Soil moisture

Assessment versions

Published (reviewed and quality assured)

• No published assessments

Rationale

Justification for indicator selection

Water retention is a major hydrological property of soil. It governs soil functioning in ecosystems. While water retention capacity is an intrinsic soil property based on clay content, structure and organic matter levels, soil moisture content is highly dynamic and is, if based on natural factors only, the balance between rainfall, evapotranspiration, surface runoff, and deep percolation. Changes in temperature and precipitation patterns and intensity will affect evapotranspiration and infiltration rates, and thus soil moisture.

Although soil moisture constitutes only about 0.005% of global water resources, it is an important part of the water cycle and is a key variable controlling numerous processes and feedback loops within the climate system (Seneviratne et al., 2010). When depleted due to the lack of precipitation, increased evapotranspiration or increased runoff, soil moisture starts to constrain plant transpiration, crop growth and thus, livestock and food production.

Scientific references

• IPCC, 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Seneviratne, S.I., T. Corti, E.L. Davin, M. Hirschi, E.B. Jaeger, I. Lehner, B. Orlowsky, and A.J. Teuling (2010) Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Science Reviews 99(3-4), 125-161, doi:10.1016/j.earscirev.2010.02.004. EEA (2012) Climate change, impacts and vulnerability in Europe 2012 (EEA Report No 12/2012). European Environment Agency, Copenhagen, accessed 30 June 2014.

Indicator definition

Past trends

This indicator shows soil moisture over a depth of one metre based on a soil water balance model (Kurnik et al., 2014a; b)

Projections

This indicator shows changes in the Palmer Drought Severity Index (Heinrich and Gobiet, 2012)

Units

• Past trends: litres per cubic metre over ten years (l/m³.10 years)
• Projections: unitless

Policy context and targets

Context description

In April 2013 the European Commission (EC) presented the EU Adaptation Strategy Package (http://ec.europa.eu/clima/policies/adaptation/what/documentation_en.htm). This package consists of the EU Strategy on adaptation to climate change [COM (2013) 216] and a number of supporting documents. One of the objectives of the EU Adaptation Strategy is better informed decision making, which should occur through bridging the knowledge gap and further developing Climate-ADAPT as the ‘one-stop shop’ for adaptation information in Europe.

In the EU’s Common Agricultural Policy (CAP) it is recognised that farmers are exposed to increasing economic and environmental risks as a consequence of climate change and increased price volatility. Member States have to establish a comprehensive farm advisory system offering advice to beneficiaries, including on the relationship between agricultural management and climate change. The CAP’s cross-compliance system related to direct payments incorporates basic standards comprising climate change aspects. One of the priorities under the support for rural development is promoting resource efficiency and supporting the shift towards a low-carbon and climate-resilient economy in the agriculture, food and forestry sectors (with a focus on increasing efficiency in water use). ‘Climate change mitigation and adaptation, and biodiversity’ is explicitly mentioned as a thematic sub-programme. Accordingly, Member States have to specify agri-environment-climate measures and forest-environment and climate commitments that go beyond the basic standards.

Targets

No targets have been specified.

Related policy documents

• Climate-ADAPT: Adaptation in EU policy sectors
  Overview of EU sector policies in which mainstreaming of adaptation to climate change is ongoing or explored
• EU Adaptation Strategy Package
  In April 2013, the European Commission adopted an EU strategy on adaptation to climate change, which has been welcomed by the EU Member States. The strategy aims to make Europe more climate-resilient. By taking a coherent approach and providing for improved coordination, it enhances the preparedness and capacity of all governance levels to respond to the impacts of climate change.
• EU Common Agricultural Policy (CAP) reform - basic regulations
  References to climate change particularly in Regulation 1307/2013 (direct payments for farmers), Regulation 1306/2013 (so-called horizontal issues such as funding and controls: Articles 12 and 93, Annex I) and Regulation 1305/2013 (rural development: Articles 5, 7, 15, 28, 34, 35, 53 and 55).
• European Drought Observatory (EDO)
  The European drought observatory (EDO) is a service run by the EC’s Joint Research Centre. The EDO pages contain drought-relevant information such as maps of indicators derived from different data sources (e.g., precipitation measurements, satellite measurements, modelled soil moisture content).

