Droughts can have an impact on forest functioning and production, and even lead to tree mortality. However, drought is an elusive phenomenon that is difficult to quantify and define universally. In this study, we assessed the performance of a set of indicators that have been used to describe drought conditions in the summer months (June, July, August) over a 30-year period (1981–2010) in Finland. Those indicators include the Standardized Precipitation Index (SPI), the Standardized Precipitation–Evapotranspiration Index (SPEI), the Soil Moisture Index (SMI), and the Soil Moisture Anomaly (SMA). Herein, regional soil moisture was produced by the land surface model JSBACH of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM). Results show that the buffering effect of soil moisture and the associated soil moisture memory can impact on the onset and duration of drought as indicated by the SMI and SMA, while the SPI and SPEI are directly controlled by meteorological conditions.
In particular, we investigated whether the SMI, SMA and SPEI are able to indicate the Extreme Drought affecting Forest health (EDF), which we defined according to the extreme drought that caused severe forest damages in Finland in 2006. The EDF thresholds for the aforementioned indicators are suggested, based on the reported statistics of forest damages in Finland in 2006. SMI was found to be the best indicator in capturing the spatial extent of forest damage induced by the extreme drought in 2006. In addition, through the application of the EDF thresholds over the summer months of the 30-year study period, the SPEI and SMA tended to show more frequent EDF events and a higher fraction of influenced area than SMI. This is because the SPEI and SMA are standardized indicators that show the degree of anomalies from statistical means over the aggregation period of climate conditions and soil moisture, respectively. However, in boreal forests in Finland, the high initial soil moisture or existence of peat often prevent the EDFs indicated by the SPEI and SMA to produce very low soil moisture that could be indicated as EDFs by the SMI. Therefore, we consider SMI is more appropriate for indicating EDFs in boreal forests. The selected EDF thresholds for those indicators could be calibrated when there are more forest health observation data available. Furthermore, in the context of future climate scenarios, assessments of EDF risks in northern areas should, in addition to climate data, rely on a land surface model capable of reliable prediction of soil moisture.
Drought can be essentially defined as a prolonged and abnormal moisture deficiency (World Meteorological Organization, 2012). However, the cumulative nature of drought, the temporal and spatial variance during drought development, and the diverse systems that drought could have an impact on make drought difficult to quantify and define universally (Heim, 2002). The American Meteorological Society (1997) classifies drought into four categories: meteorological or climatological drought, agriculture or soil moisture drought, hydrological drought, and socio-economic drought. Drought is principally induced by a lack of precipitation. Furthermore, high atmospheric water demand, due to warm temperatures, low relative humidity, and changes in other environmental variables, often coincides with the absence of precipitation (Hirschi et al., 2011). Through land–atmosphere interactions, prolonged meteorological drought can further exacerbate soil moisture drought, or even hydrological drought (Mishra and Singh, 2010; Tallaksen and Van Lanen, 2004).
A number of drought indicators have been developed in the past in order to quantify the characteristics of the different drought types and their potential impacts on diverse ecosystems and societies (Heim, 2002). The most prominent and widely used drought indicator is the Standardized Precipitation Index (SPI), which has been recommended as a standard drought indicator by the World Meteorological Organization (WMO) due to its flexibility for various timescales, simplicity in input parameters and calculation, as well as effectiveness in decision making (Sheffield and Wood, 2011; Hayes et al., 2011). The SPI was developed to provide a spatially and temporally invariant comparison of drought determined by precipitation at different timescales (McKee et al., 1993, 1995). The Standardized Precipitation–Evapotranspiration Index (SPEI) is developed based on the SPI, and, in addition to precipitation, also accounts for temperature impacts on drought (Vicente-Serrano et al., 2010). Soil moisture status has been explored through the Soil Moisture Anomaly (SMA) and Soil Moisture Index (SMI). The SMA has been adopted in the Coupled Model Intercomparison Project (CMIP) in order to study soil moisture drought in present and future projections in Global Circulation Models (GCMs) (Orlowsky and Seneviratne, 2013). The SMI (also referred to as Relative Extractable Water – REW) is often used to investigate soil water related plant physiology issues, as it can represent the relative plant available water in the root zone (Lagergren and Lindroth, 2002; Granier et al., 1999). Those drought indicators are globally applicable. However, only few studies have examined drought indicators against drought impact data in regional level (Blauhut et al., 2015). Drought studies in northern Europe are quite rare due to the low occurrence of drought. Nevertheless, a soil moisture index calculated with simulated soil moisture have been tested with the forest health observation data in Finland in Muukkonen et al. (2015).
