OF HILLSLOPE RUNOFF COMPONENTS H. Holzmann
SIMULATION RESULTS AND INTERPRETATION Runoff comparison
Due to the short duration of the observations, the model was calibrated for the entire period. Only the first 10 days were excluded in the objective function, allowing a compensation of errors of the initial condition estimation. Both for daily and hourly temporal resolutions, the total hillslope runoff could be modelled well.
The correlation coefficient of 0.95 and a Nash-Suttcliffe (NS) value of 0.89 confirm the good performance.
Fig 4 shows that the total discharge peaks agree between observed and simulated data; only small runoff peaks in July were underestimated by the model.
For the specific runoff components, bigger discrepancies between observed and simulated data exist and are indicated in Fig 5 by grey ellipses. The upper layer flow – interpreted as near surface flow – was reliably simulated for the bigger peaks, while smaller runoff events were underestimated by the model.
The correlation coefficient for the near surface runoff was 0.90 and the NS-coefficient was 0.76. The model parameters (depths of outlets and recession constants) were optimised using equally weighted square errors of the three specific runoff components corresponding to equation (2). As the highest discharges occurred for layer 1, the deviations in the objective function gave higher weight to the parameters of layer 1.
The interflow layer was not described so well, correlation coefficient of 0.65 and NS-coefficient of 0.1 show rather weak agreement. The observed intermittent base flow component was not described by the model at all. To prevent the model from percolating too much water – which could not be observed in nature – the calibration resulted in very high retention constants k3 and k4 (see Fig 3), reflecting percolation and groundwater flow, which made the simulated base flow delayed and smoothly varying and not corresponding to the observed runoff pattern.
Some difficulties are also caused by the variable contributing area of the plot, which is not a priori known.
At the beginning of some rainfall events, the hydraulic head gradient is directed rather perpendicular to the soil surface. With increasing soil moisture content the flux will be more directed parallel to the surface.
Therefore, the runoff measuring troughs receive water from an area which is small at the beginning but gets progressively larger during the course of the event. A closed water balance then cannot be easily made.
Total Hillslope Runoff - Daily Data
Specific Discharge (mm/d) 0102030
06/01/1995 07/01/1995 07/31/1995 08/30/1995 09/29/1995 10/29/1995 Observed
Simulated
Fig 4: Simulation of Total Hillslope Runoff.
Layer 1 (Near Surface)
Discharge (mm/d) 051525
06/01/1995 07/01/1995 07/31/1995 08/30/1995 09/29/1995 10/29/1995
Observed Simulated
Layer 2 (Interflow)
Discharge (mm/d) 02468
06/01/1995 07/01/1995 07/31/1995 08/30/1995 09/29/1995 10/29/1995
Observed Simulated
Layer 3 (Baseflow)
Discharge (mm/d) 012345
06/01/1995 07/01/1995 07/31/1995 08/30/1995 09/29/1995 10/29/1995
Observed Simulated
Layer 1 (Near Surface)
Discharge (mm/d) 051525
06/01/1995 07/01/1995 07/31/1995 08/30/1995 09/29/1995 10/29/1995
Observed Simulated
Layer 2 (Interflow)
Discharge (mm/d) 02468
06/01/1995 07/01/1995 07/31/1995 08/30/1995 09/29/1995 10/29/1995
Observed Simulated
Layer 3 (Baseflow)
Discharge (mm/d) 012345
06/01/1995 07/01/1995 07/31/1995 08/30/1995 09/29/1995 10/29/1995
Observed Simulated
Fig 5: Simulation of specific runoff components.
Soil moisture comparison
The upper storage of the conceptual model represents the soil column (see Fig 3). The water content scale starts with the wilting point (WP), percolation and interflow is initiated at field capacity (FC) and surface runoff at saturation (saturation excess flow).
According to the model concept (see Fig 3), the state variable bw1 of the model upper storage (soil storage) can be interpreted as
h t
t
bw1( )=(
θ
( )−θ
WP)⋅∆ (3)where θ(t) … average instantaneous soil moisture content θWP … average soil moisture content at wilting point
∆h … depth of soil column
By solving equation (3) with the observed soil moisture data θ(t), the corresponding estimate of the state variable bw1 can be compared with the model results. Fig 6 shows the simulated state variable and the soil water storage according to the TDR measurement at a 40 cm depth estimated by means of equation (3).
