Soil moisture deficit

Assessment versions

Published (reviewed and quality assured)

• 22 Mar 2021, 04:10 PM
  - Soil moisture deficit

Rationale

Justification for indicator selection

Climate is one of the main determinants of ecosystem composition and functioning, providing a multitude of ecological functions and services that human well-being depends upon (MEA, 2005). Extreme climate events, such as drought, can alter ecosystem processes, such as nutrient, carbon and water cycling, in ways that are not yet well understood (Vose et al., 2016).

An increase in the frequency and duration of drought events might contribute to global warming through positive carbon-climate feedback mechanisms if temperate ecosystems are turned from carbon sinks to carbon sources (Ciais et al., 2005). This might contribute to the irreversible degradation of ecosystems and the loss of their services (Anderegg et al., 2012). For example, droughts tend to slow nutrient uptake by plants and reduce the absorption of foliar nutrients, with premature leaf senescence. This results in forest dieback episodes that can severely reduce carbon exchange between the atmosphere and biosphere (Lewis et al., 2011). Recent large dieback episodes have had global impacts on carbon cycles, including increasing carbon release from biomass and reducing carbon uptake from the atmosphere, although impacts may be offset by vegetation regrowth in other regions (Vose et al., 2016).

Multiannual or severe droughts can also have substantial impacts on hydrological and stream biogeochemical processes, whereas indirect effects of droughts on ecosystems can be widespread and devastating. Notable recent examples of indirect impacts include insect and pathogen outbreaks and increased wildfire risk (Van Lanen et al., 2017). Available evidence suggests a non-linear relationship between drought intensity and bark beetle outbreaks: moderate droughts reduce the occurrence of outbreaks whereas long, intense droughts can increase the occurrence (Vose et al., 2016).

Drought disturbances push coupled natural-human systems (i.e. land systems) beyond their adaptive capacity and trigger important socioecological feedback loops (Crausbay et al., 2017). For example, a drought may result in ecological impacts that feed back to alter natural systems — namely the selection of drought-adapted traits or species, range shifts or ecoclimatic teleconnections (e.g. Stark et al., 2016) — with little influence on the ecosystem services provided. Alternatively, a drought may produce only minor ecological effects that do not feed back to natural systems but have larger effects on ecosystem services that alter connected human sub-systems, leading to, for instance, a reduction in crop yields. Finally, drought can induce ecological impacts and ecosystem service losses that are extreme and drive a persistent state change in both the human and natural systems, such as vegetation type conversion or mass human migrations (e.g. the Dust Bowl migration) (Crausbay et al., 2017).

Understanding and monitoring the pressures of drought on terrestrial ecosystems allow a better understanding of potential changes to ecosystem services that are linked to human well-being and, as a result, of how to address disparate problems in land systems such as poverty and biodiversity conservation.

Numerous operational drought definitions have been proposed according to different disciplinary perspectives (Heim, 2002). Although the primary driver of drought is a shortage of precipitation, its definition may depend on, among other factors, location, time of year, soil type, land use class and the context of the impact. For example, following Dracup et al. (1980) and Wilhite and Glantz (1985), meteorological (lack of precipitation), agricultural (decline in soil moisture), hydrological (low streamflow) and socioeconomic droughts are often distinguished. Agricultural drought can be thought of as the result of a shortage of precipitation over a particular timescale that leads to a soil moisture deficit that limits water availability for terrestrial ecosystems (Sepulcre-Canto et al., 2012). Therefore, pressure on terrestrial ecosystems is mainly driven by agricultural drought and the temporal patterns and persistence of soil moisture deficits.

