articulo topmodel

Modelling the hydrological behaviour of a mountain catchmentusing TOPMODEL

L. Holkoa, A. Lepistob

aInstitute of Hydrology, Slovak Academy of Sciences, Ondrasovecka 16, 031 01 Liptovsky Mikulas, SlovakiabFinnish Environment Agency, Kesakatu 6, 00260Helsinki, Finland

Received 11 December 1995; revised 30 May 1996; accepted 5 July 1996

Abstract

The mathematical catchment model TOPMODEL was used to simulate the hydrological beha-viour of a mountain catchment at Jalovecky Creek, Western Tatras, Slovakia. The model providedadequate results in simulation of daily runoff from the catchment for the period 1 August 198731October 1993. Air temperature inversions, typical of certain periods in mountain catchments, causedoverestimation of simulated runoff because of treatment of snowfall as rainfall. A single value of thetemperature limit for solidliquid precipitation was also not appropriate for some events. Similarly,the single value of the degreeday factor for the entire period used in the snow subroutine has led tohigher simulated snowmelt runoff in some years. Hourly data were used for runoff simulation duringthe short period between 15 August and 7 September 1993. The results indicate that more effort willbe required to improve the simulation, although the total simulated runoff for the whole period wasclose to the measured runoff. The areal extent of the saturated area calculated by TOPMODEL forthe two short-term events was comparable with the results based on isotopic runoff separation.However, saturated areas estimated by TOPMODEL may provide both event and pre-event water,whereas areas contributing new water estimated by the isotopic method provide pre-event water bydefinition.q 1997 Elsevier Science B.V.

1. Introduction

Water-related phenomena connected with migration of chemical substances within thecatchment require a better understanding of the flow paths of water. Processes of runoffgeneration are still not completely understood, despite the progress made during the last 30years. It is now generally accepted that runoff in many natural catchments is dominated bysubsurface flow (Hewletts concept of variable contributing areas), but the mechanisms thatare responsible for rapid delivery of subsurface water to the stream are still open to discussion.

0022-1694/97/$17.00q 1997 Elsevier Science B.V. All rights reservedPII S0022-1694(96)03237-4

Journal of Hydrology 196 (1997) 361377

The quasi-physically based mathematical model, TOPMODEL (Beven and Kirkby,1979), can be used to model the dynamics of contributing areas. The number of applica-tions of the model in different catchments has recently increased (e.g. Hornberger et al.,1985; Durand et al., 1992; Robson et al., 1993; Quinn and Beven, 1993; Lepisto, 1994).This reflects the ability of the model to use distributed information on catchmenttopography and its relatively simple structure (which needs only few parameters, butprovides good results in simulation of catchment runoff); moreover, it tests the internalprocesses acting in the catchment that are connected with runoff generation. As pointedout by Durand et al., 1992, detailed analysis of the model is required in a variety ofenvironments.

In this study, the model has been applied in the mountain catchment which representshydrological conditions in the highest part of the Carpathians. The aim of the modellingwas (1) to test the applicability of the model for runoff simulations in the given catchment,(2) to compare the fractions of saturated areas given by TOPMODEL and by the isotopicmethod.

2. Brief description of topmodel

As pointed out by Beven et al. (1995), TOPMODEL is not intended to be a traditionalmodel package, but more a collection of concepts. Therefore, many versions of the modelexist and are used for different purposes. In this study, the single subcatchment versionwas used, to which a snow subroutine was added at the University of Linkoping, Sweden.Since the concept and detailed description of the model can be found elsewhere (Bevenand Kirkby, 1979; Beven et al., 1995), only the basic ideas are given here (Beven et al.,1995; Wolock, 1993). TOPMODEL is based on the variable source area concept of runoffgeneration (Hewlett and Hibbert, 1965). The basic model assumptions are:

1. Dynamics of the saturated zone can be approximated by successive steady-state repre-sentations.

2. Hydraulic gradient of the saturated zone can be approximated by the local surfacetopographic slope, tanb; groundwater table and saturated flow are parallel to thelocal surface slope;

3. Distribution of downslope transmissivity with depth is an exponential function ofstorage deficit or depth to the water table.

It follows from these three assumptions that the main factor determining the relationshipbetween catchment mean water table (storage deficit) and local water table level (storagedeficit) is the topographic index (Kirkby, 1975), given as ln (a/tanb) wherea is the area ofthe hillslope per unit contour length that drains through the given point.

