Increase In Rainfall Events Research Articles

Influence of rainfall on foraging behavior of a tropical seabird

AbstractAcquiring resources for self-maintenance and reproduction is a key challenge for wild animals, and the methods that individuals employ are, in part, shaped by environmental conditions that vary in time and space. For birds, rainfall may affect behavior, impairing senses and increasing energetic costs, but its consequences on movement patterns are poorly explored. We investigated the influence of rainfall on the foraging behavior of the magnificent frigatebird, Fregata magnificens. This peculiar tropical seabird lacks feather waterproofing and is known to track environmental conditions while searching for food. Thus, its foraging behavior should be highly sensitive to the effects of rainfall. By GPS-tracking chick-rearing adults, we showed that frigatebirds did not avoid areas with rainfall during foraging trips, nor did rainfall influence trip characteristics. However, rainfall decreased time devoted to foraging and increased time spent perching. Moreover, it affected flight mode, inducing birds to fly slower and at lower altitudes. Wind speed, which was not correlated with rainfall, only affected behavior during night-time, with strong winds decreasing time spent perching. Our results indicate that rainfall does not affect the spatial distribution of foraging frigatebirds but does alter fine-scale foraging behavior by reducing flight activity. We suggest that the ongoing environmental change in this region, including an increase in rainfall events, has the potential to impair foraging and negatively affect fitness.

Analysis of climate extremes indices over northeast Brazil from 1961 to 2014

An analysis of climate extremes indices was conducted for maximum temperatures (TMax), minimum temperatures (TMin), and daily rainfall data (PRCP) over the northeast region of Brazil for the period of 1961–2014. The indices were calculated for 96 weather stations using RClimDex software in a sub-regional study based on cluster analysis, as well as for each individual weather station, after a rigorous process of quality control, gap filling of missing values, and homogenization. The Mann-Kendall non-parametric trend test was employed to assess the statistical significance of the series. The results indicate unequivocal signs of heating. In the past decades, there were predominant trends of decrease in the percentage of cold nights (−8.4% days/decade), increase in the percentage of the number of warm nights (10.6% days/decade), increase in the number of days per decade in which the minimum temperature exceeded the threshold of 20 °C, and a trend showing an increase in heat waves. This was corroborated by the declining trend in the number of consecutive days in which TMin did not exceed the 10th percentile, and the increase in the number of consecutive days in which TMax exceeded the 90th percentile in the data distribution. With regard to PRCP, in most weather stations where there is a significant increase in consecutive dry days, there is also a trend of significant increase in consecutive wet days, thereby intensifying the seasonality, with the dry seasons becoming drier and the rainy seasons wetter. In most weather stations, a reduction was found in the total annual precipitation and in the frequency of rainy days. Moreover, in many stations, an increase in rainfall events that surpasses the threshold of 95% and 99% in the distribution was discovered. These factors highlight concerns of a region of Brazil marked by the scarcity of perennial water resources and the threat of desertification.

A local-regional scaling-invariant Bayesian GEV model for estimating rainfall IDF curves in a future climate

Future changes in rainfall patterns induced by climate changes will affect society and ecosystems, and quantifying these changes is of utmost importance for the management of hydroclimate risk. In particular, the estimation of intensity-duration-frequency (IDF) curves for rainfall data is a routine procedure in urban hydrology and hydraulic studies and should be revisited to reflect future changes in rainfall variability. In this work, we propose a novel methodology based on the scaling-invariant property of rainfall duration versus intensity to estimate parameters of a generalized extreme value (GEV) distribution at sub-daily scales. A Bayesian inference framework is developed so that uncertainties are reduced and can be easily propagated to IDF curves. The proposed model can be employed to: (i) improve local (at-site) GEV estimates for sites with limited rainfall records; (ii) estimate GEV parameters at sub-daily scales and construct IDF curves for sites where only daily rainfall records are available (partially gauged sites); (iii) construct regional IDF curves for homogeneous hydrologic regions; and (iv) update local and regional IDF curves from simulations of future daily rainfall. The model is tested using historical rainfall data from 18 gauges located in the Han River Watershed in South Korea, and projected climate change scenarios RCP 6 and RCP 8.5 from the Met Office Hadley Centre HadGEM2-AO model. When considering historical data, the results show that the model satisfactorily estimate IDF curves for both gauged and partially gauged sites. In future scenarios, the model reveals a substantial increase in rainfall events of rare intensity (large return periods), mostly due to changes in the rainfall variability rather than changes in the average rainfall. Particularly, for a 100-year return period event, we expect an increase of about 23% in scenario RCP 6 and about 30% under scenario RCP 8.5 when projected using regional IDF curves. To the best of our knowledge, this is the first statistical approach in the literature to assess future changes in regional IDF curves, which in our opinion is more suitable than evaluating local estimates only.

GCM出力に基づいた日本域における降雨イベントの将来変化に関する研究

There is growing concern on a global scale about the significant increase of extreme meteorological events in recent years, e.g., torrential rains, heavy snows, and severe hurricanes. The current study conducted frequency and scale analysis of torrential rains to examine future variations of rainfall events due to global warming, based on GCM (Global Climate Model) outputs calculated under the present condition (1979-2008), near-future condition (2015-2044), and future condition (2075-2104) by the KAKUSHIN Program of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT). DAD analysis was also attempted to clarify whether and how the statistical relationship will change in the future with global warming between maximum areal rainfall and the area and duration of each rainfall event. Results show that the occurrence frequency of torrential rains could markedly increase in some regions of Japan, and that DAD relationship could significantly change in the future, and there could be a numerical increase in rainfall events which depart from the regression curve of DAD relationship due to a greater rainfall intensity or larger area of rainfall than most other events. Visual inspections of each event suggest that such extreme events could be attributed to Baiu front activated by the sufficient amount of water vapor and typhoons strengthened by the higher sea surface temperature due to global warming in the future.