The Influence of the Agroecological Resources of Crimea on thePrimary and Secondary Metabolites of AligoteGrapes

This research focused on examining the interrelationships between the natural conditions for growing grapes, as well as the quantitative and qualitative characteristics of the harvest. These are important criteria for the scientifically grounded selection of a territory for planting a vineyard, selecting varieties and determining the use of the resulting products. The characteristics of six model vineyards of the Aligote cultivar, located in various natural zones and viticultural regions of the Crimea, were analyzed. The values of climatic indicators were calculated, including the growing degree days above 10∘C (∑Т ∘С10), growing degree days above 20∘C (∑Т ∘С20), Huglin index, Winkler index, average growing season temperature, average September temperature, ratio ∑Т ∘С20/∑Т ∘С10,total precipitation during the year, total precipitation during the growing season, total precipitation in September, and Selyaninov hydrothermal coefficient. These were calculated usinggeoinformation and mathematical modeling for the locations of the analyzed vineyards. The content of the primary metabolites (total sugars, titrated acids and calculated indicators based on them) and secondary metabolites (phenolic components, oxidase activity) of grapes from the model vineyards were analyzed. The range of variation in the studied indicators within the analyzed territories was calculated, and the nature and magnitude of the relationships between the indicators were revealed. A cluster analysis of the analyzed vineyards was carried out and clusters were distinguished according to the degree of similarity in climatic parameters, as well as the content of the primary and secondary metabolites of the grapes. Keywords: grapes, agroecological factors, primary and secondary grape metabolites, ampeloecological zoning, terroir

Analysis of anomalies in the control probes of the Rozgrund dam

The subject of the contribution is to clarify the causes of anomalous development of water levels in some observation objects of the Rozgrund dam. To clarify the anomalous development of the water levels, a detailed analysis of the development of water levels in all observation probes built on the dam, the water levels in the reservoir and the daily total precipitation was performed. An important knowledge is the amplitudes of fluctuations in water levels in observation probes and in the water levels in the reservoir. The calculations of correlation coefficient, expressing the relationship between water level in individual boreholes and the water level in the reservoir are another step in assessment of anomalies. Based on the knowledge obtained, it was possible to assess the degree of the influence of the water level in the reservoir on the development of the water level regime in observation objects. At the same time, it was also possible to detect the existence of other effects influenced the water levels in the probes, such as the leakage into their surroundings caused by precipitation or the impact of waters flowing from hillslopes.

Analysis of spatial patterns of trends in the frequency and intensity of Indian precipitation

bl ‘’kks/k Ik= esa lewps Hkkjr ds 129 ekSle dsanzksa ds fy, 1910 ls 2000 rd dh le;kof/k ds nSfud o"kkZ ds fjdkM+ksaZ dks ,df=r fd;k x;k gS A blds ckn fofHkUu ekSle foKkfud mi[kaM+ksa ds fy, ek/; okf"kZd o"kkZ ds ekuksa ds vuqlkj bu dsanzksa dks ukS fHkUu&fHkUu {ks=ksa esa ck¡Vk x;k gS A izR;sd {ks= ds fy, gj ik¡p izfr’kr ds varjky ij dqy o"kkZ vkSj o"kkZ dh ckjackjrk dk foLr`r fo’ys"k.k fd;k gS A bu ifj.kkeksa ls lkekU;r% Hkkjr ds yxHkx lHkh Hkkxksa esa o"kkZ dh deh dk irk pyk gS tcfd dsoy mRrj if’peh Hkkxksa esa o"kkZ esa o`f) ns[kh xbZ gS A o"kkZ ds izfr lSadM+k oxZ varjkyksa ds vuqlkj fd, x, gekjs fo’ys"k.k ls ;g irk pyrk gS fd fo’ks"k :Ik ls ns’k ds vk/ks Hkkx if’peh {ks= esa vfro`f"V dh ?kVuk,¡ ckj&ckj gksrh gSa A Hkkjrh; o"kkZ ds LFkkfud vk;keksa ij izdk’k Mkyus okys gkmxVu bR;kfn (2001) ds vkbZ- ih- lh- lh- ds oSKkfud ewY;kadu vkSj vU; v/;;uksa ds lkFk gekjs ifj.kke O;kid :Ik ls esy [kkrs gSa A We assembled daily precipitation records for 129 weather stations spread all over India for the time period 1910 to 2000. Next we classified these stations into nine different regions according to the mean annual precipitation values for the different India meteorological sub-divisions. We conducted detailed analysis of total precipitation and the frequency of precipitation for each five-percentile interval for every region. In general, our results show a decrease in precipitation throughout much of India with only the northwest showing an increase. Our analyses by precipitation percentile class intervals show that the most extreme events have become more frequent, particularly in the western half of the country. Our results are broadly consistent with the IPCC Scientific Assessment by Houghton et al. (2001) and other studies focusing on the spatial dimensions of Indian precipitation over time.