Boreal forests have been recognized as a
Soil moisture strongly regulates transpiration and photosynthesis for most terrestrial plants, consequently modulating water and energy cycles of the landscape, as well as biogeochemical cycles of the plants (Seneviratne et al., 2010; Bréda et al., 2006). Nevertheless, ground observed soil moisture is limited in time and space (Seneviratne et al., 2010). Regional analysis is necessary to fully capture the spatial heterogeneity of the impacts of drought on ecosystem functioning (Aalto et al., 2015). In recent years, a multi-decadal global soil moisture record that incorporates passive and active microwave satellite retrievals has become available (Liu et al., 2012). However, microwave remote sensing can only provide surface soil moisture in the upper centimetres of the soil. Land surface models (LSMs) are valuable tools to derive spatial maps of soil moisture in deeper soil layers, for instance, the root-zone soil moisture, which is of particular importance in many climate studies (Hain et al., 2011; Rebel et al., 2012; Seneviratne et al., 2010).
This study aims to improve our understanding of the properties of different drought indicators (including SPI, SPEI, SMA, and SMI), and assess their ability to indicate the Extreme Drought that affects Forest health (EDF) in boreal forests in Finland. The EDF is defined in this study according to the extreme drought in Finland in 2006, which caused visible impacts on forest appearance compared to normal years (Muukkonen et al., 2015). For the soil moisture drought indicators (SMA, SMI), regional soil moisture was simulated by the JSBACH LSM of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) with its five layer soil hydrology scheme. Thus, this study also aims to gain insights into the capability of the five layer soil hydrology scheme with its parameters in the JSBACH LSM to simulate soil moisture dynamics across Finland. The outcome of this study provides suggestions on the selection and interpretation of drought indicators for estimating EDF risks in boreal forests in future climate scenarios.
Our study area is focused on Finland (Fig. 1). Finland is a northern
European country, situated between 60 and 70
The temperature of Finland is generally moderate, compared to many other
places at the same latitudes (Tikkanen, 2005). This is because the
westerly winds bring warm air masses from the North
Atlantic Ocean in winter, while in summer they bring clouds that
decrease the amount of incoming solar radiation. However, the
continental high pressure system located over the Eurasian continent
occasionally influences the climate causing warm and cold spells in
summer and winter, respectively. The precipitation in Finland is
influenced by the Scandinavian mountain range, which blocks large
amounts of moisture that are transported from west to east. Both
temperature and precipitation show spatial variations along a south to
north gradient. The annual mean surface temperature is about
5–6
In addition to mineral soils, a high areal fraction of peatland is typical for Finland, especially in the north of the country. Shallow soil areas accompanied with bare rocks are mostly located around the coastline in southern Finland and are also found in north-west Finland, which is a part of the Scandinavian mountain range.
Coniferous forest, including Scots pine and Norway spruce, is the dominant forest type in Finnish boreal forests (Finnish Statistical Yearbook of Forestry 2012, 2012). Broadleaved forest accounts for less than 10 % of the forest area. In total, 75 % of the total forest land area is located on mineral soils. In the past, large areas of unproductive peatlands have been drained to grow forests in Finland, as a result of the originally high proportion of pristine peatlands and timber production requirements (Päivänen and Hånell, 2012).
Characteristics of the three micrometeorological sites. The plant functional types and the soil types in the JSBACH site simulations corresponding to observed tree species and soil types at the three sites are shown in brackets.
