Abstract
Robust sub-seasonal and seasonal drought forecasts are essential for water managers and stakeholders coping with water shortage. Many studies have been conducted to evaluate the performance of hydrological forecasts, that is, streamflow. Nevertheless, only few studies evaluated the performance of hydrological drought forecasts. The objective of this study, therefore, is to analyse the skill and robustness of meteorological and hydrological drought forecasts on a catchment scale (the Ter and Llobregat rivers in Catalonia, Spain), rather than on a continental or global scale. Meteorological droughts were forecasted using downscaled (5 km) probabilistic weather reforecasts (ECMWF-SEAS4). These downscaled data were also used to produce hydrological drought forecasts, derived from time series of streamflow data simulated with a hydrological model (LISFLOOD). This resulted in seasonal hydro-meteorological reforecasts with a lead time up to 7 months, for the time period 2002–2010. These monthly reforecasts were compared to two datasets: (1) droughts derived from a proxy for observed data, including gridded precipitation data and discharge simulated by the LISFLOOD model, fed by these gridded climatological data; and (2) droughts derived from in situ observed precipitation and discharge. Results showed that the skill of hydrological drought forecasts is higher than the climatology, up to 3–4 months lead time. On the contrary, meteorological drought forecasts, analysed using the Standardized Precipitation Index (SPI), do not show added value for short accumulation times (SPI1 and SPI3). The robustness analysis showed that using either a less extreme or a more extreme threshold leads to a large change in forecasting skill, which points at a rather low robustness of the hydrological drought forecasts. Because the skill found in hydrological drought forecasts is higher than the meteorological ones in this case study, the use of hydrological drought forecasts in Catalonia is highly recommended for management of water resources.
Highlights
Drought events are one of the most costly weather-related natural hazards, because their effects can be widespread and long-lasting
Data comparison Gridded observed precipitation and discharge data spanning the period between 1990 and 2016, obtained from the LISFLOOD model (Simulation Forced with Observations, hereafter referred to as “SFO” for both precipitation and discharge, Section 2.2.2) were used as proxy for observations to calculate the skill of reforecasts of drought in precipitation and drought in discharge, respectively, for the years 2002 to 2010
This study shows that forecasting skill remains positive for lead times up to 3–4 months, and that it proves to be useful to look at longer time scales for hydrological drought forecasts
Summary
Drought events are one of the most costly weather-related natural hazards, because their effects can be widespread and long-lasting. Even though the term drought might seem straightforward, it is not unambiguous, as many different definitions have been proposed. We use the definition proposed by Tallaksen and Van Lanen (2004): a sustained period of below-normal water availability. The following three types of natural droughts can be determined (Wilhite, 2000; Tallaksen and Van Lanen, 2004): (1) meteorological drought: below-normal precipitation; (2) soil moisture drought: below-normal soil moisture content; and (3) hydrological drought: below-normal (ground)water levels and discharge. According to Heinrich and Gobiet (2012); Orlowsky and Seneviratne (2013); Russo et al (2013); Prudhomme et al (2014); Pascual et al (2015); Wanders and Van Lanen (2015); Wanders et al (2015) and Van der Wiel et al (2019), climate change will lead to drier conditions in many regions and river basins, causing drought events to occur more frequently, and increasing their impacts. Reliable and robust sub-seasonal and seasonal drought forecasts are essential for water managers and stakeholders
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