Magnesium Isotopic Compositions of International Geological Reference Materials

Magnesium isotopic compositions are reported for twenty‐four international geological reference materials including igneous, metamorphic and sedimentary rocks, as well as phlogopite and serpentine minerals. The long‐term reproducibility of Mg isotopic determination, based on 4‐year analyses of olivine and seawater samples, was ≤ 0.07‰ (2s) for δ²⁶Mg and ≤ 0.05‰ (2s) for δ²⁵Mg. Accuracy was tested by analysis of synthetic reference materials down to the quoted long‐term reproducibility. This comprehensive dataset, plus seawater data produced in the same laboratory, serves as a reference for quality assurance and inter‐laboratory comparison of high‐precision Mg isotopic data.     

Time trend analysis of some agroclimatic variables during the last half century over Iran

Theoretical and Applied Climatology - We assessed the trends of precipitation, maximum and minimum temperature (Tmax and Tmin), diurnal temperature range (DTR), water requirement of autumn-planted...     

The daily travel of villagers to the city in a case study of Northern Iran

Rural urban migration can often result in decreased agricultural output in countries with scarce land resources. It also can hasten the socio-economic problems associated with the overcrowded cities of the developing world. As only 10% of Iran is arable it is particularly vulnerable to threats to food security and self-sufficiency. This paper examines the daily flow of villagers to a major city near the vital staple crop producing rural district of Lakan, where out-migration is already occurring. Although most discussions of daily travel to cities is typically relegated to commuting, this paper aims to analyze travel for the purpose of access to a variety of facilities and services that villages in the rural district of Lakan lack including secondary schools, post offices, banks, and even telephones and newspaper stands. The paper emphasizes that daily journeys make permanent moves to the city more likely because of the major inconvenience that results from the scarcity of basic facilities within Lakan villages. Travel to a distant city for staple foods at grocery stores or for access to secondary schools, for example, could intensify future out-migration because the heavy inconvenience of constant travel might eventually outweigh the benefits of maintaining residency in villages. The paper utilizes information about daily travel to Rasht to recommend various government construction projects and initiatives to improve the quality of life for villagers and avert future rural–urban migration and the reduction in staple crop production that might result.     

The accountability imperative for quantifying the uncertainty of emission forecasts: evidence from Mexico

Governmental climate change mitigation targets are typically developed with the aid of forecasts of greenhouse-gas (GHG) emissions. The robustness and credibility of such forecasts depends, among other issues, on the extent to which forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of GHG emissions in the country. We use those projections to produce probabilistic forecasts of GHG emissions for Mexico. We contrast our probabilistic forecasts with Mexico’s governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive climate change mitigation targets.

POLICY INSIGHTS

• No minimum transparency and quality standards exist to guide the development of GHG emission scenario forecasts, not even when these forecasts are used to set national climate change mitigation targets.

• No accountability mechanisms appear to be in place at the national level to ensure that national governments rely on scientifically sound processes to develop GHG emission scenarios.

• Using probabilistic forecasts to underpin emission reduction targets represents a scientifically sound option for reflecting in the target the uncertainty to which those forecasts are subject, thus increasing the validity of the target.

• Setting up minimum transparency and quality standards, and holding governments accountable for their choice of forecasting methods could lead to more robust emission reduction targets nationally and, by extension, internationally.

     

Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives

Multi-criteria decision-making methods support decision makers in all stages of the decision-making process by providing useful data. However, criteria are not always certain as uncertainty is a feature of the real world. MCDM methods under uncertainty and fuzzy systems are accepted as suitable techniques in conflicting problems that cannot be represented by numerical values, in particular in energy analysis and planning. In this paper, a modified TOPSIS method for multi-criteria group decision-making with qualitative linguistic labels is proposed. This method addresses uncertainty considering different levels of precision. Each decision maker’s judgment on the performance of alternatives with respect to each criterion is expressed by qualitative linguistic labels. The new method takes into account linguistic data provided by the decision makers without any previous aggregation. Decision maker judgments are incorporated into the proposed method to generate a complete ranking of alternatives. An application in energy planning is presented as an illustrative case example in which energy policy alternatives are ranked. Seven energy alternatives under nine criteria were evaluated according to the opinion of three environmental and energy experts. The weights of the criteria are determined by fuzzy AHP, and the alternatives are ranked using qualitative TOPSIS. The proposed approach is compared with a modified fuzzy TOPSIS method, showing the advantages of the proposed approach when dealing with linguistic assessments to model uncertainty and imprecision. Although the new approach requires less cognitive effort to decision makers, it yields similar results.     

Effective environmental factors in the distribution of vegetation types in Poshtkouh rangelands of Yazd Province (Iran)

The objective of this research was to study the relationships between environmental factors and vegetation in order to find the most effective factors in the separation of the vegetation types in Poshtkou rangelands of Yazd province. Sampling of soil and vegetation were performed with randomized-systematic method. Vegetation data including density and cover percentage were estimated quantitatively within each quadrat, and using the two-way indicator species analysis (TWINSPAN), and vegetation was classified into different groups. The topographic conditions were recorded in quadrat locations. Soil samples were taken in 0–30 and 30–60 cm depths in each quadrat. The measured soil variables included texture, lime, saturation moisture, gypsum, acidity (pH), electrical conductivity, sodium absorption ratio, and soluble ions (Na⁺, K⁺, Mg²⁺, Cl⁻, CO₃²⁻, HCO₃⁻ and SO₄²⁻). Multivariate techniques including principal component analysis (PCA) and canonical correspondence analysis (CCA) were used to analyse the collected data. The results showed that the vegetation distribution pattern was mainly related to soil characteristics such as salinity, texture, soluble potassium, gypsum, and lime. Totally, considering the habitat conditions, ecological needs and tolerance range each plant species has a significant relation with soil properties.     

Modelling of piping collapses and gully headcut landforms:Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.     

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO,BAT and COA algorithms

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.     

Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.     

Vulnerability evaluation of a coastal aquifer via GALDIT model and comparison with DRASTIC index using quality parameters

In recent years, environmental assessments of groundwater resources have resulted in the development of models that help identify the vulnerable zones. An aquifer is investigated using both GALDIT and DRASTIC indices. The GALDIT model is developed to determine the vulnerability of coastal aquifers in terms of saltwater intrusion whereas the DRASTIC model is generally applicable to all aquifers. Having compared the results of both the GALDIT and DRASTIC models with quality parameters, the salinity model proved to be more appropriate in identifying the vulnerability of coastal aquifers. The results show a Pearson correlation coefficient between TDS and the GALDIT vulnerability map of 0.58 while the corresponding value for the DRASTIC index is 0.48.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR A. Fiori