The absolute variability of the soil moisture storage is reliably modelled, but the observed data show a more pronounced increasing trend than the modelled data and the drainage phases of observed soil moisture variations tend to be delayed. Some deviation may be due to the fact that the soil profile depth considered in (3) was about 90 cm, while the TDR data correspond to a depth of 30 to 50 cm only.
Soil water storage (mm) 010203040
08/05/1995 08/16/1995 08/27/1995 09/07/1995 09/18/1995 09/29/1995 Modelled soil water storage
Observed soil water storage
Fig 6: Modelled (conceptual model) and observed (equation (3)) soil water storage.
CONCLUSIONS
Conceptual rainfall-runoff models find a broad range of application at the basin scale. This contribution shows that this type of model is also applicable for the plot scale. But the analysis of actual processes based on field observations indicated that some shortcomings should be recognized. (1) The model’s quick flow response is based on a saturation excess flow generation mechanism and can only occur if the soil storage is full. The model output will also generate interflow. The observations showed that quick flow occurred only during certain events. Therefore a submodel representing the Hortonian overland flow may better describe the natural processes and should be added to the model. (2) The observed runoff for the third (lowest) layer should not be interpreted as groundwater flow, as the soil layer in which it is generated is not permanently saturated. It could rather be described as an intermittent perched groundwater flow term, initiated at a predefined average soil water content, higher than the field capacity. A model adaptation consisting of raising the elevation of the percolation outlet can allow for this effect. (3) The spatial boundary of the plot-scale water balance unit can vary in time. This makes the definition of the contributing area difficult and event-dependent. This is not considered in the model. (4) Some model parameters refer to soil physical properties such as soil depth, wilting point and field capacity, and have therefore a better physical meaning on the plot scale and on the scale of Hydrological Response Units (HRU) than on the basin scale, where the retention parameters cannot be directly estimated from physical parameters, but are rather a function of the catchment size and morphological conditions. Future research will focus on these relations to also investigate the applicability of the conceptual model for ungauged catchments or to find upscaling
procedures for parameter estimation. (5) The combined approach of in-situ experiments and the application of computer models provides useful insight into the system behaviour, the conceptualisation of the processes and their limitations.
REFERENCES
Atkinson, T.C. (1978) Techniques for measuring subsurface flow on hillslopes. In : Hillslope Hydrology.
Editor: M. J. Kirkby. Chichester, New York, Brisbane, Toronto: John Wiley & Sons. S. 1-42.
Hager, H., Holzmann, H. (1997) Hydrologic functions of natural forest ecosystems in an alpine catchment.
(in German), Final report of research project HÖ/94 by order of the Austrian Academy of Sciences, Vienna..
Holzmann, H., Sereinig, N. (1997) In situ measurements of hillslope runoff components with different types of forest vegetation. In: FRIEND’97 - Regional Hydrology: Concepts and Models for Sustainable Water Resource Management (Proc. of the Postojna Conf., Slovenia). IAHS Publ. No. 246.
Holzmann, H., Nachtnebel, H.P., Sereinig, N. (1998) Small scale modelling of runoff components in an alpine environment. In Kovar, K. et.al. (edts.) Hydrology, Water Resources and Ecology in Headwaters (Proceedings of the HeadWater’98 Conf.. Meran, April 1998). IAHS Publ.
No.248, 231-238.
Holzmann, H., Nachtnebel, H.P. (2002) Sequential development of a conceptual hydrological model considering alpine basin processes. In Rizzoli, A. & Jakeman (Edts.), Integrated assessment and decision support. Proc. of IEMSS conference, Lugano, Vol.1, 416-421.
Nachtnebel, H.P., Holzmann, H. (1999) Development of an operational runoff forecasting system for the Austrian Danube basin. (in German), Report to the customer. Vienna.
Peters, D. L., Buttle, J. M., Taylor, C. H., LaZerte, B. D. (1995) Runoff production in a forested, shallow soil, Canadian Shield basin. Water Resources Research. Vol. 31, No. 5, 1291-1304.