Scientific references

• Anderegg WRL, Kane JM, Anderegg LDL, 2013. Consequences of widespread tree mortality triggered by drought and temperature stress. Nature Climate Change, 3, 30–36. doi:10.1038/nclimate1635.
• Ciais P, Reichstein M, Viovy N, Granier A, Ogee J, Allard V, Aubinet M, Buchmann N, Bernhofer C, Carrara A et al. 2005. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437: 529–533.
• Crausbay, S.D., A.R. Ramirez, S.L. Carter, M.S. Cross, K.R. Hall, D.J. Bathke, J.L. Betancourt, S. Colt, A.E. Cravens, M.S. Dalton, J.B. Dunham, L.E. Hay, M.J. Hayes, J. McEvoy, C.A. McNutt, M.A. Moritz, K.H. Nislow, N. Raheem, and T. Sanford, 2017  'Defining Ecological Drought for the Twenty-First Century'. Bulletin of the American Meteorological Society 98(12), pp. 2543-2550
• Dracup JA, Lee KS Jr, Paulson EG (1980) On the definition of droughts. Water Resources Research 16(2) pp. 297–302
• Lewis, S.L.; Brando, P.M.; Phillips, P.L., et al., 2011. The 2010 Amazon drought. Science. 331(6017): 554.
• Eklundh, L., and Jönsson, P., 2017. TIMESAT 3.3 with seasonal trend decomposition and parallel processing - Software Manual. Lund University, 92 pp.
• Heim, R.R., 2002. A review of twentieth-century drought indices used in the United States. Bull. Am. Meteorol. Soc. 83, 1149–1165.
• Jönsnon P., Eklundh L., 2004. TIMESAT – a program for analyzing time-series of satellite sensor data Computers and Geosciences, 30, 833 – 845.
• MEA, Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being: Synthesis Millennium Ecosystem Assessment, World Resources Institute, Washington, DC, USA Island Press: Washington, DC, USA. accessed 6 August 2019.
• Van Lanen, H.A.J., et al., 2017. Climatological risk: droughts. In: Poljanšek, K., Marín Ferrer, M., De Groeve, T., Clark, I. (Eds.). Science for disaster risk management 2017: knowing better and losing less. EUR 28034 EN, Publications Office of the European Union, Luxembourg, Chapter 3.9, doi: 10.2788/688605.
• Vose, James M.; Clark, James S.; Luce, Charles H.; Patel-Weynand, Toral, eds. 2016. Effects of drought on forests and rangelands in the United States: A comprehensive science synthesis. Gen. Tech. Rep. WO-93b. Washington, DC: U.S. Department of Agriculture, Forest Service, Washington Office. 289 p.
• Jin, H. and Eklundh, L., 2014, A physically based vegetation index for improved monitoring of plant phenology’,Remote Sensing of Environment152, pp. 512-525.  https://doi.org/10.1016/j.rse.2014.07.010
• Mann, H. B., 1945, 'Nonparametric Tests Against Trend' Econometrica 13, pp. 163-171.   https://doi.org/10.2307/1907187
• Sepulcre-Canto, G., et al., 2012, ‘Development of a combined drought indicator to detect agricultural drought in Europe’ Natural Hazards and Earth System Sciences 12(11), pp. 3519-3531. DOI:  10.5194/nhess-12-3519-2012
• Stark, S. C., et al., 2016, ‘Toward accounting for ecoclimate teleconnections: intra- and inter-continental consequences of altered energy balance after vegetation change’ Landscape Ecology31, pp. 181-194.
• Van Lanen, H. A. J., et al., 2017, ‘Climatological risks: droughts’ in: Poljanšek, K. et al. (eds): Science for Disaster Risk Management 2017: Knowing better and losing less, Publications Office of the European Union, Luxembourg. https://ec.europa.eu/jrc/en/publication/science-disaster-risk-management-2017-knowing-better-and-losing-less
• Wilhite, D. A. and Glantz, M. H., 1985, ‘Understanding the drought phenomenon: the role of definitions’ Water International 10(3), pp. 111-120,  https://doi.org/10.1080/02508068508686328

Indicator definition

This indicator shows the annual deviation in soil moisture content of each 500-m grid cell from the long-term (1995-2019) average. Negative soil moisture anomalies indicate that the annual average availability of soil moisture to plants drops to such a level that it has the potential to affect terrestrial vegetation and, hence, cause persistent changes in ecosystem condition. Negative long-term averages and negative trends in the annual data indicate increasing pressures on vegetation and ecosystems, and thus represent a climatic driver that should be considered in EU nature restoration plans. Therefore, the indicator can inform policy action on ecosystem restoration in the EU but also on adaptation to climate change.

Units

Area measured in km²and standard deviation; extent measured as area affected by soil moisture deficit as a percentage (%) of total country area.

