Testing of Drought Exceedance Probability Index (DEPI) for Turkey using PERSIANN data for 2000-2021 period

Drought is a climatic event that threatens the environment and human life with an ambiguity of location and time. Recently, droughts can be analyzed for different periods with the help of different mathematical methods and developing technology. This study aims to perform a drought analysis in 126 designated study points of Turkey. The analyzed data includes monthly total precipitation values between March 2000 and February 2021, obtained from PERSIANN system (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). Monthly precipitation totals of these designated points were used as input parameters in the Drought Exceedance Probability Index (DEPI) which is a new drought analysis method. The analysis was conducted separately for the whole of Turkey from January to December. Moreover, the findings were compared with the Standardized Precipitation Index (SPI), a globally accepted and commonly used drought index, to measure the drought detection performance of DEPI. SPI was calculated for periods of 6, 12 and 24 months. Pearson correlation coefficients between drought values of SPI-6, SPI-12 and SPI-24 and DEPI results were calculated. The second part of the study includes possible trend of drought determined by the Mann-Kendall trend analysis method. Both DEPI and SPI results and trend analysis results were mapped and visualized with the help of ArcGIS package program. The highest correlation is between DEPI and SPI-12 with 0.75, while the lowest correlation is between DEPI and SPI-24 with a value of 0.62. SPI monthly drought maps indicated the wettest months were January and February, while the driest months were March and July. Besides the DEPI monthly drought maps, the wettest months were October and November, while the driest months were May and June. The Mann-Kendall trend maps showed a significant increase in drought for summer.

The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

Climate-Growth Relations of Abies georgei along an Altitudinal Gradient in Haba Snow Mountain, Southwestern China

Climate warming has been detected and tree growth is sensitive to climate change in Northwestern Yunnan Plateau. Abies georgei is the main component of subalpine forest in the area. In this study, A. georgei ring width chronologies were constructed at four sites ranging from 3300 to 4150 m a.s.l. in Haba Snow Mountain, Southeastern edge of Tibetan Plateau. We analyzed the relationship between four constructed chronologies and climatic variables (monthly minimum temperature, monthly mean temperature, monthly maximum temperature, monthly total precipitation, the Standardized Precipitation-Evapotranspiration Index, and monthly relative humidity) by using response function analysis, moving interval analysis, and redundancy analysis. Overall, the growth of A. georgei was positively affected by common climatic factors (winter moisture conditions, autumn temperature, and previous autumn precipitation). At low and middle-low sites, May moisture condition and previous December precipitation controlled its radial growth with positive correlations. At middle-high and high sites, previous November temperature was the key factor affecting tree growth. The result of moving interval analysis was consistent with correlation analyses, particularly for May moisture at low altitudes.

Assessment and monitoring of drought in Chitradurga district of Karnataka using different drought indices

Drought is a natural disaster due to less precipitation than the normal that can occur irrespective of climate regimes. Impact assessment of drought and monitoring are the most important mitigation stratregies to combat the drought effects. As the single index cannot assess all the drought conditions, in the present study multi-variate indices approach has been used to assess and monitor drought. Five indices were assessed using precipitation data such as deciles index (DI), percent normal (PN), China-Z index (CZI), Z-Score index (ZSI) and standardized precipitation index (SPI). Monthly total precipitation data was used to calculate drought events occurred during the period 1967–2017 in different talukas of Chitradurga district of Karnataka, India. The assessment revealed that SPI, CZI and ZSI performance was similar in identifying drought. PN was very much responsive for the rainfall events that occurred during the particular year however, it exhibited variations in dry conditions. DI was not that much satisfactory in identifying drought conditions. Among the five indices assessed, SPI seems to be the best indicator to predict the drought onset than the other four drought indices. Therefore SPI can be recommended for assessing and monitoring the drought in Chitradurga district of Karnataka, India.

Winter Precipitations of Northern Part of Farming-pastoral Zone and Hulunbuir Grassland

In January 2018 a high record of monthly total precipitation in northern China drew our attention. This number is 4 times more than that in normal winters over the past 30 years, and its location is in northern China. Thus our research region is composed by the northern part of the farming-pastoral zone and the Hulunbuir Grassland. We target our research at understanding the phenomena and causes of such high precipitations. We explore the heavy precipitation locations, and use dynamical analyses on different pressure levels to find out the cause of the high score. We analyze wind fields, geopotential heights and relative humidity for the pressure levels of 200 hPa, 500 hPa, 700 hPa and 850 hPa. We find that the location of the highest monthly total precipitation in January 2018 is on the mountain, whereas the spots of heavy precipitations during one event are not located on the mountain. Zooming in January 2018, it is the precipitation frequency that drastically increased, not the number of heavy precipitation events. The dynamical analyses show that the heavy precipitation events in January 2018 are mainly caused by appearance of cyclones either in or near the research region at high geopotential heights.