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

The copula-based joint distribution can construct a fire risk model to improve forest fires' early warning system, especially in Kalimantan. In this study, we model and analyze the copula-based joint distribution between climate conditions and hotspots. We used several climate conditions, such as total precipitation, dry spells, and El Nino-Southern Oscillation (ENSO). We used copula functions with sample size reduction to construct the joint distributions and the copula regression model to estimate the fire size. The results show that the probability of extreme hotspots number during normal ENSO conditions is very rare and almost near zero during La Nina. Other than that, extreme hotspot event (more severe than in 2019) during El Nino is more sensitive to total precipitation than dry spells based on the conditional survival function. However, the copula regression model found that the model used dry spells as a climate condition better than total precipitation. In this model, the 95% confidence interval of the expected hotspots can cover all actual hotspots data.

Elevation-Dependent Trends in Precipitation Observed over and around the Tibetan Plateau from 1971 to 2017

The Tibetan Plateau (TP) are regions that are most sensitive to climate change, especially extreme precipitation changes with elevation, may increase the risk of natural disasters and have attracted attention for the study of extreme events in order to identify adaptive actions. Based on daily observed data from 113 meteorological stations in the Tibetan Plateau and the surrounding regions in China during 1971–2017, we calculated the annual total precipitation and extreme precipitation indices using the R ClimDex software package and explored elevation-dependent precipitation trends. The results demonstrate that the annual total precipitation increased at a rate of 6.7 mm/decade, and the contribution of extreme precipitation to total precipitation increased over time, and the climate extremes were enhanced. The annual total, seasonal precipitation, and precipitation extreme trends were observed in terms of elevation dependence in the Tibetan Plateau (TP) and the surrounding area of the Tibetan Plateau (TPS) during 1971–2017. There is growing evidence that the elevation-dependent wetting (EDWE) is complex over the TP. The trends in total precipitation have a strong dependence on elevation, and the EDWE is highlighted by the extreme precipitation indices, for example, the number of heavy precipitation days (R10) and consecutive wet days (CWD). The dependence of extreme precipitation on elevation is heterogeneous, as other extreme indices do not indicate EDWE. These findings highlight the precipitation complexity in the TP. The findings of this study will be helpful for improving our understanding of variabilities in precipitation and extreme precipitation in response to climate change and will provide support for water resource management and disaster prevention in plateaus and mountain ranges.

Quantifying the joint distribution of drought indicators in Borneo fire-prone area

Borneo island is prone to fire due to its large peat soil area. Fire activity in Borneo is associated with regional climate conditions, such as total precipitation, precipitation anomaly, and dry spells. Thus, knowing the relationship between drought indicators can provide preliminary knowledge in developing a fire risk model. Therefore, this study aims to quantify the copula-based joint distribution and to analyze the coincidence probability between drought indicators in Borneo fire-prone areas. From dependence analysis, we found that the average of 2 months of total precipitation (TP), monthly precipitation anomalies (PA), and the total of 3 months of dry spells (DS) provides a moderate correlation to hotspots in Borneo. The results show the probability of the dry-dry period is 26.63, 17.66, and 18.54 % for TP-DS, PA-DS, and TP-PA, respectively. All of these are higher than the probability of the wet-wet period, which is 25.01, 16.12, and 17.98 % for TP-DS, PA-DS, and TP-PA, respectively. Through the probability, the return period of TP-DS in the dry-dry situation 3.2 months/year, meaning the dry situation in total precipitation and dry spells that occur simultaneously could appear about 3 months in a year on average. Furthermore, the return period of PA-DS and TP-PA in the dry-dry situation is 2.12 and 2.22 months/year, respectively. Moreover, the probability of dry spells in dry conditions when given total precipitation in dry conditions is higher than given precipitation anomalies in dry conditions.

