Current drought monitoring and early warning systems use different indicators for monitoring drought conditions and apply different indicator thresholds and rules for assigning drought intensity classes or issue warnings or alerts. Nevertheless, there is little knowledge on the meaning of different hydro-meteorologic indicators for impact occurrence on the ground. To date, there have been very few attempts to systematically characterize the indicator–impact relationship owing to sparse and patchy data on drought impacts. The newly established European Drought Impact report Inventory (EDII) offers the possibility to investigate this linkage. The aim of this study was to explore the link between hydro-meteorologic indicators and drought impacts for the case study area Germany and thus to test the potential of qualitative impact data for evaluating the performance of drought indicators. As drought indicators two climatological drought indices – the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) – as well as streamflow and groundwater level percentiles were selected. Linkage was assessed though data visualization, extraction of indicator values concurrent with impact onset, and correlation analysis between monthly time series of indicator and impact data at the federal state level, and between spatial patterns for selected drought events. The analysis clearly revealed a significant moderate to strong correlation for some states and drought events allowing for an intercomparison of the performance of different drought indicators. Important findings were strongest correlation for intermediate accumulation periods of SPI and SPEI, a slightly better performance of SPEI versus SPI, and a similar performance of streamflow percentiles to SPI in many cases. Apart from these commonalities, the analysis also exposed differences among federal states and drought events, suggesting that the linkage is time variant and region specific to some degree. Concerning “thresholds” for drought impact onset, i.e. indicator values concurrent with past impact onsets, we found that no single “best” threshold value can be identified but impacts occur within a range of indicator values. Nevertheless, the median of the threshold distributions showed differences between northern/northeastern versus southern/southwestern federal states, and among drought events. While the findings strongly depend on data and may change with a growing number of EDII entries in the future, this study clearly demonstrates the feasibility of evaluating hydro-meteorologic variables with text-based impact reports and highlights the value of impact reporting as a tool for monitoring drought conditions.
Drought is a complex natural hazard with severe environmental and socioeconomic impacts. According to the UN Convention to Combat Drought and Desertification, drought is a “naturally occurring phenomenon that exists when precipitation has been significantly below normal recorded levels” (UN General Secretariat, 1994). Although little can be done to prevent this naturally occurring hazard, actions can be taken to reduce the societal vulnerability to drought. Such actions include the development of drought monitoring and early warning (M & EW) systems and drought plans to enhance drought preparedness (e.g., Wilhite et al., 2000; Wilhite and Knutson, 2008; Wilhite and Svoboda, 2000). Drought M & EW systems are based on different drought indicators or indices, which are variables describing drought conditions derived from predominantly meteorological or hydrological data. Knowledge on drought conditions expressed through an indicator, however, does not directly translate into understanding when and where drought impacts will occur given the complexity of how a prolonged precipitation deficit propagates though the hydrological cycle and interacts with environmental and socioeconomic factors. Nevertheless, information on the occurrence, timing, and severity of a drought impact is usually what matters most to stakeholders. Therefore there is a vital need for research on the link between commonly used drought indicators and impacts (e.g., Kallis, 2008; Stagge et al., 2015a; Stahl et al., 2012).
Especially for the development of drought plans knowledge on the relationship between drought indicators and impacts is important to infer meaningful threshold values triggering a management response (Steinemann and Cavalcanti, 2006; Steinemann, 2003, 2014). A recent survey among state drought managers in the US revealed that drought indicators and derived trigger values are often used without clarity about the relevance or effectiveness of this indicator (Steinemann, 2014). One reason for little consensus on the appropriateness of different indicators for drought M & EW is sparse and patchy data for “ground truthing” drought indicators, i.e. evaluating drought indicators with impact information. Since drought is a slow-onset “creeping” hazard (Gillette, 1950) with multifaceted impacts on different domains and sectors it is less visible than, for instance, earthquakes or floods. Apart from some exceptions (e.g., agricultural yield statistics) it is challenging to find information on the variety of drought impacts, which are mainly non-structural (not associated with physical damages to buildings, infrastructure, and other assets) and difficult to quantify in monetary terms (Logar and van den Bergh, 2013). To address these shortcomings, an online database for collecting user-based reports on drought impacts was launched in the United States some years ago (US Drought Impact Reporter (DIR); Wilhite et al., 2007). For Europe, a similar system has been recently established, however as a research database with a focus on past drought events, rather than as a real-time monitoring tool. This European Drought Impact report Inventory (EDII), which was broadly modeled after the US Drought Impact Reporter, compiles text-based reports on drought impacts from a variety of sources (Stahl et al., 2012; Stahl et al., 2015). Inventories like the DIR or the EDII offer the possibility to evaluate drought indicators with information on impact occurrence.
