Decoupling of impact factors reveals the response of German winter wheat yields to climatic changes.

Yield development of agricultural crops over time is not merely the result of genetic and agronomic factors, but also the outcome of a complex interaction between climatic and site-specific soil conditions. However, the influence of past climatic changes on yield trends remains unclear, particularly under consideration of different soil conditions. In this study, we determine the effects of single agrometeorological factors on the evolution of German winter wheat yields between 1958 and 2015 from 298 published nitrogen (N)-fertilization experiments. For this purpose, we separate climatic from genetic and agronomic yield effects using linear mixed effect models and estimate the climatic influence based on a coefficient of determination for these models. We found earlier occurrence of wheat growth stages, and shortened development phases except for the phase of stem elongation. Agrometeorological factors are defined as climate covariates related to the growth of winter wheat. Our results indicate a general and strong effect of agroclimatic changes on yield development, in particular due to increasing mean temperatures and heat stress events during the grain-filling period. Except for heat stress days with more than 31°C, yields at sites with higher yield potential were less prone to adverse weather effects than at sites with lower yield potential. Our data furthermore reveal that a potential yield levelling, as found for many West-European countries, predominantly occurred at sites with relatively low yield potential and about one decade earlier (mid-1980s) compared to averaged yield data for the whole of Germany. Interestingly, effects related to high precipitation events were less relevant than temperature-related effects and became relevant particularly during the vegetative growth phase. Overall, this study emphasizes the sensitivity of yield productivity to past climatic conditions, under consideration of regional differences, and underlines the necessity of finding adaptation strategies for food production under ongoing and expected climate change.

Snow cover trend and hydrological characteristics of the Astore River basin (Western Himalayas) and its comparison to the Hunza basin (Karakoram region).

A large proportion of Pakistan's irrigation water supply is taken from the Upper Indus River Basin (UIB) in the Himalaya-Karakoram-Hindukush range. More than half of the annual flow in the UIB is contributed by five of its snow and glacier-fed sub-basins including the Astore (Western Himalaya - south latitude of the UIB) and Hunza (Central Karakoram - north latitude of the UIB) River basins. Studying the snow cover, its spatio-temporal change and the hydrological response of these sub-basins is important so as to better manage water resources. This paper compares new data from the Astore River basin (mean catchment elevation, 4100 m above sea level; m asl afterwards), obtained using MODIS satellite snow cover images, with data from a previously-studied high-altitude basin, the Hunza (mean catchment elevation, 4650 m asl). The hydrological regime of this sub-catchment was analyzed using the hydrological and climate data available at different altitudes from the basin area. The results suggest that the UIB is a region undergoing a stable or slightly increasing trend of snow cover in the southern (Western Himalayas) and northern (Central Karakoram) parts. Discharge from the UIB is a combination of snow and glacier melt with rainfall-runoff at southern part, but snow and glacier melt are dominant at the northern part of the catchment. Similar snow cover trends (stable or slightly increasing) but different river flow trends (increasing in Astore and decreasing in Hunza) suggest a sub-catchment level study of the UIB to understand thoroughly its hydrological behavior for better flood forecasting and water resources management.

Variabilidade de índices de chuva nos estados de Santa Catarina e Rio Grande do Sul / Variability of rainfall index in the states of Santa Catarina and Rio Grande do Sul

Sendo a chuva uma das variáveis climáticas de maior influência no meio ambiente, na economia e na sociedade, este estudo objetiva analisar a variabilidade climática de diferentes índices de chuva nos Estados do Rio Grande do Sul e de Santa Catarina. Foram utilizados dados diários de chuva de 32 estações hidrológicas, onde foram obtidos cinco índices de chuva. O coeficiente angular da Regressão Linear foi utilizado para analisar a tendência climática dos índices de chuva nas escalas sazonal e anual. Os meses seguintes foram definidos como representativos de cada um dos períodos sazonais: de dezembro a fevereiro (verão), de março a maio (outono), de junho a agosto (inverno) e de setembro a novembro (primavera). Desde meados do século XX, houve aumento na quantidade de chuva, no número de dias chuvosos e de forma mais discreta, no número e na intensidade dos eventos extremos de chuva, principalmente na primavera e no outono. Com a função de autocorrelação aplicada em algumas estações hidrológicas, foi encontrado uma periodicidade da chuva anual em torno de 3, 9-11 e 18 anos nas estações com as séries de dados mais extensas (Pomerode e Rio Negro).

As the rain of the climatic variables of greatest influence on the environment, economy and society, this study aims to analyze the climatic variability of different rainfall index in the States of Rio Grande do Sul and Santa Catarina. Were used daily rainfall data of 32 hydrological stations, which were obtained five rainfall index. The angular coefficient of linear regression was used to analyze the climate trend of rainfall index in seasonal and annual scales. The following months were defined as representative of each of the seasons: December-February (summer), March-May (autumn), June-August (winter) and September-November (spring). Since the mid-twentieth century, there was an increase in the amount of rainfall, the number of rainy days and more discreetly, in the number and intensity of extreme rainfall events, especially in spring and autumn. With the autocorrelation function applied in some hydrological stations, was found a periodicity of annual rainfall of about 3, 9-11 and 18 years at stations with the longest data series (Pomerode and Rio Negro).