scholarly journals An inventory of Alpine drought impact reports to explore past droughts in a mountain region

2021 ◽  
Vol 21 (8) ◽  
pp. 2485-2501
Author(s):  
Ruth Stephan ◽  
Mathilde Erfurt ◽  
Stefano Terzi ◽  
Maja Žun ◽  
Boštjan Kristan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Altitude ◽  
Alpine Region ◽  
Hydrological Drought ◽  
Southern Region ◽  
Mountain Region ◽  
Drought Impact ◽  
Drought Impacts ◽  
Impact Data ◽  
The Impact

Abstract. Drought affects the European Alpine mountain region, despite a humid climate. Droughts' damaging character in the past and increasing probability in future projections call for an understanding of drought impacts in the mountain regions. The European Drought Impact report Inventory (EDII) collects text reports on negative drought impacts. This study presents a considerably updated EDII focusing on the Alpine region. This first version release of an Alpine Drought Impact report Inventory (EDIIALPS) classifies impact reports into categories covering various affected sectors and enables comparisons of the drought impact characteristics. We analysed the distribution of reported impacts on the spatial, temporal and seasonal scale and by drought type for soil moisture drought and hydrological drought. For the spatial analysis, we compared the impact data located in the Alpine region to the whole of Europe. Furthermore, we compared impact data between different climatic and altitudinal domains (the northern region vs. the southern region and the pre-Alpine region vs. the high-altitude region) and between the Alpine countries. Compared to the whole of Europe, in the Alpine region agriculture and livestock farming impacts are even more frequently reported, especially in the southern region. Public water supply is the second most relevant sector but overall less prominent compared to Europe, especially in spring when snowmelt mitigates water shortages. Impacts occur mostly in summer and early autumn, with a delay between those impacts initiated by soil moisture and those initiated by hydrological drought. The high-altitude region shows this delay the strongest. From 1975 to 2020, the number of archived reports increases, with substantially more impacts noted during the drought events of 1976, 2003, 2015 and 2018. Moreover, reported impacts diversify from agricultural dominance to multi-faceted impact types covering forestry, water quality, industry and so forth. Though EDIIALPS is biased by reporting behaviour, the region-specific results of negative drought impacts across the water-rich European mountain region demonstrate the need to move from emergency response to prevention and preparedness actions. These may be guided by EDIIALPS' insights to regional patterns, seasons and drought types.

scholarly journals An Alpine Drought Impact Inventory to explore past droughts in a mountain region

2021 ◽  
Author(s):  
Ruth Stephan ◽  
Mathilde Erfurt ◽  
Stefano Terzi ◽  
Maja Žun ◽  
Boštjan Kristan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Altitude ◽  
Hydrological Drought ◽  
Southern Region ◽  
Study Region ◽  
Drought Impact ◽  
Drought Impacts ◽  
Impact Data ◽  
The Impact ◽  
Alpine Space

Abstract. Drought affects even mountain regions, despite a humid climate. Droughts' damaging character in the past and an increasing probability in future projections call for an understanding of drought impacts in the European Alpine region. The European Drought Impact Report Inventory (EDII) collects text reports on negative drought impacts. This study presents a considerably updated EDII focusing on the study region of the greater Alpine Space. This first version release of an Alpine Drought Impact Inventory (EDIIALPS) classifies impact reports into categories covering various affected sectors and enables comparisons of the drought impact characteristics. We analyzed the distribution of reported impacts on the spatial, temporal and seasonal scale, and by drought type for soil-moisture and hydrological drought. For the spatial analysis, we compared the impact data located in the Alpine Space' to entire Europe. Further, we compared impact data between different climatic and altitudinal domains (Northern vs. Southern region, pre-Alpine vs. high-altitude region), and between the Alpine countries. Compared to entire Europe, in the Alpine Space agriculture and livestock farming impacts are even more frequently reported, especially in the Southern region. Public water supply is second most relevant sector, but overall less prominent compared to Europe, especially in spring when snowmelt mitigates water shortages. Impacts occurred mostly in summer and early autumn with a delay between those impacts initiated by soil-moisture and those by hydrological drought. The high-altitude region showed this effect the strongest. From 1975 to 2020, the number of archived reports increased, with substantially more impacts noted during the drought events of 1976, 2003, 2015 and 2018. Moreover, reported impacts diversified from agricultural dominance to multi-facetted impact types covering forestry, water quality, industry and so forth. Though EDIIALPS is biased by reporting behaviour, the amount of more than 3200 compiled reports on negative drought impacts demonstrates the need to move from emergency actions to better preparedness. These may be guided by EDIIALPS' insights to regional patterns, seasons and drought types.

