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Hail represents a major threat to agriculture in Switzerland, and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damage is challenging. Here, we build and systematically assess an open-source model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. In contrast, for grapevine, decreasing model resolution below 1 km tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about twice as often as observed (high frequency bias and false alarm ratio), but it also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %), while overall model skill, as measured by the Heidke skill score (HSS), remains largely unchanged (0.41–0.44). We argue that, ultimately, the best threshold therefore depends on the relative costs of a false alarm versus a missed event. Finally, the frequency of false alarms is substantially reduced and skill is improved (HSS = 0.54) when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skilful and is best done at 8 km resolution for field crops and 1 km for grapevine. [ABSTRACT FROM AUTHOR]

Hail remains a major threat to agriculture in Switzerland and beyond and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damages is challenging. Here, we build and systematically assess a model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops, model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. On the contrary, for grapevine, a lower model resolution tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about two times too often (high frequency bias and number of false alarms) but also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %) while overall model skill remains largely unchanged. We argue that, ultimately, the best threshold selection therefore depends on the user need and the costs of a false alarm or a missed event. Finally, the frequency of false alarms can be substantially reduced when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skillful and is best done at 8 km resolution for field crops and 1 km for grapevine. They further allow different users of such models to identify the suitable threshold for their application, taking into account associated trade-offs. [ABSTRACT FROM AUTHOR]

Urine patches from grazing cattle are hotspots of nitrous oxide (N2O) emissions. The default IPCC emission factor for urine patches (EFurine) is 0.77% for wet climates and 0.32% for dry climates. However, literature reports a considerable range of cattle urine EF values and urine characteristics used in experimental studies, revealing contrary results on the effects of urine patch characteristics and seasonal pattern. Therefore, we examined N2O emissions and corresponding EFurine values in relation to urine patch characteristics (urine N concentration, urine volume, patch area, urine composition) and environmental drivers (precipitation, water filled pore space, soil temperature). Ten artificial urine application experiments were performed from July 2020 to June 2022 on a pasture located in Eastern Switzerland. Urine N concentration, patch area, volume and urine N composition showed no significant effects on the EFurine value (p > 0.05). EFurine varied, however, strongly over time (0.17–2.05%). A large part of the variation could be predicted either by cumulative precipitation 20 days after urine application using a second order polynomial model (Adj. R2 = 0.60) or average WFPS 30 days after urine application using a linear model (Adj. R2 = 0.45). The derived precipitation model was used to simulate EFurine weekly over the last 20 years showing no significant differences between the seasons of a year. The resulting overall average EFurine was 0.67%. More field studies are needed across sites/regions differing in climate and soil properties to implement a country-specific EF3 for Switzerland and to improve the quantification of N2O emissions at the national scales. [ABSTRACT FROM AUTHOR]

In the semi-arid Peruvian Andes, the growing season is mostly determined by the timing of the onset and retreat of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa basin (RSB) reported decreasing predictability of the onset of the rainy season and further challenges related to changes in rainfall characteristics. Previous studies based on time series of local rain gauges however, did not find any significant changes in either the timing or intensity of the wet season. Both in-situ and satellite rainfall data for the region lack the necessary spatial resolution to capture the highly variable rainfall distribution typical for complex terrain, and are often questionable in terms of quality and temporal consistency. To date, there remains considerable uncertainty in the RSB regarding hydrological changes over the last decades. In this study, we overcome this limitation by exploiting satellite-derived information on vegetation greenness to reveal a robust and highly resolved picture of recent changes in rainfall and vegetation phenology across the region: As the semi-arid climate causes water availability (i.e. precipitation) to be the key limiting factor for plant growth, patterns of precipitation occurrence and the seasonality of vegetation indices (VIs) are tightly coupled. Therefore, VIs can serve as an integrated proxy of rainfall. By combining MODIS Aqua and Terra VIs for 2000-2020 and several datasets of precipitation, we explore recent spatio-temporal changes in vegetation and water availability. Furthermore, we examine their links to El Niño Southern Oscillation (ENSO). While different rainfall datasets tend to be incoherent in the period of observation, we find significant greening over the majority of the RSB domain in VI data, particularly pronounced during the dry season (Austral winter). This indicates an overall increase of plant available water over time. The rainy season onset and consequently the start of the growing season (SOS) exhibits high inter-annual variability and dominates the growing season length (LOS). The end of the growing season (EOS) is significantly delayed in the analysis which matches the observed dry-season greening. By partitioning the results into periods of three stages of ENSO (neutral, Niño, Niña), we find an earlier SOS and an overall increased season length in years associated with El Niño. However, the appearance of Niño/Niña events during the analysed period cannot explain the observed greening and delayed EOS. While our study could not corroborate anecdotal evidence for recent changes in the SOS, we confirm that the SOS is highly variable and conclude that rainfed farming in the RSB would profit from future efforts being directed towards improving medium-range forecasts of the rainy season onset. [ABSTRACT FROM AUTHOR]