Cross-border climate vulnerabilities of the European Union to drought.

European Union's vulnerability to climate change stretches far beyond its borders because many of its economic sectors, such as meat and dairy, use raw materials sourced from far afield. Cross-border climate vulnerability is a relatively new subject in scientific literature, while of high societal and economic relevance. We quantify these climate vulnerabilities with a focus on drought risk and assessed them for 2030, 2050, 2085 and for RCP 2.6 and 6.0 climate scenarios. Here we find that more than 44% of the EU agricultural imports will become highly vulnerable to drought in future because of climate change. The drought severity in production locations of the agricultural imports in 2050 will increase by 35% compared to current levels of drought severity. This is particularly valid for imports that originate from Brazil, Indonesia, Vietnam, Thailand, India and Turkey. At the same time, imports from Russia, Nigeria, Peru, Ecuador, Uganda and Kenya will be less vulnerable in future. We also report that the climate vulnerabilities of meat and dairy, chocolate (cocoa), coffee, palm oil-based food and cosmetic sectors mainly lie outside the EU borders rather than inside.

Translating large-scale climate variability into crop production forecast in Europe.

Studies show that climate variability drives interannual changes in meteorological variables in Europe, which directly or indirectly impacts crop production. However, there is no climate-based decision model that uses indices of atmospheric oscillation to predict agricultural production risks in Europe on multiple time-scales during the growing season. We used Fast-and-Frugal trees to predict sugar beet production, applying five large-scale indices of atmospheric oscillation: El Niño Southern Oscillation, North Atlantic Oscillation, Scandinavian Pattern, East Atlantic Pattern, and East Atlantic/West Russian pattern. We found that Fast-and-Frugal trees predicted high/low sugar beet production events in 77% of the investigated regions, corresponding to 81% of total European sugar beet production. For nearly half of these regions, high/low production could be predicted six or five months before the start of the sugar beet harvesting season, which represents approximately 44% of the mean annual sugar beet produced in all investigated areas. Providing early warning of crop production shortages/excess allows decision makers to prepare in advance. Therefore, the use of the indices of climate variability to forecast crop production is a promising tool to strengthen European agricultural climate resilience.

Hydrogeological modelling for the watershed management of the Mar Menor coastal lagoon (Spain).

The Mar Menor is the largest lagoon along the Spanish Mediterranean coast. It suffers from eutrophication and algal blooms associated with intensive agricultural activities and urban pressure in the surrounding Campo de Cartagena plain. A balanced discharge of groundwater, carrier of algal nutrients such as nitrate, is essential to ensure the integrity of the coastal lagoon and the availability of groundwater resources inland. We here present a 3D hydrogeological model of the unconfined Quaternary aquifer that discharges into the lagoon. The model couples both surface water balance and groundwater dynamics and has been calibrated to available data in the period 2000-2016. The calibrated model allows understanding of the current state of the aquifer and its link to the lagoon. The potential discharge has been quantified in both space and time and falls between 69.5 and 84.9 hm3/yr during dry and wet periods, respectively (with values of nitrate discharge of 11.4-11.8 Mkg/yr in the absence of aquifer sink terms, e.g., leakage to deeper aquifers and pumping from groundwater wells). The predictive capabilities of the calibrated model can be used to test the impact of different integrated management scenarios on the surface-groundwater dynamics of the catchment. Three plausible management scenarios are proposed that include localized and distributed groundwater pumping (drains and groundwater wells, respectively). Results show the effectiveness of the scenarios in reducing the groundwater and nitrate discharge into the lagoon. The disadvantages of the proposed scenarios, including potential seawater intrusion, need to be balanced with their relative merits for the sustainable development of the region and the survival of the Mar Menor ecosystem. The modelling approach proposed provides a valuable tool for the integrated and holistic management of the Campo de Cartagena-Mar Menor catchment and should be of great interest to similar hydrological systems with high ecological value.

Valuing investments in sustainable land management in the Upper Tana River basin, Kenya.

We analyze the impacts of investments in sustainable land use practices on ecosystem services in the Upper Tana basin, Kenya. This work supports implementation of the Upper Tana-Nairobi Water Fund, a public-private partnership to safeguard ecosystem service provision and food security. We apply an integrated modelling framework, building on local knowledge and previous field- and model-based studies, to link biophysical landscape changes at high temporal and spatial resolution to economic benefits for key actors in the basin. The primary contribution of this study is that it a) presents a comprehensive analysis for targeting interventions that takes into account stakeholder preferences, local environmental and socio-economic conditions, b) relies on detailed, process-based, biophysical models to demonstrate the biophysical return on those investments for a practical, decision-driven case, and c) in close collaboration with downstream water users, links those biophysical outputs to monetary metrics, including: reduced water treatment costs, increased hydropower production, and crop yield benefits for agricultural producers in the conservation area. This study highlights the benefits and trade-offs that come with conducting participatory research as part of a stakeholder engagement process: while results are more likely to be decision-relevant within the local context, navigating stakeholder expectations and data limitations present ongoing challenges.

Q fever in The Netherlands: the role of local environmental conditions.

The Netherlands is facing a Q fever epidemic in which dairy goats are implicated. People living close to an affected farm have an increased risk. However, no human cases were reported around a number of farms with serious Q fever problems. To assess the role of local environmental conditions which may add to the transmission or risk of Q fever, we gathered datasets on vegetation, land use, soil characteristics, and weather conditions in 5 km areas around infected farms. Areas without transmission had a higher vegetation density and relatively shallow groundwater conditions. Vegetation and soil moisture are relevant factors in the transmission of Coxiella burnetii from infected farms to humans, by reducing the amount of dust available for dispersion of the bacteria. The findings suggest that intensive goat and sheep husbandry should be avoided in areas that are characterized by a combination of arable land with deep groundwater and little vegetation.