The gridded meteorological data compiled by the Finnish Meteorological
Institute (FMI gridded observational data) are interpolated products
from stand meteorological observations in Finland (Aalto et al.,
2013). In this study, daily FMI gridded observational data were used
on a 0.2
In addition, meteorological and soil moisture data at three
micrometeorological sites were used as meteorological forcing for site
level simulations and for a comparison of modelled and observed soil
moisture, respectively (Fig. 1; Table 1). Soil parameters derived from
observations are only available for the Hyytiälä site (water
content at saturation
(
We adopted the yearly forest drought damage percentage in Finland from Muukkonen et al. (2015), who based their analysis on the forest health observation data from a pan-European monitoring programme ICP Forests (the International Co-operative Programme on the Assessment and Monitoring of Air Pollution Effects on Forests). The visual forest damage symptom inspections have been carried out by 10–12 trained observers during July–August since 2005, following internationally standardized methods (Eichorn et al., 2010) and national field guidelines (e.g. Lindgren et al., 2005). When a single sample tree in a site showed drought symptoms, it was recognized as a drought damage site. Therefore, uncertainties can rise from different personal interpretations and inappropriate time point of the visual inspections.
A 4-year (2005–2008) period of forest health observation data were analysed in Muukkonen et al. (2015). The summer of 2006 was extremely dry, and 24.4 % of the 603 forest health observation sites over entire Finland were affected, in comparison to 2–4 % damaged sites in a normal year. In southern Finland, 30 % of the observational sites showed drought symptoms.
JSBACH (Raddatz et al., 2007; Reick et al., 2013) is the LSM
of the Max Planck Institute for Meteorology Earth
System Model (MPI–ESM) (Stevens et al., 2013; Roeckner et al.,
1996). It simulates energy, hydrology, and carbon fluxes within
the soil–vegetation continuum and between the land surface system
and the atmosphere. Diversity of vegetation is represented by
plant functional types (PFTs). A set of properties are attributed
to PFTs with respect to the various processes JSBACH is accounting for.
For soil hydrology, a bucket scheme was originally used, in which the
maximum water that can be stored in the soil moisture reservoir (
In order to more adequately simulate the soil hydrology, a five layer soil hydrology scheme has been newly introduced in JSBACH (Hagemann and Stacke, 2015). The five layer structure is defined with increasing layer thickness (0.065, 0.254, 0.913, 2.902, and 5.7 m) and reaches almost 10 m depth below the surface. However, the soil depth to the bed rock, determines the active soil layers. Therefore, in the five layer soil hydrology scheme, the root zone is differentiated into several layers, and there could be soil layers below the root zone, which transport water upwards for transpiration when the root zone has dried out. Moreover, evaporation from bare soil can occur when the uppermost layer is wet, while the whole soil moisture bucket must be largely saturated in the bucket scheme. For a more detailed description of the five layer soil hydrology scheme in JSBACH and how it affects soil moisture memory, see Hagemann and Stacke (2015).
In this work, the regional JSBACH simulation was driven by the
prescribed meteorological data (1980–2011) simulated by the regional
climate model REMO (Jacob, 2001; Jacob and Podzun, 1997), whose
temperature and precipitation biases were corrected with the FMI
gridded observational data (Aalto et al., 2013). A quantile–quantile
type bias correction algorithm was applied to daily mean temperature
(Räisänen and Räty, 2013), while daily cumulative
precipitation was corrected using parametric quantile mapping
(Räty et al., 2014). The ECWMF ERA-Interim reanalysis (Dee
et al., 2011) was used as lateral boundary data for the climate
variables and as the initial values of surface climate variables for the REMO
simulation. Both the regional JSBACH and REMO simulations were
conducted in the Fennoscandian domain centred on Finland with
a spatial resolution of 0.167
In addition, simulations were carried out for the three measurement sites with the observed local meteorological forcing. The characteristics of the sites together with the corresponding model settings are described in Table 1.
Prior to the actual regional and site level JSBACH simulations, long-term spin-ups were conducted to obtain equilibrium for the soil water and soil heat.
