Moving from drought hazard to impact forecasts

Abstract

Present-day drought early warning systems provide the end-users information on the ongoing and forecasted drought hazard (e.g. river flow deficit). However, information on the forecasted drought impacts, which is a prerequisite for drought management, is still missing. Here we present the first study assessing the feasibility of forecasting drought impacts, using machine-learning to relate forecasted hydro-meteorological drought indices to reported drought impacts. Results show that models, which were built with more than 50 months of reported drought impacts, are able to forecast drought impacts a few months ahead. This study highlights the importance of drought impact databases for developing drought impact functions. Our findings recommend that institutions that provide operational drought early warnings should not only forecast drought hazard, but also impacts after developing an impact database.

Skill of large-scale seasonal drought impact forecasts

“…In general, the skill of drought impact forecasts using BSS is fair for short LTs and poor for longer LTs implying that for shorter LTs a reasonable distinction can be made between impact and no impact. This is especially the case for situations where the observed data show prolonged periods with drought impacts (Sutanto et al, 2019b).…”

“…1). Bachmair et al (2016), Bachmair et al (2017), and (Sutanto et al, 2019b) provide a detailed explanation of the use of RF to develop drought impact forecasting functions.…”

“…We only present the skill of drought impact forecasts for Case 4 because the number of drought impact functions that can be developed for Case 5 is limited. This is due to the large differences in the number of reported impacts for each impacted sector between the German NUTS-1 regions (Stahl et al, 2016;Sutanto et al, 2019b). For example, for Case 5, drought impacts on Agriculture and Livestock Farming or Forestry can be forecasted in BW and BV regions only, respectively (Figure not shown).…”

A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies

“…Carrão, Naumann, and Barbosa () mapped global patterns of drought risk, revealing low spatial correlation between hazard occurrence and drought risk. Sutanto, van der Weert, Wanders, Blauhut, and Van Lanen () analyzed gridded drought indices and drought impact reports with machine learning to forecast drought impacts on a European level. Albrecht () combined a global disaster loss database with European survey data to analyze how natural hazard‐related disasters affect social capital.…”

A General Overview of the Risk-Reduction Strategies for Floods and Droughts

“…It not only provides flood risk assessment but also directly translates the risk into the expected socio-economic impacts [123]. Along with flood or drought warning and prediction models, the integration of different information and tools, such as geographic information systems (GISs), satellite images, consensus data, the locations of critical structures, and socio-economic impact assessment tools, is necessary [123,124]. Impact assessment can be classified according to its evaluation time into pre-or during-disaster assessment.…”