Developing and using models

Computer-based models are a well-established scientific approach to describe and understand a complex system. In understanding what models can do and what their limitations are, it is necessary to differentiate between:

1. Models which provide a simplified representation of a selected part of reality. The selection can be spatial, describing a specific region, and/or topical, describing a specific issue like climate. A choice is made on the level of detail, often depending on data availability.

2. Scenarios which use models to help assess (‘project’) future developments and their likelihoods or test management options and assess their effects (e.g. impacts of land use change on the climate).

Basically modelling can provide an insight into the interactions of socio-economic and natural systems related to land management.

Because of the complexity, separate models describing various aspects of the system (e.g. carbon cycle, carbon sequestration, erosion, ecosystem services, water cycle, socio-economics and labour) are combined. This integrated ‘model architecture’ can be applied either at the local level, investigating different land man-agement practices and climate change scenarios, and/or at the landscape level, where models have the potential to integrate local land management practices and show the impacts of their combi-nations within the larger landscape.

A typical challenge for research and the use of models is to address complex interactions and dependencies within a water-shed; for example of reservoirs built and the water storage and release regulated for downstream users. There will be questions such as: What is the amount of sediment inflow into the reservoir?

What are the consequences on the storage and the downstream sediment load? What is the minimum water flow for downstream users? What is the optimal balance between hydropower gen-eration and water availability for irrigation during dry seasons?

And: How is it possible to regulate and minimize the damaging impacts of floods and droughts? Each of these can be addressed with appropriate models.

Models based on quantitative data can be used, for example, for the description of the effects of different land management practices on productivity, soil biodiversity, and water demands.

Chapter 6     The contribution of research 131

Models based on qualitative data are used to visualize complex interrelationships amongst users, decision-makers, resources, and regulations (e.g. ‘constellation analysis’, see Approach page 175 and Video) in order to detect driving forces and barriers.

When new models are developed, or existing ones are combined, the time needed for model testing and calibration/ validation, and actually running scenarios is often underestimated. It can take years and considerable effort to develop and adapt models – and get reliable results.

For the incorporation of knowledge from local or regional stake-holders in model development repeated rounds of workshops are necessary. These workshops should also support trust building in the modelling exercise and its results.

Models are used for specific purposes and thus have to be robust enough to produce reliable results but also reflect the complex-ity needed. Therefore, prioritization is important. New and better structured models can improve our understanding of complexity (with suitable model architecture). Alternatively, existing models with newly developed user-friendly applications can fit the pur-pose. In implementation-oriented research (IOR), modelling can be used as a tool to facilitate collective decision-making processes and to foster knowledge exchange between different stakeholder groups. When discussing a model during workshops, stakeholders from various backgrounds tend to develop a deeper, joint, under-standing of the cross-cutting nature of land management. As a longer-term planning tool scientific models are often used as a basis for Decision Support Systems (DSS) (see Chapter 6 page 137).

The following gives an overview of the range of possibilities of modelling by describing examples used in very different contexts.

Combining existing models to assess land management options in a river basin

This example demonstrates the variety of already existing models capturing different aspects of the overall land management sys-tem of the São Francisco River Basin, Brazil. The models were com-bined to better understand water, land use, climate interactions in the region, and to make facts and their interrelationships more transparent as a basis for decisions-making (Table 6.1).

Some of these models are aimed at decision-making based on existing simulations. This category of models concerns topics and challenges such as water quantity (SWIM), hydro-economic condi-tions, hydrodynamic calculacondi-tions, water quality (Moneris), global land use (MAgPIE), and biodiversity modelling with MaxEnt.

SWIM simulates future water quantities and river flow under dif-ferent land use patterns (MAgPIE results) and includes difdif-ferent climate change scenarios (Figure 6.1). The aim of these modelling efforts is to better estimate how much water will be available for agricultural irrigation, hydropower generation and other uses in the future. A model-based upstream/ downstream water manage-ment system has been proposed for improved allocation of water, including different options for integrating the ecosystem of the region into the model as a ‘water user without a voice’. All simula-tions are exemplary calculasimula-tions, meant to feed into the prioritiza-tion for water allocaprioritiza-tion and resultant decision-making.