A large body of literature exists on the vast amount of drought indicators (for recent reviews see Heim Jr., 2002; Keyantash and Dracup, 2002; Zargar et al., 2011) and many studies have assessed the linkage between different hydro-meteorologic indicators (e.g., Anderson et al., 2011; Hao and AghaKouchak, 2014; Haslinger et al., 2014; Keyantash and Dracup, 2002; Steinemann, 2003; Vicente-Serrano et al., 2012). While fewer studies explored the relationship between drought indicators and a quantitative impact variable, such as agricultural yield or a vegetation response proxy (e.g., Ceglar et al., 2012; Mavromatis, 2007; Potop, 2011; Quiring and Ganesh, 2010; Quiring and Papakryiakou, 2003; Rossi and Niemeyer, 2010; Sepulcre-Canto et al., 2012; Vicente-Serrano et al., 2012), only three studies have exploited text-based reports of drought impacts for evaluating the meaning of drought indicators or the statistical modeling of the likelihood of impact occurrence (Dieker et al., 2010; Blauhut et al., 2015; Stagge et al., 2015a). The value of incorporating impact information into drought M&EW lies in moving from a hazard-based, reactive to a risk-based, proactive approach of drought management, as often postulated (Wilhite et al., 2000). Drought indicators only characterize the hazard, leaving room for interpretation whether and when this will trigger impacts. Depending on the vulnerability of a system a given hazard intensity will or will not evoke adverse environmental, economic or social effects. Vulnerability assessment is a common tool for closing the gap between hazard information and knowledge of risk of a certain region or exposed entity (e.g., Birkmann et al., 2013; Kallis, 2008; Knutson et al., 1998); its outcome, however, will strongly depend on the quality of available indicator data and assumptions made (Naumann et al., 2014). Directly evaluating drought indicators with impact occurrence allows, in theory, gaining insight into the cause–effect relationship of a physical water deficit without any assumptions on vulnerability. Nevertheless, there are numerous challenges and potential sources of bias during the collection of drought impact information (Lackstrom et al., 2013); text-based impact reports thus only represent a proxy for impact occurrence.
Given the limited knowledge on the potential of qualitative impact data for
evaluating the meaning of drought indicators, this study aims at exploring
the link between hydro-meteorologic drought indicators and text-based
information of drought impacts. To test the feasibility of linking
indicators with impacts, Germany was chosen as a case study given its good
coverage in the EDII and availability of hydro-meteorologic data.
Specifically, we ask the following research questions:
Is there a discernible link between drought impact occurrence derived
from text-based information and different hydro-meteorologic indicators commonly
applied for operational drought monitoring and early warning (M & EW) systems? If there is a link, which indicator or set of indicators best explain
drought impact occurrence for the case study area Germany? Can impact occurrence be attributed to a specific indicator threshold?
Overview on study area and data. Left panel: federal states of
Germany overlain by raster displaying SPI or SPEI resolution (0.25
Four indicators were selected representing drought propagation in different
domains of the hydrological cycle: the Standardized Precipitation Index (SPI)
(McKee et al., 1993), the Standardized Precipitation Evaporation Index (SPEI)
(Vicente-Serrano et al., 2010), and two hydrological indicators, namely
streamflow percentiles (
Monthly streamflow percentiles are based on daily records of streamflow for
several gauging stations per federal state. Time series of monthly
groundwater percentiles originate from weekly to monthly readings of
groundwater levels or spring discharge for several monitoring stations per
state. Figure 1 displays the spatial distribution of stations (amount of
streamflow and groundwater gauging stations per federal state, respectively:
28/15 (BW), 69/26 (BV), 21/18 (BB), 19/18 (HE), 38/42 (LS), 7/4 (MP), 23/18 (NW),
20/18 (RP), 9/9 (SH), 3/0 (SL), 23/10 (SX), 16/14 (ST), no data (BE).
