Diego Gómez

Hydrological extremes display long‐term trends and natural oscillations in response to climatic and socioeconomic factors. Knowing how and why these extremes are shifting is crucial for increasing water resilience in Europe. This work analyses trends in floods and droughts in European catchments between 1951 and 2020. Using the OS LISFLOOD hydrological model and non‐stationary extreme value analysis, we disentangle the effects of dynamics in climate, encompassing climate variability and climate change, land use, water abstraction and reservoirs on river flow extremes. Our results reveal spatial and temporal variations in magnitude of hydrological extremes. Land use changes have generally increased flood and drought hazards, while water demand primarily intensified droughts. Reservoirs, conversely, have decelerated the hydrological cycle. Our work delivers insights into the intricate connections between climate, society, and water in European river basins, enabling the development of effective strategies for enhancing resilience to extreme water events under global change.

Global challenges such as climate change and population growth require improvements in crop monitoring models. To address these issues, this study advances the identification of potato crop phenological stages using satellite remote sensing, a field where cereals have been the primary focus. We introduce a methodology using Sentinel-1 (S1) and Sentinel-2 (S2) time series data to pinpoint critical phenological stages—emergence, canopy closure, flowering, senescence onset, and harvest timing—at the field scale. Our approach utilizes analysis of NDVI, fAPAR, and IRECI2 from S2, alongside VH and VV polarizations from S1, informed by domain knowledge of the spectral and morphological responses of potato crops. We propose the integration of NDVI and VH indices, NDVI_VH, to improve stage detection accuracy. Comparative analysis with ground-observed stages validated the method’s effectiveness, with NDVI proving to be one of the most informative indices, achieving RMSEs of 12 and 14 days for emergence and closure, and 17 days for the onset of senescence. The integrated NDVI_VH approach complemented NDVI, particularly in harvest and flowering stages, where VH enhanced accuracy, achieving an overall R² value of 0.80. The study demonstrates the potential of combining SAR and optical data for post-season crop phenology analysis, providing insights that can inform the development of new methods and strategies to enhance on-season crop monitoring and yield forecasting.

Recent extreme wildfires worldwide have raised concerns about the accelerating impacts of climate change. Assessing the socioeconomic impacts of wildfires is challenging due to uncertainties in risk drivers and observational records. Here, we implement a high-resolution data modelling framework to quantify fire season length, population exposure to fire weather, and wildfire economic damage in Europe for a range of global warming scenarios. Climate change is expected to lengthen the fire season across Europe, particularly in southern regions already prone to fire-conducive weather. While the south already faces extended periods of high fire danger, population in central and northern Europe will be increasingly exposed to adverse fire weather conditions. Present direct wildfire damages of €2.4 billion per year could nearly double with warming of 3°C or more. Mediterranean regions will bear the highest economic burden, with annual maximum damages reaching 5–10% of their regional economy. Our findings advocate for stringent climate mitigation, fire-resistant ecosystems, and resilient communities near fire-prone areas.

Remote sensing is a valuable tool in precision agriculture due to its spatial and temporal coverage, non-destructive method of data collection, and cost-effectiveness. In this study, we measured the canopy reflectance of potato (Solanum tuberosum L.) crops on a plant-by-plant basis with a handheld spectrometer instrument. Our study pursues two primary objectives: (1) determining the optimal temporal aggregation for measuring canopy signals related to potato yield and (2) identifying the best spectral bands in the 350–2500 nm domain and vegetation indices. The study was conducted over two consecutive years (2020 and 2021) with 60 plants per plot, encompassing six potato varieties and three replicates annually throughout the growth season. Employing correlation analysis and dimensionality reduction, we identified 23 independent features significantly correlated with tuber yield. We used multiple linear regression analysis to model the relationship between the selected features and yield and to compare their influence in the fitted model. We used the Leave-One-Out Cross-Validation (LOOCV) method to assess the validity of the model (RMSE = 702 g and %RMSE = 29.2%). The most significant features included the Gitelson2 and Vogelmann indices. The optimal time period for measurements was determined to be from 56 to 100 days after planting. These findings may contribute to the advancement of precision farming by proposing tailored sensor applications, paving the way for improved agricultural practices and enhanced food security.

