reach had been

411 Some aspects ofhydrodynamic models

Fig. 3 General layout of the Blanice River reach ("Prazâk" location)

examined twice (in the years 1977 and 1978), it was possible to compare bed level changes which occurred during the one year period. The above noted knowledge of the velocity distribution in every cross section enables the authors to compare the velocity distribution coefficient a calculated from carefully measured vel­ocity profiles with the value estimated by the program.

After verification of the program by data from the experimental reach, it was decided to apply it also to several Czech rivers with various hydraulic conditions, according to the classification given before. It turned out very soon that the most suitable profiles were those which had been permanently used by the Czech Hydromet-eorological Institute for rating curves at control sections.

From a rather long list of profiles, preference was given to the following rivers (profiles): Labe (Labskâ), Berounka (Srbsko), Divokâ Orlice (Kostelec nad Orlici), Morava (RaSkov), Bëlâ (Castolovice).

Figs. 2, 5, and 6 show the basic geometrical, roughness and velocity characteristics at some of the above mentioned stations.

Rivers from mountainous and adjacent areas (the first and third type) have been considered since it was necessary to specify limits

E. Zeman & R. Zizka 412

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Fig 4 Example of cross-section of the Blanice River ("Pra2ak" location)

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d. (m

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Fig. 5 Cross-section of the Berounka River (Srbsko)

413

CHMU 1005/87 HK

D1VOKÂ ORLICE pf. 0 Kostelec

Some aspects of hydrodynamic models

0,00

8 9 10 11 12 13 14 15 16 17 18

Fig. 6 Cross-section of the Divokâ Orlice River (Kostelec n/0)

for the application of the program. The main goal of this part was to concentrate on hydraulic and geometric parameters with the greatest influence on the overall accuracy of calculations by the program.

A different approach has been applied for the verification of water surface profile computations under supercritical flow condi­tions. A rectangular channel with a constant bed slope has been used. Pavlovskiy's analytical method has been applied for verifi­cation purposes.

All important results are presented in Table 1.

DISCUSSION

Using the results presented in the earlier sections, it is possible to discuss the water surface profile calculation procedure and its applicability.

Calibration by the Vltava river data was completed by Ratsiranto (1987) with good results. The maximum difference between the given water level and the computed one did not exceed 0.05m for Q = 722.0 m3 /s. Velocity profiles were not available for the reach and that is why the Coriolis number determined by the program could not be compared with the measured one.

Accuracy of the Coriolis number has been checked by data from Srbsko on the Berounka river. For a carefully chosen cross-section Srbsko with bankful width b„ = 57 m and hydraulic depth d, = 0.66 m detailed velocity distribution profiles are known. The difference

E. Zeman & R. Zizka 414

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415 Some aspects ofhydrodynamic models

Aa = + 2.7% shown in Table 1 seems to be negligible in comparison with other examples also presented in the paper.

Another calibration based on data from the Blanice river provid­ed rather interesting results. Table 1 and Figs. 3 and 4 show bas­ic characteristics of cross-sections. The reach has a low degree of irregularity and during field experiments both banks were quite well maintained. Top widths of all cross-sections No. 0-5 fulfil­led the condition of b„ < 15 m while the depth d, was between 0.39 and 1.05 m. The channel has at least three various values of Manning roughness n for bed and adjacent slopes at the cross-sec­tion.

Table 1 clearly shows that the accuracy of the estimate of the Coriolis number Op for the Blanice river is much lower than for the Berounka. Negative values of Aa appear quite frequently since the value of a_ is underestimated. It may be assumed that the proce­dure of the estimation of a„ is responsible for the discrepancy. When the top width b„ of the cross-section is small enough as oc­curred in the case of the Blanice river, the influence of singular­ities (stones, scours, obstacles and vegetation) becomes an impor­tant factor which may influence the real velocity distribution in the river.

The results obtained have led to the conclusion that an inaccur­ate guess of the Coriolis number a„ had no direct effect on water level calculation, since the velocity head did not exceed the value of 0.02 m. Description of roughness (n or k) at the given cross-section has to be as complete as possible. In the case of the Blanice river one value of equivalent roughness for a cross-section was absolutely insufficient for an estimate of a„. It turned out that at least three values for banks and river bed were necessary.

Table 1 also shows differences for other water courses examined. Results of the study have led to the following conclusion for

further applications of the model: for velocity heads h < 0.05 m, the error in a^ has negligible influence on water level calcula­tions. Even for higher values of h , the influence should be minor as long as the degree of nonuniformity (9/3 )(d /d ) is low. For water level calculations in mountainous streams it is convenient to know the velocity distribution and roughness in several cross-sections within a given reach for calibration purposes.

Fig. 7 shows an example of the evaluation of eq. (3) in a graphical form. It is clear that the difference in schemes (1 to 3) depend only on the various values of Op. The error will be accumulated in every cross-section within the given reach. If there is a monotone profile within the given reach (as in Fig. 3) it leads to significant error in water level. This is of practical importance for the design of flood-control structures. An under­estimation of structure parameters could lead to severe financial damages and losses of human lives.

Since the eq. (6) is generally valid, all the conclusions mentioned above are valid also for ID hydrodynamic models for unsteady flow conditions.

In flood events another factor could influence water level com­putations. The commonly used set of equations for steady or un­steady flows is not necessarily applicable to alluvial channels. Their bed may be covered with bed forms ranging from ripples and

E. Zeman & R. Zizka 416

No.

1

3

3

BCE

1,74

1,33 1,05

elevation (m)

11,35

11,36

11,38

h v (m)

0,24

0,18 0,13

Q = 16,38m3/s

v s 1,22m/s

Fig.7 Evaluation of eq. (3) for various values of Og (Divokâ Orlice River at Kostelec n/0)

dunes to antidunes. For alluvial channels a more general set of equations is then required. The most advanced theory was presented by Ackers and White (see Breusers, 1986). The program discussed in this paper is designed in such a way that it is possible to include alluvial roughness procedure without problems.

The introduction of inaccurate a can also lead to other diffi­culties. Numerical experiments have shown that the impact of an error in a had rather important influence on the value of the Froude number. If otp = 1.05 ( a commonly used value) is substi­tuted in eq. (3) instead of the correct value = 1.74, then Fr* = 0.51 instead of Fr* = 0.98 is obtained. The general impact of such an error is clearly shown in Fig. 7. The Coriolis number a plays an important role whenever Froude number is to be computed. Even for the channel of a rather simple cross sectional geometry (as for the Divokâ Orlice river, Fig. 6) at the point of minimum specific energy characterized by wide and flat flood plains, two points of minimum specific energy can be computed for certain discharges.

SUMMARY AND CONCLUSIONS

Recent developments in hydrology and hydraulics have made it feas­ible to construct hydrological models based on more advanced and comprehensive mathematical models. A hydrodynamic model for river routing is usually included in such models. Bad results of a hyd­rodynamic model would have feedback influence on other program units used in the hydrologie model in the form of inner boundary conditions. Incorrect estimates of a may have a significant impact on roughness data.

417 Some aspects of hydrodynamic models

A generalized computer program for the computation of water level in natural water courses is presented with a step-by-step description of the applied methods and procedures. Its general form makes it a powerful and useful tool for water management whenever open channel hydraulics is to be considered.

Examples have been included to show the capability and limits of applications of the program. The program cannot be used to compute water levels in reaches where hydraulic jumps and/or changes from subcritical to supercritical flows occur without an interruption. It is not recommended to apply the program for alluvial channels.

The program is suitable even for computers of the lowest class (64 kb memory seems to be the limit).

The study has led so far to the following conclusions: The comparison of correction coefficients a and g based on

measurements and those inferred only from geometrical and hydraulic parameters clearly shows the limits of applications of ID hydro-dynamic models in the mountainous and/or adjacent areas.

(a) For river channels where velocity head h > 0.20 m, bankful width b„ < 15 m and 0.6 < Fr* < 1.3 this type or program should be applied with special care.

(b) The authors recommend to avoid applying simplified numerical models without calibration and verification, especially within the above mentioned range.