Policy context and targets

Context description

Although there are no specific targets related to this indicator, in May 2020, the EU adopted a biodiversity strategy to 2030, related to protecting and restoring nature (EC, 2021). The strategy states that ‘The biodiversity crisis and the climate crisis are intrinsically linked. Climate change accelerates the destruction of the natural world through droughts, flooding and wildfires, while the loss and unsustainable use of nature are in turn key drivers of climate change’. Droughts negatively affect agricultural ecosystems and food security, the resilience of forest ecosystems and the ability of green urban spaces to protect people against heatwaves. In particular, the impacts of extended droughts on ecosystems need to be assessed because they can lead to significant loss of vegetation productivity, irreversible damage to the condition of ecosystems and land degradation.

For the EU, the opportunity cost of not reaching the headline target of the 2020 biodiversity strategy of halting the loss of biodiversity and ecosystem services has been estimated at EUR 50 billion per year (EC, 2015) . In addition to undermining economic benefits, the continuing loss of biodiversity means that ecosystems and the societies that rely upon them will become more fragile and less resilient in the face of challenges such as climate change, pollution and habitat destruction. Droughts have an impact on several land and soil functions, as well as ecosystem services, both in urban and rural areas. For example, droughts have an impact on the availability of water resources for human use in agriculture, cause habitat loss, the migration of local species and their replacement by alien species in open rural systems, and consequently cause soil erosion and biodiversity degradation. By putting pressure on natural ecosystems, droughts have hampered the achievement of the 2020 EU biodiversity strategy’s objectives.

Pressure from droughts on natural ecosystems also plays an important role in the implementation of the EU strategy on green infrastructure. In contrast to the most common ‘grey’ (human-made, constructed) infrastructure approaches that serve one single objective, green infrastructure approaches promote multifunctionality, which means that the same area of land is able to perform several functions and offer multiple benefits if its ecosystems are in a healthy state. More specifically, green infrastructure aims to enhance nature’s ability to deliver multiple valuable ecosystem goods and services, potentially providing a wide range of environmental, social, climate change adaptation and mitigation, and biodiversity benefits. Drought diminishes the normal condition of ecosystems and their capacity to provide services that could be integrated into green infrastructures.

Under EU legislation adopted in May 2018, EU Member States have to ensure that greenhouse gas emissions from land use, land use change and forestry are offset by an at least equivalent removal of CO₂ from the atmosphere in the period 2021-2030. Ultimately, the capacity of forests and soils on a given area of land to remove carbon from the atmosphere will depend on a number of natural (regional/geographical) circumstances such as variations in growing conditions (temperature, precipitation and droughts) and natural disturbances (storms, fires) as well as past and present management practices (e.g. rotation lengths, which affect the distribution of age classes in forest stands). By measuring changes in emissions and removals relative to business-as-usual projections, these circumstances (such as drought pressure) will be ‘factored out’, so that only changes related directly human-induced activities are measured. This also provides incentives for improving the current situation and gives an equal value to mitigation whether through sequestration or conservation, or material and energy substitution.

The role of the CAP is to provide a policy framework that supports and encourages producers to address economic, environmental (i.e. relating to resource efficiency, soil and water quality, and threats to habitats and biodiversity) and territorial challenges, while remaining coherent with other EU policies. This translates into three long-term CAP objectives: viable food production, the sustainable management of natural resources and climate action, and balanced territorial development. Given the pressure from droughts on natural resources, agriculture must improve its environmental performance through more sustainable production methods. Farmers also have to adapt to challenges stemming from changes to the climate by pursuing climate change mitigation and adaption actions (e.g. by developing greater resilience to disasters such as flooding, drought and fire). Understanding the spatiotemporal distribution of drought pressures on land will contribute to a better, faster and more informed implementation of CAP reforms and improve the quality of life of rural populations in Europe.

Targets

There are no specific targets related to this indicator.

