Glacial Geomorphology of Mt. Asralt Khairkhan and Mt. Baga Khentii Saridag in Khentii Mountain Range, Northeastern Mongolia

This study provides a map of glacial geomorphology of Mt. Asralt Khairkhan and Mt. Baga Khentii Saridag in Khentii Mountain Range in northeastern Mongolia. In the Khentii Mountain Range, only two types of glacial landforms including glacial cirques and terminal moraines are observed for the paleoglacial reconstructions of Mt. Asralt Khairkhan and Mt. Baga Khentii Saridag, and are mapped based on a mapping from 30 m resolution ASTER DEM and Landsat 8 satellite imagery. Spatial distribution of the glacial landforms indicates that the Khentii Mountain Range has experienced valley glaciations with small ice caps centered on the high mountains. The glacial cirques mark numerous glacial erosional landforms, while the terminal moraines indicate glacial advances and depositional landforms at outlets of valleys. Outer limits of these glacial landforms show their maximum glaciation of 10.0 km2 and 0.7 km2 for Mt. Asralt Khairkhan and Mt. Baga Khentii Saridag, respectively. This study demonstrates that spatial analysis of the glacial landforms is crucial for reviewing the paleoglacier extent. The map of the glacial geomorphology will be a key for further detailed studies in paleoglaciology and paleoclimate of the mountain range.

Hydrochemical Characterisation and Assessment of the Level of Contamination of Groundwater Collected by Private Waterworks in the Town of Moundou in the South of Chad

Groundwater is the main source of drinking water for large cities in most African countries. In Moundou, for example, the conventional groundwater supply system is failing. To compensate for this state failure, the population is building boreholes and wells, most of which tap the surface water table, generally referred to as the “water table”. The aim of this study is to characterize these waters in order to assess their level of contamination and, by extension, the degree of pollution of the water table. Major elements such as: Chloride (Cl-), Sulfate (SO42-), Nitrate (NO3-), Calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+) were analysed by Liquid Chromatography and the Bicarbonate ion (HCO3-) was determined by the titrimetric method. The methodology applied is based on a combination of hydrochemical techniques and statistical analysis (PCA and CHA). A sampling campaign was carried out during high-water periods. The results of the physico-chemical analyses show mineralization ranging from 7.29 to 3670 μS/cm, with an average of 487.44 μS/cm. The groundwater studied is generally acidic, with a pH ranging from 3.26 to 6.41. Based on their anions, they are classified into four main hydrochemical facies: chloride and sulphate facies, calcium and magnesium facies, sodium and potassium facies and bicarbonate facies. The various correlations between major ions and statistical analyses have enabled us to identify three hydrogeochemical processes involved in water mineralization. The dominant process is silicate hydrolysis, followed by cation exchange, then anthropogenic input, which influences mineralization by polluting the water.

The Pleistocene Glaciation in the Karakoram Mountains:Reconstruction of Past Glacier Extensions and Ice Thicknesses

Geomorphological and Quaternarygeological field- and laboratory data (Fig.1) are introduced and interpreted with regard to the maximum Ice Age (LGM) glaciation of the Central-and South Karakoram in the Braldu-, Basna-, Shigar-and Indus valley system as well as on the Deosai plateau between the Skardu Basin and the Astor valley (Fig.2). These data result from two research expeditions in the years 1997 and 2000. They show that between c. 60 and 20 Ka the Central Karakorum and its south slope were covered by a continuous c. 125,000 km2 sized ice stream network. This ice stream network flowed together to a joint parent glacier, the Indus glacier. The tongue end of the Indus glacier reached down to 850 ～ 800 m a.s.l. In its centre the surface of this Indus ice stream network reached a height of a good 6000 m. Its most important ice thicknesses amounted to c. 2400 ～ 2900 m.

