A quantitative analysis to objectively appraise drought indicators and model drought impacts

Abstract

Abstract. Drought monitoring and early warning is an important measure to enhance resilience towards drought. While there are numerous operational systems using different drought indicators, there is no consensus on which indicator best represents drought impact occurrence for any given sector. Furthermore, thresholds are widely applied in these indicators but, to date, little empirical evidence exists as to which indicator thresholds trigger impacts on society, the economy, and ecosystems. The main obstacle for evaluating commonly used drought indicators is a lack of information on drought impacts. Our aim was therefore to exploit text-based data from the European Drought Impact report Inventory (EDII) to identify indicators that are meaningful for region-, sector-, and season-specific impact occurrence, and to empirically determine indicator thresholds. In addition, we tested the predictability of impact occurrence based on the best-performing indicators. To achieve these aims we applied a correlation analysis and an ensemble regression tree approach, using Germany and the UK (the most data-rich countries in the EDII) as test beds. As candidate indicators we chose two meteorological indicators (Standardized Precipitation Index, SPI, and Standardized Precipitation Evaporation Index, SPEI) and two hydrological indicators (streamflow and groundwater level percentiles). The analysis revealed that accumulation periods of SPI and SPEI best linked to impact occurrence are longer for the UK compared with Germany, but there is variability within each country, among impact categories and, to some degree, seasons. The median of regression tree splitting values, which we regard as estimates of thresholds of impact occurrence, was around −1 for SPI and SPEI in the UK; distinct differences between northern/northeastern vs. southern/central regions were found for Germany. Predictions with the ensemble regression tree approach yielded reasonable results for regions with good impact data coverage. The predictions also provided insights into the EDII, in particular highlighting drought events where missing impact reports may reflect a lack of recording rather than true absence of impacts. Overall, the presented quantitative framework proved to be a useful tool for evaluating drought indicators, and to model impact occurrence. In summary, this study demonstrates the information gain for drought monitoring and early warning through impact data collection and analysis. It highlights the important role that quantitative analysis with impact data can have in providing "ground truth" for drought indicators, alongside more traditional stakeholder-led approaches.

Drought in a human-modified world: reframing drought definitions, understanding, and analysis approaches

“…Data-driven statistical models have used time series or spatial variability of these impact occurrences as a response variable in regression and classification tree models ( Fig. 6c; Stagge et al, 2015a;Bachmair et al, 2016b;Blauhut et al, 2015). These studies have also shown that impact generation is more complex than previously assumed and may be caused by the cooccurrence of several extremes, lagged effects, and seasonality (Stagge et al, 2015a).…”

The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

“…Other studies have suggested a link between impact severity and the number of records reporting it (e.g. Hernández Varela et al, 2003;Bachmair et al, 2016), but this needs to be taken carefully since the media coverage of a drought event is highly influenced by the sociopolitical context in the affected area (Sonnett et al, 2006;Llasat et al, 2009). A few additional aspects concerning reliability were noticed while processing the records from the press: Accuracy The information on drought occurrence reported in the newspaper may not be accurate.…”

“…quantitative information) is considered as a benchmark of drought impact on agriculture. The correlation of remote sensing data, especially SPI and NDVI, to agriculture yield data has been widely researched and applied (see Bachmair et al, 2016, for a review). This second type of impact data was included to provide a comparison of the results obtained in the correlation to text-based impact data and results obtained with the most commonly used type of impact data, and discuss the advantages and limitations of one with respect to the other.…”

“…These have the objective to collect text-based impact records systematically with the aim to increase their availability and accessibility. Recent studies have explored the links of the EDII records to drought indicators (Bachmair et al, 2015(Bachmair et al, , 2016Blauhut et al, 2015), though these have focused on the national scale, and it is recognised that further development is required to allow analysis at the subnational scale .…”

Analysis of hydrological data with correlation matrices: technical implementation and possible applications

“…Bachmair et al (2016) for instance, found that the accumulation periods of SPI and SPEI best linked to drought impacts vary from one region to the other and also depend on the kind of impact considered. In the Aichfeld example, the SPI6 exhibits the highest correlation with the SGI, suggesting that it is a reasonable indicator for drought impacts on groundwater, in particular pointing to low groundwater levels and thus low aquifer storage and discharge, which might be associated with other adverse environmental or economic effects.…”