A set of hydro-meteorological indicators were analysed. The SPI, SPEI, and SMA are standardized indicators that show the degree of anomalies to long-term means over the aggregation period, while SMI describes the instantaneous soil moisture status normalized with total soil moisture storage available to plants. In this study, daily SMI was used. The SPI, SPEI, and SMA were calculated with a 4-week (28 days) aggregation time frame, but they were updated every day with running inputs over the 30-year period. Both the 4-week aggregation time frame and 30-year study period are considered to be of sufficient duration climatologically under WMO guidelines (World Meteorological Organization, 2012). The SPI and SPEI were calculated using both the FMI gridded observational data set and the regional JSBACH forcing data described in the previous chapter, while SMA and SMI were computed with the layer 2 soil moisture from the regional JSBACH simulation. In addition, the SMIs were derived from site soil moisture observations, as well as from site JSBACH simulations. The layer 2 soil moisture from the JSBACH simulations was used, because the soil moisture in the shallower layer (layer 1) is highly sensitive to small changes in climatic variables, and the soil moisture dynamics in the deeper layers are excessively suppressed. Furthermore, the layer 2 is representative of the root zone in forest soils.
The SMI is a measure of plant available soil water content relative to the maximum plant available water in the soil (Betts, 2004; Granier et al., 2007; Seneviratne et al., 2010). The soil water above field capacity cannot be retained, and produces gravitational drainage and usually flows laterally away. The soil water below the wilting point is strongly held by the soil matrix to such an extent that the plants are unable to overcome this suction to access the water (Hillel, 1998).
The SMI is calculated as follows:
Note that soil water content can exceed
The SMA is an index relevant to plant functioning (Burke and Brown, 2008). The SMA depicts the deviation of the soil moisture status in a certain period of a year to the soil moisture climatology over this period. It can be normalized by the standard deviation of the soil moisture in this respective period over all years, for direct comparison with the other standardized drought indicators, e.g. SPI, SPEI.
The SMA in this study is calculated following the method of Orlowsky
and Seneviratne (2013):
The SPI inspects the amplitudes of precipitation anomalies over a desired period with respect to the long-term normal. The homogenized precipitation series is fitted into a normal distribution to define the relationship of probability to precipitation (Edwards and McKee, 1997). In this work, a Pearson type III distribution is adopted because it is more flexible and universal with its three parameters in fitting the sample data than the two parameter Gamma distribution (Guttman, 1994, 1999). The parameters of the Pearson type III distribution are fitted by the unbiased probability-weighted moments method. Typically, the timescales of SPI range from 1–24 months. The reduced precipitation under various durations can illustrate the impacts of drought on different water resources (Sivakumar et al., 2011). A time frame of less than 1 month is not recommended as the strong variability in weekly precipitation may lead to erratic behaviour in the SPI (Wu et al., 2007). However, the “moving window” of a minimum of 4 weeks with daily updating is acceptable (World Meteorological Organization, 2012). Furthermore, attention should be paid when interpreting the 1 month SPI to prevent misunderstanding. Large values in the 1 month SPI can be caused by relatively small departures from low mean precipitation (World Meteorological Organization, 2012).
The SPI is a probabilistic measure of the severity of a dry or wet event. An arbitrary drought classification with specific SPI thresholds was defined by McKee et al. (1993). Recently, an objective method based on percentiles from the United States Drought Monitor (USDM) has been recommended for defining location-specific drought thresholds (Quiring, 2009). For calculating SPI, we used the SPI function in R package SPEI version 1.6 (Beguería and Vicente-Serrano, 2013).
The SPEI is similar to SPI mathematically, but also accounts for the impact of
temperature variability on drought through atmospheric water demand, in addition to the water supply
from precipitation (Vicente-Serrano et al., 2010). The SPEI is based on a climatological
surface water balance, which is calculated as the differences between precipitation and ET
In order to evaluate the ability of JSBACH simulated soil moisture to detect drought,
the SMI series at the three study sites from the site and the regional
(the model grids where the sites are located) JSBACH simulations were
compared with the observed soil moisture data over the common data
coverage periods (Table 1). SMI based on the Hyytiälä
observational data was calculated with
The temporal and spatial coherency between the drought indicators was investigated at the regional level. The time correlations over our study period between the SPI calculated with the observational data set and the SPI calculated with the JSBACH forcing data were derived for the grid boxes in Finland. The same approach was adopted for the SPEIs. Moreover, the time correlations between the meteorological-based drought indicators (SPI, SPEI) calculated with the JSBACH forcing data and the soil moisture-based drought indicators (SMI, SMA) calculated with the JSBACH simulated soil moisture were derived for the grid boxes in Finland, as well as the time correlation between SMI and SMA. Furthermore, the spatial and temporal evolution of drought depicted by indicators was compared through time–latitude transections.