The hydrological behaviour of the catchment in TOPMODEL is modelled as follows:Precipitation enters the catchment either as rain or snow, depending on the air temperature,and contributes accordingly to the rise of the snow water equivalent or infiltrates into thesoil. Infiltrating water, along with the water draining from other parts of the catchment,causes the groundwater table to rise. If the groundwater table rises to the land surface, thearea becomes saturated. Precipitation or snowmelt on saturated areas produces saturation

362 L.Holko, A. Lepisto/Journal of Hydrology 196 (1997) 361377

excess overland flow. Infiltration excess overland flow is calculated according to theGreenAmpt method. Any water stored in the saturated subsurface zone is assumed tomove downslope as subsurface flow. Total flow is calculated as a sum of overland andsubsurface flows.

Evaporation and its structure are calculated according to air temperature, presence ofsnow in the catchment and interception. No evaporation is assumed if the air temperatureis below 58C and if there is snow on the land surface. If the air temperature is between 5and 108C, a linear increase in potential evaporation is assumed.

If the air temperature is below 08C, precipitation increases the snow water equivalent.The snow cover is set to hold as much as 10% of water. The snow starts to melt when theair temperature exceeds the critical value which is given as the model parameter. The rateof melting depends on the difference between the air temperature and this critical value,and on the value of the degreeday factor given as a parameter.

3. Catchment and input data

Jalovecky Creek catchment (22.1 km2) is located in the Western Tatra Mountains.Hydrological research in the catchment started in 1986 and its main aim is to improvewater balance computations in the mountain environment. Elevation ranges between 800and 2178 m a.s.l., with a mean of 1500 m a.s.l. The slopes in the catchment are rathersteep, with a mean value of 288. Mean annual air temperature at the catchments meanelevation is 3.58C, mean precipitation is 1435 mm and mean runoff is 858 mm.

Catchment bedrock is formed mainly by Paleozoic crystalline schists, gneisses, mig-matites and granodiorites which cover 93% of the catchment area. The rest is made up byMesozoic complexes (limestone, dolomite, shale). Due to the intensive tectonic activityduring geological development of the Carpathians and the presence of glaciers in thePleistocene, the bedrock is densely fissured and the weathered zone can be fairly deep.The bedrock is covered by glacial and glaciofluvial sediments and shallow soils. Soil typesare represented by Cambisols, Podzols and Rankers (Kostka, 1995). From the hydrogeo-logical point of view, there are good conditions for groundwater recharge and dischargewithin the catchment (Melioris, 1980). Vegetation is represented by forests (45% of thearea, with spruce dominating), dwarf pines (31%) and alpine meadows and open bedrockareas (24%). Because the catchment is situated within the Tatra National Park, humanactivities are restricted to tourism only.

The version of TOPMODEL used in the present study requires precipitation, potentialevapotranspiration and the values of topographic indexes as input data. Topographicindexes were calculated with the computer program GRIDATB distributedtogether with TOPMODEL (Beven et al., 1995). The calculation was based ontopographic data from the map of scale 1:10 000 and the grid size used in the calculationswas 100 100 m.

Two files with precipitation, runoff and potential evapotranspiration data were preparedfor the simulations. The first includes daily data from the period 1 August 198731October 1993. The second contains hourly data from the period 15 August 19937September 1993.

363L.Holko, A. Lepisto/Journal of Hydrology 196 (1997) 361377

Daily runoff was based on hourly water level records measured at the catchment outlet.Estimates of daily precipitation were derived from weekly totals measured by the rain-gauge located at the mean elevation of the catchment according to daily measurements atthe nearest meteorological station, which is approximately 8 km from the catchment out-let. Since November 1988, the reliability of areal precipitation estimates has been verifiedby data from six other gauges located in the catchment. Mean daily temperature wascalculated from hourly measurements at the mean elevation of the catchment. A roughestimate of the potential evapotranspiration was given by Thornthwaites method(Thornthwaite, 1948). Monthly totals of potential evapotranspiration were divided bythe number of days to obtain estimates of the daily values.

It is obvious that the results of modelling depend on the quality of input data. The abovementioned data included some uncertainties in the areal estimates of precipitation, char-acterization of air temperature for a relatively large catchment with frequent inversionsituations in late autumn and early spring with a single value, and the rough estimates ofpotential evapotranspiration. The hourly data for a short period between 15 August and 7September 1993 was therefore used to avoid most of the above uncertainties.

