Model Acronym: SWIM

Model Full Name: Soil and Water Integrated Model


Main medium: terrestrial
Main subject: hydrology, biogeochemistry, vegetation
Organization level: ecosystems
Keywords: river basin, spatially distributed, multiple sub-basins, multiple hydrotopes, three-level spatial disaggregation, runoff, groundwater, water quality, crop yield, nutrient cycling, nutrient transport, erosion, sediment transport, climate change, land use change, continuous-time.



Valentina Krysanova
Potsdam Institute for Climate Impact Research
P.O.Box 601203, Telegrafenberg
14412 Potsdam, Germany
phone: +49-(0)331-288-2515
fax: +49-(0)331-288-2600


SWIM: Valentina Krysanova & Frank Wechsung, Potsdam Institute for Climate Impact Research
SWAT-Modules: J.G. Arnold, P.M. Allen, G.T. Bernhardt, R. Srinivasan, R.S. Muttiah, C. Walker, P.T. Dyke, 1993, USDA & Texas A&M University
MATSALU-Modules: V. Krysanova, A. Meiner, J. Roosaare, A. Vasilyev, 1989, Estonian Ac. Sci.


A1. Model Overview


SWIM (Soil and Water Integrated Model) is a continuous-time spatially distributed river basin model, simulating hydrology, vegetation, erosion and nutrients (nitrogen, N, and phosphorus, P) (Fig. 1). The model can be applied to river basins with the area from 100 to 10000 km2, or (after validation in representative sub-basins) to regions of that order of magnitude. The model is described in Krysanova et al., 1998.




Fig. 1 Flow chart of the SWIM model, integrating hydrological processes, crop/vegetation growth and nutrient dynamics


A three-level disaggregation scheme (Fig. 2) plus a vertical subdivision into a maximum of 10 soil layers are implemented in the SWIM model for mesoscale basins: "basin - sub-basins – hydrotopes". A mesoscale basin is firstly subdivided into sub-basins of a reasonable average area and after that hydrotopes are delineated within every sub-basin, based on land use and soil types. A hydrotope is a class of elementary units in the sub-basin, which have the same land use and soil type. Climate is homogeneous at the second level of disaggregation - for sub-basins.



Fig 2. Three level disaggregation scheme 'basin - sub-basins – hydrotopes‘ implemented in SWIM



In the case of regional applications, if the boundaries of the region do not coincide with boundaries of any river basin, Thiessen polygons for climate stations (or grid cells of a reasonable area) can be considered as the second level of disaggregation instead of sub-basins.


SWIM is coupled to the Geographic Information System GRASS (Geographic Resources Analysis Support System) to extract spatially distributed parameters of elevation, land use, soil types, and groundwater table. If spatially-distributed input data like Digital Elevation Model, soil and land use maps are available in the ARC/INFO format, they can be easily transferred to GRASS. The SWIM/GRASS interface supplies necessary input files for the basin and sub-basins.


A2. Model history


SWIM was developed in the Potsdam Institute for Climate Impact Research on the basis of two other models:

·        SWAT (Arnold et al., 1993 & 1994) and

·        MATSALU (Krysanova et al., 1989).


The SWAT model was developed in the Blackland Research Center (USDA ARS, Temple, Texas) to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions. To satisfy this objective, the model is process-based, uses readily available inputs, is computationally efficient, and enables users to study long-term impacts. SWAT incorporates features of several ARS models and is a direct outgrowth of the SWRRB model. Specific models that contributed significantly to the development of SWAT were CREAMS, GLEAMS, and EPIC. The MATSALU model developed in Estonia on the basis of CREAMS has the similar structure as SWAT.