Analyzing Trends in Rainfall and Their Impacts in Water Management in a Cerrado Region in Brazil

This study presents a trend analysis related to a Cerrado Region in Brazil surrounded by multiple climatic influences and which lived a recent water crisis (2016-2018). This crisis could be associated with climatic changes or population growth. To verify the first possibility, an analysis was performed on a series of rainfall data (21 rain gauges spread throughout the region) divided by season periods (December/January/February – DJF, March/April/May – MAM, June/July/August – JJA, September/October/November – SON, and Water Year – WY) to provide information about the presence of trends or lack thereof. Four statistics tests were used in this procedure: Cox-Stuart, Mann-Kendall, Spearman, and Wald-Wolfowitz. The overall results indicate that the percentage of gauges/periods displaying trends by the Mann-Kendall was 10.48%, Cox-Stuart 9.52%, Spearman 12.38, and Wald-Wolfowitz 8.57%. Of these gauges/periods, 70% were classified as highly skewed, 10% as moderately skewed, and 20% as symmetric. Most of the trends are concentrated in the JJA period where it registered about 22 mm of rainfall average while the annual mean total precipitation is ~1500 mm.

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

Forest fires have become a national issue every year and get serious attention from the government and researchers, especially in Kalimantan. The copula-based joint distribution can construct a fire risk model to improve the early warning system of forest fires. This study aims to model and analyze the copula-based joint distribution between climate conditions and hotspots in Kalimantan. We constructed the bivariate joint distributions between climate conditions, either total precipitation or dry spells, and hotspots with sample size reduced by ENSO conditions, i.e., La Nina, normal, and El Nino. From the joint distribution, fire risk models are calculated using conditional probability and copula regression. The results show that the relationship between climate conditions and hotspots in La Nina and normal ENSO conditions have an upper tail dependence but no lower tail dependence. Meanwhile, the relationship has both upper and lower tail dependences during El Nino. There is an outlier in normal ENSO conditions with more hotspots than normally, i.e., in September 2019. The probability is very low during normal ENSO conditions, i.e., less than 2%. The only relatively high probability is during El Nino, i.e., more than 10%. Moreover, the copula regression models show that the model given specific dry spells is better than that given specific total precipitation as climate condition. The copula regression for hotspots given specific total precipitation and ENSO conditions has the RMSE value of 1339 hotspots and the R2 value of 60.70%. Meanwhile, the copula regression for hotspots given specific dry spells and ENSO conditions has the RMSE value of 1185 hotspots and the R2 value of 69.21%.

Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above: (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

Partitioning Solid and Liquid Precipitation over the Tibetan Plateau Based on Satellite Radar Observations

Whether precipitation falls in the form of rain or snow is of great importance to glacier accumulation and ablation. Assessments of the phase-aware precipitation have been lacking over the vast area of the Tibetan Plateau (TP) due to the scarcity of surface measurements and the low quality of satellite estimates in this region. In this study, we attempt a satellite radar-based method for this precipitation partition, in which the CloudSat radar is used for snowfall while the Global Precipitation Measurement Mission radar is used for rainfall estimation. Assuming that a 11-year snowfall and a 5-year rainfall estimates represent the mean states of precipitation at each phase, the phase partition characteristics including its annual mean, spatial pattern, seasonal dependence and variation with elevations are then discussed. Averaged over the highland area (over 1 km above sea level) in TP, the annual total precipitation is estimated to be around 400 mm, of which about 40% fall as snow. The snowfall mass fraction is about 45% in the northern and 30% in the southern part of TP, and about 80% in the cold and 30% in the warm half year. Surface elevation is found to be a high-impact factor on total precipitation and its phase partition, generally with total precipitation decreasing but snowfall fraction increasing with the increase of elevation. While there are some shortcomings, the current approach in combining snowfall and rainfall estimates from two satellite radars presents a useful pathway to assessing phase-aware precipitation over the TP region.