REFERENCES

Blalock, M.E. & Sturm, T.W. (1981) Minimum Specific Energy in Compound Open Channel, J. Hyraul. Div., ASCE, 107, (HY-6)

Breusers, H.N.C. (1986) Sediment Transport 1, Lecture Notes, IHE, Delft

Cunge, J.A., Holly, F.M. & Verwey, A. (1981) Practical Aspects of Computational River Hydraulics, Pitman

Feldman, A.D. (1981) HEC Models for Water Resources Simulation Theory and Experience, Advances in Hydroscience, 12

French, R.H. (1985) Open-Channel Hydraulics, McGraw-Hill, New York

Henderson, F.M. (1966) Open-Channel Flow, MacMillan, New York Kolâf, V., Bern,J. & Patocka, C. (1983) Hydraulika, SNTL/ALFA, Praha Liby, J. (1977) Rychlostni soucinitel C -Chézyho rovnice v

otevfenych korytech se zvysenou drsnosti, Prâce a studie - sesit 148, VÛV, Praha

Macan, J., Podzimek, J. & ftihâk, V. (1983) Program pro vypoCet prûbëhu hladin (manual), Povodi Vltavy, Praha

Ratsiranto, V.G. (1987) Ustâleny nerovnomërny pohyb v pfirozenych a upravenych korytech, Diploma thesis, Tech. University, Praha

Reed, J.R. & Wolfhill, A.J. (1976) Evaluation of Friction Slope Models Rivers. In: Symposium on Inland Waterways for Navigation, Flood Control and Water Divisions, Colorado State University, Fort Collins

Shearman (1976) Computer Applications for Step Backwater and Floodway Analysis, U.S. Geological Survey, Open File Report, Washington, D.C.

Hydrology ofMountainous^4reoi (Proceedings of the Strbské Pleso Workshop, Czechoslovakia, June 1988). IAHS Publ. no. 190, 1990.

Hydrological impact of deforestation in the central Himalaya

M. J. HAIGH Geography Unit, Oxford Polytechnic Headington, Oxford, England

J. S. RAWAT, H. S. BISHT Department of Geography, Kumaun University Almora, U.P., India

ABSTRACT Deforestation is the most serious environmental problem in Uttarakhand, home of the Chipko Movement, the Third World's leading nongovernmental organization (NGO) dedicated to forest con­servation. This group exists because of the rural people's concern for the loss of forests and their personal experience of the envi­ronmental consequences. Despite this, it has become fashionable for scientists from some international organizations to argue there is little evidence for recent deforestation, desertification, acce­lerated erosion and increased flooding in the region. This paper tries to set the record straight. It summarizes results collected by field scientists in Uttarakhand. These data reinforce the popu­lar view that deforestation and environmental decline are very ser­ious problems. Preliminary results from the Kumaun University/Ox­ford Polytechnic instrumented catchment study are appended. This catchment is set in dense Chir (Pinus roxburghii) forest on a steep slope over mica schist in a protected wildlife sanctuary on the ur­ban fringe at Almora, U.P. The results demonstrate a pattern of sediment flushing associated with the rising flows of the Monsoon.

INTRODUCTION

Deforestation is the most serious environmental problem in Uttar­akhand, the Himalaya of Uttar Pradesh, India (Fig. 1). This tract, which covers nearly 52 thousand km2 on the western borders of Nepal, is home of the "Chipko" Movement, the Third World's leading NGO devoted to forest conservation (Haigh, 1988a). This group achieved international notice through its successful campaigns to persuade the Indian Government to reform destructive forest poli­cies and control damaging surface mine operations in the Himalaya, and through its Gandhian approach to development.

Chipko exists because of the concern of the rural people, par­ticularly the rural women, for the loss of their forests and be­cause of their personal experiences of the environmental consequen­ces (Jain, 1984). As time goes by, the daily chores of gathering fuelwood and bearing water require longer and longer journeys. As time goes by, obtaining timber to build newly wed children a house, becomes more and more difficult. Worse, each monsoon brings the threat of more severe problems. Bhatt (1980) writes: "In Chamoli District alone there are at least 450 villages where homes, fields

419

M. J. Haigh et al. 420

and forests are crumbling due to the repeated visitations of floods and landslides". The symptoms of environmental degradation every­where.

Despite this, it has become fashionable for scientists from some international organizations to argue that there is little real evi­dence for accelerated recent deforestation, desertification, ero­sion, or an increasing flood hazard in the Himalaya (Pearce, 1986; Ramsay, 1987; Hamilton, 1987). These writers tend to stress the variability in ("uncertainties surrounding") the data which is pro­duced by researchers in the Himalaya (Ives, 1987; Thompson & War-burton, 1985). They base their own generalisations on studies in the Middle Hills of Nepal. The Kathmandu area has one of the long­est histories of development in the Himalaya and one which con­trasts very sharply with the much larger tracts of the Himalaya where development has become a problem during the last century. These writers also tend to base their hydrological observations exclusively on the publications of foreign workers in Nepal, which has yet to establish a major hydrological station. Writings from India, Pakistan, and even other mountain areas tend to be ignored.

There are, of course, very good reasons why friends of Nepal might not wish that nation to be linked to flooding and sediment pollution problems which extend beyond its boundaries (Chalise, 1986). However one may sympathise with that wish, the scientific evidence is different. Further, it is critical to the well being of neighbouring nations, with large mountain areas of their own, to be aware of the problems which exist in their own hill country and to channel their resources towards the solution of those problems.

This paper attempts to set the record straight. It is a summary of the results collected by research workers in Uttarakhand, India. These results tend to support the disturbing images of environment­al degradation put forward by most NGOs and independent environmen­tal researchers of the Himalayan region. The paper also includes a note on the first data from the first hydrological monitoring sta­tion in the Central Himalaya, which has been established by Kumaun University and Oxford Polytechnic at Almora.

THE ENVIRONMENTALIST'S MODEL OF ENVIRONMENTAL DECLINE IN THE HIMALAYA

Originally, the Himalaya of Uttarakhand were largely mantled with trees. However, the expansion of population during the British period caused the forests to become stressed by the needs of the subsistence economy. British attempts to conserve the forest re­sources through closure resulted in a massive increase in pressure on those forest lands left in local control, forest destruction through political protest, and the alienation of local people from any feeling of responsibility for the forest resource (Pant, 1922). Military demands during two world wars, the growing timber and fuelwood demands, and tree theft (which may account for 25% of all timber exploitation in the hills) all added to the degradation of the forests. To this has been added the growth of the road network which has both directly and indirectly contributed to forest dest­ruction through clearance, landsliding and facilitating tree theft

421 Hydrological impact of deforestation

78 80 82 84

Fig. 1 The Uttar Pradesh Hill Region. 1) Uttarkarshi, 2) Dehra Dun, 3) Tehri, 4) Chamoli, 5) Pauri, 6) Pithoragarh, 7) Almora, 8) Nainital.

(Haigh et al., 1987; Haigh 1983, 1984). Meanwhile, the population of just 4.787 million (1981) continues to expand at a rate of around 2.3% p.a. In its wake, agricultural extension proceeds at about 1.5% p.a. and livestock at about 0.18 cattle units p.a. (Shah, 1982).

So, over the years, the tree cover has become depleted. Once, the tree canopy, the undergrowth and particularly the leaf litter, intercepted the bulk of the incident rainfall which dripped and seeped slowly to a deep, open structured soil with a large and well developed biota. Little remained at the soil surface to cause sur­face runoff. However, a proportion of the water which infiltrated was trapped by the vegetation and returned to the atmosphere as évapotranspiration. The remainder soaked to the groundwater to feed springs, seeps, and to provide the base flow of perennial streams.

Then, during the last century, and especially during the last few decades, the mountains suffered severe forest degradation, much of which was due to processes with origins outside the mountains themselves: colonial, commercial and national, some of which was due to pressures building up in local communities. The forest cov­er of the hills was reduced precipitously and that which remained suffered thinning due to lopping for timber. Overgrazing and burn­ing to improve pasture prevented regeneration.

Now, there remained much less vegetation to protect the soil. The soil became more vulnerable to erosion and more easily compac-

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423 Hydrological impact of deforestation

ted. The reduced vegetation cover allowed more water to reach the soil more rapidly and returned less to the atmosphere. Surface runoff began to occur more frequently and, as it did so, rates of erosion began to rise. Erosion rapidly reduced the depth of the soil and thus its capacity to store water. As time passed, more and more water ran across the ground surface, and less and less soaked into the ground. As a result, the groundwater table became lowered, springs ran dry and the landsurface suffered desertifica­tion. As the tree roots in the soil rotted away, the steep hill­sides became prone to an increased frequency of landsliding. Soil mobilised by erosion and rubble from landslides was dumped in streams and river channels causing their beds to aggrade rapidly. Perennial streams became buried beneath channel debris. Perennial streams became ephemeral due to the decline in the water table and reduction in base flow.