How models are combined depends on the questions that they need to answer. For example, a specific model architecture was built to assess impacts of land use change on soil productivity and fertility, biomass production, watershed functions and envi-ronmental services in small mountainous catchments in southern China. LUCIA (Land Use Change Impact Assessment) is a dynamic and spatially explicit landscape-scale model (http://lucia.uni-hohenheim.de). Emphasis was on material flows in the landscape that connect upland/ upstream and lowland/ downstream areas.

The model architecture consists of five main modules:

• Hydrology/ soil water

• Soil nutrients

• Organic matter decomposition

• Plant growth

• Land use and management options.

Assessing impacts of climate change on biodiversity and eco-system services

Models can also be used to assess future environmental con-ditions and their impact on species distribution and ecosystem service provision; for example in relation to climate change and sea level rise. Here modelling can be an important tool to bet-ter understand the challenges that may arise in the future, and to evaluate possible solutions.

In a collaborative German modelling project the following mech-anism has been established: as a first step, climate simulations based on different emission scenarios, as well as assumed sea level rises, are used to determine the hydrological conditions in the region simulated for the period from 2010-2100. In a second step, species distribution models project the occurrence of plant species in the landscape, based on the modelled hydrological con-ditions, the known (and assumed constant) soil characteristics and probable land uses. The models thus integrate data from differ-ent sources of the inter- and transdisciplinary work, and quantify the spatial and time-bound conditions. In a final step, the future hydrological conditions, land use and plant species distributions are used to calculate the ecosystem services (Figure 6.2). Thus, changes over time as well as trade-offs and synergies of ecosys-tem services can be analysed for the whole scenario period of 90 years.

Model name What it describes What it can be used for

‘MAgPIE’ Global land use allocation influenced by population growth, climate change, trade (São Francisco River Basin in high resolution)

Designing plausible scenarios for future land use pattern (e.g. input into SWIM, hydro-economic model, Moneris)

‘SWIM’ Climate and land use change impacting water availability in the São Francisco River Basin

Testing management scenar-ios: how much water is available if managing reservoirs in a spe-cific way, or, what management is required to reach a target (and discharges as input into hydro-economic, hydrodynamic and Moneris models)

‘Moneris’ Nitrogen and Phosphorus emissions into the São Francisco River Basin

Detecting dominant sources of emissions in order to direct action at the major polluters Hydrodynamic

models 2D or 3D representation of water dynamics at different scales

Estimating the retention time of pollution and direction of dilution movement in a given stretch of the water body (e.g.

how will pollution spread when starting new net-cage aqua-culture?)

Hydro-eco-nomic models

Water demand under cur-rent/ future land use pat-terns, water infrastructure and technical, environ-mental, and institutional constraints (semi-arid region of the São Fran-cisco River Basin)

Suggesting an economically efficient water allocation based on the economic value of water and evaluating economic effects of operational reservoir rules, environmental flows and institutional constraints

‘MaxEnt’

biodiversity model

Species habitat and species distribution for the area of one specific natural biome (Caatinga)

Identifying patches for prioritization of conservation (hotspots or coldspots)

Table 6.1: Different models applied in the São Francisco River Basin, Brazil

Based on this chain of models it is possible to analyse the effects of a changing climate, sea level rise and changing land use on eco-system services. This helps us to understand how sensitive a land-scape is to environmental and land use changes, and enables an analysis of possible solutions to address changing climatic condi-tions over a long period of time time (http://geoportal-glues.ufz.

de/comtess_app.html).

Combining models at different scales

To study the hydrogeology in south-west Madagascar, where water scarcity is one of the key challenges, it was essential to distinguish between large-scale hydrogeology in an area of about 40,000 km² and small-scale hydrogeology in selected villages and their sur-rounding area (100 km²). At the larger scale, rough estimates and information from literature were used to develop a general under-standing of the hydrogeology of the area (Figure 6.3). In the tar-get villages of the project, detailed investigations combined with framework conditions calculated by the large-scale model permit-ted estimates of the local hydrogeology. General hydrogeological methods and assumptions were combined with the results of spe-cific field studies, especially focussing on wells and groundwater.

