GIS and Hydrological Models as Tools for Habitat Modelling in Alluvial Plains

Stefan TAEGER

1 Introduction

The coupling of Geographic Information Systems and hydrological models creates a very powerful tool for processing fundamental ecological data and for the modelling of riparian zones (SÖNDGERATH ET AL. 2001, RIEDL ET AL. 2005). They can be utilised in addition to other applications to create, in particular, habitat models that can represent and simulate the relationship between the target species of conservation and their habitat. Thus a tool is available for landscape planning that not only allows planning-relevant species occurrences to be documented and evaluated, but furthermore, is also able to represent functional relationships between species occurrences and habitat structures.

The following article shows the design of such a model on the example of animal-ecological habitat models for the target species of Avifauna in a section of the biosphere reserve “Spreewald” in Brandenburg. The aim was to create habitat models that can depict the habitat attachment of selected bird species to biotope types and functions. Moreover, the models should be able to prognosticate the impact of management options (agriculture, water management) on habitat suitability. The focus of this paper is on the presentation of the model components, the integration of the fundamental data in the various system components and the design of the overall model.

The study area lies in the Spree lowland. It has an area of approximately 500 hectares and is completely enclosed by an inland dyke. In this way, it is possible to regulate the water level of the internal water system, depending on water levels in the surrounding watercourse. The flow of incoming water to the area is regulated by a weir in the east; the outlet is in the west. Between these two barrages is a height difference of only 1.5 m resulting in very low flow gradient of max. 0.3 ‰ (see RIEDL & TAEGER 2009).

2 Modelling Approach

2.1 Overview

The overall model can depict both, the habitat suitability as well as the impact of various management options in the local planning scale. That means the scaling model must, with the aid of all model components (species selection, fundamental data, results), be focused on the provision of concrete results for the areas. In addition, the model should be developed especially for use in lowland landscapes in which the hydrological balance and agricultural use are the dominant location factors. This results in a modular structure in which the key components are a GIS-based analysis to determine the habitat preferences of the investigated bird species and a hydrological model to simulate the hydrologic

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balance. The results from the two sub-models are combined in a third module which calculates future habitat suitability based on scenarios. These results are in turn delivered to a GIS for display. An overview of the model design is provided in Figure 1.

Fig. 1: Model components and working cycles within the project

2.2 The model components: Methods and Materials

2.2.1 Model Part 1: Analysis of habitat preferences

The habitat preferences are identified on the basis of an index-based procedure (Electivity-Index, BLASCHKE 1997, JUST 2005). The potential key factors for the analysis of habitat preferences are deterministically selected, whereby the selection is oriented towards known habitat preferences of the species. A variable selection following statistical methods does not take place. The following fundamental data are included in the model:

Brooding evidence from territory mapping Biotope types (according to biotope mapping of Brandenburg) Small structural types of open land and woodland (e.g. grassland structure, coverage,

proportion of dead wood, shrub layer) Selected landscape metrics

Spatial superposition of brooding evidence and all the available data concerning the area characteristics of potential key factors is used to determine which of the factors is relevant for explaining the occurrences of the species. A central part of the analysis is the evaluation of biotope types. This also explicitly takes into account spatial parameters such as location, shape and distribution of the biotope types. Hereby selected species-specific Landscape Metrics (LSM) are applied. The sub-model provides statements on current habitat preferences of the selected species. As a result, the preference indices of the investigated species are available for each biotope type. These form the basis for the simulation of the expected future habitat suitability following transitions in land use.

2.2.2 Model Part 2: Modelling the hydrological balance

To calculate the hydrological balance influenced by ground and surface water factors numerical models are the standard. Two-or three-dimensional modelling represent the current standard in the field of groundwater models, while surface runoff is typically

GIS: Analysis of the habitat preferences the bird species for conservation 1

Hydrologic Model: modelling of the hydrological balance and outlet 2

Prediction model: Simulation of future conditions of nature and Landscape (Scenarios) 3

GIS: Visualisation of future habitat suitability 4

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represented using one-or two-dimensional models. As a rule, the influence of groundwater in the calculation of surface runoff is neglected.

