PIK Report No. 27

7. Appendix: PIK Available Model Toolbox 1996

7.1 Hydrology Sector


P (Purpose): Daily runoff dynamics for regions, subdivided into vegetation and elevation zones with the components of snow accumulation and melt, soil moisture accounting, runoff generation, routing procedure.
I (Driving Inputs): P (precipitation), T (temperature), E-pot (potential evapotranspiration)
O (Origin): Swedish Meteorological and Hydrological Institute, Norrköpping


P: Reproduction of the hydrological behaviour of catchments by continuous simulation using a specific topographic index.
I: P and, for specific tasks, other climate parameters
O: Univ. of Lancaster, UK


P: Hydrological catchment model EGMO coupled with GIS ARC/INFO. Variable spatial disaggregation by means of GIS-data such as land use, soil types, climate, and vegetation types. The time steps vary between minutes and days partially with automatic step width control.
I: P and, for specific tasks, other climate parameters
O: Inst. f. Wasserwirtschaft (IfW), Berlin
PIK and Büro für Angewandte Hydrologie (BAH), Berlin


P: River profile dependent discharge model with variable time step of minutes to a few hours.
I: P
O: PIK and predecessors


P: Comprehensive modelling system for simulations of water flow, water quality, and sediment transport processes. Time steps vary between minutes and days, areas between single soil profiles and several river catchments.
I: P and, for specific tasks, other climate parameters
O: Danish Hydraulic Institute, Horsholm


P: Watershed model integrating hydrology, vegetation growth, erosion and nutrient balances for areas up to 20,000 km2. The time step is one day, if step width is not automatically controlled. SWIM is based on the two following models SWAT and MATSALU.
I: P, T, W (wind velocity)


P: Watershed model for predictions of effects of management decisions on water, sediments, and chemical loads for rural basins. A daily dynamics is realized.
I: P and other task dependent climate parameters
O: Texas A&M Univ., Temple, TX
USDA ARS, Grassland, Soil and Water Res. Lab., Temple, TX


P: System of four models (water balance, nutrient balance, stream flow, and sea bay nutrient cycling) describing eutrophication effects in the Matsalu Bay coming from a rural watershed.
I: P and other task dependent climate parameters
O: Estonian Acad. of Sci., Tallinn

7.2 Forest Sector


P: Patch model for succession based on annual biomass dynamics and tree species composition including competition and decay processes.
I: P, T, (monthly means or sums), S (hours of sunshine)
O: Univ. of Lund, Sweden


P: same as FORSKA
I: P, T (monthly means)
O: ETH Zürich and PIK


P: Patch model for the daily dynamics of a pine ecosystem, in particular, the growth and death of biomass for the different tree compartments.
I: R, T, P, H, SO2, N
O: PIK and predecessors

7.3 Agricultural Sector


P: The Erosion-Productivity Impact Calculator consists of the components of hydrology (water surface runoff, percolation, lateral subsurface flow, evapotranspiration, water table dynamics), erosion (water, wind), nutrient balance (nitrogen, phosphorus), crop growth (manifold of crops), tillage, plant environment control, economics. The time step is one day.
I: P, T, R, (W, H)
O: USDA-ARS, Grassland Soil and Water Res. Lab., Temple, TX


P: Physiologically detailed crop model for the daily dynamics of ontogenesis, growth, and yield under water stress and nitrogen stress. The model was developed for winter wheat and further extended to a family of crop models by adaptation to crops such as summer wheat, summer barley, winter barley, and winter rye.
I: P, T, R, H, W
O: PIK, ZALF, and predecessors


P: Model for ontogenesis, growth, and yield of wheat and maize.
I: P, T, R, W, H
O: Ritchie


P: Tool box for nitrogen dynamics in soil and plant
O: TU München, Informatik im Pflanzenbau, Freising
GSF Forschungszentrum für Umwelt und Gesundheit, Inst. f. Bodenökologie, Neuherberg


P: The Global Trade Analysis Project is an equilibrium model and provides aggregated information about the influence of global or regional climate changes on demand-supply relations and prices.
O: Univ. of Purdue, Australia


P: Regionalisation of global effects and results under special regional (German) aspects. The amount of agricultural products, the corresponding supply, the real trade amounts and the prices will be calculated.
O: FAL, Inst. f. Marktwirtsch., Braunschweig


P: Agricultural area model for transformation of whole-regional results concerning production, trade, and consumption to subregions and subregion-specific enterprises with area optimization.
O: Univ. Bonn

Crop models available through model bank CAMASE:


P: Patch model for potato growth
Origin: Washington State Univ.


