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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-405
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Aug 2018

Research article | 07 Aug 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China

Feng Ma1,2, Lifeng Luo2, Aizhong Ye1, and Qingyun Duan1 Feng Ma et al.
  • 1State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 2Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, USA

Abstract. Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below -1 indicate drought events. The skill of the forecasting systems was evaluated in terms of Anomaly Correlation (AC) and Brier skill score (BSS). The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanism. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of initial hydrologic conditions (ICs) during cold and dry seasons. During wet seasons, there's no skillful hydrological predictions since lead-2 month because the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions is the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological predictability during the wet seasons in the MHRB.

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Short summary
Predicting meteorological droughts more than 2 months in advance became difficult due to low predictability, leading to weak skills for hydrological droughts in wet seasons. Hydrological drought forecasts showed skills up to 3–6 lead months due to memory of initial hydrologic conditions in dry seasons. Human activities have increased hydrological predictability during wet seasons in the MHRB. This fills gaps in understanding drought and predictability predictions in endorheic and arid basins.
Predicting meteorological droughts more than 2 months in advance became difficult due to low...
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