An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

応用水文 = Applied hydrology

Heat and rainfall extremes have intensified over the past few decades and this trend is projected to continue with future global warming1–3. A long persistence of extreme events often leads to societal impacts with warm-and-dry conditions severely affecting agriculture and consecutive days of heavy rainfall leading to flooding. Here we report systematic increases in the persistence of boreal summer weather in a multi-model analysis of a world 2 °C above pre-industrial compared to present-day climate. Averaged over the Northern Hemisphere mid-latitude land area, the probability of warm periods lasting longer than two weeks is projected to increase by 4% (2–6% full uncertainty range) after removing seasonal-mean warming. Compound dry–warm persistence increases at a similar magnitude on average but regionally up to 20% (11–42%) in eastern North America. The probability of at least seven consecutive days of strong precipitation increases by 26% (15–37%) for the mid-latitudes. We present evidence that weakening storm track activity contributes to the projected increase in warm and dry persistence. These changes in persistence are largely avoided when warming is limited to 1.5 °C. In conjunction with the projected intensification of heat and rainfall extremes, an increase in persistence can substantially worsen the effects of future weather extremes. Climate models project an increase in summer weather persistence for the northern mid-latitudes. In a 2 °C world, two-week-long hot-and-dry conditions increase by up to 20% for eastern North America. The chance of a week of heavy rainfall increases by 26%, adding to the risk of extremes in the future.

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Summer weather becomes more persistent in a 2 °C world

In this research, the number of patients with Covid-19 and the number of deaths due to this disease in France, Germany, Iran, Italy, Spain, United Kingdom, and Unites States America are considered. First, the relations between the considered countries are studied using Pearson’s correlation. Then, based on the spread rate of Covid-19, these countries are categorized using principal component analysis.

Principal Component Analysis to Study the Relations between the Spread Rates of COVID-19 in High Risks Countries

A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging Ensemble and Support Vector Machine (SVM) classifier, was proposed for the spatial prediction of landslides at the district of Mu Cang Chai, Viet Nam. In the present study, 248 past landslides and fifteen geo-environmental factors (curvature, elevation, distance to rivers, slope, aspect, river density, plan curvature, distance to faults, profile curvature, fault density, lithology, distance to roads, rainfall, land use, and road density) were considered for the model construction. Different evaluation criteria were applied to validate the proposed hybrid model such as statistical index-based methods and area under the receiver operating characteristic curve (AUC). The single SVM and the Naive Bayes Trees (NBT) models were selected for comparison. Based on the AUC values, the proposed hybrid model BSVM (0.812) outperformed the SVM (0.804) and NBT (0.8) models. Thus, the BSVM is a promising and better method for landslide prediction.

Bagging based Support Vector Machines for spatial prediction of landslides

Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid regions, leads to enormous damage to agriculture, economy, and environment. In this study, the seasonal Standardized Precipitation Index (SPI) drought index and time series models were employed to model and predict seasonal drought using climate data of 38 Iranian synoptic stations during 1967–2014. In order to model and predict seasonal drought ITSM (Interactive Time Series Modeling) statistical software was used. According to the calculated seasonal SPI, within the study area, drought severity classes 4 and 3 had the greatest occurrence frequency, while classes 6 and 7 had the least occurrence frequency. Results indicated that the best fitted models were Moving-Average or MA (5) Innovations and MA (5) Hannan-Rissenen, with 60.53 and 15.79 percentage, respectively. On the other hand, results of the prediction as well, indicated that drought class 4 with the highest percentages, was the most abundant class over the study area and drought class 7 was the least frequent class. According to results of trend analysis, without attention to significance of them, observed seasonal SPI data series (1967–2014), in 84.21% of synoptic stations had a negative trend, but this percentage changes to 86.84% when studying the combination of observed and predicted simultaneously (1967–2019).

/pdf/modeling-prediction-and-trend-assessment-of-drought-in-iran-4g7ejpff74.pdf

Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index

Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model.