Detection of Legionella species, the influence of precipitation on the amount of Legionella DNA, and bacterial microbiome in aerosols from outdoor sites near asphalt roads in Toyama Prefecture, Japan

Background Legionellosis is caused by the inhalation of aerosolized water contaminated with Legionella bacteria. In this study, we investigated the prevalence of Legionella species in aerosols collected from outdoor sites near asphalt roads, bathrooms in public bath facilities, and other indoor sites, such as buildings and private homes, using amoebic co-culture, quantitative PCR, and 16S rRNA gene amplicon sequencing. Results Legionella species were not detected by amoebic co-culture. However, Legionella DNA was detected in 114/151 (75.5%) air samples collected near roads (geometric mean ± standard deviation: 1.80 ± 0.52 log10 copies/m3), which was comparable to the numbers collected from bathrooms [15/21 (71.4%), 1.82 ± 0.50] but higher than those collected from other indoor sites [11/30 (36.7%), 0.88 ± 0.56] (P < 0.05). The amount of Legionella DNA was correlated with the monthly total precipitation (r = 0.56, P < 0.01). It was also directly and inversely correlated with the daily total precipitation for seven days (r = 0.21, P = 0.01) and one day (r = − 0.29, P < 0.01) before the sampling day, respectively. 16S rRNA gene amplicon sequencing revealed that Legionella species were detected in 9/30 samples collected near roads (mean proportion of reads, 0.11%). At the species level, L. pneumophila was detected in 2/30 samples collected near roads (the proportion of reads, 0.09 and 0.11% of the total reads number in each positive sample). The three most abundant bacterial genera in the samples collected near roads were Sphingomonas, Streptococcus, and Methylobacterium (mean proportion of reads; 21.1%, 14.6%, and 1.6%, respectively). In addition, the bacterial diversity in outdoor environment was comparable to that in indoor environment which contains aerosol-generating features and higher than that in indoor environment without the features. Conclusions DNA from Legionella species was widely present in aerosols collected from outdoor sites near asphalt roads, especially during the rainy season. Our findings suggest that there may be a risk of exposure to Legionella species not only in bathrooms but also in the areas surrounding asphalt roads. Therefore, the possibility of contracting legionellosis in daily life should be considered.

Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey

Condensed water vapor in the atmosphere is observed as precipitation whenever moist air rises sufficiently enough to produce saturation, condensation, and the growth of precipitation particles. It is hard to measure the amount and concentration of total precipitation over time due to the changes in the amount of precipitation and the variability of climate. As a result of these, the modelling and forecasting of precipitation amount is challenging. For this reason, this study compares forecasting performances of different methods on monthly precipitation series with covariates including the temperature, relative humidity, and cloudiness of Muğla region, Turkey. To accomplish this, the performance of multiple linear regression, the state space model (SSM) via Kalman Filter, a hybrid model integrating the logistic regression and SSM models, the seasonal autoregressive integrated moving average (SARIMA), exponential smoothing with state space model (ETS), exponential smoothing state space model with Box-Cox transformation-ARMA errors-trend and seasonal components (TBATS), feed-forward neural network (NNETAR) and Prophet models are all compared. This comparison has yet to be undertaken in the literature. The empirical findings overwhelmingly support the SSM when modelling and forecasting the monthly total precipitation amount of the Muğla region, encouraging the time-varying coefficients extensions of the precipitation model.

The Potential Global Distribution of Sirex juvencus (Hymenoptera: Siricidae) under Near Current and Future Climatic Conditions as Predicted by the Maximum Entropy Model

Wood wasp species in the genus Sirex are known pests of forestry. They cause significant economic losses due to their impacts on plant health and wood quality. S. juvencus (Hymenoptera: Siricidae), widely distributed in Asia, Europe, and North America, is known to negatively impact forestry, infesting Picea, Pinus, Larix, Abies, Cupressus, and Pseudotsuga species. This pest destroys plants by depositing eggs, mucus, and its obligate mutualistic fungus, Amylostereum areolatum. Its obligate mutualistic fungus is to provide nutrition for S. juvencus larva. Despite its extensive distribution range, little is known about which environmental variables significantly impact current and future distribution patterns of S. juvencus for pest control and monitoring. Here we used the maximum entropy model in conjunction with occurrence points of S. juvencus and environmental variables to predict the current and future global potential distribution of S. juvencus. We used the jackknife method and Pearson’s correlation analysis to select the environmental variables that influence the geographic distribution of S. juvencus, which resulted in the inclusion of the monthly average maximum temperature in February, the max temperature of warmest month, monthly average minimum temperature in July, monthly total precipitation in June, precipitation of the driest month, monthly total precipitation in September, and the temperature annual range. Temperature and precipitation are mainly likely to drive the distribution enabled by its obligate mutualistic fungus and the potential to co-infect with other Sirex species. The high temperature and low humidity influence S. juvencus eggs and larvae directly and indirectly via fungus-growth, which enables the larvae to survive. Furthermore, S. juvencus may increase its distribution to moderately suitable areas due to competition or dependency on other Sirex species during the infestation. Under the future climatic conditions, the highly suitable area increased by 32.79%, while the moderately suitable area, low suitable area, and unsuitable area increased by 28.14%, 3.30%, and 2.15%. Under climate changes, S. juvencus may spread in previously unsuitable areas rapidly.

