Abstract
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 the 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 score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model” assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. 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 mechanisms. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was 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 drought predictability during the wet seasons in the MHRB.
Highlights
Drought is among the most costly natural hazards in many parts of the world
To evaluate the performance of seasonal drought prediction system, we first examined the predictability and forecast skill of North American Multi-Model Ensemble (NMME) meteorological predictions based on SPI3 series in terms of anomaly correlation (AC) metric (Fig. 3)
The predictability is higher in autumn and winter than that in summer and spring, which corresponds to a higher forecast skill in autumn and winter
Summary
Drought is among the most costly natural hazards in many parts of the world. It is defined as a prolonged period of below-average rainfall, leading to water shortages in soil and the hydrologic system. Drought can have a substantial effect on many sectors, such as agriculture, ecosystem and economy, and its impacts can vary from region to region. Statistical, dynamic and hybrid (statistical–dynamic) methods have been used for drought predictions (Mariotti et al, 2013; Hao et al, 2018; Mishra and Singh, 2011; Pozzi et al, 2013; Luo and Wood, 2007; Luo et al, 2008). The statistical method is based on the historical relationship between some aspects of drought and a number of predictors (e.g., large-scale climate signals). The dynamic method relies on the skill of state-of-the-art general
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