Abstract
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.
Highlights
Drought is a creeping phenomenon that slowly covers and affects extensive areas (Wilhite, 2000; Tallaksen and Van Lanen, 2004; Mishra and Singh, 2010; Van Loon, 2015)
To evaluate the skill of seasonal drought impact forecasts using the random forest (RF) and logistic regression (LR) approaches, we divided the study into three parts with the following objectives (Fig. 1): (1) evaluation of the skill in forecasting drought hazards using re-forecast data from 2002 to 2010, which is a first step in the forecasting chain; (2) evaluation of the skill of the drought impact functions based upon the RF and LR approaches using proxy observed data and a split-sampling method; and (3) testing the skill of developed drought impact functions based upon the RF and LR approaches to forecasting drought impacts using re-forecast data from 2002 to 2010
Group 1 relates to agricultural drought, group 2 relates to streamflow drought, group 3 relates to hydrological drought linked to ecosystems, and group 4 relates to meteorological drought and heat
Summary
Drought is a creeping phenomenon that slowly covers and affects extensive areas (Wilhite, 2000; Tallaksen and Van Lanen, 2004; Mishra and Singh, 2010; Van Loon, 2015). Sutanto et al (2019b), have conducted a first study that explored the possibility of forecasting the drought impacts using hydro-meteorological drought indices – including the SPI, SPEI, and standardized runoff index (SRI, Shukla and Wood, 2008) – and the EDII database with data from Germany. They only used the RF method to forecast drought impacts for four different lumped impacted sectors (i.e., impact groups). To evaluate the skill of seasonal drought impact forecasts using the RF and LR approaches, we divided the study into three parts with the following objectives (Fig. 1): (1) evaluation of the skill in forecasting drought hazards using re-forecast data from 2002 to 2010 (box G), which is a first step in the forecasting chain; (2) evaluation of the skill of the drought impact functions based upon the RF and LR approaches using proxy observed data and a split-sampling method (box L); and (3) testing the skill of developed drought impact functions based upon the RF and LR approaches to forecasting drought impacts using re-forecast data from 2002 to 2010 (box O)
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