New findings concerning the Pleistocene Glaciation of the Leh Basin,Ladakh(34°03'N/77°38'E)

Like for most parts of High Asia,researches concerning the Pleistocene landscape evolution of the Leh Basin(34°03' N/77°38' E) have also left contradictions.To push this topic,three up to now unexplored Ladakh Range tributaries of the Leh Basin(Stagmo-,Arzu-and Nang-Valley) have been investigated.U-shaped profiles,transfluence passes,moraine mantled and glacially rounded peaks and ridges,roches moutonnées,glacial flank polishings and ground moraines document the former glaciation of the study area.The ice fillings of these tributaries reached a minimum thickness up to 540 m.Even at the valley outlets and on the orographic right side of the Leh Basin,the glaciation was more than 350 m thick.Based on these empirically extracted results,theoretical snow line considerations lead to the conclusion that the whole Leh Basin was filled up by a former Indus-Valley glacier.An ice injection limited to the nourishment areas of the Ladakh Range valleys could not have caused the reconstructed ice cover(down to 3236 m a.s.l.),which is proved by extended ground moraine complexes.Only an Indus ice stream network(most likely during the LGP),nourished by inflowing glaciers of the Ladakh-and Stok Range,explains the widespread existence of the glacial sediments at the outlets of the investigated valleys.

内蒙古鄂尔多斯高原古冰缘遗迹科学考察研究进展

冰缘遗迹(特别是冷生楔形构造及融冻褶皱)是重建古气候及第四纪晚期多年冻土环境的重要证据。内蒙古鄂尔多斯高原是我国北方地区冰缘现象最为发育的地区之一。为准确了解鄂尔多斯高原冰缘遗迹类型及其分布特征、区域冻土演化历史等,中国科学院西北生态环境资源研究院和荷兰自由大学共同组成科研小组,于2018年5—6月组织了"鄂尔多斯高原冰缘遗迹科学考察"。考察区域涉及靖边—城川—乌审旗—鄂尔多斯东胜区一带约12000km2的范围。考察内容主要包括鄂尔多斯高原冰缘遗迹类型及特征、分布区域、各类型冰缘遗迹所指示的气候条件的初步推断等。结果表明:冻融褶皱和冷生楔体构造是鄂尔多斯高原主要存在的两大类冰缘遗迹。基于本次考察中关于冰缘遗迹的分布与特征等新发现,并综合前人研究成果,初步推断:在气温极低、多年冻土非常发育的时段,有利于形成各类冷生楔状构造,如冰楔假形和大型原生砂楔等;在气候转暖、多年冻土退化,但还没有全部融化完阶段,可能形成融冻褶皱;区域性大面积分布和成群出现的融冻褶皱一般反映较暖气候环境下,多年冻土层上部已退化到一定程度。基于光释光(OSL)年代测试结果,结合冰缘遗迹的特征及其所指示的古气候环境,初步重建了鄂尔多斯5万年以来的冻土环境变化序列。区内多年冻土在多年冻土最大期(LPM,25~19ka BP)时最发育,以大面积连续多年冻土为主;之后,随气温转暖,总趋势呈退化状态,多年冻土分布逐渐变为片状→岛状→零星斑状,直至现今全部融完变为深季节冻土区。

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

Ecogeomorphic Feedbacks in Semiarid Rangelands:A Review

The ecogeomorphic processes occurring on semiarid rangelands are reviewed, with emphasis on the source-sink relations and positive feedback loops that existed between shrub patches and intershrub areas, and the way livestock presence affected these interactions. Compared with intershrub areas, the shrub patches had a higher soil porosity, infiltration capacity, water-holding capacity, hydraulic conductivity, structural stability, and organic matter content, and lower bulk density. These differences derived from a host of processes whose intensity was less in the shrub patches, including raindrop impact, mechanical crust formation, overland water flow, soil erosion, evaporative moisture loss, and flock trampling. There was also greater shading of the soil surface; soil and litter deposition; water accumulation; microbial, fungal, and mesofaunal activities in the shrub patches. The overland flow of water carried soil and litter from the intershrub areas to the shrub patches and resulted in microtopographic modifications that tended to strengthen these source-sink relations. Grazing had an impact on these processes, not only at the shrub-intershrub scale but also within the intershrub areas, through the creation of highly compacted trampling routes. The combined role of the above ecogeomorphic processes was to maintain the rangeland＇s functionality. Without these inter-relationships, water loss, soil erosion, and nutrient depletion would occur at the hillside scale, causing degradation of the landscape.