Delayed soil-moisture and hydrological drought impacts in mountain regions

2021 ◽  
Author(s):  
Ruth Stephan ◽  
Mathilde Erfurt ◽  
Kerstin Stahl
Keyword(s):  
Soil Moisture ◽  
Hydrological Cycle ◽  
High Elevation ◽  
Hydrological Drought ◽  
Southern Region ◽  
Seasonal Patterns ◽  
Mountain Regions ◽  
Drought Impacts ◽  
Wide Range ◽  
Alpine Space

<p>Droughts are among the most damaging natural hazards leading to a wide range of impacts also in the water-rich mountain regions. In recent years, drought conditions and impacts have been reported frequently in the European Alpine region, possibly because climate change affects mountain environments more rapidly than others. However, in mountain regions the propagation of drought signals through the hydrological cycle and into different environmental and societal impact occurrence are poorly understood, especially regarding the impacts’ seasonal patterns and delayed effects. This study aims to improve the understanding of the droughts’ characteristics and their impacts from the high elevation headwater regions down to plateau and foothill areas. Specific climate conditions in high elevation regions determine an alpine environment, economy and society that differs from the pre-Alpine regions. Subsequently, impacts are expected to vary as well and indices for drought monitoring may have to be selected specifically for such a region. The European Drought Impact Report Inventory (EDII) compiles text-reports on negative drought impacts across Europe and classifies them into 15 categories with 108 subtypes. An updated version focusing on the ‘Alpine Space’ released as EDII<sub>ALPS</sub> V1.0 contains more than 3,200 reports about drought impacts. The most relevant categories are Agriculture and livestock farming and Public water supply. This data allowed an analysis of the seasonal patterns of drought impacts in several categories for four sub-regions in the Alpine Space: pre-Alpine vs. high-elevated region, Northern vs. Southern region. Assigning the impacts subtypes to drought type, soil-moisture drought (SMD impacts) and hydrological drought (HD impacts) allowed the derivation of smoothed seasonal “impact regimes”. The peak of HD impacts occurred later in the year than the SMD impact peak, most clearly in the high-elevation region, with the latest increase of HD impacts in May and strongest decrease between November-December. This pattern is less clear for the Southern region. SMD indices and HD indices that may be used for monitoring and early warning need to be targeted to and tested for capturing these delays.</p>

scholarly journals Drought impact links to meteorological drought indicators and predictability in Spain

2021 ◽  
Author(s):  
Herminia Torelló-Sentelles ◽  
Christian Franzke
Keyword(s):  
Random Forest ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Random Forest Model ◽  
The Arctic ◽  
Drought Impact ◽  
Forest Model ◽  
Drought Impacts ◽  
Drought Indicators ◽  
Impact Data