Many of these stations are used for the federal states' hydrological
forecasting systems and thus represent stations with good data quality. Note
that streamflow gauging stations represent a variety of catchments varying
in size and catchment characteristics, many of them being anthropogenically
influenced. For more details on the selection of gauging stations per state
see Kohn et al. (2014). The reference period for the calculation of monthly
streamflow and groundwater level percentiles is 1970–2011. Similar to the
spatial aggregation of SPI or SPEI, different indicators metrics for streamflow or
groundwater level percentiles were calculated per federal state: mean
(
Information on drought impacts originates from the European Drought Impact
report Inventory (EDII) (Stahl et al., 2012). According to the EDII a
“drought impact” is a negative environmental, economic or social effect
experienced under drought conditions. Consequently, precipitation
shortfalls, anomalously low levels of soil moisture, water levels or
streamflow without negative consequences (for water uses, ecosystems,
agricultural yields etc.) or at least serious concerns, are not regarded as
drought impacts. EDII entries are based on text-based impact reports. These
reports come from a variety of sources such as governmental or NGO reports,
books, newspapers/digital media or journal articles. Each drought impact
report in the inventory contains (1) a spatial reference (different levels of
geographical regions including the European Union NUTS (Nomenclature of
Territorial Units for Statistics) regions standard), (2) a temporal reference
(at least the year of occurrence), and (3) an assigned impact category (there
are 15 impact categories with further division into impact subtypes):
agriculture and livestock farming, forestry, freshwater aquaculture and
fisheries, energy and industry, waterborne transportation, tourism and
recreation, public water supply, water quality, freshwater ecosystems,
terrestrial ecosystems, soil system, wildfires, air quality, human health
and public safety, and conflicts). More information on each drought impact
report is available in the inventory but is not used for the analysis.
Examples of drought impacts are crop losses, reduced production of thermal
and nuclear power plants, impaired navigability of streams, local water
supply shortage, or increased mortality of aquatic species, to name a few.
Stahl et al. (2012) provide further information on the EDII and all impact
entries can be searched and viewed online at
About 30 % of the EDII entries represent impacts that occurred in
Germany (761 impact reports as of the contents of August 2014). For the
statistical analysis the qualitative information on drought impacts was
converted into monthly time series of number of drought impact occurrences
per state. The following decisions were made during the conversion of
“drought impact reports” (EDII entries) into “drought impact
occurrences” (hereafter termed Spatial reference: an impact report often contains information on drought
impacts that occurred at several locations and/or impacts representing different
impact subtypes. An impact report was converted into several Temporal reference: impact reports indicating a month for start and end
of drought impact occurrence were converted accordingly. If only the season was
provided, drought impacts were assumed to have occurred during each month of that
season (winter
For the analysis, the time period 1970–2011 was chosen. Out of all impact
reports for Germany, 685 fell into the time period 1970–2011; 38 % of
these entries had either country-level information only or no month/season
indicated and was thus discarded. The conversion of the remaining impact
reports resulted in 1569 drought impact occurrences with spatial and
temporal reference (state-level and month). In addition to the number of
Number of drought impact occurrences and onsets per federal state.
The linkage between drought indicators and impacts was assessed through data visualization, correlation analysis, and extraction of indicator values concurrent with impact onset. Two approaches were followed: (1) linkage between time series of indicator–impact data per state to gain insight into the spatial variability of the indicator–impact relationship, and (2) linkage between spatial patterns of indicator–impact data for selected drought events.
For this approach only years with at least one
Information on selected drought events: duration and number of drought impact occurrences and onsets.
To determine the relationship between drought indicators and impacts we
computed Spearman rank correlation coefficients and corresponding
significance levels for
time series of time series of SPI and time series of
The cross-correlation analysis was only carried out for impact time series
with at least 10 months with impact occurrence (see Table 1 for months with
Moreover, indicator values associated with drought impact onset were
extracted from each drought indicator time series per federal state. Since
indicator values concurrent with past impact onset may represent thresholds
for impact occurrence, we hereafter use the term indicator “threshold”
when referring to the former. If
For this approach the link between spatial patterns of indicator–impact data
across the federal states was investigated for selected drought events. A
drought event is defined as a time period of drought impact occurrence after
a time with no impacts; we set a threshold of 35 mean mean and mean
Correlation for
Rank correlation coefficients (
Figure 3 displays correlation coefficients between time series of drought
indicators and
When focusing on commonalities in correlation patterns for
Distribution of
Median of indicator distribution (
Acc.
