4.5 Article

Projection of future drought and extreme events occurrence in Goodwater Creek Experimental Watershed, Midwestern US

Journal

HYDROLOGICAL SCIENCES JOURNAL
Volume 66, Issue 6, Pages 1045-1058

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2021.1906878

Keywords

agriculture; drought; climate change; future extreme weather; modeling; soil moisture

Funding

  1. USDA-ARS [58-3622-4-021]
  2. Climate Change Impacts on Hydrology and Productivity in Goodwater Creek Experimental Watershed
  3. USDA [5070-12130-006-00D]

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This study predicts the future occurrence of drought and wet periods based on various factors, indicating an increased frequency and duration of future droughts primarily due to a decline in summer precipitation. Despite an increase in precipitation, the study shows an increased risk of spring droughts in the future, emphasizing the importance of process representation with hydrologic models for drought computation.
Predicting the impacts of projected change in precipitation (P) and temperature (T) on occurrence of drought and extreme events is essential for managing natural resources and setting policy. This study compares future occurrence of excessively dry and wet periods based on P, T, stream flow, soil moisture, and extreme P and T events. The comparisons are based on coupled future climate projections from multiple Earth system models downscaled using site-specific weather data and hydrologic model outputs for the Goodwater Creek Experimental Watershed, Missouri, USA. The use of multiple drought indices, downscaled climate data, and process model output facilitated drought prediction for different land surface processes and its comparison. The P and T extremes and droughts were calculated using standardized indices. The results based on drought and extreme indices indicate increased frequency and duration of drought in the future, primarily due to a projected decline in summer precipitation resulting in summer droughts. The streamflow and soil water-based drought indices indicated increased spring drought risks in the future despite a precipitation increase, indicating the importance of process representation with hydrologic models for drought computation.

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