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Modelling the soil
The soil is important since it represents the main land storage of heat and water that is available for subsequent release into the atmosphere. The multi-layer soil model has a fairly realistic representation of the vertical density and temperature profiles of the soil which allows a good representation of its thermal properties.
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There is an exchange of heat, moisture and momentum between the atmosphere and underlying surface according to the vegetation type.
The multi-layer soil model
The IFS multi-layer soil model uses four layers to represent the top ~1.3m of soil and the complex heat fluxes and interactions between them. These are sufficient to represent as correctly as possible all timescales from one day to one year. The soil model represents the vertical structure of the soil and the evolution of soil temperature and liquid water content in each layer. The heat and moisture energy flux are represented by the model:
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- larger rainfall rates can result in compaction of the soil surface leading to reduction in infiltration. This can lead to rapid runoff and flash floods, even over dry soil. See Fig2A.1.4.5-10.
- infiltration rates are influenced by higher rainfall intensities.
- runoff can result in erosion where bare or sparse vegetation.
- model representation of downward percolation of moisture can be inhibited when some soil layers are modelled as frozen. Model water is trapped above a frozen layer and is incorrect. Sometimes large sub-surface runoff can result. Changes in IFS to the hydraulic conductivity of frozen soil will reduce the problem.
Fig2A.1.4.5-1: Schematic of four-level soil model with land surface tiles. Surface heat and moisture are illustrated in the schematic using the maximum selection of six different surfaces ("tiles"). The four layers of soil have differing moisture contents which vary:
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Table2.1.4.5-1: List of symbols for parameters shown in Fig2A.1.4.5-1.
Soil temperature
Soil temperature is a forecast variable in IFS. It needs to be initialised at each analysis cycle but there are relatively few directly measured observations. Soil surface (skin) temperature is derived from the expected air temperature structure in the lowest 2 m together with energy fluxes (from HTESSEL) and an analysis of observed screen level (2 m) temperatures.
Soil moisture
Soil moisture is a measure of the water content within the ground. Dry soil is made up of soil particles and spaces between them. The proportion of space found in dry soil is a function of the soil type and this varies with location. Some or all of these spaces can be filled by water and the total amount of water held within an unsaturated soil is termed soil moisture.
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See also Section 9.2.1.3 Errors associated with soil moisture.
Evapotranspiration
Plant evapotranspiration efficiency varies between the permanent wilting point (PWP) and field capacity (CAP). Evapotranspiration efficiency:
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Evapotranspiration depends on the type and cover of vegetation and these depend upon the soil moisture.
Variations with location and vegetation in the model
In the ECMWF model:
- The soil type and the associated SAT value is a function of location. It does not vary with depth nor with time of year.
- The proportion of bare soil is a function of location. It does not vary with time of year.
- PWP and CAP are functions of location. They do not vary with time of year. They are primarily a function of soil type but also depend on:
- type of high vegetation.
- type of low vegetation.
- cover of high vegetation.
- cover of low vegetation.
- Land partly or completely covered in snow is treated rather differently as fluxes from the snow surface are also incorporated.
Measurement of soil moisture
Soil moisture is a forecast variable in IFS. It needs to be initialised at each analysis cycle but there are very few directly measured observations. Soil surface (skin) moisture is derived from:
- the expected air temperature and moisture structure in the lowest 2 m together with energy fluxes (from HTESSEL) and an analysis of observed screen level (2 m) humidities.
- satellite soil moisture data from the ASCAT sensor on the MetOp satellites.
- data from the Soil Moisture and Ocean Salinity satellite mission (SMOS) is used for operational monitoring (see Fig2A.1.4.5-2).
Fig2A.1.4.5-2: Measurements from the Soil Moisture and Ocean Salinity satellite mission (SMOS) polar orbiter satellite data. At L-band frequency (1.4 GHz) the surface emission is strongly related to soil moisture over continental surfaces. Surface radiation at this frequency is influenced by the vegetation layer (and hence soil moisture if the vegetation type is known), but proximity of lakes etc cause difficulties with interpretation.
Model derived evapotranspiration
Model evaporation from land surfaces is the sum of evapotranspiration from plants and evaporation from bare soil. Both depend on the soil moisture. However, evapotranspiration also depends crucially on relative humidity and wind speed in the lowest layer of the atmosphere.
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The 2m temperature and humidity are diagnostic parameters of the model, so their analysis only has an indirect effect on atmosphere through the soil and snow variables.
Soil moisture plots
Soil moisture plots are structured as above. The range of possible values is divided into three bands, which themselves are separated by the PWP and CAP discontinuities:
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- soil moisture variables for the top layer (Layer 1, 7cm deep), This layer has the greatest impact on the atmosphere.
- average soil moisture variables for the top three layers (Layer 1, 2 and 3, 1 metre deep). This is derived by summing the contributions from Layer 1 (0-7cm), Layer 2 (7-28cm depth) and Layer 3 (28-100cm depth), weighted according to thickness. Layers 2 and 3 (and indeed 4) have an impact on the atmosphere via:
- plants with deeper roots.
- moisture transfer between soil layers. This can operate both:
- upwards by capillary action.
- downwards under gravity.
Soil moisture charts
Fig2A.1.4.5-3: Examples of Soil Moisture at T+00 and T+192 DT 00UTC 06 March 2023.