The potato (Solanum tuberosum L.) is one of the most important food crops and is consumed by more than a billion people worldwide. Despite the fact that crop growth models are widely used to optimize crop management and agricultural decision-making, they fail to provide spatial information, whereas satellite-based methods do, and in a cost-effective manner. In order to improve satellite-based crop yield prediction models, we developed a methodology based on Sentinel-2 (S2) data using Machine Learning (ML) techniques. In addition, we included and compared the commonly used Normalized Difference Vegetation Index (NDVI) to a newly developed spectral indicator, the Potato Productivity Index (PPI). Our results showed the capacity of our ML models to predict potato yield with great accuracy (coefficient of determination (R²) = 0.77 and Root Mean Square Error (RMSE) = 15.42%), using S2 bands 2, 3, 4, 5, 8A, and 9, as well as the PPI index. This index aims to provide information concerning plant photosynthetic activity as well as its water stress. The Random Forest (RF) model using the S2 bands and the PPI index as predictors obtained better predictive results (R² = 0.77, RMSE = 15.42%) for potato crops than using S2 bands and NDVI (R² = 0.66, RMSE = 16.88%). This study demonstrates the suitability of our models to predict potato yields in the region studied and improves on a previous approach presented by the same authors in terms of model interpretability and inter-annual variability.

The Simultaneous Nadir Overpass (SNO) method was developed by the NOAA/NESDIS to improve the consistency and quality of climate data acquired by different meteorological satellites. Taking advantage of the reduced impact induced by the Bidirectional Reflectance Distribution Function (BRDF), atmospheric effects, illumination and viewing geometries during an SNO, we created a sensor comparison methodology for all spectral targets. The method is illustrated by applying it to the assessment of data acquired by the Landsat 8 (L8), Sentinel-2A (S2A), and Sentinel-2B (S2B) optical sensors. Multiple SNOs were identified and selected without the need for orbit propagators. Then, by locating spatially homogeneous areas, it was possible to assess, for a wide range of Top-of-Atmosphere reflectance values, the relationship between the L8 bands and the corresponding ones of S2A and S2B. The results yield high coefficients of determination for S2 A/B with respect to L8. All are higher than 0.980 for S2A and 0.984 for S2B. If the S2 band 8 (wide near-infrared, NIR) is excluded then the lowest coefficients of determination become 0.997 and 0.999 from S2A and S2B, respectively. This methodology can be complementary to those based on Pseudo-Invariant Calibration Sites (PICS) due to its simplicity, highly correlated results and the wide range of compared reflectances and spectral targets.

The climate in southern Iceland has warmed over the last 70 years, resulting in accelerated glacier dynamics at the Solheimajoküll glacier. In this study, we compare glacier terminus locations from 1973 to 2018, to changes in climate across the study area, and we derive ice-surface velocities (2015–2018) from satellite remote-sensing imagery (Sentinel-1) using the offset-tracking method. There have been two regional temperature trends in the study period: cooling (1973–1979) and warming (1980–2018). Our results indicate a time lag of about 20 years between the onset of glacier retreat (−53 m/year since 2000) and the inception of the warming period. Seasonally, the velocity time series suggest acceleration during the summer melt season since 2016, whereas glacier velocities during accumulation months were constant. The highest velocities were observed at high elevations where the ice-surface slope is the steepest. We tested several scenarios to assess the hydrological time response to glacier accelerations, with the highest correlations being found between one and 30 days after the velocity estimates. Monthly correlation analyses indicated inter-annual and intra-annual variability in the glacier dynamics. Additionally, we investigate the linkage between glacier velocities and meltwater outflow parameters as they provide useful information about internal processes in the glacier. Velocity estimates positively correlate with water level and negatively correlate with water conductivity between April and August. There is also a disruption in the correlation trend between water conductivity and ice velocity in June, potentially due to a seasonal release of geothermal water.