Hourly precipitation was measured by a tipping bucket raingauge located in the catch-ment, and areal estimates were checked by measurements of other seven raingauges in thecatchment. Runoff and air temperature data were measured as mentioned above. Hourlyvalues of potential evapotranspiration were derived from the daily estimates calculated bythe method mentioned above. The calculation was based on the temperature index, i.e.Eh = Th(Ed/Td), whereE andT denote potential evapotranspiration and air temperaturerespectively, h stands for hourly values and d for daily totals.

The areal extent of saturated areas calculated by TOPMODEL was compared with theresults from isotopic runoff separation. Deuterium was used as a tracer in a two-compo-nent separation model. According to this model, the event water fraction (contribution ofwater from precipitation to runoff) is calculated as:

Xevent=1 [(ds dp)=(dg dp)] (1)

whered is the relative isotopic ratio of the sample (e.g.2H/1H) with respect to that ofthe standard Vienna SMOW, andds, dp and dg represent isotopic composition ofstreamwater, precipitation and groundwater respectively. On the assumption thatthe event water flows to the stream channel as a result of precipitation (or snowmelt)falling on saturated areas in the catchment, the fraction of saturated area (eventwater-contributing area) can be estimated by dividing the discharge by the rate ofprecipitation (snowmelt) and multiplying the result by the event water fraction(Rodhe, 1987).

Saturated area fractions were calculated for the summer months in 1992 and 1993(monthly data) and for two events at the end of August 1993 (mean values forthe events). Monthly weighted means of precipitation and runoff used in the calculationof monthly values ofXevent were based on the samples collected once a week. Thedeuterium content of groundwater was assumed to equal the annual mean of deuter-ium in precipitation; during the two events at the end of August 1995 the deuteriumcontent in the groundwater was assumed to equal that of the stream baseflow beforethe events.


4. Results and discussion

4.1. TOPMODEL parameters

The number of parameters depends on the processes which the model takes into accountduring the simulations. The version of TOPMODEL used in this study contains the snowsubroutine, calculates infiltration excess, and considers interception of precipitation. Con-sequently, the number of parameters has increased. The influence of different parameterson the results of simulations varied. The most important parameters were:

1. szf (m1) scaling model parameter; this, together with the next parameterT0,indirectly controls the simulation of the recession limb of a hydrograph;

2. alnT0 catchment mean value of lnT0, whereT0 (m2 h1) is the lateral transmissivity

of the soil when it is just saturated;3. aK0 catchment mean value of saturated conductivity at the surface,K0 (m h

1). Thisparameter is used during the modelling of fluxes in the unsaturated zone;

4. dth1, dht2 these parameters outline the basic soil structure; dth1 (m) represents thedifference between soil water content at saturation point and at field capacity, dth2 (m)is the difference between field capacity and wilting point;

5. cmelt, csf, ttmelt parameters of the snow subroutine; csf () is the correction factorthat takes snow interception losses into account, cmelt (mm day1 8C1) is the degreeday factor and ttmelt (8C) represents the limiting air temperature above which the snowcover starts to melt.

Preliminary runs were carried out for the Jalovecky Creek catchment to select an initialset of values for all the parameters and to choose which parameters to optimize. After thesatisfactory modelling of the recession limbs for the majority of runoff events, the para-meters szf, alnT0 and aK0 were kept constant and the soil and snow subroutine parameterswere allowed to vary in the manual optimization. Automatic optimization did not lead tobetter results. The values of the most important parameters are given in Table 1.

Parameter aK0 was set as the value of saturated conductivity of the soil samples takenfrom the catchment, although from its definition it follows that the real value ofK0 (as wellas that ofT0) can hardly be measured. Due to the porous character of the catchment, the

Table 1Main TOPMODEL parameters for simulations with daily data during the period 1 August 198731 October 1993and hourly data during the period 15 August7 September 1993; szf is scaling model parameter, dth1 and dth2 arestorage parameters of soils, aK0 is catchment mean value of saturated conductivity, cmelt is degreeday factor,csf is snow interception factor, ttmelt is limiting air temperature for the start of snowmelt and alnT0 is thecatchment mean value of lateral transmissivity when the soil is just saturated

szf dth1 (m) dth2 (m) aK0(m h1)

cmelt(mm day1 8C1)

csf ttmelt(8C)

alnT0(m2 h1)

daily data 4.0 0.13 0.15 0.125 2.5 0.9 1.5 1.0hourly data 6.3 0.13 0.15 0.125 2.5a 0.9a 1.5a 0.55

aSince simulation with hourly data was performed for the summer period, the snow subroutine parameters werenot used.


thickness of the capillary fringe was assumed to be negligible as well as the area of thebare rocks in the catchment.