Methodology

Methodology for indicator calculation

Past trends

Soil moisture was estimated using an algorithm for calculating the water balance at the surface and in the sub-surface horizons (up to 1m depth) as described in Kurnik et al. (2014a). The model uses the recommendations of the Food and Agriculture Organisation of the United Nations (FAO) for estimating the available water content in soils (Allen et al., 1998). It calculates soil moisture by adding and subtracting the losses and gains in the various parameters of the soil water budget, expressed in terms of the water column (in millimetres).

Written in volumetric units, the soil water balance (SWB) can be represented by Equation 1:

D * Capital delta_SWB / Capital delta_t = RR(t) – ET_A(t) – SRO(t) – DP(t) (1)

where D (in millimetres) is the depth of the modelled soil profile (root zone), and Capital delta_SWB (in cubic metres per cubic metre) is the change of the water volume over an area with depth D between two consecutive steps (Capital delta_t). RR (in millimetres per day) is the amount of precipitation at the surface, ET_A (in millimetres per day) is the actual evapotranspiration, SRO (in millimetres per day) is the surface runoff and DP (in millimetres per day) is the deep percolation.

Thus, as precipitation increases, actual evapotranspiration (ET_A), total runoff (SRO) and deep percolation (DP) decrease the soil moisture content in the root zone. The parameters are defined as described in Kurnik et al. (2014a). Pedotransfer functions were used to calculate the field capacity, wilting point and soil saturation point characteristics required to calculate the SWB.

In addition to climate data, the equation takes account of other parameters such as, land cover, phenological phases, and hydrological soil properties.

Input data: The model uses a range of inputs as specified in Kurnik et al. (2014a). Daily meteorological station data were obtained from the European Climate Assessment and Data set (ECA&D), compiled by the Royal Netherlands Meteorological Institute (KNMI). Land cover information from the Corine Land Cover 2006 project (CLC2006) was used to identify the type of land cover and to calculate the crop coefficient for three phenological stages (start, middle and end of season). Intra-annual variation of the crop coefficient k_c was calculated from the Normalised Difference Vegetation Index (NDVI), applying the same linear relationship between k_c and NDVI for all stations and over the whole validation period. Hydrological soil properties (namely soil saturation point, field capacity and wilting point) were calculated using soil characteristics (soil texture and soil organic matter) from the European Soil map (ESDB version 2.0).

Projections

Calculation of the Palmer Drought Severity Index (PDSI) opposes atmospheric water supply to soil water demand using a rather simple soil-water balance model.

The PDSI measures the deviation from climatically normal soil moisture conditions for the current month without regarding conditions of preceding months, accounting for local climate features.

It can be classified as follows:

PDSI: Class
≥4.00: Extremely wet
3.00 to 3.99: Very wet
2.00 to 2.99: Moderately wet
1.00 to 1.99: Slightly wet
0.50 to 0.99: Incipient wet spell
0.49 to −0.49: Near normal
−0.50 to −0.99: Incipient drought
−1.00 to −1.99: Mild drought
−2.00 to −2.99: Moderate drought
−3.00 to −3.99: Severe drought
≤−4.00: Extreme drought