P: Patch model for sugar beet growth with optical and Radar reflection
O: Agricultural Univ. Wageningen


P: Soil-Plant model with daily dynamics of soil water and nitrogen and growth of crops such as winter wheat and summer barley.
I: P, T, R, H, W
O: Royal Vet. and Agric. Univ., Copenhagen


P: Patch model for soy bean yield of the SUCROS model family.
I: P, T, R, W, H
O: Agricultural Univ. of Wageningen


P: Summer barley model of the SUCROS model family including competition and plant density effects.
I: P, T, R, W, H
O: Agricultural Univ. of Wageningen


P: Winter wheat model of the SUCROS model family for regional effects of temperature and CO2.
I: P, T, R, W, H
O: Agricultural Univ. of Wageningen


P: Patch model for winter wheat growth including optical and Radar reflection.
I: P, T, R, W, H
O: Agricultural Univ. of Wageningen


P: Model family for daily or hourly growth dynamics of crops such as wheat, maize, and barley.
I: P, T, R, W, H
O Univ. of Alberta, Dept. of Soil Science, Edmonton

7.4 Modelling, Simulation, Decision Support Systems


P: Modelling and simulation systems for ecological and related models with hierarchical structure, macro- and vector processes and ecological interpretation of the phenomena to be modelled, which internally are treated as terms of difference or recurrence equations
O: PIK and predecessors


P: Universal system for multicriteria analysis of problems with an assigned set of alternatives and a set of rating criteria. An expert system for choosing and applying the best of eight basic methods.
O: Economy Univ. Prague


P: Developmental environment for the design of expert systems or AI-supported object-oriented models.
O: Neuron Data, Palo Alto, CA

7.5 Global Models


P: Global interrelations between climate (atmosphere and ocean), biosphere (terrestrial environment) and society (energy and industry). It is a substantially extended version of IMAGE 1 with concern to geographically explicit modelled biosphere and the feedback mechanisms, in particular, climate feedback.
I: Demographic, economic, technological parameters, and control policies
O: RIVM, Bilthoven (NL)


P: Translated MOSES-version of the famous global model with the same name mapping interrelations between the five dynamic components population and health, agriculture and food production, capital and industrial production, non-renewable resources, and persistent pollution.
O: MIT, Boston; PIK

FBM 2.1

P: The Frankfurt Biosphere model (FBM) is a process oriented global vegetation model on a 0.5ox0.5o grid to compute the net primary productivity (NPP) and standing biomasses dependent on climate and soil properties. The basic differential equations are parameterized according to the vegetation type.
I: Time series of temperature, precipitation, and solar radiation in a monthly resolution, water holding capacity of the soil, global vegetation distribution (ecosystem map).
O: Inst. for Physical and Theoretical Chemistry, Univ. Frankfurt

PLAI 1.0

P: PLAI computes the vegetation dynamics (so far mainly short term dynamics, mainly derived from the FBM, i.e. with the same spatial resolution) dependent on climate and soil properties and out of these results the vegetation distribution. Plant functional types (PFT) are used instead of biomes. Simple rules yield in a ratio of these PFTs. PLAI is available on the SP2 parallel computer.
I: Time series of temperature, precipitation, and solar radiation in a monthly resolution, water holding capacity of the soil.


P: Rule based model to relate the climate to the corresponding equilibrium vegetation distribution which is given in terms of 19 biomes, optional also the distribution of potential agricultural regions.
I: see PLAI (2)
O: C. Prentice, W. Cramer, et al.