Related policy documents

• COM(2013) 249 final Green infrastructure (GI): enhancing Europe’s natural capital
  EC, 2013, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions — Green infrastructure (GI): enhancing Europe’s natural capital (COM(2013) 249 final of 6 May 2013).
• Common agricultural policy
  The common agricultural policy is about our food, the environment and the countryside.
• EU Biodiversity Strategy for 2030
  The European Commission has adopted the new  EU Biodiversity Strategy for 2030 and an associated Action Plan (annex)  - a comprehensive, ambitious, long-term plan for protecting nature and reversing the degradation of ecosystems. It aims to put Europe's biodiversity on a path to recovery by 2030 with benefits for people, the climate and the planet. It aims to build our societies’ resilience to future threats such as climate change impacts, forest fires, food insecurity or disease outbreaks, including by protecting wildlife and fighting illegal wildlife trade. A core part of the  European Green Deal , the Biodiversity Strategy will also support a green recovery following the COVID-19 pandemic.
• SWD/2015/0187 final
  EC, 2015, Commission staff working document — EU assessment of progress in implementing the EU biodiversity strategy to 2020 (SWD(2015) 187 final).

Methodology

Methodology for indicator calculation

Data for this soil moisture deficit indicator are derived at the pixel level and express the average soil moisture deviation from long-term average conditions and relative trends in soil moisture anomalies during the vegetation growing seasons for the period 2000-2019. To estimate relative trends, it is assumed that the time series of soil moisture anomalies can be represented as a linear function of time (t), as follows:

(Y_t) ̂ = (β₀ + β₁)  × (t + ε)

where (Y_t ) ̂ denotes the estimated soil moisture anomaly for the growing season at yeart, β_0 is the estimated soil moisture anomaly att = 0, β_1*t represents the rate of change in soil moisture anomalies as a function of time and ε is the error term of the model. Once the model parameters β_0 and β_1 are estimated for each pixel through a least-squares fit, the relative soil moisture anomaly trend (rSMAt) can be computed as follows:

rSMAt = ((Y_19 ) ̂ - (Y_00) ̂)/(max┬(t∈{00,…,19})〖Y_t 〗 - min┬(t∈{00,…,19} )〖Y_t 〗 ) × 100

where (Y_00 ) ̂ and (Y_19 ) ̂ denote, respectively, the estimated soil moisture anomalies for the years 2000 and 2019, and max┬(t∈{00,…,19} )〖Y_t 〗 and min┬(t∈{00,…,19} )〖Y_t 〗 denote, respectively, the observed maximum and minimum soil moisture anomalies during the period 2000-2019. A relative trend was chosen instead of the absolute trend in standard deviations, to scale the indicator in a measurement unit that is independent of any associated statistical distribution.

The vegetation growing season period was derived from the MODIS 8-day plant phenology index (Jin and Eklundh, 2014), with data smoothed for each year in the period 2000-2019 with a double logistic fitting curve using TIMESAT software (Jönsson and Eklundh, 2004).

To account for outliers in the trends, which can occur by means of changes in land cover due to the impacts of natural events or human land use activities (such as irrigation or clear cuts), it was decided to remove all pixels for which land cover flows between 2000 and 2018 were identified by the Corine Land Cover accounting layers. Similarly, to avoid the inclusion of trends derived from spurious regressions, only statistically significant trends (p-value < 0.05) were included in the final indicator. The significance of the trends was based on the non-parametric Mann-Kendall trend test (Mann, 1945).

Methodology for gap filling

All input data sets were derived from global sources with wall-to-wall coverage of the land surface. No gap filling was needed.

Methodology references

No methodology references available.

Data specifications

EEA data references

• Vegetation productivity 2000-2016 provided by European Environment Agency (EEA)
• Soil moisture deficit during the vegetation growing season, annual time-series, 2000-2019 provided by European Environment Agency (EEA)

Data sources in latest figures

Uncertainties

Methodology uncertainty

Accuracy is determined by the remote-sensing vegetation signal provided by the Copernicus Land Monitoring Service and by the precipitation and soil moisture values provided by the Copernicus Emergency Service.

Data sets uncertainty

The various conceptual generalisations and scientific assumptions that are intrinsic in the Distributed Water Balance and Flood Simulation Model (Lisflood) — for example regarding soil physics, land use, canopy cover and meteorological data interpolation — and also the calibration of the model, may produce in some cases large approximations of the actual soil moisture content, and a progressive divergence from the real conditions.

Rationale uncertainty

No uncertainty has been identified.