Decreasing nutrient concentrations in soils and trees with increasing elevation across a treelineecotone in Rolwaling Himal, Nepal

At a global scale, tree growth in alpine treeline ecotones is limited by low temperatures. At a local scale, however, tree growth at its upper limit depends on multiple interactions of influencing factors and mechanisms. The aim of our research was to understand local scale effects of soil properties and nutrient cycling on tree growth limitation, and their interactions with other abiotic and biotic factors in a near-natural Himalayan treeline ecotone. Soil samples of different soil horizons, litter, decomposition layers, and foliage samples of standing biomass were collected in four altitudinal zones along three slopes, and were analysed for exchangeable cations and nutrient concentrations, respectively. Additionally, soil and air temperature, soil moisture, precipitation, and tree physiognomy patterns were evaluated. Both soil nutrients and foliar macronutrient concentrations of nitrogen(N), magnesium(Mg), potassium(K), and foliar phosphorus(P) decrease significantly with elevation. Foliar manganese(Mn) concentrations, bycontrast, are extraordinarily high at high elevation sites. Potential constraining factors on tree growth were identified using multivariate statistical approaches. We propose that tree growth, treeline position and vegetation composition are affected by nutrient limitation, which in turn, is governed by low soil temperatures and influenced by soil moisture conditions.

The Glacial(MIS 3-2)Outlet Glacier of the Marsyandi Nadi-icestream-network with its Ngadi Khola Tributary Glacier(Manaslu-and Lamjung Himalaya):The Reconstructed Lowering of the Marsyandi Nadi Ice Stream Tongue down in to the Southern Himalaya Foreland

For the reconstruction of past climate variations,investigations on the history of glaciers are necessary.In the Himalaya,investigations like these have a rather short tradition in comparison with other mountains on earth.At the same time,this area on the southern margin of Tibet is of special interest because of the question as to the monsoon-influence that is connected with the climate-development.Anyhow,the climate of High Asia is of global importance.Here for the further and regionally intensifying answer to this question,a glacial glacier reconstruction is submitted from the CentralHimalaya,more exactly from the Manaslu-massif.Going on down-valley from the glacial-historical investigations of 1977 in the upper Marsyandi Khola(Nadi) and the partly already published results of field campaigns in the middle Marsyandi Khola and the Damodar- and Manaslu Himal in the years 1995,2000,2004 and 2007,new geomorphological and geological field- and laboratory data are introduced here from the Ngadi(Nadi) Khola and the lower Marsyandi Nadi from the inflow of the Ngadi(Nadi) Khola down to the southern mountain foreland.There has existed a connected ice-stream-network drained down to the south by a 2,100-2,200 m thick and 120 km long Marsyandi Nadi main valley glacier.At a height of the valley bottom of c.1,000 m a.s.l.the Ngadi Khola glacier joined the still c.1,300 m thick Marsyandi parent glacier from the Himalchuli-massif(Nadi(Ngadi) Chuli) – the south spur of the Manaslu Himal.From here the united glacier tongue flowed down about a further 44 km to the south up to c.400 m a.s.l.(27°57'38 "N/84°24'56" E) into the Himalaya fore-chains and thus reached one of or the lowest past ice margin position of the Himalayas.The glacial(LGP(Last glacial period),LGM(Last glacial maximum) Würm,Stage 0,MIS 3-2) climatic snowline(ELA = equilibrium line altitude) has run at 3,900 to 4,000 m a.s.l.and thus c.1,500 altitude meters below the current ELA(Stage XII) at 5,400-5,500 m a.s.l.The reconstructed,maximum lowering of the climatic snowline(ΔELA = depression of the equilibrium line altitude) about 1,500 m corresponds at a gradient of 0.6°C per 100 altitude meters to a High Glacial decrease in temperature of 9°C(0.6 × 15 = 9).At that time the Tibetan inland ice has caused a stable cold high,so that no summer monsoon can have existed there.Accordingly,during the LGP the precipitation was reduced,so that the cooling must have come to more than only 9°C.

