Abstract. Drought affects many regions worldwide, and future climate projections imply that drought severity and frequency will increase. Hence, the impacts of drought on the environment and society will also increase considerably. Monitoring and early warning systems for drought rely on several indicators; however, assessments of how these indicators are linked to impacts are still lacking. Here, we explore the links between different drought indicators and drought impacts within six sub-regions in Spain. We used impact data from the European Drought Impact Report Inventory database and provide a new case study to evaluate these links. We provide evidence that a region with a small sample size of impact data can still provide useful insights regarding indicator–impact links. As meteorological drought indicators, we use the Standardised Precipitation Index and the Standardised Precipitation Evapotranspiration Index; as agricultural and hydrological drought indicators, we use a Standardised Soil Water Content Index and a Standardised Streamflow Index and a Standardised Reservoir Storage Index. We also explore the links between drought impacts and teleconnection patterns and surface temperature by conducting a correlation analysis, and then we test the predictability of drought impacts using a random forest model. Our results show that meteorological indices are best linked to impact occurrences overall and at long timescales between 15 and 33 months. However, we also find robust links for agricultural and hydrological drought indices, depending on the sub-region. The Arctic Oscillation, Western Mediterranean Oscillation, and the North Atlantic Oscillation at long accumulation periods (15 to 48 months) are top predictors of impacts in the northwestern and northeastern regions, the community of Madrid, and the southern regions of Spain, respectively. We also find links between temperature and drought impacts. The random forest model produces skilful models for most sub-regions. When assessed using a cross-validation analysis, the models in all regions show precision, recall, or R2 values higher than 0.97, 0.62, and 0.68, respectively. Thus, our random forest models are skilful in predicting drought impacts and could potentially be used as part of an early warning system.

Abstract. Drought affects many regions worldwide and future climate projections imply that drought severity and frequency will increase. Hence, the impacts of drought on the environment and society will also increase considerably. Monitoring and early warning systems for drought rely on several indicators; however, assessments on how these indicators are linked to impacts are still lacking. Here, we explore the links between different drought indicators and drought impacts within six sub- regions in Spain. We used impact data from the European Drought Impact Report Inventory database, and provide a new case study to evaluate these links. We provide evidence that a region with a small sample size of impact data can still provide useful insights regarding indicator-impact links. As meteorological drought indicators, we use the Standardised Precipitation Index and the Standardised Precipitation-Evapotranspiration Index, as agricultural and hydrological drought indicators we use a Standardised Soil Water Index and, a Standardised Streamflow Index and a Standardised Reservoir Storage Index. We also explore the links between drought impacts and teleconnection patterns and surface temperature by conducting a correlation analysis and then test the predictability of drought impacts using a Random Forest model. Our results show meteorological indices are best linked to impact occurrences overall, and at long time scales between 15 and 33 months. However, we also find robust links for agricultural and hydrological drought indices, depending on the sub-region. The Arctic Oscillation, Western Mediterranean Oscillation and the North Atlantic Oscillation at long accumulation periods (15 to 48 months), are top predictors of impacts in the northwest and northeast regions, the Community of Madrid, and the south regions of Spain respectively. We also find links between temperature and drought impacts. The Random Forest model produces skilful models for most sub- regions. When assessed using a cross-validation analysis, the models in all regions show precision, recall, or R2 values higher than 0.97, 0.62 and 0.68 respectively. Since we find the models to be skilful, we encourage other types of impact data to be used to investigate these links and to predict drought impacts.

Abstract. Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall, and cities becoming larger and denser, the frequency, magnitude and impact of these events are expected to increase. Pluvial floods can occur in urban areas within minutes. A fast and reliable flood warning system should thus be implemented in flood-prone cities to warn the public of upcoming floods and save lives and reduce damage. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.

Supplementary materialFigure S1. Laboratory dripping experiment results for the less sensitive (cave-designed) acoustic sensors. In relation to the optical sensor's records, the occurrence of the acoustic sensors' records is reported.

<p>More than half of the world’s population now resides in cities and the amount of urban population is expected to further increase during the coming decades. Urbanization and the associated changes in land use/land cover can have a notable impact on the climate at local and regional scales. Specifically, several studies recently concluded that urbanization can modify the temporal and spatial properties of precipitation. On top of that, global warming is expected to enhance the magnitude and frequency of short-duration heavy precipitation, with consequential effects on the severity and frequency of urban pluvial flood events. Therefore, improving our understanding of the separate and combined effects of urbanization and climate change on short-duration precipitation is imperative for flood risk assessments and planning of future cities. To this end, we investigate the impact of climate change and urbanization on the space-time properties of precipitation by conducting current and future simulation scenarios over cities with different climates using the Weather Research and Forecasting (WRF) physically-based climate model. The results of this study elucidate the important role of urban land cover on the spatial stucture of precipitation under a changing climate.</p>