[1] Information from 846 N 2 O emission measurements in agricultural fields and 99 measurements for NO emissions was summarized to assess the influence of various factors regulating emissions from mineral soils. The data indicate that there is a strong increase of both N 2 O and NO emissions accompanying N application rates, and soils with high organic-C content show higher emissions than less fertile soils. A fine soil texture, restricted drainage, and neutral to slightly acidic conditions favor N 2 O emission, while (though not significant) a good soil drainage, coarse texture, and neutral soil reaction favor NO emission. Fertilizer type and crop type are important factors for N 2 O but not for NO, while the fertilizer application mode has a significant influence on NO only. Regarding the measurements, longer measurement periods yield more of the fertilization effect on N 2 O and NO emissions, and intensive measurements (!1 per day) yield lower emissions than less intensive measurements (2-3 per week). The available data can be used to develop simple models based on the major regulating factors which describe the spatial variability of emissions of N 2 O and NO with less uncertainty than emission factor approaches based on country N inputs, as currently used in national emission inventories.

Understanding global future river flood risk is a prerequisite for the quantification of climate change impacts and planning e ective adaptation strategies 1. Existing global flood risk projections fail to integrate the combined dynamics of expected socioeconomic development and climate change. We present the first global future river flood risk projections that separate the impacts of climate change and socioeconomic development. The projections are based on an ensemble of climate model outputs 2 , socioeconomic scenarios 3 , and a state-of-the-art hydrologic river flood model combined with socioeconomic impact models 4,5. Globally, absolute damage may increase by up to a factor of 20 by the end of the century without action. Countries in Southeast Asia face a severe increase in flood risk. Although climate change contributes significantly to the increase in risk in Southeast Asia 6 , we show that it is dwarfed by the e ect of socioeconomic growth, even after normalization for gross domestic product (GDP) growth. African countries face a strong increase in risk mainly due to socioeconomic change. However, when normalized to GDP, climate change becomes by far the strongest driver. Both highand low-income countries may benefit greatly from investing in adaptation measures, for which our analysis provides a basis. Between 1980 and 2013, the global direct economic losses due to floods exceeded $1 trillion (2013 values), and more than 220,000 people lost their lives 7. Global flood damages have been increasing steeply over the past decades, so far mainly driven by steady growth in population and economic activities in flood-prone areas 8,9. Future increases in flood frequency and severity due to changes in extreme weather are expected 1,9. Such increasing trends in flood risk may have severe direct humanitarian and economic impacts and lasting long-term negative effects on economic growth 10,11. In 2015, several major international policies are being initiated or renewed that may catalyse flood risk adaptation and hence risk reduction, such as the Sustainable Development Goals, Conference of the Parties (COP) 21, and the Sendai Framework for Disaster Risk Reduction. Such efforts require global understanding of the drivers of flood risk change in the future. Past efforts to enhance this understanding have focused on the global-scale mapping of present-day flood hazard 12,13 and risk 4,5 and future changes in global flood exposure and risk 14 due to either climate change 6,15,16 or socioeconomic development 8,17. One recent study 18 combined global socioeconomic and climate change into future global flood risk projections for the first time, however, this work did not reveal regional patterns nor quantify the drivers of risk change. Furthermore, no study has so far accounted for installed and maintained flood protection standards (FPS; ref. 10).

Globally, economic losses from flooding exceeded $19 billion in 2012, and are rising rapidly. Hence, there is an increasing need for global-scale flood risk assessments, also within the context of integrated global assessments. We have developed and validated a model cascade for producing global flood risk maps, based on numerous flood return-periods. Validation results indicate that the model simulates interannual fluctuations in flood impacts well. The cascade involves: hydrological and hydraulic modelling; extreme value statistics; inundation modelling; flood impact modelling; and estimating annual expected impacts. The initial results estimate global impacts for several indicators, for example annual expected exposed population (169 million); and annual expected exposed GDP ($1383 billion). These results are relatively insensitive to the extreme value distribution employed to estimate low frequency flood volumes. However, they are extremely sensitive to the assumed flood protection standard; developing a database of such standards should be a research priority. Also, results are sensitive to the use of two different climate forcing datasets. The impact model can easily accommodate new, user-defined, impact indicators. We envisage several applications, for example: identifying risk hotspots; calculating macro-scale risk for the insurance industry and large companies; and assessing potential benefits (and costs) of adaptation measures.

Abstract. There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate, which can be used for strategic global flood risk assessments. The framework estimates hazard at a resolution of ∼ 1 km 2 using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood-routing model, and more importantly, an inundation downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IM-AGE). We performed downscaling of the hazard probability distributions to 1 km 2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard estimates has been performed using the Dartmouth Flood Observatory database. This was done by comparing a high return period flood with the maximum observed extent, as well as by comparing a time series of a single event with Dartmouth imagery of the event. Validation of modelled damage estimates was performed using observed damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate. The framework estimates hazard at high resolution (~1 km²) using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood routing model, and importantly, a flood extent downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km² resolution with a new downscaling algorithm, applied on Bangladesh as a first case-study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard and damage estimates has been performed using the Dartmouth Flood Observatory database and damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models