Drought is one of the main natural hazards affecting the economy and the environment of large areas. Droughts cause crop losses, urban water supply shortages, social alarm, degradation and desertification. In this study, the spatial characteristics of annual and seasonal drought were evaluated based on climate data from 16 synoptic stations during the period of 1980–2010 in south of Iran. To estimate the drought severity used modified Reconnaissance Drought Index (RDI) and to prepare maps, ArcGis10.2 software was used. Results showed in annual drought, percent of areas with normal condition, severe dry and extreme dry condition have had significant increasing trend (0.95 level). In winter drought, the percentage of areas with severe dry and extreme dry condition have had significant increasing trend (0.95 level). In spring drought, percent of areas with moderate dry has had significant increasing trend (0.95 level), in summer drought, percent of areas with moderate dry has had increasing trend (insignificant) and in autumn drought, percent of areas with severe dry has had significant increasing trend (0.95 level). Other classes of drought in different time scales had not significant trend. Result showed that the percentage of area with dry condition is increasing, this can be effective on the agricultural activities, agricultural productions, water resource management and other activities.

Analysis of Changes in Spatial Pattern of Drought Using RDI Index in south of Iran

Drought is a climatic phenomenon that can occur in various regions with different climate conditions. Generally, drought has negative impacts on different fields such as environment, rangelands, and water resources. The agricultural section (especially rain-fed agriculture) is one of the parts that is directly affected by different types of drought especially meteorological and agricultural droughts. The standardized precipitation evapotranspiration index (SPEI) is one of the newest and most applied indices to assess drought characteristics. In this paper, a modification is suggested for SPEI with the substitution of observed precipitation (OP) with effective precipitation (EP) to evaluate drought, with an emphasis on consideration of drought effects on agricultural section. To calculate EP, Food and Agriculture Organization of the united nation method (FAO), US Bureau of Reclamation (USBR), the Simplified version of Soil Conservation Service of the US Department of Agriculture method (USDA-SCS simplified), and the CROPWAT version of USDA-SCS method (USDA-SCS CROPWAT) were used. To compare the calculated SPEI based on OP (SPEIOP) and EP (SPEIEP) (based on different EP calculation methods), the correlation coefficients (CC) between SPEIOP and SPEIEP in four synoptic stations with at least 30 years of climatic data and annual yield loss (%) in winter wheat (Triticum sativum) (simulated using AquaCrop model) in the suitable reference periods for agricultural drought were used. Results showed, in Fasa, Drodzan, and Zarghan stations, the CC between SPEI based on EP using the USBR method (SPEIUSBR) and annual YL% had the highest values (in 42.11%, 68.42%, and 36.84% of Triticum sativum all reference periods, respectively). In Shiraz station, the CC between SPEI based on EP using the FAO method (SPEIFAO) and annual YL% had the highest values (in 47.37% of all reference periods). In all stations, the SPEIUSBR had the most reference periods with significant CC at 0.05 or 0.01 levels.

Modified version for SPEI to evaluate and modeling the agricultural drought severity

Landslides are as the movement of soil on slopes that they are one of the most common natural hazards in many mountainous areas. Landslides are recognized as an important natural hazard in many countries. So in the study, the geographic information system-based the fuzzy quantifier is used to determinate the landslide susceptibility modeling in the north of Khorramabad, west of Kermanshah Province, Iran. To determine the landslide susceptibility modeling generated aspect, some input data were prepared such as the digital elevation model, lithology, slope, land use, river, road, fault, and precipitation maps. Fuzzy map showed that almost all of the area was medium landslide susceptibility that had the value close to 1. Fuzzy-AHP model showed that 77.62% of the study area had medium landslide susceptibility and this method was a useful tool for forecasting of landslide susceptibility status in each case study.

Abdol Rassoul Zarei

Papers

Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index

Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model.

Analysis of Changes in Spatial Pattern of Drought Using RDI Index in south of Iran

Modified version for SPEI to evaluate and modeling the agricultural drought severity

Landslide Susceptibility Mapping Using Fuzzy-AHP