Soil moisture dynamics at the three micrometeorological sites:
According to the forest health observation data, we consider the 30 % forest damaged sites in southern Finland as the fraction of the area influenced by the severe drought in 2006, which is a reasonable assumption based on the dense and even distribution of observation sites over southern Finland. Based on this information, we utilized the cumulative area distributions of the SMI and SMA over southern Finland during the driest 28-day period of southern Finland in 2006 (i.e. in the case of SMI, this is the lowest 28-day-running mean value averaged over southern Finland) to derive their thresholds for this kind of extreme drought. Herein, as SMA was calculated with 28-day-running means for soil moisture, the same time window was adopted for SMI to be consistent with SMA. The SPEI threshold for extreme drought is selected as 2 % of the SPEI data series, according to the recommended percentile classification (Quiring, 2009).
In general, the timing of dry spells in summer in most of the years of the simulated soil moisture corresponded well with the observations at the three sites (Fig. 2). There was good agreement between the minimum values reached by the simulated and observed SMIs in summertime at Hyytiälä. The late summer of 2006 was noticeable as being extremely dry in the simulations and observations at Hyytiälä and Sodankylä. At Kenttärova, the extent of the SMI was quite different in the regional and site JSBACH simulations. This was mainly because different soil types are prescribed for this site, which affects not only the soil hydrology but also the values of SMI. In the regional simulation, Kenttärova was situated in a peat soil area, while in reality and in the site simulation the site is classified within a mineral soil area. The soil type in an individual grid for the regional simulation is homogeneous and defined according to the soil type with the highest coverage. The summer of 2010 was the driest among the three years at Kenttärova according to the observation, and the timing of the driest period after mid-summer shown in the observations was successfully captured by the site simulation. Moreover, the soil at Kenttärova was mostly unsaturated during those three years, even in the site simulations where it was realistically represented as a mineral soil. This is related to the small amount of precipitation during those years.
The diverse features of soil moisture among these sites in
wintertime were captured by JSBACH. The soil tends to be saturated at
Hyytiälä in winter, whereas at Sodankylä and Kenttärova there
is a winter recession period of soil moisture when the soil tends to dry out.
At Hyytiälä, the difference is due to infiltration of snowmelt water
during intermittent periods when air temperature is above 0
Percentiles of the time correlation coefficients across the grid
boxes over Finland. The time correlations over the study period between the
SPIs and SPEIs derived from the JSBACH forcing data and the observational
data set (SPI
Overall, the timing of summer dry spells and the winter characteristics of the observed soil moisture at the three sites were well captured by the simulated soil moisture, although the simulated soil moisture shows larger amplitudes and a faster response to changes in water inputs. The discrepancies in soil moisture between the site and the regional JSBACH simulations are mainly due to the differences in precipitation in summertime and in surface temperature during winter in the meteorological forcing data, as well as different soil types in specific locations. The latter is related to the difference in scales between the regional grid and the site. Soil characteristics tend to be heterogeneous, so that the characteristics may vary on scales from a metre to a kilometre. While for modelling on the regional grid, effective soil characteristics are chosen that represent the average characteristics of a grid box.
Latitude–time transections of
Time correlations over the study period between
SPEI
The time correlations between the regional results of those drought indicators over our study period showed high correlation coefficients over Finland (Fig. 3). The medians of the time correlation coefficients over the whole of the country were greater than 0.6; with the 5 % percentiles also greater than 0.5, with the exception of the correlation coefficient between SMA and SPI. The agreement between SPEIs calculated with the JSBACH forcing data and the FMI gridded observational data set was better than that for SPIs. Furthermore, the soil moisture-based drought indicators revealed a better correspondence with SPEI than with SPI, which is reasonable as SPEI is based on the water balance. Therefore, in the following, we will focus on SPEI as the climatic driver indicator, and as there was a good correlation between the JSBACH forcing data and the FMI gridded observational data-based SPEIs, we restricted the data set by using the JSBACH forcing data-based SPEI, which was better related to the two soil moisture-based drought indicators from the model. Moreover, the correlation between SPEI and daily SMI was higher than that between SPEI and SMA. This is especially true for peatland areas while the correlations in mineral soil areas are more similar (see regional maps in the Supplement). This results from different soil moisture memory effects in those soil types.