Flooding became more serious because of the increased volume of water in the environment, because of the increased frequency and volume of surface runoff and because of the rising levels of affec­ted river beds. Landsliding became more frequent because of the toe erosion caused by the increased monsoon floods, because of deforestation and because of road construction. Inevitably, part of the problem of increased sediment loads and higher flood peaks was passed downstream to affect areas hundreds of miles from the deforested headwaters. For example, the Alaknanda flood of 1970 left 2 m deep deposits of debris at Srinagar (Garhwal), 100 km from the flood source and blocked a 10 km reach of the Ganga Canal near Hardwar 300 km from the flood's source (Bhatt, 1980). Essentially similar versions of this model have received widespread and regular publication (e.g. Uttarakhand Seva Nidhi, 1986; Haigh, 1984; Myers, 1984, 1986; Ashish, 1979).

EVIDENCE FROM UTTARAKHAND

Deforestation and forest degradation

Official statistics suggest that the forest cover of Uttarakhand is about 67 %. However, despite an active program of planting, even the area of forest under the control of the Uttar Pradesh Forest Department seems to have declined by 5 X in the period 1965 - 1980 (Kumar, 1981). Gupta (1979) attempted to use satellite imagery to quantify actual forest cover and suggested that just 37.5 X of the area is currently forested. Tiwari et al. (1986) found that about 29 X of Uttarakhand was forested but that 'good forest', with a crown canopy greater than 60 X, accounted for only 4.4 X of the land.

Tiwari & Singh (1987) surveyed 200,000 ha in Naintal and Almora Districts using aerial photographs in support of field data. Some 45 X of this area is not forested, 23 X being given over to agri­culture, 15 X to scrub or grassland. Just 2 % of the area is clas­sed as wasteland due to erosion but 85 X of the agricultural land suffers erosion problems. The rate of agricultural extension into forest land is 1.5 X p.a. Fifty-five percent of the area is classed as forest, but only 6 X of this woodland has a crown canopy which exceeds 60 X, whilst 21 X has a crown canopy of less than

M. J. Haigh et al. 424

40 % which indicates widespread forest thinning and degradation.

Rainfall interception and runoff

During the monsoon seasons of 1981 and 1982, Pathak et al. (1985) undertook studies of rainfall interception at six forest sites in Uttarakhand. Five of these sites had a canopy cover in excess of 70 X and only one as low as 38 X. Throughfall rates ranged from 75 to 92 % and canopy interception from 8 to 25 X. Workers at Dehra Dun in the foothills have reported that pine forest (1,156 trees /ha) is capable of intercepting 22 X of incident rainfall (Dabral et al., 1986), whilst densely coppiced Sal may intercept as much as 34 X (Ghosh & Subba Rao, 1979).

Pathak et al. (1985) found that stemflow accounted for less than 1 % of the total rainfall (0.3 - 0.9 % ) . Dabral et al. (1968), however, considered that stemflow in Himalayan pine could reach 4 X of the rainfall, and 6 to 9 % in broadleaved forest (Ghosh & Subba Rao, 1979).

At the ground surface, the litter layer intercepted some 7 to 10 X of the total rainfall (Pathak et al., 1985). Ghosh & Rao (1979) estimate litter interception as 5 % whilst Dabral et al. (1968) measured 7.6 % beneath pine (P. roxburghii), 9 X beneath Sal and 8.9 % beneath teak (Tectona grandis).

Most of the remaining rainfall is intercepted by the undergrowth or goes to infiltration (59 - 84 %; see Pathak et al., 1985). Sur­face runoff did not develop for showers of less than 10 mm. Meij-erink (1974) confirms that runoff events are rare and that much of the sediment which is mobilised is trapped on man-made or forested river terraces where the infiltration capacity is high. He observ­ed near Mussoorie that the runoff generated by a 0.4 km2 catchment would disappear on a terrace fringe of 200 to 300 m. At Bhaintan, on the Rishikesh-Tehri road, Mohan & Gupta (1983) measured infil­tration rates under different land uses. Stable infiltration rates beneath thin forest averaged 12.5 mm/hour (range 3.5 to 20.0 mm/ hour: 4 test sites). On terraced crop land, the average was 33.6 mm/hour (range: 8.0 - 41.5 mm/hour: 5 test sites) whilst one test on grassland gave a final infiltration rate of 21.5 mm/hour. It is widely accepted that well maintained back-sloping terraces are best practice in the region for agricultural soil and water conserva­tion. (Poorly maintained terraces, unfortunately, are a different matter, and terrace abandonment and breakdown is a growing problem in Uttarakhand). Mohan & Gupta (1983) relate the poor infiltration capacity of their forest to compacted soils and poor litter capac­ity and their results contrast sharply with those of Ghosh & Rao (1979). These workers compared infiltration rates beneath Sal for­est and on neighbouring agricultural fields. The stable infiltra­tion rate beneath dense, well developed Sal plantation, 38 mm/hr, was twice that on neighbouring, unterraced agricultural lands.

In forest, very little of the incident rainfall remains to gen­erate surface wash on the hillslopes. Pathak et al. (1985) meas­ured surface runoff as 0.2 to 1.3 X on their dense forest, 25 m2

plots (Table 1). Overland flows were conspicuously higher from the site with the least forest cover (1.3 % vs 0.8 - 0.2 % ) . These data lead this team to postulate that subsurface soil flows were

425 Hydrological impact of deforestation

major pathways for soil loss (Singh et al., 1983). However, The Pathak team results do contrast with those reported from the Murree Hills, Pakistan. Here, a team from the Pakistan Forest Institute measured runoff on a 45° slope covered by a deep (1.5 m) Typic Hap-ludoll developed underneath Chir Pine (Raeder-Roitzsch & Masrur, 1969; Choudhri & Nizami, 1985). The Pakistani team employed 4 m2

runoff plots and found that while forested plots yielded only 4 X of the rainfall as runoff, dense grass and young trees gave 17 to 18 X, thin grass 28 to 38 X, and bare soil 47 X. Runoff jumped from 4 to 11 % in the twelve months after tree harvesting (Masrur & Hanif, 1972).

Desertification and floods

Seth & Khan (1960) have indicated' that the moist broadleaved for­ests of the Lesser Himalaya may return as much as 50 X of the inci­dent rainfall back to the atmosphere through évapotranspiration. Pot studies suggest that Chir Pine has a lower transpiration rate than the local broadleaves but that it consumes more water than either Sal or the Oaks which Chir is tending to replace on many Uttarakhand hillsides (Raturi & Dabral, 1986). In Japan, it was discovered that on losing a 69 % pine cover to nematode infection, the water yield of one 69 ha catchment increased by 110 mm. Base flows were 50 to 100 X greater at different seasons of the year and the reason was the reduction in évapotranspiration following the death of the trees (Abe & Tani, 1985). By contrast, when Eucalypts were planted in a denuded watershed in the same area, total runoff was reduced by 28 X (Mathur et al., 1976).

Preliminary results from the Fazegut hydrological station in Swat, Pakistan, on what is currently badly degraded and overgrazed grassland (63 - 78 X exposed rock) show that, of a 57 to 61 mm rainfall event, 49 to 58 X was converted to runoff, and of a 19.5 to 21.4 mm event, 5 to 23 X was converted to runoff (P.F.I. 1987 -unpublished data). In Uttarakhand, it has been reported, that on a 4.6 ° slope of silt clay loam, natural grass covers contribute 21 X of the rainfall as runoff whilst compacted, bare fallows, return 71 X (Dhruva Narayna, 1987). Mishra et al. (1979) relate runoff to the leaf area of the vegetation. Studies in the Siwalik foothills, from a 24.2 ° sloping grassed watershed, indicated that as the grass yield increased from 65 to 85 q/ha so runoff decreased from 38 to 31 X of the incident rainfall (Agnihotri et al-, 1985). In sum, less vegetation means more runoff.

Nevertheless, it is an oft repeated, if less easily substantiat­ed complaint of the hill's people, that streams are drying up as a result of forest degradation. However, there is more than anecdot­al evidence for this phenomenon, which appears to be caused by dramatic changes in the infiltration and water storage capacity of the soils in areas which have suffered forest degradation.

