Climate Change Scenarios and the Dragon Fruit Climatic Zoning in Brazil

BACKGROUND:The presente paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC in Brazil. We used temperature and precipitation data from 4,942 cities collected on the NASA / POWER platform from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041-2060 and 2061-2080 associated with four IPCC climate change scenarios. The spatialization and interpolation of data occurred according to the aptitude classes designed to meet the thermal and water needs of the crop. RESULTS: Forecasts of increase in temperature indices and reduction in accumulated rainfall were found in all Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction in areas at low risk for the cultivation of pitaya. In the South and Southeast regions, a large part of the areas remained suitable for the production of this fruit until 2080. CONCLUSION:The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas apt for economic production be restricted.

Evaluation and Assimilation of FY-3C/D MWHS-2 Radiances in the RMAPS-ST

Currently, humidity information can be obtained from the Microwave Humidity Sounder-2 (MWHS-2) mounted on the polar-orbiting satellites FY-3C and FY-3D. However, making full use of the MWHS-2 data remains a challenge, particularly in the application of regional numerical weather models. This study is the first to include MWHS-2 radiance data in the Rapid-refresh Multi-scale Analysis and Prediction System—Short-term (RMAPS-ST) regional model. The results and impact of MWHS-2 radiance data assimilation were investigated and evaluated. It is found that MWHS-2 radiance data can be effectively assimilated in the RMAPS-ST after a series of quality control and variational bias correction. Benefits could be obtained in the reduction of background departures for each humidity sounding channel. Assimilation experiments over a period of one month were carried out, and the impacts of MWHS-2 radiances were quantitatively analyzed on the forecasts of RMAPS-ST system. The results showed that MWHS-2 saw a small but significant improvement for low-level humidity of short-range forecast, by 16.5% and 3.2% in terms of mean bias and root-mean-square error, respectively. The positive impact on short-range forecast also can be found for middle and low level temperature and wind. For quantitative precipitation forecast, the assimilation of MWHS-2 radiances increased the score skills of different rainfall levels in the first 12 h forecast by an average of 1.4%. There was a slight overall improvement in the 24-h precipitation forecast for over-estimation and false alarm of 3-h accumulated rainfall below 1.0 mm, with 0.75% and 0.36%, respectively. The addition of MWHS-2 radiance data gives a small positive impact on low-level humidity, temperature, and wind in the RMAPS-ST regional model, and it also improves short-range forecast of rainfall, particularly in the first 12 h of the forecast.

Comparison of rainfall from ordinary and automatic rain gauges in Karnataka

The establishment of a network of AWS is one of the very important components under modernization programme of IMD. This study discusses the comparison of 24 hrs accumulated rainfall from ordinary and automatic rain gauges in 11 co-located stations of Karnataka. Results show that Bangalore, Gadag, Honnavar, Dharwad, Haveri, Tumkur, Bidar and Kodagu have bias within ±5 mm exhibiting good performance while Chamarajanagar, Raichur and Bijapur have bias within ±20 mm. The correlation coefficient between two datasets is strong and positive for all the stations except Chamarajnagar, Raichur and Bijapur. The t-test shows that the difference between means of two datasets is not statistically significant at 95% confidence. Further, AWS data is able to show changes in various meteorological parameters along with the movement of the cyclone. This has highlighted the utility of AWS data in extreme weather events like a tropical cyclone

Seasonal epidemiology of gastrointestinal nematodes of cattle in the northern continental climate zone of western Canada as revealed by internal transcribed spacer-2 ribosomal DNA nemabiome barcoding

Background Gastrointestinal nematode (GIN) epidemiology is changing in many regions of the world due to factors such as global warming and emerging anthelmintic resistance. However, the dynamics of these changes in northern continental climate zones are poorly understood due to a lack of empirical data. Methods We studied the accumulation on pasture of free-living infective third-stage larvae (L3) of different GIN species from fecal pats deposited by naturally infected grazing cattle. The field study was conducted on three organic farms in Alberta, western Canada. Grass samples adjacent to 24 fecal pats were collected from each of three different pastures on each farm. Internal transcribed spacer-2 nemabiome metabarcoding was used to determine the GIN species composition of the harvested larvae. The rotational grazing patterns of the cattle ensured that each pasture was contaminated only once by fecal pat deposition. This design allowed us to monitor the accumulation of L3 of specific GIN species on pastures under natural climatic conditions without the confounding effects of pasture recontamination or anthelmintic treatments. Results In seven out of the nine pastures, grass L3 counts peaked approximately 9 weeks after fecal deposition and then gradually declined. However, a relatively large number of L3 remained in the fecal pats at the end of the grazing season. Nemabiome metabarcoding revealed that Cooperia oncophora and Ostertagia ostertagi were the two most abundant species on all of the pastures and that the dynamics of larval accumulation on grass were similar for both species. Daily precipitation and temperature across the whole sampling period were similar for most of the pastures, and multiple linear regression showed that accumulated rainfall 1 week prior to sample collection had a significant impact on the pasture L3 population, but accumulated rainfall 3 weeks prior to sample collection did not. Conclusions The results suggest that the pasture L3 population was altered by short-term microclimatic conditions conducive for horizontal migration onto grass. Overall, the results show the importance of the fecal pat as a refuge and reservoir for L3 of cattle GIN on western Canadian pastures, and provide an evidence base for the risk assessment of rotational grazing management in the region. Graphical Abstract

Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.

The Use of Laser Precipitation Monitor (LPM) of Disdrometer and Weather Radar to Determine the Microphysics Characteristics of Extreme Rainfall in Jakarta. (Jakarta Flood Case Study February 25, 2020)

Heavy rains on February 24, 2020, caused flooding in most parts of Jakarta and its surroundings. The one-day observation of accumulated rainfall from the Laser Precipitation Monitor (LPM) was recorded at 358.6 mm/day at the Kemayoran station on February 25, 2020, at 00.00 UTC (07.00 Jakarta Time). In this study, analysis of the microphysical characteristics of extreme rainfall using LPM installed at Kemayoran meteorology station and weather radar at Cengkareng meteorology station with a spatial radius of 250 km. LPM is used to measure the diameter of the raindrops, the velocity of falling raindrops, LPM reflectivity, and the amount of accumulated rainfall with time resolution per minute and stored in excel data format. While the weather radar is used to measure the reflectivity spatially and temporally in the data volume format (.vol). The method used is, first, to find the relationship between LPM reflectivity and the amount of LPM rainfall with regression analysis. Second, the radar reflectivity is converted into estimated rainfall intensity for the Jakarta area and its surroundings. The results of this study found a relationship between LPM reflectivity (X) and rainfall accumulation LPM (Y) to form a regression relationship with the formula Y = 0.013X with R2 = 0.3777. Based on the record of the LPM time series, the peak of rainfall occurred at 18.17 UTC with 1000 raindrops, the maximum fall speed was 10 m/s, and the maximum diameter is 8.5 millimeters. Based on the results of microphysical measurements of LPM, spatial plots, and vertical cross-section radar, it can be concluded that flooding in Jakarta is due to heavy rain from convective clouds.

Ianos - A hurricane in the Mediterranean

During 15-21 September 2020, an intense medicane, named Ianos, formed over the warm Mediterranean Sea. Following a path of approximately 1900 km, Medicane Ianos affected Greece resulting in four casualties and devastating damages in the western and central parts of Greece. Persistent gale force 1-minute winds up to 44 ms‒1 and wind gusts up to 54 ms‒1 were recorded in Cephalonia island (Ionian Sea), while record-breaking amounts of accumulated rainfall have been recorded in several Ionian Islands, as well as in parts of Central Greece. Analysis of the available observations showed that Ianos was the most intense medicane ever recorded in the Mediterranean. This paper aims at investigating the genesis and evolution of the medicane, based on in situ observations, satellite measurements and model analyses. Towards that objective, Meteosat Second Generation (MSG) SEVIRI imagery, combined with lightning data permitted to follow the evolution of convective activity during the various phases of Ianos. This investigation is complemented with upper-air model analyses in order to evaluate the synoptic environment within which Ianos has formed and was sustained during 7 days. Finally, the Global Precipitation Mission Core Observatory satellite (GPM-CO) overpasses over Medicane Ianos provided invaluable information about its 3-D structure, especially during its most intense phase.

Mating marks on museum specimens reveal breeding patterns in species of Pterostichus Bonelli (Carabidae, Pterostichini)