The model SWIM was developed with an intention to incorporate the best features of both SWAT and MATSALU and to be transferable to other basins in Europe. During the last three years SWIM was extensively tested and validated in a number of mesoscale basins in Germany (mainly belonging to the Elbe river drainage area) regarding different processes – hydrological, vegetation, nutrients and erosion. Though still SWIM has many common modules with SWAT, there are certain essential differences, like the three-level spatial disaggregation scheme implemented in SWIM, a new water routing routine based on the Muskingum method, and a new method for the adjustment of net photosynthesis and evapotranspiration to higher CO2. Besides that, validation of SWIM with the daily time step is an advantage in comparison with SWAT, which is usually validated only with monthly and annual time steps.


Model structure


The SWIM model includes hydrological processes coupled to nutrient cycles, crop/vegetation growth, and erosion.


B1. Hydrological processes


The simulated hydrological system (Fig. 3) consists of four control volumes: the soil surface, the root zone, the shallow aquifer, and the deep aquifer. The soil column is subdivided into several layers in accordance with the soil database. The water balance for the soil column includes precipitation, surface runoff, evapotranspiration, percolation and subsurface runoff. The water balance for the shallow aquifer includes groundwater recharge, capillary rise to the soil profile, lateral flow, and percolation to the deep aquifer.




Fig. 3 Flow chart of hydrological processes in soil as implemented in SWIM


Surface runoff is estimated as a non-linear function of precipitation and a retention coefficient, which depends on soil water content, land use and soil type (modification of the Soil Conservation Service (SCS) curve number method, Arnold et al., 1990). The method was adapted to German conditions by validation in seven mesoscale river basins of different size (all in the Elbe drainage area) and with different climatic conditions, land use and soils. Besides, it is possible to use another common method for runoff simulation in SWIM: to calculate runoff as a function of precipitation and soil saturation independently of land use and soil type. Lateral subsurface flow (or interflow) is calculated simultaneously with percolation. It appears when the storage in any soil layer exceeds field capacity after percolation and is especially important for soils having impermeable or less permeable layer below several permeable ones. Potential evapotranspiration is estimated using the method of Priestley-Taylor. Actual evaporation from soil and actual transpiration by plants are calculated separately.


B2. The nutrient module


The nitrogen and phosphorus modules (Fig. 4) include the following pools: nitrate nitrogen, active and stable organic nitrogen, organic nitrogen in the plant residue, labile phosphorus, active and stable mineral phosphorus, organic phosphorus, and phosphorus in the plant residue, and the flows: mineralisation, fertilization, plant uptake, lateral flows with surface and subsurface runoff, leaching into ground water, loss with erosion, sorption/desorption of P, and denitrification for N. Regarding the lateral transport, the runoff and leaching are more important for nitrogen than for phosphorus. The latter is mainly transported with erosion.




Fig 4 Nitrogen and phosphorus flow charts as implemented in SWIM


Amounts of nitrogen contained in direct runoff, lateral subsurface flow, and leaching from soil profile are estimated as the products of the volume of water and the average daily concentration. Organic N transport by sediments and sediment transport of P are simulated with a ‚loading function‘ of J. Williams (Arnold et al., 1994) from the sediment yield, concentration of P or organic N in the top soil layer, and the enrichment ratio. The latter is logarithmically related to sediment concentration.


A robust approach was suggested for modelling nutrient dynamics in mesoscale basins (Krysanova et al., 1998) and it was applied for modelling nitrogen dynamics in the Stepenitz and Zschopau basins with SWIM. The robust approach has the following requirements:

  1. the flow chart of nutrient dynamics should be as simple as possible, but should include the main pools of nutrients and flows between them;
  2. the validation procedure should be based not only on observational data in the basin under study (usually only measured nutrient concentrations in the basin outlet), but also on the regionally-available information about different nutrient balance components; and
  3. the results should not be interpreted as exact predictions, but more as indicators of possible trends and qualitative differences.


For application of the robust approach, the published regional data on nitrogen dynamics in soils and nitrogen flows for northern Germany were collected from literature to obtain regionally-specific ranges for the validation of simulated nitrogen balance in different hydrotopes.


