Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of population affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System, which has been set up to provide an overview on upcoming floods in large world river basins. The Global Flood Awareness System is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the Earth

The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Density Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as Normal-Score Transform. In this paper some possible problems caused by small sample sizes for the applicability in flood forecasting systems will be discussed and illustrated by examples. For the practical implementation commands and examples from the freely available and widely used statistical computing language R (R Development Core Team, 2011) will be given (represented in Courier font) and possible solutions are suggested by combining extreme value analysis and non-parametric regression methods

A deeper understanding of the intricate relationship between the two components of the Asian summer monsoon (ASM)-the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM)-is crucial to improve the subseasonal forecasting of extreme precipitation events. Using an innovative complex network-based approach, we identify two dominant synchronization pathways between ISM and EASM-a southern mode between the Arabian Sea and southeastern China occurring in June, and a northern mode between the core ISM zone and northern China which peaks in July-and their associated large-scale atmospheric circulation patterns. Furthermore, we discover that certain phases of the Madden-Julian oscillation and the lower frequency mode of the boreal summer intraseasonal oscillation (BSISO) seem to favour the overall synchronization of extreme rainfall events between ISM and EASM while the higher-frequency mode of the BSISO is likely to support the shifting between the modes of ISM-EASM connection.

Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale

This study examines the European heatwaves' predictability at subseasonal timescales. Land surface feedbacks and tropical convection, due to their variability at intraseasonal timescales, are taken into consideration and their potential role in extending the predictability beyond the medium range (10 days) is explored. A classification of European heatwaves into five heatwave types is used to discriminate the effects of surface feedbacks and of tropical variability among the different heatwave types. The classification is computed in terms of circulation patterns. By inferring the near‐surface temperature through atmospheric circulation, we aim to identify the predictable component of the heatwave events. All five heatwave circulation patterns are characterized by persistent anticyclonic anomalies located over the region with maximum temperatures. We show that soil moisture deficit is not a required precondition for the occurrence of heatwaves over most of Europe. However, heatwave events over southern Europe exhibit some sensitivity to dry conditions. We use a simplified index to describe the dominant mode of tropical convection at intraseasonal timescales. The index, based on precipitation anomalies, represents the evolution of the Boreal Summer Intraseasonal Oscillation (BSISO). We find that episodes with strong BSISO amplitudes characterized by enhanced convection over India, Bay of Bengal and China sea favour the occurrence of heatwave events over Russia. The results highlight the role of tropical intraseasonal variability in enhancing the predictability of some extreme temperature events over Europe.

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Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate their effects. This paper tries to assess the predictive capabilities of an integrated drought monitoring and forecasting system based on the Standard precipitation index (SPI). The system is firstly constructed by temporally extending near real-time precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with forecasted fields as provided by the ECMWF seasonal forecasting system and then is evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar patterns in the South and North West Africa, while there is a low correlation in the tropical region which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at larger time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in two to four months lead time

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