Abstract. Drought affects many regions worldwide and future climate projections imply that drought severity and frequency will increase. Hence, the impacts of drought on the environment and society will also increase considerably. Monitoring and early warning systems for drought rely on several indicators; however, assessments on how these indicators are linked to impacts are still lacking. Here, we explore the links between different drought indicators and drought impacts within six sub- regions in Spain. We used impact data from the European Drought Impact Report Inventory database, and provide a new case study to evaluate these links. We provide evidence that a region with a small sample size of impact data can still provide useful insights regarding indicator-impact links. As meteorological drought indicators, we use the Standardised Precipitation Index and the Standardised Precipitation-Evapotranspiration Index, as agricultural and hydrological drought indicators we use a Standardised Soil Water Index and, a Standardised Streamflow Index and a Standardised Reservoir Storage Index. We also explore the links between drought impacts and teleconnection patterns and surface temperature by conducting a correlation analysis and then test the predictability of drought impacts using a Random Forest model. Our results show meteorological indices are best linked to impact occurrences overall, and at long time scales between 15 and 33 months. However, we also find robust links for agricultural and hydrological drought indices, depending on the sub-region. The Arctic Oscillation, Western Mediterranean Oscillation and the North Atlantic Oscillation at long accumulation periods (15 to 48 months), are top predictors of impacts in the northwest and northeast regions, the Community of Madrid, and the south regions of Spain respectively. We also find links between temperature and drought impacts. The Random Forest model produces skilful models for most sub- regions. When assessed using a cross-validation analysis, the models in all regions show precision, recall, or R2 values higher than 0.97, 0.62 and 0.68 respectively. Since we find the models to be skilful, we encourage other types of impact data to be used to investigate these links and to predict drought impacts.

Evaluation of 2018 drought and effectiveness of adaptation measures in the Netherlands

2020 ◽  
Author(s):  
Marjolein H.J. van Huijgevoort ◽  
Janine A. de Wit ◽  
Ruud P. Bartholomeus
Keyword(s):  
Soil Moisture ◽  
The Netherlands ◽  
Capillary Rise ◽  
Hydrological Drought ◽  
Water Levels ◽  
Severe Drought ◽  
Drought Event ◽  
Groundwater Levels ◽  
The Impact

<p>Extreme dry conditions occurred over the summer of 2018 in the Netherlands. This severe drought event led to very low groundwater  and surface water levels. These impacted several sectors like navigation, agriculture, nature and drinking water supply. Especially in the Pleistocene uplands of the Netherlands, the low groundwater levels had a large impact on crop yields and biodiversity in nature areas. Projections show that droughts with this severity will occur more often in the future due to changes in climate. To mitigate the impact of these drought events, water management needs to be altered.</p><p>In this study, we evaluated the 2018 drought event in the sandy regions of the Netherlands and studied which measures could be most effective to mitigate drought impact. We have included meteorological, soil moisture and hydrological drought and the propagation of the drought through these types. Droughts were determined with standardized indices (e.g. Standardized Precipitation Index) and the variable threshold level method. Investigated measures were, for example, higher water levels in ditches, reduced irrigation from groundwater, and increased water conservation in winter. We also studied the timing of these measures to determine the potential for mitigating effects during a drought versus the effectiveness of long term adaptation. The measures were simulated with the agro-hydrological Soil–Water–Atmosphere–Plant (SWAP) model for several areas across the Netherlands for both agricultural fields and nature sites.</p><p>As expected, decreasing irrigation from groundwater reduced the severity of the hydrological drought in the region. Severity of the soil moisture drought also decreased in fields that were never irrigated due to the effects of capillary rise from the groundwater, but, as expected, increased in currently irrigated fields. Increasing the level of a weir in ditches had a relatively small effect on the hydrological drought, provided water was available to sustain higher water levels. This measure is, therefore, better suited as a long term change than as ad hoc measure during a drought. The effectiveness of the measures depended on the characteristics of the regions; for some regions small changes led to increases in groundwater levels for several months, whereas in other regions effects were lost after a few weeks. This study gives insight into the most effective measures to mitigate drought impacts in low-lying sandy regions like the Netherlands.</p>

scholarly journals Skill of large-scale seasonal drought impact forecasts

2020 ◽  
Vol 20 (6) ◽  
pp. 1595-1608
Author(s):  
Samuel J. Sutanto ◽  
Melati van der Weert ◽  
Veit Blauhut ◽  
Henny A. J. Van Lanen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Large Scale ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Learning Approaches ◽  
Discriminative Ability ◽  
Drought Impact ◽  
Drought Impacts ◽  
Impact Data