The study area has very high groundwater levels and a watercourse with low flow gradient. In addition, large areas are completely inundated during the winter months. Due to the intensive interaction between groundwater and surface water it is necessary to link groundwater and surface water models here. The construction of the coupled hydrological model is based on the following data:

Terrain levels from laser scan DTM Cross-sections of the watercourse Ground water level (eight monitoring stations in the study area) Water levels of the external watercourse as a boundary condition for the model Land uses in the study area (taking into account hydrological unevenness)

The coupled model is based on a computational grid, which reflects the topographical structure of the project area, and in particular effective hydrologically active structures such as watercourses, dams and depressions. The entire flow space of the models will be subdivided by so-called “finite elements” in the form of a three-or four-sided grid structure (see Fig. 2).

At each node, the hydrological parameters of water level, flow velocity and duration of inundation are calculated. As a result, comprehensive information, covering the whole area, is available about water levels, water depth, depth to water table and inundation duration. These values can now be calculated for selectable variants of flood management (see Fig. 3).

 Fig. 2: Calculation of the surface drainage network (Source: KLAUKE 2005)

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Fig. 3: Calculated water levels to represent flooded areas (Source: KLAUKE 2005)

2.2.3 Model Part 3: simulation and forecasting of future habitat suitability

A central goal of the model development is a representation of the effects of different management options on the habitat suitability for the investigated species. The sub-model for the simulation has to assimilate the data from the sub-models 1 (biotope types for representing habitat suitability) and sub-model 2 (results of the hydrological model). The simulation model has the ability to calculate future hydrological balances based on defined progression lines. Based on the altered site conditions, future peculiarities of the biotopes are predicted and used as the basis for the representation of future habitat suitability.

The first step for the simulation of future habitat quality is the assessment of possible trends regarding agricultural use and water management. Five development scenarios are to be established which depict the different management approaches, particularly with regard to inundation management and agricultural intensity. All possible tendencies are taken into account, from possible usage intensification to the complete cessation of agricultural activities.  

For each of the five scenarios a simplified idealised hydrograph of the ground water level (see Section 2.2.2) were developed based on the actual water level hydrographs. Based on these parameters, the hydraulic “water level” and “inundation duration” are calculated for each scenario and integrated into the simulation model.

Parallel to this, a development series for a period of up to 25 years is to be set up for all habitat types occurring in the study area based on the hydrological boundary conditions and the agricultural usage and intensity (see Fig. 4). The foundation for this is formed from examples in the literature (especially GOEBEL 1996) and reference areas, which already have comparable local hydrological parameters and uses (“Space for time substitution”).

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Hydrology Agriculture

original biotope type 2005 (actual state)

Changes in the ground water level relative to the actual state

Exceeding biotope specific maximum inundation depths and duration for changing water levels

adjustment of agricultural use (intensification – curtailment) to changes in hydrological balance

Exceeding a biotope-dependent “reaction time” referenced to changes in the hydrological balance development of a modified Biotope

Biotope type future (2010, 2030 – Forecast)

Fig. 4: Factors influencing developmental series of biotopes for polder management

The simulation model was developed for the research project by using a DELPHI programming environment (see RATHKE, 2005). The transference of fundamental data from the sub-models 1 and 2 happens in ASCII text format. In the simulation model, the entire study area is in the form of a computational grid similar to that of the hydrological model. Fig 5 shows a detail from the network using the example of the area in the south east of the study area at the inlet structure (weir).