Almora Town, the traditional capital of Kumaun, eastern Uttar­akhand, straddles a ridge with steep sloping sides. Since 1560 its demand for timber and fuel has stripped away local forest cover. The area once possessed some 360 springs, many are enshrined in local place names, but today, less than 10 X still function (Joshi et al., 1983). A survey of springs in the Gaula River catchment in

M. J. Haigh et al. 426

Nainital District found that 45 X have gone dry in recent memory (Bartarya, 1988; Valdiya, 1985; Moddie, 1985). In Uttarakhand as a whole, 55 X of the springs are said to have dried up during the last 20 years while the number of villages suffering water scarcity may have increased by 40 X in the last 13 years (Uttarakhand Seva Nidhi, 1986).

Detailed studies of channel capacities and sediment load are be­ing undertaken by J.S. Rawat in the Nana Kosi Basin of Kumaun (Rawat & Bisht, 1986). Early returns from the Jamthara Gadh catch­ment indicate that streams are much reduced in deforested subcatch-ments (Rawat, 1987). The base flow in the Gaula river may have decreased by a third in the last decade (Bartarya, 1988; Valdiya, 1985).

A Sal forest watershed near Dehra Dun in the foothills of Garh-wal, western Uttarakhand, was cleared for agriculture as part of a paired catchment study. The volume of runoff increased by 15 X and the peak rate of runoff by 72 X compared to an undisturbed catch­ment (Sastry et al., 1986). Another Sal forest site involving paired 6 ha catchments on a 20 to 30° slope was employed for an ex­amination of the effects of thinning. One tree in five was removed (Subba Rao et al., 1985). This treatment did not affect the volume of flow but flood peaks increased (8.6 X) in the thinned catchment, especially during more intense rainstorms in the first year after cutting when canopy interception was reduced from 18 to 13 % (Megahan, 1988). Planting Eucalypts in a degraded catchment, also near Dehra Dun, reduced discharge by 28 Z and the peak flow by 73 X (Mathur et al-, 1976).

Isolated from the emotive issues of Indo-Nepali politics, most researchers agree that a reduction in the forest cover tends to lead to an increase in flood peaks and in runoff, especially if this is accompanied by substantial reductions in infiltration. Quite obviously, the waters formerly lost to évapotranspiration have to go somewhere and if this is not into the ground, and not into the base flow of the major rivers, then other options are limited. The land area considered by India to be flood prone has doubled in the 10 years to 1989, from 20 to 40 million ha (Govern­ment of India, 1980).

Sediment production and channel response

In 1978, the Mussoorie Bypass was built across a partially defores­ted hillside. The new road blocked four parallel, ephemeral, stream channels draining similar, 1 km2 catchments on a 40 ° slope. Two channels drained protected forest and two drained deforested scrubland. The monsoon turned the unfinished road into an effic­ient bedload sediment trap. Some 50 m3 of sediment was trapped be­neath each forested catchment compared to 230 and 370 m3 beneath the deforested, grassland and scrub, catchments (Haigh, 1982).

Singh, Pandey and Pathak (Singh et al., 1983; Pandey et al., 1984) have compared runoff and sediment yield from forested and de­forested catchments in Kumaun using 25 m3 runoff plots. Pandey ejt al. (1983) calculate soil losses due to surface runoff and suffo-sion as 3.2 t/km2 and 6.2 t/km2 from oak forest and non-forest sites respectively (Table 2). The use of 25 m2 runoff plots for

427 Hydrological impact of deforestation

this study, of course, eliminates runoff generated upslope, sediment contributions due to channelled flows, to mass movement, and to human interference, notably through construction and road clearance. Consequently, the runoff and soil losses indicated tend to be on the low side (Table 2 but cf Masrur & Hanif, 1972).

Hill roads are the cause of many landslides and rate as major sources of sediment. Estimates of the rates of sediment production by roadways in the Central Himalaya run from 430 to 550 m3/km of roadway/year (Patnaik, 1978; Valdiya, pers. comm). However, the results of detailed case studies suggest that these figures may be an underestimate (Bansal & Mathur, 1976; Haigh, 1988b).

Landslides, however, are more widespread, and according to popu­lar belief, their frequency is increasing rapidly (Bhatt, 1980; Bahuguna, 1982). Despite this Meijerink (1974), working in the Aglar valley of Garhwal, calculates that superficial debris slides generate only 0.5 t/km2/year of debris of which a fifth reaches the river channel. He also calculates that the catchment's long term, geological sediment yield is 9 to 45 t/km2/year. By contrast, soil losses as high as 15600 t/km2/year have been measured on 4.6 ° slope tilled fallow in the Dehra Dun area.

Dhruva Narayana (1987) estimates the sediment yield of the Tehri Dam's catchment as 1940 t/km2/year. The sediment loads carried by Himalayan rivers can be very high. Das (1987) sampled the sediment loads of the Bhagirathi and Bhilangana rivers above the new Tehri High dam in Garhwal. Between July and October, these rivers carry a silt load of the order of 2 kg/m3 and 44 kg/m3, respectively.

Table 2 Overland Flow and Runoff during Monsoon Season in Central Himalayan Forests (Pandey et al., 1983)

Sites Tree Density stems/ha

Forest (Quercus spp) 148 Forest 116 Forest 78 Forest 33 Road Debris Deposit 0 Landslide (21 yrs) 0 Landslide (13 yrs) 0 Cropland (maize) 0

Ground Cover X

47 41 38 19

12 31 18 46

Overland Flow X of rainfall

0.44 0.49 0.44 0.45

0.60 0.45 0.60 0.57

Soil Loss t/km2

2.5 3.7 2.6 4.0

8.1 4.2 6.2 6.4

Many channels in the area raise their beds by up to 10 cm a year (Bahunga, 1978). Sediment-choked channels are not uncommon in the hills, especially in areas close to construction activity. The sediment is supplied by deforestation, road construction and clear­ance, and by landslides which are often due to the channel itself. A survey of a kilometer reach of second order channel in a defores­ted area below Mussoorie in 1978 discovered 9 fresh landslides of more than 20 m3 contributing more than 1,000 m3 to the channel bed.

M. J. Haigh ei al. 428

However, the most spectacular illustration of this process is to be found at Raj pur near Dehra Dun. Here, in 1919, the Kaulagarh Bridge was built across a small perennial stream. The bridge sof­fit cleared the bed of the perennial rock floored stream by 19.5 m. However, just upstream, deforestation triggered landsliding which dumped an enormous volume of sediment in the channel. In 1979, the bridge cleared a wide boulder run by just 1 m (Photographs in Haigh, 1984; Nossin, 1971).

HYDROLOGY OF HIMALAYAN PINE FOREST: PRELIMINARY RESULTS

The first hydrological monitoring station in Kumaun commenced oper­ation in March 1987. The project, founded as a research collabora­tion between Oxford Polytechnic and Kumaun University, is establi­shed at Almora, in the heart of the lesser Himalaya (altitude: 1,615 m). The ultimate aim is to compare the hydrological behav­iour of forested and deforested hillsides. However, to date, only one (the control) catchment is producing data.

The control catchment is the Animal Park Watershed, which drains a nature preserve on the outskirts of Almora. This preserve is securely fenced and policed by forest guards to prevent incursion. The Park is a rare example of undisturbed pine forest in the Almora area. The tree canopy is 82 % and ground cover by vegetation and litter exceeds 75 X.

The gauged stream drains a small (area: 1.1 km2) elongated (1.9 km) catchment developed on the upper convexity of a steep hillside (channel slope > 25°). The catchment is underlain by im­permeable mica schist, perhaps with some interbedded quartzites. These strata belong to the Almora Crystalline group.

Instrumentation consists of a v-notch weir, established accord­ing to British Standard specifications, with a stilling pond pro­tected by an efficient sediment trap. A Hindustan Clockworks chart recording stage recorder is set in the stilling pond adjacent to the weir. A recording rainguage has been established nearby and these records are calibrated against data collected at the Viveka-nanda Laboratory for Hill Agriculture of the Indian Council of Ag­ricultural Research some kilometres distant. Runoff records are supported by a weekly program of sampling of suspended sediment and dissolved loads.

