F. Hellie, G. Peschke ✝, C. Seidler, D. Niedel
International Graduate School Zittau, Markt 23, D-02763 Zittau, Germany
ABSTRACT
Hydrological processes are characterised by a high degree of spatial variability. Therefore a spatial subdivision of the basins has to be carried out in the preprocessing of distributed precipitation-runoff models. To include more process knowledge into the simulation of runoff production and to overcome the problem of overparameterisation a functional discretisation is necessary. Thus, an expert system (XPS) was developed that allows a process-oriented subdivision of the basins. It offers the possibility to identify spatial units with functional hydrological similarity focussing on the homogeneity of runoff generation processes. In this publication the methodology and the structure of the XPS are presented and a result of its application in a mesoscale basin is shown.
Keywords runoff generation, spatial distribution, regionalisation, expert system
INTRODUCTION
One of the fundamental qualities of the landscape is the regulation of runoff. The runoff generation processes in a catchment are influenced by many different factors and thus, they are characterised by great spatial and temporal variability. Knowledge about the spatial distribution of runoff components and their temporal behaviour is an important basis for regional planning, water conservation, water projects and flood forecasting. It is useful to solve different hydrological problems.
The classical discretisation is based on the tope-principle. Hydrological similarity is often defined only by formal structural similarity based on relief, soils, land use and other geoparameters, and derived from the overlay functions of the GIS. A large number of elementary discretisation units can arise. In the process of up-scaling it is necessary to aggregate these units to reduce their number per se as well as the number of parameters that have to be determined for the precipitation-runoff model. Here we investigate a method for the subdivision of catchments functionally - based on the runoff generation processes.
METHODOLOGY
The required subdivision should be handled by methods of Artificial Intelligence because our knowledge about runoff processes is gained to a considerable extent by empirical knowledge containing significant uncertainties. Therefore an expert system was developed named XPS-FLAB (Peschke et al., 1999;
Zimmermann, 1999) as an instrument of regionalisation in order to identify process-related spatial units with a dominant runoff generation process. We attached great importance to the quick runoff components (Table 1). Basics of the knowledge-based system XPS-FLAB are generally available information, such as maps of soil types, geological formations, land use, stream networks and a digital elevation model.
These data on geofactors and basin features represent fuzzy knowledge. To reach the desired goal of a functional spatial discretisation, two steps are necessary:
1. GIS supported overlay of information to generate the smallest units with identical feature combinations.
2. Classification of these units and aggregation to areas with functional hydrological similarity (up-scaling).
The GIS solves the problem of data acquisition and management and generates the smallest spatial units (topes) from the overlay of different geo-information. The knowledge-based XPS-FLAB represents process understanding (semantic) and determines how a precipitation-runoff model should be parameterised
for simulating basin runoff. Rules and facts evaluate the topes resulting from the GIS application considering the runoff generation process (Fig 1). To derive this rule system we used our own long-time experiences in hydrological experimental work (e.g., Etzenberg, 1998; Peschke et al., 1998; Müller and Peschke, 2000) as well as the investigation results from other teams (e.g., Gutknecht and Kirnbauer, 1996; Kirnbauer and Haas, 1998; Merz and Plate, 1997; Uhlenbrook, 1999; Tilch et al., 2002). Different feature combinations may produce equal runoff components and the units with the same processes are aggregated and represented in a map. Thus, the XPS is a module of qualitative evaluation and classification of landscape elements.
Furthermore it can be coupled with a distributed precipitation-runoff model for a quantitative description of the basin. In a first step, the system indicates dominant processes under given conditions. If the input data (e.g. precipitation features) or the state of the system (e.g. antecedent soil moisture or plant cover) were changed, other processes would become dominant (Fig 3). Threshold values are used to consider this variability, e.g. when the soil moisture surpasses a given value, the interflow process gains dominance or when the precipitation reaches a specific intensity, infiltration gives way to quick overland flow.
spatial varying input data from general available information
than runoff process = delayed interflow
List of results Map of spatial
Fig 1: Structure of the XPS-FLAB.