The water courses, based on triangular elements and the biotope boundaries become visible in the computational grid (Fig. 5 centre). The hydrological variations for each development scenario are calculated for each base geometry (about 2 million triangles with edge lengths

 

Fig. 5: Surface model (centre) with corresponding area of biotope types (left) and aerial photo (above right) (Source: RATHKE 2005 modified)

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of 1 - 5 m depending on the topography). Concurrently, the changes in the actual biotopes dependant on the above mentioned factors for each triangular element are shown. A forecast of up to 25 years in variable time steps can be carried out in this way. Thus, the temporal structural dynamics of biotope coverage as a developmental series becomes visible for the entire study area. This procedure is repeated for each species studied and each scenario.

2.2.4 Model Part 4: Visualisation of results

The vector data is imported into a desktop GIS and, after a review of the geometries, visualised in the form of habitat suitability maps based on biotopes. The maps then show the present and future potential suitability as breeding habitats in three classes (ideal, generally suitable or unsuitable) for each scenario in variably selectable time increments.

3 Results

As a result, large-scale and tangible area representations of habitat suitability are available with an associated three-stage key (see above). The habitat suitability is essentially represented by means of biotopes (see Fig. 6). However, it is also possible to consider species-specific other key factors such as small structure types, providing comprehensive coverage is available. The example given here is the additional use of landscape structure dimensions (landscape metrics, e.g. edge density, structural diversity). Taking them into account significantly increases the validity of habitat models for species known to colonize border structures and Ecotones.

Figure 6 shows the development of habitat suitability for Emberiza schoeniclus which prefers mosaics of reed swamps, hygrophilic scrubs and abandoned pastures. The scenario with intensive agriculture and lowering of groundwater table causes a drop in suitable breeding habitats. The scenario with cessation of agriculture and waterlogging shows an increasing amount of suitable breeding habitats, especially in the lower regions where damp meadows develop into reed swamps and structural diversity increases.

Fig. 6: Habitat suitability map for Emberiza schoeniclus (reed bunting): Status Quo 2005 (left), forecast 2030 – scenario with intensive agriculture (middle), forecast 2030 – scenario with cessation of agriculture and waterlogging (right)

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4 Conclusions and Outlook

The results show that the presented conceptual model can plausibly and comprehensibly portray potential habitat suitability for the investigated species based on a few key factors. The method presented is particularly suitable for projects where only a few key factors (due to project resources, issues or data-location) are to be examined for their relevance for habitat suitability. The simulation of future changes in habitat suitability displays long-lasting processes particularly well. Short term effectual changes in biotope quality and thus the habitat suitability are less visible. The reason for this is to be found primarily in the selection of the investigated species, not the model design. All investigated species were adopted to landscape structures which need several years to develop. Waders for example, which are able to make short term changes in damp meadows visible, were underrepresented in the study.

A technical enhancement of the model can be performed by optimising the interfaces between the “heart of the model”, the forecasting software “HabiMod” and the GIS. As mentioned above, the fundamental data for the simulation is loaded as ASCII data. The results of the simulation are passed on to the GIS as vector data. The manual import of data is generally very expensive. Furthermore the export of over 2 million constituent areas of the presented study area further burdened the system stability when read into the GIS. Not only is it necessary to automate the transfer routines, which is computationally easy to implement. Moreover, the underlying computational grid (see Section 2.2.3) for future applications should have a lower resolution. Recent experiences have shown that the resolution of the basic geometries (Fig. 6) chosen, at least for this study of the bird species, was too high.

The model design is generally suitable for use in natural areas, where the hydrological balance and agricultural use are the decisive location factors. The problem lies in the availability of suitable fundamental data for modelling. All available data for the study area was not suitable as a model bases relating either to the content and spatial resolution or the currency of the data. The further establishment of habitat models in practice, however, still requires more stringent evaluation of existing data, for example from ongoing monitoring programs. In Germany for example, the monitoring program of biodiversity in Northrhine-Westfalia, called “ecological area sample” delivers data of biotope types and the occurrence of bird species in suitable scales. It would be desirable to provide comparable databases for all relevant natural landscape units in Germany as a reference frame for large scale habitat modelling.

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