Abstract. Forecasting of drought impacts is still lacking in drought early-warning systems (DEWSs), which presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (logistic regression and random forest) to predict drought impacts with lead times up to 7 months ahead. The observed and forecasted hydrometeorological drought hazards – such as the standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and standardized runoff index (SRI) – were obtained from the The EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) DEWS. Reported drought impact data, taken from the European Drought Impact Report Inventory (EDII), were used to develop and validate drought impact functions. The skill of the drought impact functions in forecasting drought impacts was evaluated using the Brier skill score and relative operating characteristic metrics for five cases representing different spatial aggregation and lumping of impacted sectors. Results show that hydrological drought hazard represented by SRI has higher skill than meteorological drought represented by SPI and SPEI. For German regions, impact functions developed using random forests indicate a higher discriminative ability to forecast drought impacts than logistic regression. Moreover, skill is higher for cases with higher spatial resolution and less lumped impacted sectors (cases 4 and 5), with considerable skill up to 3–4 months ahead. The forecasting skill of drought impacts using machine learning greatly depends on the availability of impact data. This study demonstrates that the drought impact functions could not be developed for certain regions and impacted sectors, owing to the lack of reported impacts.

Alpine drought impact chains for sector-based climate-risk assessments

2021 ◽  
Author(s):  
Stefano Terzi ◽  
Mathilde Erfurt ◽  
Ruth Stephan ◽  
Kerstin Stahl ◽  
Marc Zebisch
Keyword(s):  
Case Studies ◽  
Drought Risk ◽  
Economic Sectors ◽  
Scientific Publications ◽  
Factors Affecting ◽  
Drought Impact ◽  
Drought Impacts ◽  
Drought Conditions ◽  
The Impact ◽  
Alpine Space

<p>Droughts are slow and silent natural hazards that can lead to long-lasting environmental, societal and economic impacts. Mountain regions are also experiencing drought conditions with climate change affecting their environments more rapidly than other places and reducing water availability well beyond their geographical locations. These conditions call for better understanding of drought events in mountains with innovative methodologies able to capture their complex interplays.</p><p>Within this context, the Alpine Drought Observatory (ADO) Interreg Project aims to further improve the understanding of drought conditions in the Alpine Space, with activities spanning from the characterization of drought types’ components in five heterogeneous case studies in Austria, France, Italy, Slovenia and Switzerland. For each case study, different sectors exposed to drought, ranging from hydropower, agriculture to tourism are considered. Moreover, specific socio-economic characteristics were collected for each sector in order to better understand the main drivers leading to drought impacts.</p><p>Starting from the risk concept in the IPCC AR5, the Impact Chains (IC) methodology has been applied to characterize the hazard, exposure and vulnerability components in the ADO case studies. IC allowed to pinpoint the main factors affecting drought risk and the relevant socio-economic sectors integrating a mixed-method approach. Quantitative data collection on the hazard and exposure components were coupled with local experts’ knowledge on the main vulnerability factors (e.g., through a questionnaire). Although validation represents a critical part of drought modelling, IC analysis and results were therefor compared with information from the European Drought Impact Inventory (EDII) on local drought impacts collected from scientific publications, unions press releases and newspaper articles over a long time period.</p><p>While drought risk assessment through IC can improve the understanding of the main drought events and their underlying factors, they also provide insights to improve planning and management of future drought events in the Alpine Space.</p>

scholarly journals Linking drought indices to impacts to support drought risk assessment in Liaoning province, China

2020 ◽  
Vol 20 (3) ◽  
pp. 889-906
Author(s):  
Yaxu Wang ◽  
Juan Lv ◽  
Jamie Hannaford ◽  
Yicheng Wang ◽  
Hongquan Sun ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Strong Relationship ◽  
Liaoning Province ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Risk ◽  
Drought Impact ◽  
Drought Impacts ◽  
Standardised Precipitation Index ◽  
The Impact