From the time–latitude transections of the selected indicators (Fig. 4), the
most exceptional dry years in our study period (e.g. 1994, 2006) can be
distinguished, as well as the exceptionally wet years
(e.g. 1981, 1998). Although there is generally a good correlation among all
three indicators in capturing drought, there are differences among them in
depicting drought durations and latitudinal extent at detailed locations and
time. First, SPEI and SMA generally show more consistent patterns extending
through a wider range of latitudes than SMI. Also, the buffering effect of
soil moisture and the associated soil moisture memory can delay and extend
dry or wet events as indicated by SMI and SMA, in comparison to those by the
SPEI. For instance, the dry period in 1992 over southern Finland in SMI and
SMA is longer than that in SPEI, and the wet period in the same year over
northern Finland as indicated by SMA starts later in comparison to SPEI;
however, this difference is not shown by SMI. Second, SMI exhibits a more
distinct south–north gradient than the other two indicators. In particular,
SMI describes more frequent droughts in the extreme southern parts of
Finland. This is because the shallow soil in those areas is more sensitive to
climate drivers. However, there is much less drought indicated by SMI in the
extreme northern part of the country (above 68
Cumulative area distribution of the
The
SMI values vary within different ranges for the peatland and mineral soil areas in
southern and northern Finland, whereas SMA and SPEI, as they are standardized indicators, show no
differences regarding the soil type or location (Fig. 5). The regionally averaged SMI over the
peatland areas mainly varies from 0.4 to 0.6 in both the south and
north of Finland, while the SPEI averaged over the same area ranges
between
Our results showed that the driest 28-day periods of southern Finland
in 2006 were the same (from 20 July to 16 August) for SMI and SMA. The
SMI and SMA thresholds for the EDF are 0.138 and
The summer drought periods
Furthermore, we compared the regional distributions of the areas
influenced by the 2006 EDF in the driest 28-day period indicated by
SMI, SMA and SPEI (Fig. 7). The SMI showed that the EDF influenced
areas were mainly located in southern Finland, whereas the SMA showed
more EDF affected areas located in the middle to northern part of the
country (mainly above 64
A more comparative analysis of the ability of the three indicators to represent EDF under the derived thresholds was conducted for the summer months of the 30-year study period (Fig. 8). As the shallow soil is quite sensitive to climate variation, areas with soil depths less than 3 m were excluded to eliminate the influence on drought period by sporadic drought episodes that would have exaggerated the number of drought days. In general, the drought periods (number of days) influenced by EDF show a better consistency among the three indicators than the mean fraction of affected areas. In general, SMI shows less area under EDFs in both southern and northern Finland than the other two indicators. In particular, the only EDF indicated by SMI in the north was for 2006, but with only a small fractional area of around 1–2 %. In the south, the SMI indicates EDF events in 1994 and 2006, with the mean influenced area larger than 5 % and the period longer than 30 days. In 2006, the mean influenced areas indicated by the SMI and SMA are similar, as are the drought periods. However, the SMA shows less mean influenced areas compared to SMI in 1994, which is related to the longer drought period indicated by SMA than SMI. The SPEI displays higher mean areas influenced by EDFs than the soil moisture drought indicators in all years, except 1990. The reason for this is that the EDF as indicated by SPEI in that year had already commenced before June, which is the first month of summer in our study. The SMA shows a prolonged effect in comparison to meteorological drought, which is not sufficiently strong to allow SMI to reach the EDF threshold due to the high initial soil moisture content.
Overall, the SMI is considered to be more capable in indicating EDFs because it directly reflects the plant available soil moisture. In boreal forests in Finland, EDFs indicated by SPEI and SMA often cannot lead to very low soil moisture that could be indicated as EDFs by SMI, due to the high initial soil moisture or presence of peat.