During the first 8 months of record, from April 17th, 1987 to January 1st, 1988, the catchment received 488.3 mm of precipitation of which 80 X fell in the monsoon season of July to September. Discharges during this period ranged from a low of 0.09 1/s on June 6th to a high of 38 1/s on August 12, 1987. Suspended sediment concentrations ranged from 0 mg/1 during May and November-December to a peak of 870 mg/1 near the close of the Monsoon in early Sept­ember. Dissolved loads proved much less variable and were, on average, of greater dimension (Durgin, 1984). The peak concentra­tion was 428 mg/1 recorded in early September while the lowest concentrations, 80 to 40 mg/1, were measured in samples collected in October through December.

Preliminary analysis of the data suggests that there is an annu­al cycle of sediment release controlled by successive phases of

429 Hydwlogical impact of deforestation

discharge. High suspended sediment loads are associated with each new sustained peak discharge. Subsequent repetition of the same discharge tends to be associated with lower suspended sediment loads. Indeed, suspended sediment loads decline to zero in the stable flow conditions at the close of the monsoon.

Table 3 Discharge recession at the close of the monsoon season of 1987: Animal Park Pine Forest Watershed, Almora, U.P.

Discharge Date Time 1/s day/month hours of

the day

3.02 2.15 1.12 0.99 0.95 0.90 0.85 0.80 0.75 0.70 0.60 0.55 0.50 0.45 0.45

19/10 19/10 19/10 19/10 30/10 30/10 1/11 2/11 3/11 4/11 4/11 12/11 21/11 25/11 10/12

17/18 18/19 19/20 20/21 12/13 16/17 13/14 16/17 15/16 15/16 16/17 24/01 11/12 16/17 24/01

The pattern suggests that, during the autumn, and especially during the dry hot summer months, mobilisable sediments accumulate in the catchment and in the stream channel. With the onset of the monsoon, these sediments are progressively flushed out by the se­quentially rising discharges. The character of this process, how­ever, requires further and more detailed investigation.

Hydrographs of individual rainfall events based on hourly data indicate that catchment response to rainfall is rapid. The flow peak is achieved in the hour following the onset of the storm. For example, on 23/4/1987 a three-hour storm deposited 2.0 + 1.0 + 3.5 = 6.5 mm. Before the storm, the flow was 0.61 1/s, this rose to 0.69 1/s in the second hour of the storm and fell back to 0.62 1/s in the hour following the storm, after which there was a long slow decline in flow rates which stabilised as 0.613 1/s 9 hours later.

The last rainfall of the monsoon season fell on the 19/10/1987. This storm deposited 8 + 2 = 10 mm in two hours. Discharge rose from 0-9 to 3.0 1/s in the first hour of the storm. Two hours later, discharge was down to 1.1 1/s, and stabilised at 1.0 1/s some 23 hours after the peak was achieved. Further recession proceeded slowly (Table 3), though not especially evenly. Abrupt drops in discharge were recorded on October 30th and again on the 4th of November. However, whether these are real, or due to faults in data collection, is not yet determined. If real, these features may relate to thresholds of exhaustion in certain categories of

M. J. Haigh et al. 430

catchment storage. It is, of course, no coincidence that most flow mimima are achieved in late afternoon when both evaporation and transpiration rates are highest.

CONCLUSION

The causes and consequences of environmental degradation in Uttar-akhand's Central Himalaya are common knowledge to local residents who have seen their environment change through their lifetime. Their observations are reinforced by the various and scattered sci­entific investigations which have been undertaken in the region. In Uttarakhand, the nature of the problems are beyond dispute. No longer are such matters considered the most appropriate topics for investigation. Instead, attention is now focused on remedial measures and upon environmental regeneration in the Himalaya (Singh, 1985). The focal problem here, however, runs beyond the scope of this paper. It concerns a restructuring of the social, economic, and agroecosysterns of Uttarakhand (Ashish, 1978; Singh et al., 1984). It concerns rural uplift and ecodevelopment (Uttarakhand Seva Nidhi, 1986; Haigh, 1988a).

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Results of international co-operation within a regional project on hydrology of mountainous areas of the countries of central and eastern Europe

A. SVOBODA Institute of Hydrology and Hydraulics, Centre of Geophysical Research, Slovak Academy of Sciences Trnavskâ 32 Bratislava, Czechoslovakia

ABSTRACT The paper describes hydrological models used in a regional project on the hydrology of mountainous areas undertaken by the following countries: Bulgaria, Czechoslovakia, the German Democratic Republic, Hungary, Poland, Romania and the Soviet Union. It also reports on the results obtained to date (1988) and gives examples of applications of some of the models described.

INTRODUCTION

Co-operation in hydrological research within the International Hydrological Programme (IHP) of UNESCO has a long tradition in the region of Central and Eastern Europe. Representatives of the National Committees (NC) for IHP of the countries from this region meet regularly to co-ordinate this co-operation. Recognizing the urgent need for co-ordinated research of runoff formation in mountainous areas and its modelling, it was decided to initiate the project "Research of hydrological processes in mountainous regions, particularly of flood formation, using mathematical models". The following countries participate in the project: Bulgaria, Czechoslovakia, the German Democratic Republic, Hungary, Poland, Romania, and the Soviet Union. The Czechoslovak NC for IHP is acting as the project co-ordinator. Because no special funds were allocated for the project activities in the participating countries, it was necessary first to identify those fields of interest where research had already started or was planned to start in the near future in which a close international co-operation would bring most benefit to participating countries. This was discussed at the first meeting of the international Working Group (WG) where it was agreed to concentrate in the first phase on examining the existing tools (mathematical models) in all participating countries, using hydrometeorological data from seven catchments with different physiographic characteristics and located in different climatic regions. These catchments are:

Rosica (101 km2), Bebresh (479 km2) - Bulgaria, Balkan range; Opor (733 km2) - Soviet Union, Eastern Carpathians; Sola (785 km2), Dunajec (706 km2) - Poland, Northern Carpathians; Hron (582 km2) - Czechoslovakia, Central Carpathians; Schwarzwasser (363 km2) - GDR, Ore Mountains (Erzgebirge).

435

A. Svoboda 436

Using these data, the following models were tested: WBCM (Bulgaria), NLC (Czechoslovakia), EGMO (GDR), GCHJ (Poland), VIDRA (Romania), and DOZHD-2 (Soviet Union). For the time being (June 1988), the following results are available:

WBCM - Rosica NLC - all catchments EGMO - Schwarzwasser, Hron GCHJ - Bebresh, Rosica, Hron, Opor (not all events) VIDRA - all catchments DOZHD-2 - Bebresh, Rosica, Hron.

Except for the hydrometeorological data, the descriptions of all models were distributed among the members of the WG, and it is intended to distribute source programs in FORTRAN IV as well as user's manuals on the standard 5 1/4" diskettes under MS DOS 3.1 as used for IBM PC/XT compatible microcomputers.

BRIEF DESCRIPTION OF THE MODELS

WBCM - The model was developed at the Institute of Hydrology and Meteorology, Bulgarian Academy of Sciences in Sofia, Bulgaria (Jankov, 1987). It is a model with distributed parameters, describing the following phases of the water cycle in a catchment: évapotranspiration, interception, surface detention, infiltration into unsaturated and saturated zones, surface runoff, interflow, and groundwater runoff. Evaporation and transpiration are modelled separately using empirical relationships based on the saturation deficit and wind velocity (for evaporation) and on the wind velocity and soil moisture deficit (for transpiration). The model accounts for changes in transpiration during a day using a sine function. Soil infiltration is expressed as an exponential function of soil moisture deficit. The surface flow component is determined from the water balance among precipitation reaching the ground, surface detention, infiltration and surface flow. Interflow is calculated from its empirical relationship to the actual soil water content, similarly as the groundwater recharge component. Groundwater flow is simulated as outflow from a two-stage linear reservoir. Surface flow and interflow are routed through a series of equal nonlinear reservoirs with power functions relating the outflow and the storage. The model allows for areal distribution of input (precipitation, saturation deficit and wind velocity) as well as of the model parameters. It needs 21 parameters; 9 of them have to be optimized, the rest can be derived from the catchment characteristics and from an analysis of the outflow hydrographs. The optimization procedure is an integral part of the model.