It was assumed arbitrarily that 20% of precipitation under all soil moisture conditionsreaches the groundwater table as macropore flow; this value was not verified by measure-ments. Different values of the parameter changed the proportions of modelled subsurfaceand overland flow, but the total simulated flow remained constant. The more remarkableinfluence of the bypass flow could be observed by the simulated soil moisture. Simulatedsoil moisture during the winter periods was kept at its maximum value which was deter-mined by parameter dth2. The lengths of these periods were shorter during the simulationswith high bypass flow (Fig. 1).

Parameters dth1, dth2 were first estimated according to the above mentioned definition(differences in water contents at saturation, field capacity and wilting point) using thesame soil samples as were estimated from the value of parameter aK0. However, it wasfound that runoff simulation was not very sensitive to the values of dth1 and dth2. Para-meter dth2 decreased or increased the absolute values of simulated soil moisture, but hadno influence on its temporal variability.

The aim of the simulation strategy was to obtain the best possible reproduction ofmeasured runoff and an adequate water balance with realistic values of model para-meters. During the simulation with the daily data (1 August 198731 October 1993), theparameters were kept constant to test the model potential in long-term runoff simulations.It is obvious that simulations in such a case do not fit the measured data (runoff, snowwater equivalent, etc.) equally well during the whole period of simulation.

Fig. 1. Influence of bypass flow parameter pmac on simulated values of catchment mean soil moisture; daily datafrom August 1987 to October 1993.


Fig. 2. Daily precipitation, measured and simulated runoff in hydrological years 1988 and 1989.


During the simulation with hourly data (15 August7 September 1993) it was necessaryto adjust some of the parameters to improve the results (Table 1).

4.2. Runoff simulation

4.2.1. Daily input dataThe results of runoff simulation with the daily input data are given in Figs 24. Con-

sidering the uncertainties in the input data, the results are reasonable. Analysis of thedifferences between measured and simulated runoff shows that:

1. Overestimation of modelled runoff during snowmelt frequently resulted from the com-bined effect of snowmelt and liquid precipitation (May 1988, 1989, 1990 and 1992).Rapid increases in the air temperature caused similar responses in meltwater produc-tion. If precipitation occurred under such conditions, the simulated snow cover meltedaway very quickly and high runoff peaks resulted. The same situation occurred in May1991 when the simulated snow water equivalent was higher than in reality. A change inthe water holding capacity of the snow cover, together with variable values of thedegreeday factor, could have improved the simulation in the above-mentioned cases.

2. Remarkable underestimation of runoff occurred in spring 1993 for reasons similar tothose given in the previous paragraph. The snowmelt at the end of April was initiatedby rainfall. High air temperatures caused rapid snowmelt. The simulated snow waterequivalent was very low and, at the time of simulated flow peak, was approaching zero.Consequently, the simulated hydrograph during the following rainless period exhibitedfast recession. The simulated ground water table dropped to the lowest level in hydro-logical year 1993 and runoff simulation recovered only at the end of rainy June. Alower value of the degreeday factor could have improved the simulation during thesnowmelt.

3. Runoff simulation in the summer periods was reasonably good. The recession limbs ofhydrographs indicate that parameters szf andT0 were appropriate for most events.Simulated runoff after the extremely high rainfall at the beginning of August 1991was overestimated, perhaps also because of the rough estimate of potential evapotran-spiration. On the other hand, simulated runoff following the extremely dry August 1992was underestimated for a long period, despite the wet September 1992, and the runoffreproduction improved only in mid-October. The simulated water table in August 1992was at the lowest level for the whole period of simulation and was almost twice as deepas the mimimum levels in other years. This extreme condition could not be simulatedsatisfactorily with the model parameters set for the whole simulation period.