Groundwater levels vary considerably with the geological condi-tions providing varying accessibility to water for the people living in the area. Three target villages in different geological areas were studied in more detail to assess groundwater levels and recharge as well as rainwater availability during the rainy and dry season (Figure 6.4). On the plateau, the major aquifer is only accessible through deep drilling and pumping stations. Alternative water

Figure 6.1: Overview of the model architecture for the São Francisco River Basin, showing the combination and interaction of the models described in Table 6.1, covering land management and water aspects across different scales, and information flow between the models. Local investigations are carried out by subprojects (TP/SP) (Hattermann et al. in preparation).

sources are small, very localized, ‘perched aquifers’ (Figure 6.4 and 6.5). In areas where aquifers are too deep for wells, rainwa-ter is collected during the rainy season in local catchments (sihan-aka) which provide water for about two months for daily drinking, cooking and washing  – as well as for watering livestock. In the coastal areas, groundwater is easily accessible but if it is overused the region is prone to salt water intrusion which makes water unsuitable for human or agricultural use. The small-scale models help in understanding the specific situation, and indicate under which conditions water availability can be improved by appropri-ate methods.

Empirical Agent-based Land-use Modelling

It is not only natural conditions that can be modelled, but human behaviour too. This method is based on ‘agents’ (people in spe-cific roles, e.g. farmers) whose decisions in reaction to changing environments determine the development of the land use. The agent-based model SEALM (SuLaMa Empirical Agent-based Land-use Model) was Land-used in Madagascar to understand the complex interactions and feedback loops between land use and land cover changes (LULCC), deforestation processes and ecosystem services.

It allows the simulation of possible future trends in land use and explores smallholder farmers’ coping strategies with respect to food insecurity. The modelling results are helpful to identify hot-spot areas of LULCC and forest fragmentation in time and space.

The model consists of different types of ‘entities’, which include the landscape, households, livestock and climate (Figure 6.6). For the design of those entities, a wide range of data was used, incor-porating social surveys, high-resolution remote sensing and field-based validation data.

Chapter 6     The contribution of research 133

Figure 6.2: The COMTESS SVT (synthesis and visualizing tool) to present spatial and temporal explicit data. The tool can show distribution for selected species under different climate scenarios and sea level rise scenarios for different land management options. Here: Peninsula Michaelsdorf (German Baltic coast) in the year 2085 under climate scenario SRES A2 for a land use ‘trend/ business-as-usual’ scenario and a sea level rise of 1.05 m (GWP = global warming potential). (COMTESS)

The model represents six important steps carried out by ‘household agents’ – the farmers – related to land use: (i) ex-ante planning and labour allocation (before action is taken); (ii) field extension;

(iii) field preparation; (iv) crop cultivation; (v) harvest; and (vi) ex-post-planning (reflecting the land use decisions after harvest) and selection of coping strategies for the next cycle. For each step, households may use different adaptation mechanisms to avoid food insecurity and increase household income: increasing or reducing the area of cultivated land depending on available capital and energy requirements, changing the allocation of their agricul-tural fields or altering their coping strategy dependent on available livestock, farm-income and capital.

Within a geographical information system (GIS), maps related to bio-physical and socio-economic data were compiled from exist-ing information and field surveys (e.g. land use, land cover, soil quality, biomass, crop yield and land ownership). Forests were additionally characterized by information relating to the forest use potential (e.g. biomass stock and growth rate), which can be determined through remote sensing and forest inventory data.

The livestock module simulates different grazing and herd man-agement strategies and the resulting effects on vegetation and land cover changes.

Global driving forces are selected that directly affect the state of the model variables and household activities such as population dynamics, climate conditions, protection of forest resources and fallow periods. During model set-up, the global variables can be changed in the user interface to simulate multiple scenarios (e.g.

different climate scenarios, population increase or different crop management strategies). Simulation outputs are explicit concern-ing space and time, and include maps of the landscape enablconcern-ing the analysis of habitat fragmentation, changes in forest area and biomass stocks. Socio-economic outputs include food security (food self-sufficiency), crop yields, household income, availabil-ity of fuel and construction wood, and related coping strategies.

The simulated scenarios and the underlying data of all maps and graphs can be exported to electronic files for further analyses and interpretation.