We found distinct and consistently placed, species- and sex-specific abrasions of the cuticle on museum specimens of 14 species of the Pterostichus Bonelli, 1810 (Carabidae, Pterostichini) subgenus Hypherpes Chaudoir, 1838. We deduced that these marks are generated during mating and, therefore, can be used to distinguish between preserved specimens of beetles that had previously mated at the time of capture and those that had not mated. In addition to describing and detailing the occurrence of the marks and providing evidence that they are the result of mating, we demonstrate their utility for inferring life history using a museum voucher collection. By scoring these indications of mating from pinned specimens, we describe life cycle patterns in two similar, relatively closely related and sympatric species of the subgenus Hypherpes, P. vicinus Mannerheim, 1843 and P. californicus (Dejean, 1828). Both were sampled during a pitfall trap study in Contra Costa, California, USA from 2014–2019 and deposited in the Essig Museum of Entomology, UC Berkeley. Both species had very low adult activity through the drought and end of drought period prior to the spring of 2017 and are significantly more abundant in the post-drought period. Based on mating marks, both species responded to accumulated precipitation ending the drought by the emergence of an active, mostly unmated cohort of adults. The spring activity peak, following the end of the drought, was dominated by unmarked and presumably unmated beetles, but samples from subsequent springs included a nearly equal mix of beetles showing mating marks and apparently unmated beetles. The beetle activity appears to correspond more with the accumulated rainfall of the preceding rainy season than with the rains of the sample year. Beetles sampled in autumn and winter (rainy season) predominantly show mating marks. The occurrence throughout the year of beetles that are marked as having mated is consistent with iteroparous beetles with a lifespan of more than one year and also consistent with dynamic phenotypic polyvariance in which the adult activity period is synchronised by adjusting development time. The dominant pattern fits with a life cycle that is typically annual univoltine, or possibly biennial semivoltine in dry years, rainy season breeding (autumn-winter) iteroparous, with adult summer aestivation and possibly facultative larval hibernation. However, unmarked and so apparently unmated individuals and teneral adults were captured during peak activity periods regardless of the season, suggesting that either the beetles diapause as teneral adults that then complete development and become active at various points during the year and/or there are multiple periods of breeding and oviposition each year in at least some portion of the population.

Dynamics of Clomazone Formulations Combined with Sulfentrazone in Sugarcane (Saccharum spp.) Straw

Herbicide formulations can alter the herbicide performance, affecting the application safety and weed control efficiency. Thus, the objective of this work was to compare the dynamics of clomazone herbicide applied single and combined with sulfentrazone on sugarcane (Saccharum spp.) straw. Laminated polypropylene containers filled with sugarcane straw (10 t ha−1) were subjected to two clomazone formulations (microencapsulated and conventional formulations; 1200 g ha−1) applied single or combined with sulfentrazone (600 g ha−1) with four replications, and the experiment was duplicated. The application was performed indoors with an automated sprayer. After application, accumulated rainfall depths (0, 5, 10, 20, 50, and 100 mm) on the treated containers were simulated soon after the herbicide applications, and the percolated waters were subsequently collected for herbicide quantification by chromatography and mass spectrometry (LC-MS/MS). The microencapsulated formulation of clomazone applied single or combined with sulfentrazone enabled the recovery of higher quantity of clomazone (>80%), with the advantage that a large percentage remained encapsulated (>70%), thus decreasing losses and increasing the product efficiency. The 30 mm simulated rainfall efficiently carried the clomazone herbicide when its microencapsulated formulation was applied, whereas its conventional formulation required higher rainfall depths (60 mm). Sulfentrazone was easily carried through the sugarcane straw by the rainfall depths when it was combined with clomazone, regardless of the clomazone formulation. The clomazone formulation affect the percolation dynamics of this herbicide through the sugarcane straw.

Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Climate change can affect the development of soybean cultivation, impacting your productivity. Thus, agrometeorological information is essential in order to improve productivity strategies. The objective of the paper was to analyze the influence and occurrence of seasonality of the following agrometeorological variables on soybean productivity: mean air temperature [TMean] (ºC), accumulated rainfall value [Rain] (mm), global solar radiation [Sr] (MJ m-2 day-1), and potential evapotranspiration [ETp] (mm), in ten-day variations of the the maximum vegetative development date (MVDD), in the 2011/2012 and 2013/2014 harvest years in the state of Paraná. The study was based on spatial distribution of variables, using univariate and bivariate Global Moran’s Indexes, and multivariate clustering analysis. To verify seasonality in the time distribution of the agrometeorological variables in the ten-day variations close to soybean MVDD, we used the circular statistics, through the mean vector length (R). Result it was identified regions of the state that have higher and lower rainfall and seasonality, also have higher and lowest productivity, respectively. That the variation in soybean productivity between harvest years was correlated with the agrometeorological variables, and rainfall volume is an important factor in productivity. The other agrometeorological variables occurred uniformly, especially in 2011/2012 harvest year, in the Northwest, Central-northern and West mesoregions. Furthermore, there was clustering of regions with similar spatial distribution of the evapotranspiration and rainfall variables in 2aDMDV2d in the 2011/2012 and 2013/2014 harvest year, showed the same spatial distribution of the agrometeorological variables and the productivity variable.