In this study, we assessed the performance of several drought indicators (SPI, SPEI, SMA, and SMI) for their ability to represent the timing and spatial extent of droughts in Finland. The SPI, SPEI and SMA are standardized indicators that describe the degrees of anomalies over a period, whereas SMI is directly related to plant available water. Those standardized indicators were calculated with 28-days-running mean inputs, while SMI is calculated with daily soil moisture. The regional soil moisture is simulated by the land surface model JSBACH with its five layer soil hydrology scheme. The simulated soil moisture can generally capture the timing of dry spells in summer and winter characteristics of the observed soil moisture at the three observation sites in Finland, although inconsistencies exist in the rates of change and amplitudes of variations in soil moisture. The SPEI showed higher time correlation coefficients with the soil moisture-based drought indicators than SPI, as SPEI takes into account the surface water balance rather than precipitation only. Further inspections of the temporal and spatial variability of SPEI, SMA, and SMI revealed that, in general, the SPEI and SMA showed latitudinal-consistent patterns, whereas the SMI described more droughts for the south than the north of Finland. The vulnerable shallow soil area along the coastline in southern Finland and the peat soil area in northern Finland are drought-prone and drought-resistant areas, respectively, as indicated by SMI. Therefore, soil characteristics impact on SMI. In addition, soil moisture buffering effects and the associated soil moisture memory can delay and extend the drought as indicated by soil moisture-based drought indicators, in comparison to those by the SPEI.
Especially, we examined the effectiveness of SPEI, SMA, and SMI to capture the Extreme Drought affecting Forest Health (EDF). The SMI was found to be more capable in spatially representing the EDF in 2006. High discrepancies were found among the indicated EDF periods and the mean fraction of affected areas by the three indicators for the summer months of the 30-year study period. The SPEI was the most sensitive drought indicator and showed the highest amount of EDFs with larger influenced areas, while the SMI showed much less EDF events than the other two indicators.
To conclude, we recommend to use SMI to indicate EDFs in boreal forest because it directly represents the plant available soil moisture, which is a synthesized result of the initial soil moisture content, soil properties, as well as climate conditions. Thus, a land surface model that produces reliable predictions of soil moisture is necessary when assessing EDF risks in boreal areas. To improve the accuracy of soil moisture-based drought indicators (especially SMI) calculated with LSM simulated soil moisture, high-quality soil type distribution and soil parameters data are essential. More sophisticated models are expected to improve simulated soil moisture; for instance, soil layers with different soil types along the soil profile, heterogeneity of soil types in a grid box and thorough consideration of the model formulations and parameters that regulate the rate of evapotranspiration, drainage and run-off. Furthermore, uncertainties associated with the drought indicators may originate from their input data (Naumann et al., 2014), therefore unbiased forcing data are of vital importance for the accurate simulation of soil moisture by a LSM (Maggioni et al., 2012).
The critical points of drought indicators leading to drought damages symptoms of forests are crucial for understanding climate impacts on forest ecosystems. In this study, the EDF thresholds for those indicators were selected only according to the statistics of the forest health observation in 2006. This might induce some uncertainties when they are used for future predictions of EDFs. The method for selecting EDF thresholds for drought indicators could be adopted and the EDF thresholds could be calibrated, when there are more observation data about forest damages induced by drought available. In addition, drought damage on different tree species could be studied. These would require more detailed information and a better monitoring at the forest observation sites. Moreover, satellite data could be explored to monitor the drought effects in boreal forests timely and across large spatial scale (Caccamo et al., 2011).
We would like to thank the EMBRACE (EU 7th Framework Programme, Grant Agreement number 282672) and HENVI (Helsinki University Centre for Environment) projects for the financial support. We give our deepest appreciation to Pentti Pirinen from FMI for providing the observational data. The authors acknowledge MPI–MET, MPI–BGC, and CSC (Hamburg) for providing JSBACH and REMO models and assistance in their use, and also the MONIMET project (LIFE12 ENV/FI/000409) for supporting the drought indicator study. This work was also supported by the Academy of Finland Center of Excellence (no. 272041), ICOS-Finland (no. 281255), and ICOS-ERIC (no. 281250) funded by Academy of Finland. Edited by: A. Gelfan