P: Simple assessment of the water balance in the soil. Computation of soil moisture and evapotranspiration by use of the Priestly-Taylor approach. Available are one and two bucket models corresponding to the number of soil layers taken into account.
I: Soil water holding capacity, temperature, precipitation, solar radiation.
O: Submodels of PLAI and BIOME


P: Computation of the global radiation sum according to Richter et al. in hourly and daily resolution as well as day length.
I: Cloudiness or sunshine duration, latitude, day.
O: Submodel of FBM and PLAI


P: Coupled climate-ocean-vegetation equilibrium model to compute short-term as well as long term dynamics of the Earth System. Using three oceanographic subcells (Indian, Pacific, Atlantic, connected by the Southern.....), an atmospheric division into 10 by 7 cells (latitude-longitude), and a continuous indication of the vegetation within a subcell, CLIMBER is able to compute climate evolutions with sufficient speed and precision.
I: Human GHG-emissions, solar radiation.
O: BBM/GAIA-Project, PIK


P: Qualitative model for a global assessment of the natural conditions for agriculture in terms of a marginality index (0.5ox0.5o grid). NATMARG uses a Fuzzy-Logic algorithm based on a evaluation tree obtained by expert opinions.
I: NPP obtained by averaging the results of five global vegetation models, soil fertility, evapotranspiration, slopes, irrigation capacity by ground and surface water, precipitation variability.


P: Multiregional model for demographic development and migration.
I: simple demographic parameters (fertility, mortality)


P: Two regional (developed and marginal regions) models for the simulation of the use and management of freshwater resources. The regions are characterized by their economic, demographic, cultural, political, hydrological and climatic characteristics and are coupled via migration from the marginal to the developed region.
I: Indicators for the above mentioned characteristics, possibly as time signals.
O: U. Luterbacher, Graduate Inst. of Internat. Studies, Geneva, CH.

7.6 Integrated Assessment models of Climate Change


P: The Edmonds-Reilly Model is a multiregional energy-CO2 model to obtain the emissions of carbon dioxide in dependence on socio-economic conditions. The regions are divided according to the structure of the energy system which is parameterized within the model.
I: functions for the demographic and economic growth as well as for the energy prices.
O: J. Edmonds & W. Reilly, Battelle Pacific Northwest Lab., Washington D.C.


P: Fully integrated assessment model which computes the avoidance costs as well as the damage costs of climate change. Based on the GLOBAL2200 model which is a general equilibrium model applied to six regions, for the ,emissions part' of MERGE, it includes simple climate and damage assessment modules.
I: MERGE requires explicit assumptions for the population and productivity growth as well as for energy distribution and savings.
O: Manne, Richels, and Mendelssohn


P: MiniCAM (Mini Climate change Assessment Model) is based on the ERM (1) and the damage part of the MERGE model (2). Further submodules used are:
a) MAGICC: a simple climate-carbon-cycle model based on an upwelling-diffusion climate model and a sea level rise model, which takes into account glaciers, the Greenland ice shelf, and the Antarctic. MAGICC explicitly allows the consideration of different greenhouse gases.
b) SCENGEN: a model to obtain the regional distribution of the damage of climate change, reproducing a possible set of regional patterns which correspond to different GCM results.
The MiniCAM computes the GHG-emissions, temperature change, sea level rise, and market and non-market damage of climate change.
I: Besides the input necessary for the above-mentioned modules (1) and (2), it requires the specification of the development of energy technologies.
O: J. Edmonds, Battelle Pacific Northwest Laboratory, Washington D.C.


P: DICE (Dynamic Integrated Climate-Economy model) is a fully integrated optimal growth model which allows optimal emission reduction profiles under different scenarios to be computed. It is a global model without any regional disaggregation.
I: Cost functions for emission reduction and damages of climate change, population and productivity growth, development of carbon intensity. Investments and emission reductions are used as policy variables (control).


P: PAGE (Policy Analysis for the Greenhouse Effect) is a multiregional (four regions) assessment model to obtain the costs of predescribed climate protection strategies. PAGE takes into account uncertainties by explicitly modelling simple probability distributions.
I: Climate Protection Strategies, GHG-emission profiles.
O: C.Hope et al., for the Environment, Nuclear Safety and Civil Protection Directorate of the Commission of the European Communities.


P: Taking into account novel approaches in fuzzy decision theories, FUZZY computes emission profiles and corresponding optimal economic growth.
I: Cost functions, population and productivity growth.
O: M. Leimbach, PIK.