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See the current soil moisture chart. Select "Layer 1 2 3" from the drop down menu for the average moisture in the top metre of the earth.
Fig2A.1.4.5-4: Relationship between soil moisture fraction and evaporation efficiency.
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See also Section 9.2.1.3 Errors associated with soil moisture.
Contrasting examples of surface and soil water budgets
Surface water budget in a typical mid-latitude agricultural landscape reacting to high rainfall in the model.
Periods of persistent rain over Britain over the winter of 2023/24 increased the soil moisture content in the river valleys and countryside around Reading. Soil water storage in all model soil layers had been consistently between 120% and 150% of field capacity but generally below saturation. Nevertheless there were areas of standing water in low-lying areas.
- On 12 Feb: There was some minor depletion of water in levels 1 and 2 due to evaporation.
- On 13 Feb: Rain caused an increase in the rate of storage in the already high water content in soil levels 1 and 2. There is only a small change in the already high fraction of field capacity in these levels.
- On 14 Feb: Rain also showed a small rise in the rate of storage in level 1 but the fraction of field capacity remains constant. Water storage from the rain is partially offset by evaporation.
Fig2A.1.4.5-5: Example of surface and soil water budget. DT12UTC 12 Feb 2024, VT12-14 Feb 20-24. Temperate mid-latitudes.
Surface water budget in desert soil reacting to extreme rainfall in the model.
A tropical system moved over the Northern Territories, Australia depositing a period of significant rainfall.
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Recycling of moisture by evaporation often has an impact on maintaining cyclones over the desert.
Fig2A.1.4.5-6: Example of surface and soil water budget. DT00UTC 21 Jan 2024, VT21-23 Jan 20-24. Desert areas.
Relationship between 2m temperature and humidity with soil moisture
This is a very complex area. It is very difficult to get things right. Cycle 49r1 included substantial changes to vegetation, flux representation, etc. It is also very challenging to balance user needs that can be contradictory. (e.g. for hydrologists and fire prediction algorithms).
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(Before modifications made in Cy49 in 14 May 2025 the effect of positive/negative increments to model soil moisture could be to increase/reduce model 2m temperatures or reduce/increase model 2m relative humidity excessively).
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Fig2A.1.4.5-7: Forecast 24h precipitation to 00Z 03Apr25, DT00Z 02Apr25. Rain over SW France is 10-30mm. The impact on soil moisture metric in Levels 1,2,3 between 00UTC 02Apr and 00UTC03 Apr is very minor. There is even some drying out in the wettest areas shown at T+0 . To change soil moisture metric appears to need a lot of rain.
Surface water budget in a dry desert
The model soil moisture charts sometimes show moisture layers below the surface in dry desert areas. There is very little ground truth so there must be some uncertainty.
Soil moisture charts consistently give an indication of water below the surface in mid-Sahara (near 23N 7E). This should not be relied on. However, it may well be correct as the area is around the oasis town of Tamanrasset in Algeria. Also there is indication of water in layers 2 and 3 in Arabia, locally as high as 60%, but there is little data to confirm this.
Fig2A.1.4.5-8: Example of soil moisture in desert areas. DT and VT 12UTC 07 Sep 2023. In the area around Tamanrasset (Algeria) and much of Saudi Arabia, soil moisture charts show dry surface layers (level 1, orange) and about 20% moisture in lower layers (levels 2 and 3, green) and locally as high as 60% in Saudi Arabia.
Fig2A.1.4.5-9: Afilal Oasis lies within the area where the model indicates soil with moderate moisture content in level 2 (~25% of Field Capacity) and level 3 (~40% of Field Capacity). Level 1 has water content below the wilting point) and remains so as there is no vegetation and roots to bring water upwards from lower layers. Nevertheless, subterranean water is locally sufficient to reach the surface at Afilal Oasis as springs. Soil moisture is a mean over a grid square. Local details and individual oases are unlikely to be captured. Soil moisture and soil type is not necessarily representative of an individual location.
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Fig2A.1.4.5-10: Example of rapid runoff from heavy rainfall despite very low soil moisture in level1 in IFS at this time. Kerr county is a hilly region prone to flash floods but in this case the dry soil moisture might be expected to absorb at least some precipitation. Nevertheless, there was flash flooding. The river level went from about 9 feet to 29 feet in the space of 90 minutes. There was a large loss of life. Users should not underestimate the potential for rapid runoff despite low soil moisture.
Considerations
- Actual soil characteristics can vary widely within a grid box. Users and forecasters should take into account the peculiarities of a location when interpreting model output.
- The assigned average soil type for a grid box is not necessarily representative of an individual location.
- Runoff can be up to 30% of rainfall in complex orography or mountainous regions.
- Soil moisture increments have less impact on 2m temperature and 2m humidity (and vice versa) since 14 May 2025.
- Recycling of moisture by evaporation from surface often has an impact on maintaining cyclones over the dessert.
- Availability of water in the soil for plant uptake allows plant roots to better extract water, especially in relatively dry conditions. Evapotranspiration can affect the surface specific humidity.
- Impacts of errors associated with soil moisture.
Additional sources of information
(Note: In older material there may be references to issues that have subsequently been addressed)
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