NLC - The model was developed at the Institute of Hydrology and Hydraulics, Slovak Academy of Sciences in Bratislava, Czechoslovakia (Svoboda, 1983 a). It is lumped model; however, it can be used for several subcatchments, thus allowing for areal distribution of input and model parameters. The model is designed to simulate direct runoff and groundwater flow. It includes the

437 International co-operation on hydrology of mountainous areas

following components: interception, évapotranspiration, infiltration, groundwater recharge, direct-runoff component, and groundwater-runoff component. Evapotranspiration is described by an empirical relationship based upon the soil water content and a season-dependent parameter. Infiltration is determined by the difference between the rainfall reaching the ground and the effective rainfall. Effective rainfall is determined by a hyperbolic-tangent-like relationship depending on the ratio between the actual and maximum soil water contents. Groundwater recharge is directly proportional to the actual soil water content. Direct runoff is modelled by a cascade of equal nonlinear reservoirs with effective rainfall as the input, groundwater flow by a single linear reservoir with the percolation into groundwater as the input. The model has 8 parameters, 4 of them (nonlinear cascade) to be determined by trial-and-error using rainfall-runoff events with high proportions of direct runoff. One parameter is determined by soil properties (maximum soil water content), one parameter (of the groundwater linear reservoir) is derived from the runoff recession curve. The last two parameters related to groundwater recharge and évapotranspiration are determined by trial and error using rainfall runoff events with substantial proportions of groundwater flow.

EGMO - The model was developed at the Institute for Water Management in Berlin, GDR, (Becker & Pfiitzner, 1987). It is a part of CAMOS - catchment modelling system capable of simulation of up to 32 catchment elements. EGMO has been developed in two versions: for a decadal - or monthly - time step, and for a daily or shorter time step. The following components are modelled within EGMO: interception, évapotranspiration, surface flow, interflow, base flow. The model assumes a horizontal partitioning of a catchment into the following sub-areas: water surfaces, impervious areas, areas with high and deep groundwater table, hillslopes. The following levels and related sub-processes are considered: interception and surface detention storage, infiltration, saturated sub-area formation, capillary zone storage, gravity water storage and flow in the unsaturated zone, groundwater recharge and storage. Interchange of water between storages is calculated mainly on the basis of empirical relationships and of water balance. The routing of all runoff elements can be performed by several submodels, e.g. convolution, lag-and-route submodel, etc. The model requires 9 areal parameters, 6 storage parameters, 2 infiltration parameters, 8 routing parameters. Nearly all areal and storage parameters can be estimated by analysis of the catchment topographical and soil maps, the routing parameters by a hydrograph analysis. The rest of the parameters is estimated by analysis of the runoff time series using different periods (dry, storm, intermediate) of runoff.

GCHJ - The model was developed at the Institute of Meteorology and Water Management in Krakow and Warsaw, Poland (Zelazinski, 1986). It is a lumped model, used mostly for simulation and forecast of isolated rainfall-runoff events and, for this reason, it does not contain an évapotranspiration subroutine. Effective precipitation (determined by a modified CN-method developed originally by the Soil Conservation Service, USA) represents the

A. Svoboda 438

input into a single linear reservoir with a limited storage simulating the effect of the unsaturated zone. The output from this element is the input into a cascade of equal linear reservoirs (Nash model) which yields the simulated outflow from the catchment. Initial conditions (storages) are determined using the antecedent precipitation index (API). Parameters of the linear cascade are related to three geomorphological catchment characteristics and to three hydraulic characteristics of the outlet cross-section. The CN-method parameter is determined from the properties of the unsaturated zone as well as the two parameters of the single linear reservoir simulating the unsaturated zone. The model is used for forecasting in the upper catchments of the rivers Sola and Dunjec in Poland.

VIDRA - The model was developed at the Institute of Meteorology and Hydrology in Bucharest, Romania (Serban & Tchiprian, 1987). It is a lumped model; however, systems with distributed parameters and/or input can be modelled by a proper decomposition of a catchment into several homogeneous sub-areas. Each of the lumped elements is subdivided into three zones (storages), in particular interception plus surface detention, the unsaturated and the saturated zone. Empirical relationships are used to define the following processes: infiltration, percolation to groundwater table, depletion of the unsaturated zone (interflow) and depletion of the saturated zone (baseflow). Surface flow plus interflow are convoluted with three two-parameter IUH's, one for effective rainfall (ER) < 3mm/h, one for 3mm/h < ER < 6mm/h, and one for ER > 6mm/h. Output from this convolution ("water yield") is transformed by a Muskingum-type routing into the final hydrograph. The model incorporates two components not used in the project: one for the simulation of snowmelt (optional in most other models), and one for an optimal reservoir flow control based on dynamic programming. The model requires optimization of 16 parameters; 4 of them can be determined from the properties of soil and vegetation cover, the rest by trial and error simulation of events with distinctly different proportions of direct runoff and baseflow.

DOZHD-2 The model was developed at the Ukrainian Regional Research Institute in Kiev, USSR (Sosedko, 1987). DOZHD-2 is a lumped model with the possibility to account for different conditions of runoff formation within the catchment by dividing it into several sub-catchments. The model incorporates three storages: surface, subsurface (soil), and the groundwater storage. Correspondingly, the following processes are modelled: surface flow, interflow and baseflow, together with évapotranspiration, infiltration, and groundwater recharge. Model input consists of time series of the following three variables: precipitation, saturation deficit, and ground wind velocity. The movement of water between the storages and the infiltration are modelled by using empirical relationships and water balance of each of the storages. Evapotranspiration is expressed in relation to the saturation deficit, wind velocity, and the maximum and actual soil water contents. Water yields from the surface-flow and interflow storages are convoluted using a Nash-type two-parameter linear IUH. The model has 13 parameters, 4 of which are determined directly

439 International co-operation on hydrology of mountainous areas

from the hydrometeorological data. The remaining parameters are optimized using low-peak events for the parameters related to baseflow storage and flow, and high-peak, events for the parameters of surface and interflow storage and routing. Another version, not used in the project, is a SNEG-2 model describing the runoff formation from snowmelt.

SOME APPLICATIONS OF THE MODELS

The limited extent of available simulation results from most of the above models does not allow to make final conclusions about the performance of the particular models or of their subroutines. However, some of the model runs helped to discover inconsistencies in some of the data sets. It is foreseen to conclude the first phase of the project by the end of 1989. The exchange of hydrometeorological data from seven catchments located in distinctly different physiographic and climatic regions of Central and Eastern Europe represents per se a valuable achievement. It is so far the most extensive exchange of data within the co-operation of the National Committees for IHP in the region; these data can be useful to other regional or bilateral co-operation projects as well.

From the results obtained so far and from the information distributed at the meetings of the WG it can be concluded that the most detailed description of the runoff formation is offered by the EGMO model. This model has also been tested for quite a long time in operational forecasting in GDR. The model was not developed specifically for mountainous catchments but the results presented for the catchment of Schwarzwasser are encouraging. Similar in the detailed description of the runoff process is the WBCM model, to a lesser extent models DOZHD-2 and VIDRA. DOZHD-2 is used for operational forecasting in catchments of Eastern Carpathians in the USSR, and it is hoped that other countries will benefit from the experience with this model after the planned exchange of the software. The use of DOZHD-2 for evaluating changes in a catchment is documented by Kotchelaba (1985) who describes a reconstruction of a 1980 high flood in the Opor and Stryj catchments. The modelling results indicated peak flows substantially lower (by 50% in the Stryj and by 30% in te Opor) than the "observed" values. This discrepancy was subsequently traced to a previously unknown break of a small dam during the flood and formation of a barrier from floating debris downstream of the gauging stations. This barrier backed up the water thus causing the extremely high stage from which the "observed" peak flows were originally estimated using flow-rating curves.

An evaluation of the effect of changes in the main river channel on the flood regime was demonstrated with the NLC model, namely with its part consisting of a series of equal nonlinear reservoirs which was also used for flood routing in natural river channels. For this case study, the catchment of the Latorica river (southern slopes of Eastern Carpathians, USSR and Czechoslovakia) was used. Analysis of flood records from 1957 and 1980 revealed that conditions of flood formation for these two events were very

A. Svoboda 440

similar, but the peak at the outlet point at Tchop in 1980 was by 100 percent higher than in 1957, mainly due to the elimination of floodplains by the construction of embankments. A combined stochastic-deterministic model approach resulted in a corrected flood-peak frequency curve for the Tchop gauge (Svoboda, 1983b).

In Romania, elements of the VIDRA model were used for expressing the changes in flood regime related to the change in cultivation practices in mountainous catchments. A model approach was used for simulation of the effect of these changes on the runoff from experimental plots and micro-catchments Obirsie and Maracina in the Eastern Carpathians (Stanciu & Zlate-Podani, 1987).