Abstract. Drought is a ubiquitous and recurring hazard that has wide-ranging impacts on society, agriculture and the environment. Drought indices are vital for characterising the nature and severity of drought hazards, and there have been extensive efforts to identify the most suitable drought indices for drought monitoring and risk assessment. However, to date, little effort has been made to explore which index (or indices) best represents drought impacts for various sectors in China. This is a critical knowledge gap, as impacts provide important ground truth information for indices used in monitoring activities. The aim of this study is to explore the link between drought indices and drought impacts, using Liaoning province (northeast China) as a case study due to its history of drought occurrence. To achieve this we use independent, but complementary, methods (correlation and random forest analysis) to identify which indices link best to drought impacts for prefectural-level cities in Liaoning province, using a comprehensive database of reported drought impacts in which impacts are classified into a range of categories. The results show that the standardised precipitation evapotranspiration index with a 6-month accumulation (SPEI6) had a strong correlation with all categories of drought impacts, while the standardised precipitation index with a 12-month accumulation (SPI12) had a weak correlation with drought impacts. Of the impact datasets, “drought-suffering area” and “drought impact area” had a strong relationship with all drought indices in Liaoning province, while “population and number of livestock with difficulty in accessing drinking water” had weak correlations with the indices. The results of this study can support drought planning efforts in the region and provide context for the indices used in drought-monitoring applications, so enabling improved preparedness for drought impacts. The study also demonstrates the potential benefits of routine collection of drought impact information on a local scale.

scholarly journals Exploring the link between drought indicators and impacts

2015 ◽  
Vol 15 (6) ◽  
pp. 1381-1397 ◽  
Author(s):  
S. Bachmair ◽  
I. Kohn ◽  
K. Stahl
Keyword(s):  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Drought Indices ◽  
Indicator Values ◽  
Drought Impact ◽  
Federal States ◽  
Drought Impacts ◽  
Drought Conditions ◽  
Drought Indicators ◽  
Impact Data

Abstract. 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.

scholarly journals Developing drought impact functions for drought risk management

2017 ◽  
Vol 17 (11) ◽  
pp. 1947-1960 ◽  
Author(s):  
Sophie Bachmair ◽  
Cecilia Svensson ◽  
Ilaria Prosdocimi ◽  
Jamie Hannaford ◽  
Kerstin Stahl
Keyword(s):  
Risk Management ◽  
Logistic Regression ◽  
Random Forest ◽  
Binary Data ◽  
Hurdle Model ◽  
Drought Risk ◽  
Drought Impact ◽  
Drought Impacts ◽  
The Impact ◽  
Drought Risk Management

Abstract. Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydrometeorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socioeconomic consequences of drought. This study tests the potential for developing empirical drought impact functions based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using Southeast England as a case study we tested the potential of three different data-driven models for predicting drought impacts quantified from text-based reports: logistic regression, zero-altered negative binomial regression (hurdle model), and an ensemble regression tree approach (random forest). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be reasonably predictable. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts and for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated and were found to have little influence on the prediction skill. For all models we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydrometeorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can provide useful information for drought risk management, and our study showcases different methodological approaches to developing drought impact functions based on text-based data.

scholarly journals Developing drought impact functions for drought risk management

2017 ◽  
Author(s):  
Sophie Bachmair ◽  
Cecilia Svensson ◽  
Ilaria Prosdocimi ◽  
Jamie Hannaford ◽  
Kerstin Stahl
Keyword(s):  
Risk Management ◽  
Logistic Regression ◽  
Random Forest ◽  
Binary Data ◽  
Hurdle Model ◽  
Drought Risk ◽  
Drought Impact ◽  
Drought Impacts ◽  
The Impact ◽  
Drought Risk Management

Abstract. Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydro-meteorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socio-economic consequences of drought. This study tests the potential for developing empirical drought impact functions based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors, and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using South–East England as a case study we tested the potential of three different data-driven models for predicting drought impacts quantified from text-based reports; logistic regression, zero-altered negative binomial regression (hurdle model), and an ensemble regression tree approach (random forest). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be predictable within reasonable limits. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross-validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts as well as for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated, and were found to have little influence on the prediction skill. For all models we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydro-meteorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can provide useful information for drought risk management, and our study showcases different methodological approaches to developing drought impact functions based on text-based data.

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