Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-3991-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3991-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Streamflow drought: implication of drought definitions and its application for drought forecasting
Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a, 6708PB, Wageningen, the Netherlands
now at: Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands
Henny A. J. Van Lanen
Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a, 6708PB, Wageningen, the Netherlands
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Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel J. Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank
Hydrol. Earth Syst. Sci., 28, 1935–1956, https://doi.org/10.5194/hess-28-1935-2024, https://doi.org/10.5194/hess-28-1935-2024, 2024
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-252, https://doi.org/10.5194/hess-2022-252, 2022
Preprint withdrawn
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Drought impacts on crops can be assessed in terms of crop yield (CY) variation. The hypothesis is that the spatiotemporal change of drought area is a good input to predict CY. A step-by-step approach for predicting CY is built based on two types of machine learning models. Drought area was found suitable for predicting CY. Since it is currently possible to calculate drought areas within drought monitoring systems, the prediction of drought impacts can be integrated directly into them.
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-600, https://doi.org/10.5194/hess-2021-600, 2021
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Short summary
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Drought effects on crops are usually evaluated through crop yield (CY). The hypothesis is that the drought spatial extent is a good input to predict CY. A machine learning approach to predict crop yield is introduced. The use of drought area was found suitable. Since it is currently possible to calculate drought areas within drought monitoring systems, the direct application to predict drought effects can be integrated into them by following approaches such as the one presented or similar.
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The FRIEND-Water program (FWP) is the oldest and the most transverse program within the UNESCO IHP. It allows large communities of hydrologists to collaborate across borders on common shared data and scientific topics, addressed through 8 large world regions. Research priorities evolve according to the projections given by the member States during the IHP councils. FWP further activities follow the IHP IX program with the support of the Montpellier UNESCO Category II Center ICIREWAD.
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The 8th Global FRIEND conference highlighted the advance in hydrological science and innovation in water management. 52 accepted papers cover study areas in precipitation and climate impact; observation, analysis and simulations of hydrologic processes; floods in the changing environments; drought monitoring and analysis; water resources and environmental impacts. The outcome of the conference presented in the proceedings will be shared and discussed widely among UNESCO IHP networks.
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Short summary
This paper provides a comprehensive overview of the differences within streamflow droughts derived using different identification approaches, namely the variable threshold, fixed threshold, and the Standardized Streamflow Index, including an analysis of both historical drought and implications for forecasting. Our results clearly show that streamflow droughts derived from different approaches deviate from each other in terms of drought occurrence, timing, duration, and deficit volume.
This paper provides a comprehensive overview of the differences within streamflow droughts...