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Many studies have reported that total precipitation is increasing across the United States with most of the increase resulting from a positive trend in the upper tail of the daily precipitation distribution. Other studies have found that low and moderate, but not high flows are also increasing across much of the United States. How can precipitation, especially that produced by intense events, increase without a corresponding increase in high flows? We analyzed trends in annual 7‐day low, average and high flows along with seasonal precipitation that is averaged over individual basins. Our findings suggest that statistically significant trends in both fall precipitation and 7‐day low flow are found in a large percentage of the basins in the upper Mississippi and Great Lakes regions of the country. A large fraction of the trends in annual precipitation can be explained by an increase in fall precipitation. By estimating trends in precipitation at the spatial scale of individual basins, we offer a simple explanation for the apparent paradox of lack of trends in high flows. At the spatial scale of individual basins, precipitation is increasing during the fall but not during the spring, the season when high flows are generally observed. The increase in fall precipitation appears to result in an increase in the low flows while the lack of trends in precipitation in spring explains the lack of widespread trends in the high flows.

Climate change is impacting agro-ecosystems, crops, and farmer livelihoods in communities worldwide. While it is well understood that more frequent and intense climate events in many areas are resulting in a decline in crop yields, the impact on crop quality is less acknowledged, yet it is critical for food systems that benefit both farmers and consumers through high-quality products. This study examines tea (Camellia sinensis; Theaceae), the world's most widely consumed beverage after water, as a study system to measure effects of seasonal precipitation variability on crop functional quality and associated farmer knowledge, preferences, and livelihoods. Sampling was conducted in a major tea producing area of China during an extreme drought through the onset of the East Asian Monsoon in order to capture effects of extreme climate events that are likely to become more frequent with climate change. Compared to the spring drought, tea growth during the monsoon period was up to 50% higher. Concurrently, concentrations of catechin and methylxanthine secondary metabolites, major compounds that determine tea functional quality, were up to 50% lower during the monsoon while total phenolic concentrations and antioxidant activity increased. The inverse relationship between tea growth and concentrations of individual secondary metabolites suggests a dilution effect of precipitation on tea quality. The decrease in concentrations of tea secondary metabolites was accompanied by reduced farmer preference on the basis of sensory characteristics as well as a decline of up to 50% in household income from tea sales. Farmer surveys indicate a high degree of agreement regarding climate patterns and the effects of precipitation on tea yields and quality. Extrapolating findings from this seasonal study to long-term climate scenario projections suggests that farmers and consumers face variable implications with forecasted precipitation scenarios and calls for research on management practices to facilitate climate adaptation for sustainable crop production.

This article shows how the array of corner reflectors (CRs) in Queensland, Australia, together with highly accurate geodetic synthetic aperture radar (SAR) techniques-also called imaging geodesy-can be used to measure the absolute and relative geometric fidelity of SAR missions. We describe, in detail, the end-to-end methodology and apply it to TerraSAR-X Stripmap (SM) and ScanSAR (SC) data and to Sentinel-1 interferometric wide swath (IW) data. Geometric distortions within images that are caused by commonly used SAR processor approximations are explained, and we show how to correct them during postprocessing. Our results, supported by the analysis of 140 images across the different SAR modes and using the 40 reflectors of the array, confirm our methodology and achieve the limits predicted by theory for both Sentinel-1 and TerraSAR-X. After our corrections, the Sentinel-1 residual errors are 6 cm in range and 26 cm in azimuth, including all error sources. The findings are confirmed by the mutual independent processing carried out at University of Zurich (UZH) and German Aerospace Center (DLR). This represents an improvement of the geolocation accuracy by approximately a factor of four in range and a factor of two in azimuth compared with the standard Sentinel-1 products. The TerraSAR-X results are even better. The achieved geolocation accuracy now approaches that of the global navigation satellite system (GNSS)-based survey of the CRs positions, which highlights the potential of the end-to-end SAR methodology for imaging geodesy.

Sentinel-1A (S1A) is an Earth observation satellite carrying a state-of-the-art Synthetic Aperture Radar (SAR) imaging instrument. It was launched by the European Space Agency (ESA) on 3 April 2014. With the end of the in-orbit commissioning phase having been completed at the end of September 2014, S1A data products are already consistently providing highly accurate geolocation. StripMap (SM) mode products were acquired regularly and tested for geolocation accuracy and consistency during dedicated corner reflector (CR) campaigns. At the completion of this phase, small geometric inconsistencies had been understood and mitigated, with the high quality of the final product geolocation estimates reflecting the mission's success thus far. This paper describes the measurement campaign, the methods used during geolocation estimation, and presents best estimates of the product Absolute Location Error (ALE) available at the beginning of S1A's operational phase.

Efficient methods to monitor forested areas help us to better understand their processes. To date, only a few studies have assessed the usability of multitemporal synthetic aperture radar (SAR) datasets in this context. Here we present an analysis of an unprecedented set of C-band observations of mixed temperate forests. We demonstrate the potential of using multitemporal C-band VV and VH polarisation data for monitoring phenology and classifying forests in northern Switzerland. Each SAR acquisition was first radiometrically terrain corrected using digital elevation model-based image simulations of the local illuminated area. The flattened backscatter values and the local area values were input to a temporal compositing process integrating backscatter values from ascending and descending tracks. The process used local resolution weighting of each input, producing composite backscatter values that strongly mitigated terrain-induced distortions. Several descriptors were calculated to show the seasonal variation of European beech (Fagus sylvatica), oak (Quercus robur, Quercus petraea) and Norway spruce (Picea abies) in C-band data. Using their distinct seasonal signatures, the timing of leaf emergence and leaf fall of the deciduous species were estimated and compared to available ground observations. Furthermore, classifications for the forest types 'deciduous' and 'coniferous' and the investigated species were implemented using random forest classifiers. The deciduous species backscatter was about 1 dB higher than spruce throughout the year in both polarisations. The forest types showed opposing seasonal backscatter behaviours. At VH, deciduous species showed higher backscatter in winter than in summer, whereas spruce showed higher backscatter in summer than in winter. In VV, this pattern was similar for spruce, while no distinct seasonal behaviour was apparent for the deciduous species. The time differences between the estimations and the ground observations of the phenological events were approximately within the error margin (±12 days) of the temporal resolution. The classification performances were promising, with higher accuracies achieved for the forest types (OA of 86% and κ = 0.73) than for individual species (OA of 72% and κ = 0.58). These results show that multitemporal C-band backscatter data have significant potential to supplement optical remote sensing data for ecological studies and mapping of mixed temperate forests.

Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer’s accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user’s accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user’s needs.