Table 1: Quick runoff components considered in the XPS-FLAB Runoff components Specification of components
1 Sealed urban areas 2 Partly sealed urban areas 3 Rock areas
4 Areas with small infiltration 1 OVERLAND FLOW
5 Hydrophobe responding areas
2 SATURATION OVERLAND FLOW 1 Overland flow from permanent saturated areas resp.
delayed saturating soils 1 Quick interflow 2 Delayed interflow 3 INTERFLOW
3 Strongly delayed interflow
4 DEEP PERCOLATION 1 Areas with mainly vertical water movement 5 NOT ASSESSED The data cannot be assessed
Fig 2: Distribution of dominant runoff components in the Mandau basin.
RESULTS AND CONCLUSIONS
Application of the XPS-FLAB in a mesoscale basin
The knowledge-based system XPS – still under development – seems to be an appropriate instrument to identify spatial units on which a certain process of runoff generation dominates. It has already been used for several basins with different basic information. As an example, its application in a mesoscale basin is shown here. The Mandau basin (294 km2) is located in the eastern part of Germany at the border with Poland and the Czech Republic (1/3 of the catchment area is in the Czech Republic). The altitude ranges between 230 and 800 m a.s.l. and the slopes reach up to 50%. Agricultural acreage (61%) dominates the land use, only 27% of the area is forested and 11% are settlements. Large parts of the basin are characterised by loess formations, silty soils with small infiltration intensities. Annual precipitation (650 mm) and annual evapotranspiration (550 mm) are similar in magnitude.
Fig 2 shows the spatial subdivision of the Mandau basin created by the expert system. A DEM, maps of land use, soil types and river network were available for the procedure. Together with integrated expert knowledge, the XPS-FLAB was able to assess these input data regarding the runoff generation processes.
However this map shows only one possible situation dependent on the geomorphological conditions in the basin. The dominant runoff generation process can alter with changing conditions. Every raster cell has special, temporally less variable features, such as slope, soil type, land use etc., and these characteristics influence the runoff processes strongly. But the actual dominant process finally depends on the degree of ground cover, the soil moisture and the nature of the precipitation event. Therefore the dominance of a process is variable and time dependent. Within the XPS-FLAB these changes are reflected by threshold values that describe the transfer from one process to another. For instance, Fig 3 shows a rough example of the differentiation of runoff components for a sloped field of luvic stagnosol and bare soil, two soil layers with a higher hydraulic conductivity of the upper one. Hydraulic conductivity is used as a substitute for infiltration intensity. Different potential reactions in the units are possible depending on antecedent soil moisture and rainfall event characteristics. The results can be presented in maps of varying system states.
Besides the list and the map of the identified runoff components, the system generates parameter files for the modeller.
Conclusions
The XPS-FLAB is a combination of strongly abstracted process modelling and the use of comprehensive experimental experience (expert knowledge). The combination of both offers the possibility of analyses based on spatial hydrological responses and on a discretisation of the investigated basin into units of equal runoff generation processes. It enables spatial discretisation, parameter reduction and a process-oriented justification of the parameters of a precipitation-runoff model. Therefore, scale transitions do not affect the transformation of parameters. A change in land use, for example, requires a new evaluation of corresponding spatial types by the XPS-FLAB, but not a new calibration. The area-related identification of runoff components also characterises spatial origins of water and transport paths, thus, it is an important basis for the consideration of the processes of transport of matter.
The developed Knowledge Based System can be enlarged with additional runoff processes if the generation conditions are well known. It seems to be necessary to consider anthropogenic changes like drainages, because they influence the runoff production strongly. In addition to that in the future other input information like remote sensing data should be used in the rule system.
ACKNOWLEDGEMENTS
The basic research has been supported by the “Deutsche Forschungsgemeinschaft” in the focal program
“Regionalisation in hydrology” and further on in the bundle-project “Runoff generation and catchment modelling”.
Fig 3: Differentiation of runoff generation processes by threshold values (rough example) P-precipitation, FC-field capacity, n-porosity, na-replenishing porosity, Θ-soil moisture
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PI > K1 yes
Overland flow no
dry Θ < 0.7 FC
moist 0.7 FC<Θ< 0.9 FC
extremely moist Θ> 0.9 FC
storage strongly delayed
interflow
saturation overland flow na = n-0.9 FC ΣP< na ΣP>na
delayed interflow