B3. Crop/vegetation growth and yield


The module representing crop and natural vegetation is an important interface between hydrology and nutrients. A simplified EPIC (Williams et al., 1984 & 1989) approach is included in SWIM (as well as in SWAT) for simulating arable crops (i. e. wheat, barley, rye, maize, potatoes) and aggregated vegetation types (i. e. 'grass', 'pasture', 'forest'), using specific parameter values for each crop/vegetation type. It is simplified mainly in the description of phenological processes in order to decrease the requirements on input information. This enables crop growth to be simulated in a distributed modelling framework at the regional scale.


A number of parameters are specified for more than 70 crop/vegetation types in the crop database attached to the model, i.e. biomass-energy ratio, harvest index, base and optimal temperature for plant growth, maximum leaf area index (LAI), fraction of growing season when LAI declines, maximum root depth, potential heat units required for maturity of crop, and some others.


Different vegetation types affect the hydrological cycle by the cover-specific retention coefficient, which influences runoff, and indirectly - the amount of evapotranspiration (ET), which is simulated as a function of potential evapotranspiration and LAI. The interaction between vegetation and nutrient supply is modelled by the plant consumption of nutrients and using nitrogen and phosphorus stress functions, which affect the plant growth.


Two different approaches can be used in SWIM for the adjustment of net photosynthesis (factor ALFA): 1) an empirical approach based on adjustment of the biomass-energy factor as suggested in EPIC and SWAT models (Arnold et al., 1994), and 2) a new semi-mechanistic approach derived by F.Wechsung from a mechanistic model for leaf net assimilation (Harley et al., 1992), which takes into account the interaction between CO2 and temperature. The second method and its application for climate change impact study with SWIM is described in Krysanova, Wechsung et al., 1999. Additionally, a possible reduction of potential leaf transpiration due to higher CO2 (factor BETA) derived directly from the enhancement of photosynthesis is taken into account in combination with both methods for the adjustment of net photosynthesis.


B4. Erosion


Sediment yield is calculated for each sub-basin with the Modified Universal Soil Loss Equation (MUSLE, Williams and Berndt, 1977), almost the same as in SWAT (Arnold et al., 1994). The only difference is that the surface runoff, the soil erodibility factor K and the crop management factor C are estimated for every hydrotope, and then averaged for the sub-basin (weighted areal average).


To estimate the daily rainfall energy in the absence of time-distributed rainfall, an assumption about exponential distribution of the rainfall rate is made. This stochastic element is included to allow realistic representation of peak runoff rates, given only rainfall and monthly rainfall intensity. Soil erodibility factor is estimated from the texture of the upper soil layer. The slope length and steepness factor is estimated from the Digital Elevation Model of a basin.


Then the sediment routing model consisting of two components operating simultaneously – deposition and degradation in the streams – is applied. Deposition in the stream channel is based on the fall velocity of the sediment particles. Stream power estimated as a function of the flow rate and the water surface slope is used to predict degradation in the reaches.


C. Model applications


The SWIM model was tested and validated sequentially for hydrology in several basins, for nitrogen dynamics, crop growth, and erosion as described in the following Table:



Basin / gauging station

or Region

Main River, sub-region (state)





Publication in


Buckener Au /Innien

Elbe, northeast of Hamburg (Lower Sachsony)



[1], [2]


Dahme / Märkisch Bucholz

Elbe, Pleistocene lowland (Brandenburg)



[1], [2]


Nuthe / Babelsberg

Elbe, Pleistocene lowland (Brandenburg)





Stepenitz / Wolfshagen

Elbe, Pleistocene lowland (Brandenburg)


hydrology, nitrogen

[2], [4], [6]


Weiße Elster / Zeitz

Elbe, mountainous sub-region, (Sachsony – Thüringia)






(regional study)

The federal state of Brandenburg

» 30000

crop growth

[2], [5], [7], [8]


Zschopau / Lichtenwalde

Elbe, mountainous sub-region (Sachsony)


hydrology, nitrogen



Mulde / Bad Düben

Elbe, mountainous and loess sub-regions (Sachsony)


hydrology, erosion

[3], [9]