In terms of thresholds for
Differences among states – when neglecting the variability and complexity
of the pattern within each state, there appears a pattern of some states
showing more negative threshold values than others. In the states RP, SL, BW
and BV Differences between SPI and SPEI and among accumulation periods – notable as well
is that
For streamflow and groundwater percentiles the onset of drought impacts is
also concurrent with a range of threshold values (not shown). The median of
thresholds lies between 0.02 and 0.23 (
Thematic maps showing selected drought indicators
(SPEI
The maps in Fig. 5 reveal that there is a reasonable agreement between the
spatial distribution of two exemplarily selected drought indicators
(SPEI
The correlation between spatial patterns of drought indicators and
Median of indicator distribution (
Acc.
Regarding the “best” indicator there is a tendency of SPEI performing better
than SPI, and SPEI or SPI outperforming streamflow and groundwater percentiles.
Nevertheless, there is much variability among events, which also applies to
the “best” SPI or SPEI timescale. Intermediate accumulation periods (roughly
3–8 months) correlate best with impact occurrence for the events of 1976 and
2006; shorter accumulation periods (2–4 months) yield the highest
Rank correlation coefficients (
Distribution of selected drought indicators concurrent with
Indicator thresholds associated with
For streamflow percentiles similar differences among events are discernible, yet the differences are weaker. Groundwater level percentiles do not follow this pattern; thresholds were lowest in 1992 and 1976.
The analysis clearly revealed a relationship between the selected hydro-meteorologic drought indicators and drought impact occurrence inferred from text-based reports. The linkage-between-time series approach (Sect. 3.1) showed a significant moderate strength of correlation for several federal states, allowing for intercomparing the performance of different drought indicators. The event-based approach (Sect. 3.2) also exposed a significant strong correlation between spatial patterns of indicator–impact data for some drought events and indicators. From these results one can infer that qualitative information on drought impacts has strong potential for evaluating the meaning of hydro-meteorologic drought indicators. This is highly relevant for improving drought M & EW systems, since drought indicators are often used without having explicitly tested their representativeness for drought impact occurrence. Despite this promising outcome, it needs to be emphasized that for some federal states and drought events only a weak or no correlation was found, and sometimes a (mostly non-significant) correlation with non-meaningful direction.
For some states no to weak correlation may be an effect of very few months
with impact occurrence (SH, BE, and SL), while this is not the case for the
states MP, BB, and LS, which are comparable to SX, NW, and HE both regarding
the number of impact occurrences and months with
Generally, there are many potential sources of error or bias concerning drought impact data. As described in Lackstrom et al. (2013), drought impact reporting is associated with numerous challenges, creating a “patchwork” of impact information. Concerning our analysis the following sources of uncertainty need to be pointed out: first, not all drought impacts become published in reports, newspaper articles or other sources; if they are published, the level of detail regarding the spatial and temporal reference likely differs. Second, not all published information will make it into the inventory if not easily found or accessible; when entering information about spatial and temporal reference and impact category further bias may be introduced. Third, the assumptions during the process of impact report quantification for this study are subjective. For instance, we simply sum up (hydrological) drought impact occurrences per month independent of impact severity or spatial extent of the impact. All drought impacts have equal weight. At the same time, we think that the amount of reported drought impacts may represent some measure of impact severity. Hence, the number of impact occurrences may provide more information than a binary target variable (impact versus no impact). Fourth, the small sample size for some of the federal states, and generally for the event-based analysis needs to be kept in mind as further error source.
Despite these limitations, the impact data used in this study provided a
reasonable proxy for the linkage with hydro-meteorologic indicators. Given
the “patchwork” nature of impact information, uncertainty associated with
the indicator data appears of lower importance (e.g., dissimilar amount of
streamflow and groundwater gauging stations per state; small number of
streamflow and groundwater gauging stations for MP, SH, and SL; choice of
probability distribution for SPI or SPEI calculation (e.g., Stagge et al., 2015b);
averaging SPI or SPEI data over regions differing in size. The reason for weak
correlations for some drought events could also lie in the method of event
delineation (e.g., impact occurrence in summer according to impact report,
assignment of start month June during automatic data processing, yet start
of meteorological/hydrological drought conditions in August). Another reason
could be low spatial variability of impact and/or indicator data not
allowing to detect a cause–effect relationship. Especially for the events
1971, 2003, and 2006, low spatial variability of impact and/or indicator
data may explain the frequent occurrence of non-significant, weak
correlations, often with non-meaningful direction of
Generally speaking, the complementary approaches of linkage-between-time series and linkage-between-spatial-patterns of indicator–impact data revealed that (1) SPEI often correlates slightly better than SPI, (2) intermediate accumulation periods of SPI or SPEI show the highest correlation, (3) streamflow percentiles are comparable to SPI in many cases, and (4) the choice of indicator metric (mean versus minimum versus percent area in drought) does not make a difference for the between-time series approach, but matters for the event-based approach (10th percentile often outperforms mean/percent area in drought).