It is probable that runoff simulation would be better if the model parameters werevariable; i.e. particular years or seasons would have been simulated. Nevertheless, theresults presented show that the model simulates the seasonal changes in runoff regimereasonably well (Fig. 5). The data presented in Table 2 show that total simulated runoff forthe whole period was only, 1% higher than the measured runoff. Simulated runoff andevapotranspiration represented 65 and 32% of measured precipitation, respectively. Asmentioned before, the input data included some uncertainties in the areal estimates ofprecipitation because of the mountainous character of the catchment. It is probable that


measured precipitation was overestimated to some extent, which resulted in the differenceof 3% in the water balance.

In conclusion, it should be noted that particular short-term runoff events may not alwaysbe simulated correctly in long-term simulations. The snow subroutine should be modifiedto improve the runoff reproduction during snowmelt. When more input data are availablein the future, the calibrated model should be verified.

4.2.2. Hourly input dataThe results of simulations with the hourly input data for the period of 15 August7

September 1993 are given in Fig. 6. Except for the rough estimate of potential evapo-transpiration, the input data are much more reliable than in the case of daily data (areal

Fig. 5. Measured and simulated monthly runoff from August 1987 to October 1993, calculated as arithmetic meanfrom the daily simulations.

Table 2Main water balance components model inputs and outputs for simulation

Measured runoff(mm)

Simulated runoff(mm)

Evapotranspiration(mm)

Interception (mm) Input precipitation(mm)

5088a 5110a 2074a 459a 7921a

59b 58b 36b 7b 116b

a Daily data from 1 August 1987 to 31 October 1993.b Hourly data from 15 August to 7 September 1993.


averages of precipitation, no air temperature inversions). However, Fig. 6 indicates thatmore effort will be necessary to improve the simulation. Total simulated runoff is 2%lower than the measured runoff (Table 2). Simulated runoff and evapotranspiration repre-sented 50 and 36% of measured precipitation, respectively.

As was mentioned above, parameters szf and alnT0 have been changed for the simula-tion with hourly data in order to achieve better runoff reproduction. The change of szf from4.0 to 6.3 resulted in substantially higher peakflows. The influence of alnT0 was notsignificant. It was reflected only in a slightly higher value of the efficiency criterion. Ifthe new values of both parameters were used with the daily input data series, the totalsimulated runoff would be overestimated as a result of the high value of szf. Since szf is ascaling parameter, its change for different runoff events seems reasonable. According toour experience, the value of szf often varies, especially for simulations with hourly data.

4.3. Snow water equivalent

Snow water equivalent has been measured in the Jalovecky Creek catchment and themeasurements are compared with TOPMODEL simulations in Fig. 7.

The simulated catchment average of snow water equivalent depends on the air tem-perature and the model parameters described above. Fig. 7 shows simulated snow waterequivalents along with maximum, minimum and arithmetic mean of field measurements.The field measurements were made only at 512 points in the catchment and the variation

Fig. 6. Hourly precipitation, measured and simulated runoff in the period 15 August8 September 1993.


between measurements was very high. Modelled values were similar to measured catch-ment means in April 1988 and, except for the winters of 1990/91 and 1991/92 (partially),all lay within the variation of field measurements.

4.4. Saturated areas

The areal extent of saturated areas and their temporal variablity given by the modelcould be very useful in the study of the runoff generation process. The mean value ofsaturated areas in the catchment given by TOPMODEL simulations with daily input datain the period 1 August 198731 October 1993 is about 10% of the catchment area (Fig. 8).Lepisto (1994) found that TOPMODEL provided approximately the same average per-centages of saturated areas as the isotopic method in a small forested catchment in south-ern Finland. A similar comparison was made for the Jalovecky Creek catchment.TOPMODEL simulations were carried out with the hourly data for the period 15August7 September 1993, and isotopic runoff separation using deuterium was madefor the runoff events.

The hydrological situation during the period is shown in Fig. 6. An intensive rainfallburst on the night of 23 August after a long dry period caused an immediate response of thecatchment. The mean contribution of event water calculated by isotopic runoff separation

Fig. 7. Simulated and measured values of catchment mean snow water equivalent.


was 8%. The areal extent of saturated areas, based on the isotopic method, was 2% of thecatchment area. According to the TOPMODEL simulation, the extent was 1.1% of thecatchment area.

The second event was caused by less intensive but prolonged rainfall on 24 and 25August. Due to problems with the sampling device, only the falling limb of the hydrographwas separated directly, giving an event water contribution of 12%. The derived arealextent of saturated area is 2.1% of the catchment (the TOPMODEL simulation gave1.7%).

