Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socioeconomic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the "According to Our Current Knowledge" (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO 2 ] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO 2 ] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO 2 ] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based on a cultivar without vernalization requirements showed good and wide adaptation potential. Few combined adaptation options

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Full affiliations in Supplement 1 at www. int-res. com/ articles/ suppl/ c075 p241 _ supp. pdf ABSTRACT: Addressing timely and relevant questions across a multitude of spatio-temporal scales, state-of-the-art interdisciplinary drought research will likely increase in importance under projected climate change. Given the complexity of the various direct and indirect causes and consequences of a drier world, scientific tasks need to be coordinated efficiently. Drought-related research endeavors ranging from individual projects to global initiatives therefore require prioritization. Here, we present 60 priority questions for optimizing future drought research. This topical catalogue reflects the experience of 65 scholars from 21 countries and almost 20 fields of research in both natural sciences and the humanities. The set of drought-related questions primarily covers drought monitoring, impacts, forecasting, climatology, adaptation, as well as planning and policy. The questions highlight the increasingly important role of remote sensing techniques in drought monitoring, importance of drought forecasting and understanding the relationships between drought parameters and drought impacts, but also challenges of drought adaptation and preparedness policies.

Highlights Crop model ensemble size and composition affect the ensemble outputs. Recommendations on adaptation are sensitive to model ensemble composition and size. The new EOA index effectively measures the confidence level of recommendations. Effective adaptation of wheat in the Mediterranean is feasible with high confidence. The EOA index can be applied to assess confidence in many other contexts.

There is a growing demand for timely, spatially distributed information regarding crop condition and water use to inform agricultural decision making and yield forecasting efforts. Thermal infrared remote sensing of land-surface temperature has proven valuable for mapping evapotranspiration (ET) and crop stress from field to global scales using energy balance models. This is because canopy temperature is strongly regulated by the transpiration flux, which is reduced under stress conditions. This study investigates the utility of an evaporative stress index (ESI), computed using the thermal-based Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model, for explaining yield variability over the Czech Republic for the period 2002−2014. ESI timeseries, representing standardized anomalies in the actual-to-reference ET ratio and an indicator of vegetation health, are compared with yield data collected for winter wheat and spring barley crops in 32 agricultural districts, comprising a range of climatic conditions within the Czech Republic. Correlations between ESI and yield anomalies vary with climatic region, with strongest correlations identified in the more drought-prone South Moravian districts and weaker relationships in the wetter highlands regions. In most regions, correlations with spring barley yield anomalies exceeded performance for winter wheat. For both crops, correlations peaked during the 1 to 2 mo period prior to the nominal harvest date. These results provide guidance for effective integration of remotely sensed moisture stress indicators within operational yield forecasting systems.

The Pannonian Basin in southeastern Europe is heavily used for rain-fed agriculture. The region experienced several droughts in the last years, causing major yield losses. Ongoing climate change, characterised by increasing temperatures and potential evapotranspiration, and by changes in precipitation distribution will likely increase the frequency and intensity of drought episodes in the future. Hence, ongoing monitoring of droughts and estimation of their impact on agriculture is necessary to adapt agricultural practices to changing weather and climate extremes. Several regional initiatives, projects and online tools have been established to facilitate drought monitoring and management in the Pannonian Basin. However, reliable systems to forecast potential drought impacts on plant productivity and agricultural yields at monthly to seasonal scales are only in their infancy, as plant response to climatic extremes is still poorly understood. With the increasing availability of high-resolution and long-term Earth Observation (EO) data and recent progress in machine learning and artificial intelligence, further improvements in drought monitoring and impact prediction capacities are expected. Here we review the current state of drought monitoring in the Pannonian Basin, identify EO-based variables to potentially improve regional drought impact monitoring and outline future perspectives for seasonal forecasts of drought impacts on agriculture.

Recent drought and a surge in days with weather conditions conducive to wildfire occurrence during 2015-2019 reminded the Czech Republic that it is not immune to this type of natural hazard. Although Central Europe has not been at the center of such events, observed climate data and climate projections suggest a tendency toward more years with wet and mild winters and dry and hot summers. To fill the existing knowledge gap, we used an ensemble of 9 fuel aridity metrics, including 3 dedicated fire weather indices, and evaluated their level of agreement with actual fire occurrence and their temporal trends. The analysis included peri-urban zones of the 36 largest cities and towns in the Czech Republic (home of 3.8 million inhabitants) and the 29 largest protected areas (covering 13.7% of the territory). Fire weather climatology, based on both the Fire Weather Index and the Forest Fire Danger Index, agreed well with the long-term frequency of fires both in peri-urban zones and within protected areas. Future projections based on regional and global model ensembles indicated a significant increase in fuel aridity and an increase in the area affected by fire-conducive conditions both around urban areas and within protected regions. In particular, the area affected by days with very high risk fire weather conditions is likely to increase significantly relative to the past 60 yr. However, the magnitude of the projected change depends to a large degree on the selected fire weather metric and whether RCM- or GCM-based scenarios are used.

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