The GCHJ model (as well as its earlier versions) has been used for a long time for short-term forecasting. For that purpose, it is combined with meteorological forecast for next 48 hours, in order to yield a reasonable lead-time in mountainous catchments of the Polish Carpathians. From this point of view, and also for its updating procedures, it is probably one of the most elaborate models used in the project. It is the only one using geomorphological characteristics, which makes it suitable for ungauged catchments (Zelazinski, 1986).

CONCLUSIONS

It is not a discovery of the project Working Group that there does not exist an universal deterministic rainfall-runoff model for all purposes of hydrological research and practice. Based on the vast exchange and use of hydrometeorological data from the mountainous catchments of the region, as well as on the experience of the WG members, it has been shown that even relatively simple models (like NLC and GCHJ) can yield valuable results if the input data are correct and if the model accounts for areal variability of input and of the model parameters. On the other hand, much can be expected from more complex models (like EGMO, WBCM) if more extensive data are available, and also a detailed information about the catchment physiography, pedology and vegetation cover. It is foreseen that software exchange together with exchange of experience will help the members of the Working Group in improving their models for their particular needs, be it research or operational applications.

ACKNOWLEDGMENTS

The active co-operation of all members of the international working group "Research of hydrological processes in mountainous regions" of the countries of Central and Eastern Europe in commenting on this paper, as well as the keen co-operation of the National Committees for IHP UNESCO of these countries, are gratefully acknowledged.

REFERENCES

Becker, A. & Pfûtzner, B. (1987) EGMO - System approach and sub-routines for river basin modelling. Acta hydrophysica, 31 (3/4), 125-141

441 International co-operation on hydrology of mountainous areas

Jankov, B.R. (1987) Modelling of runoff formation from rainfall in mountainous catchments. (In Bulgarian). PhD Thesis, Inst, of Hydrology and Meteorology of the Bulgarian Academy of Sciences, Sofia

Kotchelaba, E.Y. (1985) Example of hydrograph reconstitution using mathematical model of flood formation from rainfall. (In Russian) Internal report, Meeting of the Working Group, Kijev

Serban, P. & Tchiprian, K. (1987) Application of the VIDRA model for flood forecasting in the basin of Opor. (In Russian) Internal report, Meeting of the Working Group, Sofia

Sosedko, M.N. (1987) Description of the mathematical model of runoff formation from rainfall and snowmelt. (In Russian) Internal report, Meeting of the Working Group, Sofia

Stanciu, P. & Zlate-Podani, I. (1987) A study of hydrological regimes in experimental basins in relation to cultivation practises. In: Water for the Future; Hydrology in Perspective (J.C. Rodda & N.C. Matalas, editors) IAHS Publ. No. 164

Svoboda, A. (1983a) Nonlinear cascade rainfall-runoff model and its application for simulation of total flow. (In Slovak with English summary). Vodohosp. Cas. 31 (3 & 4), 288-296

Svoboda, A. (1983b) Evaluation of man-induced changes in the catchment upon runoff. (In Slovak) Research Report, Inst, of Hydrology and Hydraulics, Slovak Academy of Sciences, Bratislava

Zelazinski, J. (1986) Application of the geomorphological instantaneous unit hydrograph theory to development of forecasting models in Poland. Hydrol. Sci. Journal, 31, (26), 263-270

Appendices

Hydrology of Mountainous Areas(Proceedings of the Strbské Pleso Workshop, Czechoslovakia, June 1988). IAHS Publ. no. 190, 1990.

APPENDIX 1

Chairmen and Key-Speakers of the Workshop

Opening Ceremony:

Prof.Dr.Jân Benetin, chairman of the Cz.Com.for Hydrology Dr.Mstislav Rousinov, repr. of UNESCO Dr.Vit Klemes, president of IAHS Doc.Dr.Ferdinand Samaj, director of SHMI, repr. of WMO Dr.Auréle Parriaux, repr. of IAH Dr.Ludovit Molnâr, chief organizer Invited guests of the Workshop Topic 1 :

Prof.Dr.Urszula Socynska, chairman Dr.Georg Gietl, key-speaker Doc.Dr.Ferdinand Samaj, convenor-WMO Topic 2 :

Dr.Karl Hofius, chairman Prof.Dr.Lev S.Kuchment, key-speaker Dr.Mstislav Rousinov, convenor-UNESCO Topic 3 :

Dr.Auréle Parriaux, chairman Doc.Dr.Jan Silar, key-speaker Dr.Vladimir Hanzel, convenor-Cz.Com.for Hydrology Topic 4 :

Prof.Herbert Lang, chairman Dr.Ales Svoboda, chairman Dr.Vit Klemes, key-speaker Prof.Dr.Milan Dzubâk, convenor-Cz.Com.for Hydrology

Conclusions :

Dr.Ludovit Molnâr, chairman - editor Dr.Mstislav Rousinov, UNESCO Dr.Georg Gietl, key-speaker Prof.Dr.Lev S.Kuchment, key-speaker Doc.Dr.Jan Silar, key-speaker Dr.Vit Klemes, key-speaker Dr.Gordon J.Young, coordinator

445

Hydrology of Mountainous Areas (Proceedings of the Strbské Pleso Workshop, Czechoslovakia, June 1988). IAHS Publ. no. 190, 1990.

APPENDIX 2

Recommendations of the Workshop

Recommendations made by editorial committee on behalf of participants of the International Workshop on Hydrology of Mountainous Areas, held at Strbské Pleso, Czechoslo­vakia, June 7-10, 1988.

Scientific issues to be pursued under the auspicies of UNESCO in the field of hydrology of mountainous areas

- Improvement of the data base needed for the development of hydrological models, with a special emphasis on new instrument and observation technologies and with the goal of alleviating the problems arising from high variability and spatial nonhomogeneity of hydrological variables by mapping of their areal distributions.

- Promotion of the study of physical processes, with an emphasis on field experiments focused on empirical veri­fications of specific hypotheses related to the indi­vidual geophysical and biophysical processes affecting the hydrological cycle.

- Organization within the IHP-IV and in collaboration with the National Committees regular small-size working seminars (workshops), on local and mainly regional bases, with the express purpose of discussing new deve­lopments, directions of research, coordination of approaches and methodologies.

Workshop materials to be published

- Beside of publication of extended abstracts of the Workshop in the UNESCO series of Technical Documents in Hydrology, publishing of the keynote addresses and selected papers within IAHS red-volume series.

Recommendation read at closing ceremony on 9th June, 1988.

447

Hydrology of Mountainous Areas (Proceedings o,f the Strbské Pleso Workshop, Czechoslovakia, June 1988). IAHS Publ. no. 190, 1990.

APPENDIX 3

List of Participants

Participants from outside of Czechoslovakia

AL-AZZAWI Saadi, Technical University, Brno *ANDRIEU Herve, L.C.P.S.-Sect.Hydrologie, Bouguenais,

France ®ASSVALL Randi Pytte, Norwegian Water Res.and Energy

Admin., Oslo, Norway BALASCH Carles, Institut J.Aimera, Barcelona, Spain BALINTH Gâbor, VITUKI, Budapest, Hungary *BATHURST James C., NERC Unit, University of Newcastle

Upon Type, U.K. BECKER Alfred, Institut fur Wasserwirtschaft, Berlin,GDR BLIDARU Spiridon, Inst.of Hydrology and Meteorology,

Bucurest, Romania BYCZKOWSKI Andrzej, Warszaw, Poland

:::CREUTIN Jean Dominique, Inst.de Mécanique de Grenoble, France

CLOTET-PERARNAU Nuria, Inst. J.Aimera, Barcelona, Spain *DIMITROV Dobri, Inst.of Hydrology and Meteorology,

Sofia, Bulgaria ENE Horia, Increst. Dept.of Mathematics, Bucuresti,

Bulgaria ®*ENGELEN Ben G. , Inst.of Earth Sciences, Amsterdam,

Netherlands *GIETL Georg, FVA Mttnchen, FRG *GIVONE-VAURIOT Christiane, Aéroport de Lyon, France *GOLF Walter, Technische Universitat, Dresden, GDR *GURTZ Joachim, Technische Universitat, Dresden, GDR ®*HAIGH Martin, Geography Unit, Oxford Polytechnic,