The two-component model failed to explain runoff generation for the following runoffevents, suggesting pre-event water contributions even higher than the total runoff. Thiswas probably caused by higher contribution of soil water, which was indicated also by thechange in the pattern of the isotopic signature of shallow soil waters monitored at onehillslope in the catchment (Holko, 1995). During the first two events, the soil water at thebottom of the slope was isotopically lighter than in its upper part. After the third event itbecame isotopically heavier. It was concluded that heavier water from the upper part of theslope substituted for the lighter water at the bottom of the slope by subsurface lateral flow.

Areal extent of saturated areas given by TOPMODEL and isotopic separation for thetwo first runoff events indicates that TOPMODEL results from the short-term simulationwere lower, but fairly close to those given by the isotopic method. However, more eventsshould be compared in order to generalize the results. Isotopic runoff separations based onthe monthly data were subject to great uncertainty. In comparison with TOPMODELsimulations, they had a similar order of magnitude.

Fig. 8. Mean monthly fractions of saturated areas simulated by TOPMODEL from August 1987 to October 1993,calculated as arithmetic means from the daily simulations.


According to TOPMODEL simulations, the contribution of the subsurface flow to thetotal simulated flow in the period 1 August 198731 October 1993 was 84% (simulationwith the daily input data), and 95% in the period 15 August7 September 1993 (simulationwith the hourly input data). The last number compares well with the above-mentionedresults of the isotopic runoff separations.

Robson et al. (1992) concluded that TOPMODEL results compared well with chemicalseparation, but pointed out that the results should be analysed carefully. The same is validfor the comparison of TOPMODEL results with those of isotopic separation. Mechanismsof the origin of the saturated areas in TOPMODEL and in the isotopic method are differ-ent. The soil in TOPMODEL may become saturated by rise of the groundwater level to thesoil surface (by infiltration or return flow), as well as by infiltration excess. Event watercalculated from the isotopic runoff separation is a priori assumed to originate from pre-cipitation or snowmelt on the areas which are already saturated. As noted by Rodhe(1987), this method gives only a rough estimate of the areal extent of saturated dischargeareas. Numerical similarity of the saturated areas given by both methods should thereforenot be interpreted in terms of their generation. A possible benefit would consist in sub-stitution of the results from isotopic runoff separation by mathematical modelling. Formore detailed conclusions, it is necessary to verify the results given by both methods byfield mapping of saturated areas. Rodhe (1987) found that the results given by18O for mostsnowmelt events compared well with field mapping.

Saturated areas predicted by TOPMODEL, derived from the simulations with the hourlydata, were distributed mostly along the stream channels. Their distribution seems tocompare well with our empirical knowledge of the catchment. However, the grid sizeof 100 m is too large for reasonable mapping of predicted saturated areas. Such an analysisis beyond the scope of this paper. Because this is one of the most exciting parts ofTOPMODEL, a higher resolution digital elevation model is being prepared which,together with measured field data, can be used for a more detailed analysis of modelledresults.

5. Conclusions

Considering the aims of this study mentioned in the Introduction, it can be concludedthat:

1. Application of TOPMODEL in a porous mountain catchment situated in the highestpart of the Carpathians showed that the model can be helpful in the study of runoffprocesses. The long-term runoff simulation was reasonably successful, although parti-cular runoff events have not always been simulated satisfactorily. Relatively goodrunoff reproduction was achieved despite the relatively simple structure of the modeland the fact that some model assumptions may not be valid in the catchment (e.g. deephomogeneous soil profiles). A more sophisticated snow subroutine could provide betterresults, but additional parameters would have to be included in the model.

2. Short-term simulation provided promising results from the point of view of reality ofmodelled areal extent of saturated areas as checked by the isotopic separation method.


The results of the first testing of the model in the catchment are promising. An impera-tive for further development of the model in the Jalovecky Creek catchment is data better quality input data (spatially distributed precipitation and temperature data), moreaccurate estimates of potential evapotranspiration, and field measurements that can beused for better verification of modelled results (e.g. snow water equivalent, soil moisture,groundwater table). Field mapping of saturated areas should be done in order to study theresponse of the catchment to precipitation events and to verify modelled results.

Acknowledgements

The authors would like to thank Prof. K. Beven and the anonymous reviewers forremarks that helped to improve the manuscript of this paper.

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