Review on Dating Methods:Numerical Dating in the Quaternary Geology of High Asia

Over the past few years, OSL and TCN datings of glacial material from High Asia have come into fashion. To this day, however, these techniques do not permit safe calibration. The intensity of the cosmic ray flux is being modulated by the solar and terrestrial magnetic fields and their secular fluctuations in the past. So far, these variations cannot be converted into the respective local TCN production rates for High Asia. We have reason to believe that the ages that are being calculated despite these uneertainties are generally overestimated. This assessment is supported by eonventional radiocarbon dates and above＂ all by the glacial chronology developed independently on the basis of the Quaternary geological method. The strongly emerging evidence for a much more extensive LGM glaciation of High Asia is, however, either being ignored or rejected by many authors, solely on the basis of the above-mentioned uncalibrated datings. This self-conceit based on the ＂dating fallacy＂, as we call it, should be avoided since it goes decidedly against the standards of the scientific method established in Quaternary geology and makes a fundamental scientific discussion impossible.

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)techniques 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.

Shear velocity criterion for incipient motion of sediment

The prediction of incipient motion has had great importance to the theory of sediment transport. The most commonly used methods are based on the concept of critical shear stress and employ an approach similar, or identical, to the Shields diagram. An alternative method that uses the movability number, defined as the ratio of the shear velocity to the particle＇s settling velocity, was employed in this study. A large amount of experimental data were used to develop an empirical incipient motion criterion based on the movability number. It is shown that this approach can provide a simple and accurate method of computing the threshold condition for sediment motion.

Monitoring of Temporal and Spatial Changes of Land Use and Land Cover in Metropolitan Regions through Remote Sensing and GIS

The use of remote sensing techniques and subsequent analysis by means of geographical information system (GIS) offers an effective method for monitoring temporal and spatial changes of landscapes. This work studies the urbanization processes and associated threats to natural ecosystems and resources in the metropolitan areas of Berlin and Erlangen-Fürth-Nürnber?Schwabach (EFNS). To compute the land use/cover (LULC) of the study areas, a supervised classification of “maximum likelihood” using Landsat data for the years of 1972, 1985, 1998, 2003, and 2015 is used. Results show that the built-up area is the dominant land use in both regions throughout the study period. This land use has increased at the expense of green and open areas in EFNS and at the expense of agricultural land in Berlin. Likewise, 5% of forest in EFNS is replaced with urban infrastructure. However, the amount of forest in Berlin increased by 3%. While EFNS experienced relatively big changes in its water bodies from 1972 to 1985, changes in water bodies in Berlin were rather slight during the last 40 years. The overall accuracy of our remotely sensed LULC maps was between 88% and 94% in Berlin and between 85.87% and 87.4% for EFNS. The combination of remote sensing and GIS appears to be an indispensable tool for monitoring changes in LULC in urban areas and help improving LU planning to avoid environmental and ecological problems.

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.

