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The effect of towns on plant phenology, i.e. advancement of spring development compared with a rural environment, via the urban heat island (UHI) phenomenon, has been shown for many towns in many countries. This work combines experimental and observational methodology to provide a better and deeper view of climatic habitat in an urban context with a view to understanding the relationship between plant development and urban climate on the intra-urban scale (by taking into account town structure). A dense network of 17 meteorological stations was set up in Rennes, France, enabling us to identify and quantify climatic changes associated with the UHI. Meanwhile, phenological observations were made during early spring (March and April) in 2005 on Platanus acerifolia and Prunus cerasus to study the relationship between climatic and phenological data. The results show that there is both a climatic gradient and a developmental gradient corresponding to the type of urbanisation in the town of Rennes. The town influences plant phenology by reducing the diurnal temperature range and by increasing the minimum temperature as one approaches the town centre. The influence of ground cover type (plants or buildings) on development is also shown. The developmental phases of preflowering and flowering are influenced to differing extents by climatic variables. The period during which climatic variables are effective before a given developmental phase varies considerably. The preflowering phases are best correlated with the mean of the minimum air temperature for the 15-day period before the observation, whereas flowering appears to be more dependent on the mean of the daily diurnal temperature range for the 8 days preceding the observation.

Assessing the impact/adaptation of human activities on/to climate change is a key issue, especially in the tropics that concentrate major anthropogenic dynamics such as deforestation and nearly two-thirds of the planetary rainfall. However, this task is often made tough because human activities such as agricultural dynamics are usually analysed at local or regional scale whereas climate related studies are led at large to global scales due to a lack of reliable data, especially in the tropics. In this article we argue that the increased spatial resolution of remote sensing-based rainfall estimates enables assessing the spatiotemporal variability of rainfall regimes at regional and local scales, thus allowing fine analysis of the interactions with human activities. We processed Tropical Rainfall Measuring Mission (TRMM) 3B42 daily rainfall estimates over the state of Mato Grosso (southern Brazilian Amazon) for the 1998-2012 study period in order to compute rainfall metrics such as annual rainfall and duration, onset and end dates of the rainy season based on the Anomalous Accumulation methodology (at a 0.25◦ spatial resolution). We then crossed these metrics with agricultural maps (produced at a 250m spatial resolution) and proved that the adoption of intensive agricultural practices such as double cropping systems is partly the result of a strategy to adapt practices to local climatic conditions. Finally, we discuss how such results raise important issues regarding the sustainability of the agricultural development model in the Southern Amazon

Satellite-derived estimates of precipitation are essential to compensate for missing rainfall measurements in regions where the homogeneous and continuous monitoring of rainfall remains challenging due to low density rain gauge networks. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) is a relatively new product (released in 2013) but that contains data since 1983, thus enabling long-term rainfall analysis. In this work, we used three decades (1983-2014) of PERSIANN-CDR daily rainfall data to characterize precipitation patterns in the southern part of the Amazon basin, which has been drastically impacted in recent decades by anthropogenic activities that exacerbate the spatio-temporal variability of rainfall regimes. We computed metrics for the rainy season (onset date, demise date and duration) on a pixel-to-pixel basis for each year in the time series. We identified significant trends toward a shortening of the rainy season in the southern Amazon, mainly linked to earlier demise dates. This work thus contributes to monitoring possible signs of climate change in the region and to assessing uncertainties in rainfall trends and their potential impacts on human activities and natural ecosystems.

International audienceDuring the last decades, several climate-modelling studies have forecasted a decrease in precipitation inSouthern Amazonia, projecting scenarios of a drier Amazon for the future in relation with deforestation (or forest cover).In this area, only a limited number of analyses using forest cover data and rainfall time series have considered the transitionalzones between the Amazon Forest and the Cerrado biome. In this work, we evaluated whether forest cover and rainfall patternsare correlated. The analysis encompassed rainfall times series from 207 rain gauges during the 1971–2010 period, and forestcover data acquired from LANDSAT5 satellite images. The results indicate that, at local level (1–15 km), both seasonal andannual precipitation values are not correlated to forest cover, whereas at the regional scale (30–50 km) contrasting to annualvalues, significant correlation occurs between forest cover and seasonal precipitation. Additionally, the study suggests that thelarger the forest areas, the greater the probabilities of those influencing precipitation at regional scale, in opposite direction tothe observed local level effects

The transformation of forest into pastures in the Brazilian Amazon leads to significant consequences to climate at local scale. In the region of Alta Floresta (Mato Grosso, Brazil), deforestation has been intense with over half the forests being cut since 1970. This article first examines the evolution of precipitation observed in this region and shows a significant trend in the decrease in total precipitation especially at the end of the dry season and at the beginning of the rainy season. The study then compares the temperatures measured in cleared and forested sectors within a reserve in the area of Alta Floresta (Mato Grosso, Brazil) between 2006 and 2007. The cleared sector was always hotter and drier (from 5% to 10%) than the forested area. This difference was not only especially marked during the day when it reached on average 2°C but also seemed to increase during the night with the onset of the dry season (+0.5°C). The Urban Heat Island effect is also evident especially during the night and in the dry season.