Methodology for gap filling

Not applicable

Methodology references

• Allen et al. (1998) Allen, R.G., Pereira, L.S., Raes, D., Smith, M. (1998) Crop evapotranspiration —guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. Food and Agriculture Organization, Rome.
• Gerten et al. (2003) Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., Sitch, S. (2003) Terrestrial vegetation and water balance—hydrological evaluation of a dynamic global vegetation model. Journal of Hydrology 286, 249–270.
• Kurnik et al. (2014a) Kurnik, B., Louwagie, G., Erhard, M., Ceglar, A., Bogataj Kajfež, L. (2014a) Analysing Seasonal Differences between a Soil Water Balance Model and in Situ Soil Moisture Measurements at Nine Locations Across Europe. Environmental Modeling & Assessment 19 (1), 19-34. doi 10.1007/s10666-013-9377-z.
• Peixoto and Oort (1992) Peixoto, J.P., and Oort, A.H. (1992) Physics of climate. Springer, Berlin.
• Heinrich and Gobiet (2012) Heinrich, G. and Gobiet, A. (2012) The future of dry and wet spells in Europe: a comprehensive study based on the ENSEMBLES regional climate models. International Journal of Climatology 32, 1951–1970. doi:10.1002/joc.2421.
• Palmer (1965) Palmer, WC (1965). Meteorological drought. U.S. Research Paper No. 45. US Weather Bureau,Washington, DC.

Data specifications

EEA data references

• No datasets have been specified here.

External data references

• The future of dry and wet spells in Europe
• An assessment of actual evapotranspiration and soil water deficit in agricultural regions in Europe (dataset URL is not available)

Data sources in latest figures

Uncertainties

Methodology uncertainty

Soil moisture can be measured or estimated by various methods, based either on in-situ measurements, spatially continuous information derived from satellite imagery, or hydrological and land surface models. 

Soil moisture measurements are expensive and do not provide a good spatial representation of the soil wetness conditions due to the high variability of soil moisture on a local scale. In most of the studies, soil moisture is simulated by land surface, hydrological and soil water balance models (Gerten et al., 2003; Piexoto, 1992). Modellers can choose the spatial and temporal resolutions of the end product, depending on the resolution of the input data and the computation capacities. The vertical distribution of the soil moisture can be represented with different compartments within the model.

Specifically for the projections (Palmer Drought Severity Index), regional climate model (RCM) simulations provided by the ENSEMBLES project are used to analyse changes in dry and wet conditions in Europe by the mid of the 21st century under the A1B emission scenario. Eight RCMs are selected to capture the uncertainties of the projected changes (Heinrich and Gobiet, 2012). 

Data sets uncertainty

Quantitative information, from both observations and modelling, on past trends and impacts of climate change on soil and the various related feedbacks, is very limited. For example, data has been collected in forest soil surveys (e.g. ICP Forests, BioSoil and FutMon projects), but issues with survey quality in different countries makes comparison between countries (and between surveys) difficult. To date, assessments have relied mainly on local case studies that have analysed how soil reacts under a changing climate in combination with evolving agricultural and forest practices. Thus, European-wide soil information to help policymakers identify appropriate adaptation measures is absent. There is an urgent need to establish harmonised monitoring networks to provide a better and more quantitative understanding of this system. Currently, EU-wide soil indicators are (partly) based on estimates and modelling studies, most of which have not yet been validated. Nevertheless, in absence of quantification, other evidence can indicate emerging risks. For example, shifting tree lines in mountainous regions as a consequence of climate change may indicate an extinction risk for local soil biota.

Finally, when documenting and modelling changes in soil indicators, it is not always feasible to track long-term changes (signal) given the significant short-term variations (noise) that may occur (e.g. seasonal variations of soil organic carbon due to land management). Therefore, detected changes cannot always be attributed to climate change effects, as climate is only one of the soil-forming factors. Human activity can be more determining, both in measured/modelled past trends (baseline), and if projections including all possible factors were to be made. The latter points towards the critical role of effective land use and management in mitigating and adapting to climate change.

For the validation of the soil moisture modelling used to calculate the past trends, soil data other than soil moisture were not available from the validation stations (Kurnik et al., 2014a).

For the projections, regional climate model (RCM) simulations provided by the ENSEMBLES project are used to analyse changes in dry and wet conditions in Europe by the mid of the 21st century under the A1B emission scenario. Eight RCMs are selected to capture the uncertainties of the projected changes (Heinrich and Gobiet, 2012).

Rationale uncertainty

No uncertainty has been specified