The finding that SPEI performed slightly better than SPI is in line with other
studies assessing the correlation between SPI or SPEI and different hydrological,
agricultural, and ecological response variables (Haslinger et al., 2014;
Potop, 2011; Stagge et al., 2015a; Vicente-Serrano et al., 2012). The
slightly better performance of SPEI highlights the importance of temperature and
increased evapotranspiration in drought development in addition to a
rainfall deficit (as for the 2003 event), as postulated by others
(e.g., Trenberth et al., 2014; Vicente-Serrano et al., 2014). Also in terms of the
“best” timescale of SPI or SPEI similar results were obtained as in other
studies. Stagge et al. (2015a), who modeled drought impact occurrence
for five European countries based on logistic regression with different
climatological drought indicators, identified an SPEI aggregation time of
3 months as best predictor for agricultural impact occurrence in Germany. For
other impact categories in Germany, e.g., energy and industry, they obtained
more complex results promoting a combination of shorter and longer
accumulation periods (Stagge et al., 2015a). The observed shift
towards stronger correlation with longer accumulation periods of SPI and SPEI for
Our finding that the “best” SPI or SPEI accumulation period differs among drought events could result from a shift in dominant impact type. For instance, the events in 1971, 1992, and 2006 show a higher fraction of agricultural impacts (see Fig. 2) as opposed to the other events with more diverse impact types, many of them evoked by low flows (e.g., impacts on waterborne transportation and energy production). Different impact types are known to have specific response times and could thus be attributed to different “best” SPI or SPEI timescales (e.g., shorter-term impacts on rain-fed agriculture versus longer-term impacts on water supply systems evoked by groundwater drought) (e.g., Stagge et al., 2015a; Vicente-Serrano et al., 2013). Overall, similar results as by Stagge et al. (2015a) are not surprising given that they also exploited EDII data to obtain binary impact information at the country level. However, it is important to test where simple and intuitive approaches like correlation and visualization of linkage patterns can yield similar results as more complex statistical models. The identified similar strength of correlation for streamflow as for SPI is noteworthy given the more complex streamflow signal stemming from several sources such as catchment area outside of the administrative area and human alteration through streamflow abstraction or augmentation. The weak correlation between groundwater levels and drought impact occurrence could be an effect of longer lag times of the groundwater response.
Apart from the above named commonalities, we observed differences in
correlation patterns among federal states and drought events, highlighting
the complexity of identifying a “best” indicator. It is known that an
individual indicator is not capable of representing the diversity and
complexity of drought conditions across space and time for different sectors
(Botterill and Hayes, 2012; Hayes et al., 2005). Nevertheless, drought
M & EW systems rely on the use of meaningful indicators and associated
triggers. Usually drought M & EW systems operate on a national or
continental scale and apply fixed rules for assigning drought intensity
classes or issue warnings or alerts. One example is the European Drought
Observatory (
Furthermore, the linkage-between-spatial-patterns approach revealed clear differences among drought events. The drivers of the inter-event variability of correlation patterns and thus “best” indicators are less clear. Likely a combination of (1) dissimilar hazard characteristics (duration and evolution of drought severity and related hazards such as heat weaves) triggering different impact types, (2) differences in geographic extent and vulnerability of affected regions, (3) potentially an impact reporting bias for certain events and/or regions, and (4) changes in resilience over time due to adaptation to previous droughts cause the differences among events. On the one hand, experiencing drought and its impacts fosters drought planning and enhances preparedness (e.g., Wilhite and Buchanan-Smith, 2005), which likely alters the indicator–impact relationship over time. On the other hand, each drought differs due to unique hazard characteristics and societal feedbacks. Thus, new types of impacts likely occur during not yet experienced events that society is not prepared for to cope with, as suggested by Van Dijk et al. (2013) analyzing the natural and human causes of the Millennium Drought in Australia and its impacts.
