Local rainfall forecast knowledge across the globe used for agricultural decision-making.

The agriculture sector is vital to the world's economy and weather and climate are key drivers that affect the productivity and profitability of agricultural systems. At the same time, weather-related risks pose significant challenges to farmers' livelihoods. Although scientific weather forecast (SFK) is available, many farmers, especially in the Global South, have limited access to this information, and they rely on local forecast knowledge (LFK) to make farming decisions. Many studies also recognize the value of combining both forecasting systems; yet, unlike SFK which is readily available, indicators for LFK needs to be collected first. Therefore, this study identifies and documents the spatial distribution of LFK use for agriculture across the globe through a systematic literature review. Results show that a high number of LFK regions with a total of around 1350 local environmental indicators were found in Africa and Asia and less in South and North America. The low usability of scientific weather forecasts is perceived as the main reason farmers use LFK instead of SFK, yet the accessibility of LFK both for scientists and users, needs to be improved. Indicators based on animals and meteorology appeared to be more frequently used for weather predictions than plant- and astronomy-based indicators. Digitalizing the LFK inventory and collecting more detailed information about the regions where LFK was identified could promote and foster research on integrating scientific and local forecasting systems. This study will draw attention to the importance of LFK in weather forecasting, maintain this knowledge and enhance it.

Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale - Case Catalonia (Spain).

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.

Moving from drought hazard to impact forecasts.

Present-day drought early warning systems provide the end-users information on the ongoing and forecasted drought hazard (e.g. river flow deficit). However, information on the forecasted drought impacts, which is a prerequisite for drought management, is still missing. Here we present the first study assessing the feasibility of forecasting drought impacts, using machine-learning to relate forecasted hydro-meteorological drought indices to reported drought impacts. Results show that models, which were built with more than 50 months of reported drought impacts, are able to forecast drought impacts a few months ahead. This study highlights the importance of drought impact databases for developing drought impact functions. Our findings recommend that institutions that provide operational drought early warnings should not only forecast drought hazard, but also impacts after developing an impact database.