Comparison of gridding methods for monthly precipitation for trend analysis in Canada

&lt;p&gt;During the last couple of decades, Canada&amp;#8217;s national and regional climate trend assessment has been based on a set of gridded temperature and precipitation monthly anomalies, known as the Canadian Gridded (CanGRD) data, which were produced using Optimal Interpolation (OI). In CanGRD, temperature anomalies and normalized precipitation anomalies at 463 stations of the Adjusted/Homogenized Canadian Climate Data (AHCCD) are interpolated to a 50-km equal-area grid over Canada. The input AHCCD precipitation data had been previously adjusted for known problems such as wind-induced gauge undercatch, wetting loss, and trace amounts; and joined stations series were also tested and adjusted. However, the performance of the CanGRD gridding method (i.e., the OI method) has never been evaluated against other gridding methods. The objective of this study is to evaluate CanGRD method against an ordinary kriging (KG) method. To this end, an observation-based ANUSPLIN-gridded monthly precipitation dataset (which is based on precipitation data from 3000+ stations) was used as the truth dataset, and ANUSPLIN estimates of monthly precipitation amounts at the 463 AHCCD stations were used as input data to the gridding models. In search for a better way to use KG, we took two approaches to apply KG: (1) KG-GP approach, in which KG was applied directly to the monthly total precipitation amounts; and (2) KG-GNGA approach, in which KG was applied separately to the monthly normals (for each calendar month) and the monthly anomalies. The gridded normals (GN) and the gridded anomalies (GA) were then combined together (GN+GA) for comparison with the gridded precipitation (GP) from the KG-GP approach to find out which of the two approaches is more skillful. The gridded anomalies (GA) from the KG-GNGA approach is comparable, and was compared with the CanGRD data, noting that in the CanGRD method, the anomalies rather than precipitation totals are gridded. In both evaluations, the gridded datasets were compared against their counterparts derived from the truth dataset using skill measurements such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pattern Correlation Score (PCS). The results show that (1) the KG-GNGA approach notably outperforms the KG-GP approach, and (2) the KG-GNGA method significantly outperforms the OI method used in CanGRD. This study is being expanded to include other gridding methods in the comparison.&lt;/p&gt;

Seasonal weather forecasts as a decision support tool in the Douro Wine Region, Portugal

&lt;p&gt;Weather conditions in a given year largely control wine production, despite all the mitigation measures that can be undertaken in the vineyards and wineries. As such, seasonal weather forecasts can be a valuable decision support tool for assisting winemakers in short to medium-term management, particularly when coupled with wine production models. Adequate and timely agricultural management grounded on predicted wine production will reduce the risks and enhance the efficiency of the sector. In this study, the performance of seasonal weather forecasts of wine production in the Portuguese Douro &amp; Port wine region (D&amp;P WR) is evaluated. However, this concept can be extended to other wine regions worldwide. A predictive logistic model of wine production is developed herein. Monthly mean air temperatures and monthly total precipitation, averaged over the periods of February&amp;#8211;March, May&amp;#8211;June, and July&amp;#8211;September, and an autoregressive component of wine production are taken into account for this purpose. The wine production time series for the D&amp;P WR and over the period 1950&amp;#8211;2017 (68 years) is categorized into three classes based on quantiles: high, normal and low production years. The empirical wine production model reveals a correct estimation ratio of nearly 2/3 when applied to independent 10%-random subsamples taken from the complete time series. The performance of the ECMWF 7-month seasonal weather forecasts (issued from February to August) to predict the temperature and precipitation variables used in the wine production model is subsequently assessed. The results show a reasonable performance in predicting these variables. Furthermore, the forecasts from May to August are clearly the best performing, as 1) more observed data is included in the empirical wine production model, and 2) the performance of seasonal forecasts for summer months is higher, owing to the local Mediterranean-type climate characteristics, with typically dry and settled atmospheric conditions in summer. The extension of this approach to other wine regions in Europe, as well as its operational application, are foreseen in the near future within the framework of the European Commission-funded action &amp;#8220;Climate change impact mitigation for European viticulture: knowledge transfer for an integrated approach &amp;#8211; Clim4Vitis&amp;#8221; [grant number 810176].&lt;/p&gt;