Glonn / Hohenkammer

Isar, loess sub-region, (Bavaria)


hydrology, erosion



Parthe / Leipzig-Thekla

Elbe, loess sub-region (Sachsonia)





References on SWIM model


  1. Krysanova, V., D.-I. Müller-Wohlfeil & A. Becker (1996) Integrated Modelling of Hydrology and Water Quality in mesoscale watersheds. PIK Report No. 18, July 1996, PIK, Potsdam.
  2. Krysanova, V., D.I. Müller-Wohlfeil & A. Becker (1998) Development and test of a spatially distributed hydrological / water quality model for mesoscale watersheds. Ecological Modelling 106 (1-2), 261-289.
  3. Krysanova, V., Becker & Klöcking, B. (1998) The linkage between hydrological processes and sediment transport at the river basin scale. In W.Summer E.Klaghover, W.Zhang (eds.) Modelling Soil Erosion, Sediment Transport and Closely Related Hydrological Processes. IAHS Publications no. 249, p. 13-20.
  4. Krysanova, V. & A. Becker (1999) Integrated Modelling of Hydrological Processes and Nutrient Dynamics at the River Basin Scale, Hydrobiologia 410, 131-138.
  5. Krysanova,V., Wechsung, F., Becker, A., Poschenrieder W. & Gräfe J. (1999) Mesoscale ecohydrological modelling to analyse regional effects of climate change, Environmental Modelling and Assessment 4, 259-271.
  6. Krysanova, V., Gerten, D., Klöcking, B., & Becker, A. (1999) Factors affecting nitrogen export from diffuse sources: a modelling study in the Elbe basin. In: L. Heathwaite (ed.) Impact of Land-Use Change on Nutrient Loads from Diffuse Sources, IAHS Publications no. 257, p. 201-212.
  7. Krysanova, V., F. Wechsung, A. Meiner & A. Vasilyev, 1999. Land use change in Europe and implications for agriculture and water resources. In: Ü. Ennuste, L. Wilder (eds.) Harmonisation with the western economics: Estonian developments and related conceptual and methodological frameworks, Estonian Institute of Economics at Tallinn Technical University, 361-384.
  8. Wechsung, F., Krysanova, V., Flechsig M. & Shaphoff, S. (1999) May land use change reduce the water deficiency problem caused by reduced brown coal mining in the state of Brandenburg? Landscape and Urban Ecology (in print)
  9. Krysanova, V., Williams, J., Bürger, G. & Österle, H. 1999. Linkage between hydrological processes and sediment transport at the river basin scale - a modelling study. UNESCO Technical Report on Hydrological Processes and Soil Erosion (accepted).


Other references


  1. Arnold, J.G., Allen, P.M., Bernhardt, G., 1993. A comprehensive surface-groundwater flow model. J. Hydrology, 142, 47-69.
  2. Arnold, J.G., Williams, J.R., Srinivasan, R., King, K.W., Griggs, R.H., 1994. SWAT, Soil and Water Assessment Tool. USDA, Agriculture Research Service, Temple, TX 76502.
  3. Krysanova V., Meiner, A., Roosaare, J., Vasilyev, A. 1989. Simulation modelling of the coastal waters pollution from agricultural watershed. Ecoogical. Modelling 49, 7-29.
  4. Williams, J.R., Berndt, H.D. 1977. Sediment yield prediction based on watershed hydrology. Trans. ASAE 20 (6), 1100-1104.
  5. Williams, J.R., Renard, K.G., and Dyke, P.T., 1984. EPIC – a new model for assessing erosion’s effect on soil productivity. Journal of Soil and Water Conservation 38 (5): 381-383.
  6. Williams, J.R., Jones, C.A., Kiniry J.R., and Spanel, D.A., 1989. The EPIC Crop Growth Model. In: The Transaction of the ASAE, Am. Soc. Of Agric. Engineers, St. Joseph, MI, USA, 32 (2), 497-511.