Headington, U.K. *HIGUCHI Keiji, Water Res.Institute, Nagoya Univ., Japan *HOFIUS Karl, Bundesanstalt fur Gewasserkunde, Koblenz,FRG *JARRETT Robert, US Dep.of the Interior, Geolog.Survey,

Denver, USA *KLEME§ Vit, IAHS, Nat.Hydrology Res.Institute, Saskatoon,

Canada *KONIAR-SCHAEFER Janina, Cracow Techn.University, Cracow,

Poland KRAUSNEKER Peter, HZB Vienna, Austria "KROG Svere, NVE, Oslo, Norway *KUCHMENT Lev S., Institut vodnych problem, Moscow, USSR *LANG Herbert, Hydrology ETH, Zurich, FRG MALITZ Gabriele, Meteorologischer Dienst der DDR, Potsdam *MEIGNIEN Xavier, CEMAGREF, Lyon Cedex, France NGANA James, Univesity of Dar-Es-Salam, Tanzania

* Participant presenting paper • Participant presenting poster

449

List of participants 450

*NICOUD Gérard-François, Université de Savoie, France *OERTER Hans, GSF Mtinchen Neuherberg, FRG OSUCH Boleslaw, Cracow, Poland *PARRIAUX Auréle, GEOLEP-EPFL, Lausanne, Switzerland RAEV Ivan, Forest Res. Institute, Sofia, Bulgaria

*REINWARTH Oskar, Com. for Glaciology, Mtinchen FRG * RODRIGUEZ Yves, Inst.de Mécanique de Grenoble, France ROGOCKY Viktor V., GGI, Branch Valday, USSR ROUSINOV Mstislav, UNESCO, Paris, France

*SEMPERE TORRES Daniel, Inst.de Mécanique de Grenoble, France

*SCHAEFER Robert, Inst.of Meteorology and Water Management, Cracow, Poland

*SCHWARZE Robert, Technische Universitat, Dresden, GDR SOCYNSKA Urszula, Warsaw University, Warszaw, Poland SOSSEDKO Mikhail, Ukr.NIGMI Goskomgidrometa., Kiev, USSR

*STANCIU Petre, Inst.of Hydrology and Meteorology, Bucuresti, Romania

*SVANIDZE Givi G., ZakNII Goskomgidrometa, Tbilisi, USSR *TOURASSE Patrick, EDF-DTG, Grenoble, France WARD Roscoe, Miami University, Oxford, Ohio, USA WIEZIK Benjamin, Cracow, Poland

*YOUNG Gordon J., Wilfrid Laurier University, Waterloo,USA

Czechoslovak participants

ADAMEOVA Zdena, Inst.of Hydrology and Hydraulics of the SAS, Bratislava

BABIAKOVA Gabriela, Inst.of Hydrology and Hydraulics of the SAS, Bratislava

BENETIN Jan, Inst.of Hydrology and Hydraulics of the SAS, Bratislava

*BLAZKOVÂ Sârka, Water Research Inst. Prague BOSNÂK Dalibor, ÛVTVS, Bratislava *BUBENÎCKOVÂ Libuse, Czech Hydromet.Institute, Prague CÎZOVÂ Magda, Slovak Hydromet.Institute, Bratislava *DOBES Peter, Slovak Hydromet.Institute, Zilina *DRAKO, Jaroslav, Slovak Hydromet.Institute, Bratislava DULOVIC Ladislav, Slovak Hydromet.Institute, Bratislava DZUBÂK Milan, Technical University, Bratislava *ELIÂS Vaclav, Inst.for Hydrodynamics of the Cz.Academy

of Sciences, Prague *FENDEKOVÂ Miriam, Comenius University, Bratislava *GABRIS Peter, ÛVTVS Bratislava *GAVENCIAK Stefan, Water Research Institute, Bratislava *HANZEL Vladimir, D.Stur Institute of Geology, Bratislava HARNOVÂ Jifina, VÛGI Brno HODANOVÂ Olga, Cz.Committee for UNESCO, Prague ®HOLKO Ladislav, Inst.of Hydrology and Hydraulics of the

SAS, Bratislava *HONZÂK Jiri, Czech Hydrometeorological Institute, Prague

451 List of participants

HYROS SOVÂ Eva, MLVHD Bratislava JANSK'Y Libor, SMS Bratislava KAFKA Ilja, Czech Hydromet.Institute, Prague *KAJAN Juraj, Slovak Hydromet. Institute, Bratislava *KA§PA:REK Ladislav, Water Research Institute, Prague KAZMUTCOVÂ Maria, IGHP Zilina KONÎC EK Alojz, Inst.of Hydrology and Hydraulics of the

SAS, Bratislava • KOSTK-A Zdenëk, Inst.of Hydrology and Hydraulics of the

SAS, Bratislava *KOVAR Pavel, Agricultural University, Prague KRÂM Pavel, Instituts of Geology, Prague *KRECE K Josef, Institute of Applied Ecology, Agricultural

University of Prague KUNSCHIvan, Slovak Hydromet.Institute, Bratislava *KUPCo Milan, Slovak Hydromet.Institute, Kosice KVËTON Radomil, Water Research Institute, Bratislava *LAPIN Milan, Slovak Hydromet.Institute, Bratislava MAJERC&K0VÂ Olga, Slovak Hydromet.Institute, Bratislava MAKEL Michal, Slovak Hydromet.Institute, Bratislava MAL? Otakar, Slovak Hydromet.Institute, Bratislava *MARE§ Karel, Technical University of Prague *MARES0VA Ivana, Technical University of Prague MATUSKA Milan, Slovak Hydromet.Institute, Bratislava MECÂR Miroslav, Technical Univeristy of Bratislava *MENDEL Oto, Inst.of Hydrology and Hydraulics of the SAS,

Bratislava *MÉSZ£J10S Ivan, Inst.of Hydrology and Hydraulics of the

SAS, Bratislava *MIHÂL<IK Ferdinand, Slovak Hydromet.Institute, Bratislava MINÂRIK Boris, Slovak Hydromet.Institute, Bratislava MISKAvY Viliam, Technical University of Bratislava

*MOLN&R Ludovit, Inst.of Hydrology and Hydraulics of the SAS, Bratislava

*MIKL&NEK Pavol, Inst.of ftydrology and Hydraulics of the SAS, Bratislava

*MUCHA. Igor, Comenius University of Bratislava NOVICKY Oldrich, Czech Hydromet.Institute, Prague

*PETROVl£ Pavol, Water Research Institute, Bratislava

*POBEHCA Ludovit, Slovak Hydromet.Institute, Zilina PODOL.INSK? Stefan, Slovak Hydromet.Institute, Banska

Bystrica POUR Josef, Geological Survey, Prague

*PRISTACHOVÂ Gabriela, Water Research Inst., Bratislava •RA§0 Jân, Inst.of Hydrology and Hydraulics of the SAS

Bratislava REPA Jân, Inst.of Hydrology and Hydraulics of the SAS,

Bratislava RONCAVK Peter, Inst.of Hydrology and Hydraulics of the SAS,

Bratislava SIKORA Anton, Water Research Inst., Bratislava SKALSKY Ctirad, VÛGI Brno

List of participants 452

STARY Milos, Technical University, Brno *SVOBODA Ales, Inst.of Hydrology and Hydraulics of the

SAS, Bratislava SZOLGAY Jan, Inst.of Hydrology and Hydraulics of the SAS,

Bratislava SAMAJ Ferdinand, Slovak Hydromet.Institute, Bratislava

*SILAR Jân, Comenius University of Prague SlMO Eduard, Inst.of Hydrology and Hydraulics of the SAS

Bratislava * SÏASTNY Pavel, Slovak Hydrome.t.:,, Institute, Kosice *TESAR Miroslav,Institute of Hydrodynamics of the Czech

Academy of Sciences, Zdikov *TRIZNA Miroslav, Inst.of Hydrology and Hydraulics of the

SAS, Bratislava *TURBEK Jozef, Slovak Hydromet. Institute, Bratislava *TURCAN Jozef, Hydroconsult, Bratislava *VRANA Kamil, D.Stur Institute of Geology,Bratislava *ZEMAN Evzen, Technical University, Prague *ZIZKA Robert, Technical University, Prague URSÎNY Pavol, Slovak Hydromet.Institute, Bratislava

®HLUBOCKY Bronislav, Slovak Hydromet.Institute, Bratislava

* Participant presenting paper • Participant presenting poster