Common to all events except 2011 is that they represent summer droughts with respect to peaks of drought impacts (see Table 2). Most drought impacts receded in the fall (1971, 1976, 1992, and 2006), while the 2003 drought was more persistent with longer-term drought impacts tapering off only in early 2004. From the hazard side, however, the droughts of 1976 and 1992 were more prolonged (e.g., Bradford, 2000; Hannaford et al., 2011; Zaidman et al., 2002). The 2011 drought was exceptional with regard to its unusual timing: after a flood in January two drought periods occurred in spring and late autumn, with November 2011 being the driest November recorded (Kohn et al., 2014). This may explain the comparably different correlation pattern for 2011, with SPI or SPEI from 1 to 8 months, streamflow and groundwater percentiles all performing similarly well showing strong correlation with impacts. While the reasons for the differences among events remain speculative, the inter-event variability suggests that the “best indicator” for drought impact occurrence is event-dependent. Nevertheless, the findings from the event-based analysis and the interpretations thereof need to be handled with care given the small sample size underlying the correlation between spatial patterns. Yet, we think the event-based analysis adds extra information on the variability over time complementing the insights from the linkage-between-time series approach.
Regarding indicator thresholds triggering the onset of drought impacts we found that (1) no single “best” threshold value can be identified but impacts occur within a range of indicator values, (2) SPEI often shows slightly lower values than the corresponding SPI, and (3) there are differences among federal states and drought events.
Our analysis revealed that a single “one size fits all” indicator
threshold does not exist. Instead, the interquartile range of the SPI or SPEI
distributions was found to span an absolute value of roughly 0.3 to 1 in
most federal states. The median of the threshold distribution, however,
could be regarded as reference value for impact onset, e.g., to be used as
trigger in drought management plans. Note that months with a high number of
impact onsets, e.g., the summer months of 2003 in BV, give strong weight to
the threshold distribution. This explains the smaller IQR in BV. The spread
of the indicator threshold distribution in most federal states is not
surprising given the differences in impacts both regarding impact type and
severity. We currently do not differentiate between impact types due to the
small sample size; we only consider all drought impacts versus hydrological
drought impacts. However, thresholds are likely specific to a certain impact
category and affected sector, as already pointed out by Botterill and
Hayes (2012). A split into more homogenous groups could lead to condensed
threshold ranges, a prerequisite for inferring meaningful triggers. The
Combined Drought Indicator by the European Drought Observatory, for
instance, which is based on SPI-1, SPI-3, anomalies of soil moisture and FAPAR
(Fraction of Absorbed Photosynthetically Active Radiation), builds on
combinations of threshold values of
A notable outcome of the analysis is differences in threshold values between southern/southwestern and most northern/northeastern states of Germany (SL, RP, BW, and BV versus SH, MP, LS, and ST). The differences mostly coincide with stronger and weaker correlation between indicator–impact time series of the southern/southwestern and northern/northeastern states, respectively. Care needs to be taken regarding any interpretations given the “soft” text-based impact data and small sample size. However, one could speculate that these differences are attributable to differences in geographic properties, manifesting in different vulnerabilities to reduced precipitation input. The northern/north-eastern states generally exhibit soils with higher sand content and thus lower water holding capacity than in the south (Bundesanstalt für Geowissenschaften und Rohstoffe, 2007). Additionally, there is lower natural water availability in the northern/northeastern federal states (Bundesamt für Gewässerkunde, 2003). This could serve as explanation for impact onset during less negative SPI or SPEI values than in the south. BB in the northeast of Germany showed similar SPI and SPEI thresholds (less negative) as the other northern states for short accumulation periods, yet not for longer ones. Other studies also report on lower soil moisture availability and higher drought vulnerability of the northeast of Germany (Samaniego et al., 2013; Schindler et al., 2007; Schröter et al., 2005). Regardless of the drivers of differences among states one could argue that assuming a fixed trigger applied to a large area varying in geographic properties may not be appropriate. For continental-scale drought M & EW a systematic assessment of differences in threshold behavior could be useful.