Climate-Driven Changes in Lake Areas for the Last Half Century in the Valley of Lakes, Govi Region, Southern Mongolia

This study presents the changes in lake areas in the Valley of Lakes, the Govi region, southern Mongolia. The recent changes in lake areas show decreases depending on vulnerability of lake basins and response of Govi landscape to the present climatic warming. During the recent 44 - 45 years (from 1970 to 2014 or 2015), modern lakes have encountered the present rapid increase in temperature, water evaporation and drying up that induced the reduction in lake areas in the Valley of Lakes. The finding of the reduction in lake areas is consistent with the trends on increasing in temperature since 1995 and fluctuating precipitation since 1975. Investigations with detailed chronology of lake sediment are needed from the lakes to review a more complete evolution of lake basins during the Late Quaternary paleoclimatic history in Mongolia and Central Asia.

Comparison of machine learning models for gully erosion susceptibility mapping

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

Sedimentological Study of Caves in the Zemo Imereti Plateau, Georgia, Caucasus Region

The Zemo Imereti Plateau is located in the easternmost part of the limestone region of western Georgia and is Caucasus’ only karst plateau. It is centrally located in a relatively elevated part of the intermountain plain of the country of Georgia. Lithostratigraphical (petrographic, mineralogical, and XRD) research on terrigenous sediments found in caves in the region was conducted in the Upper Cretaceous limestones located at different hypsometric levels (400 - 700 m). This study focused on allochthonous deposits, which, due to sedimentological features, contains significant and complete information for paleogeographic reconstruction as opposed to the autochthonous sediments. Source provinces of the sediments’ origin were determined using petrographic analysis. Lithological study of the terrigenous sediments indicated their origin from the Racha range, as well as their transportation mechanisms, and sedimentation conditions during deposition. Approximate ages (the end of the Middle Pleistocene and the beginning of the Late Pleistocene) of ancient terrestrial sediments in Rganisklde Cave were achieved by taking into account the geological and geomorphological development of the region. Lithostratigraphical analysis of the cave deposits and modern archaeological data indicate that the formation of the caves in the Zemo Imereti plateau took place mainly during the end of the Middle Pleistocene and in the beginning of the Pleistocene;while on the southern slope of the Racha range and in the surrounding area of Ertso-Tsona (Caucasus southern slope), they formed in the Early Pleistocene.

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.

Geoecological Monitoring of Karst Water in Georgia, Caucasus (Case Study of Racha Limestone Massif)

Karst groundwater is the major natural resource of drinking water for many countries in the world. Especially in karstic regions, karst water requirements for settlements are provided from karst aquifers. Also, we should consider, that karst groundwater is becoming more and more valuable for drinking water supply. Thus, karst groundwater quality and permanent ecological monitoring are very important for populations. Moreover, if we consider that the karst landscape is the extremely sensitive system towards anthropogenic activities, since exaclty the anthropogenic activities largely identify the karst water pollution-turbidity causing factors. This paper presents a new study regarding the quality of the karst groundwater of the study area, which contains important resource of drinking water. In the mentioned study, 12 water samples were collected from different locations of the 4 main karst springs (Krikhula, Dolabistavi, Kidobana and Sakishore) during the spring and summer of 2014 and 2015 years. The main aim was to identify chemical compositions (Ni, Ag, Co, Cd, Zn, Pb, Al, Mg, Fe, F, Cu), and also, it was important to detect Escherichia coli (E. coli). Our research regarding all these chemical compositions shows that all the values are low and under the environmental limit according to the Georgian standards. We measured chemical parameters of all these samples by Atomic Absorption Spectroscopy (AAS) in the chemical laboratory of Ivane Javakhishvili Tbilisi State University, country of Georgia.

SWPT:An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors

The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources.Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner.Hence,this study aimed at developing a user-friendly geographic information system(GIS)tool,Sub-Watershed Prioritization Tool(SWPT),using the Python programming language to decrease any possible uncertainty.It used geospatial-statistical techniques for analyzing morphometric and topohydrological factors and automatically identifying critical and priority sub-watersheds.In order to assess the capability and reliability of the SWPT tool,it was successfully applied in a watershed in the Golestan Province,Northern Iran.Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds.It provided a cost-effective approach for prioritization of sub-watersheds.Therefore,the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed.