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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Watershed Planning within a Quantitative Scenario Analysis Framework
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Moving from drought hazard to impact forecasts.

Samuel J Sutanto1, Melati van der Weert2, Niko Wanders3

  • 1Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a, 6708PB, Wageningen, The Netherlands. samuel.sutanto@wur.nl.

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|November 1, 2019
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Summary

This study shows that forecasting drought impacts is feasible using machine learning. Developing drought impact databases is crucial for improving early warning systems and drought management.

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Area of Science:

  • Hydrology
  • Climate Science
  • Data Science

Background:

  • Current drought early warning systems primarily forecast drought hazards (e.g., river flow deficits).
  • Information on forecasted drought impacts, essential for effective drought management, is largely unavailable.
  • Integrating impact forecasting into early warning systems is a critical unmet need.

Purpose of the Study:

  • To assess the feasibility of forecasting drought impacts.
  • To develop and evaluate machine-learning models that relate hydro-meteorological drought indices to reported drought impacts.
  • To highlight the importance of drought impact data for enhancing drought management strategies.

Main Methods:

  • Utilized machine learning algorithms to connect forecasted hydro-meteorological drought indices with historical drought impact data.
  • Trained models using over 50 months of reported drought impacts.
  • Assessed the predictive capability of the models for future drought impacts.

Main Results:

  • Machine learning models demonstrated feasibility in forecasting drought impacts several months in advance.
  • Model performance was positively correlated with the availability of extensive historical drought impact data (over 50 months).
  • The study confirms the critical role of comprehensive drought impact databases.

Conclusions:

  • Forecasting drought impacts is achievable and can significantly enhance drought management.
  • The development and maintenance of robust drought impact databases are essential for creating effective drought impact functions.
  • Operational drought early warning systems should expand their scope to include impact forecasting alongside hazard forecasting.