In addition, the inter-event variability of thresholds associated with impact onset suggests that a “best” threshold is time variant. The analysis revealed comparably lower values associated with drought impact onset for the longer-duration, more severe events of 1976, 2003, and 2011. However, some events did not affect all states but were spatially concentrated (1992: focus on north-eastern Germany; 1976/2011: focus on the southwest). Differences in indicator thresholds among events could hence be a result of drought event characteristics, or an effect of location given the differences in threshold values between the south/north. For drought management plans aiming at withstanding a certain “design” drought, historical droughts of similar severity and duration could be jointly analyzed to derive reference thresholds triggering certain management actions during future events. While the visualization of indicator values corresponding to impact onset is a very simple approach, the suitability of threshold ranges can be easily judged. This was shown to be an important criterion for effective communication with stakeholders (Steinemann and Cavalcanti, 2006; Steinemann, 2014).
We explored the link between hydro-meteorologic indicators and drought impacts for the case study area Germany to illustrate the potential of qualitative impact data for evaluating the meaning of drought indicators. The analysis clearly revealed a relationship between selected drought indicators (SPI, SPEI, streamflow and groundwater level percentiles) and drought impact occurrence inferred from text-based reports of the European Drought Impact report Inventory (EDII). Through data visualization, extraction of indicator values concurrent with impact onset, and correlation analysis several general conclusions concerning the performance of indicators, “best” indicator timescale, and thresholds associated with impact onset can be drawn. The notable differences in indicator–impact relationship among the federal states in Germany and among drought events, however, suggest that the linkage is time variant and region specific to some degree. We think that this study is a proof of concept and a first step in the direction of systematically characterizing the relationship between drought indicators and text-based impact reports. While the findings on “best” indicators and thresholds for impact onset strongly depend on data and may change with a growing number of impact reports in the future, the aim was to demonstrate the feasibility of evaluating hydro-meteorologic variables used for drought M & EW with text-based impact reports. The complementary approaches of linkage between time series of indicator–impact data per state and linkage between spatial patterns for selected drought events proved to be a simple, yet effective methodology for deriving strong hypotheses on general patterns of the indicator–impact relationship. Consequently, this study highlights the value of impact reporting as a tool for monitoring drought conditions and stresses the necessity to further develop drought impact inventories.
Funding to the project DrIVER by the German Research Foundation DFG under the international Belmont Forum/G8HORC's Freshwater Security programme (project no. STA-632/2-1) and to the EU-FP7 DROUGHT R & SPI project (contract no. 282769) is gratefully acknowledged. We thank Lukas Gudmundsson for the provision of SPI and SPEI gridded data developed within the DROUGHT R & SPI project. We further thank the following agencies of the German federal states for supplying streamflow and groundwater level data through the Bundesanstalt für Gewässerkunde-funded project “Extremjahr 2011”: Bayerisches Landesamt für Umwelt (LfU), Hessisches Landesamt für Umwelt und Geologie (HLUG), Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (LANUV), Landesamt für Umwelt und Arbeitsschutz Saarland (LUA), Landesamt für Umwelt, Naturschutz und Geologie Mecklenburg-Vorpommern (LUNG), Landesamt für Umwelt, Wasserwirtschaft und Gewerbeaufsicht Rheinland-Pfalz (LUWG), Landesanstalt für Umwelt, Gesundheit und Verbraucherschutz Brandenburg (LUGV, Regionalabteilungen Ost, Süd, West), Landesanstalt für Umwelt, Messungen und Naturschutz Baden-Württemberg (LUBW), Landesbetrieb für Hochwasserschutz und Wasserwirtschaft Sachsen-Anhalt (LHW), Landesbetrieb für Küstenschutz, Nationalpark und Meeresschutz Schleswig-Holstein (LKNM), Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten- und Naturschutz (NLWKN), Ruhrverband, Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (LfULG), Staatliches Amt für Landwirtschaft und Umwelt Vorpommern (StALU-VP), Thüringer Landesamt für Umwelt und Geologie (TLUG), Landesamt für Landwirtschaft, Umwelt und ländliche Räume (LLUR), and Wasser- und Schifffahrtsverwaltung des Bundes (WSV). Last, we thank A. van Dijk, C. Svensson, and an anonymous referee for providing valuable suggestions for improving the manuscript. Edited by: P. Tarolli Reviewed by: A. van Dijk and another anonymous referee