Showing papers in "Environmental Earth Sciences in 2019"
TL;DR: In this paper, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map.
Abstract: Multi-hazard assessment modeling comprises an essential tool in any plan that aims to mitigate the impact of future natural disasters. For a particular area they can be generated by combining assessment maps for different types of natural hazards. In the present study, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map. Evaluation of the reliability of the proposed model predictions was performed through uncertainty analysis of the variables that we used for producing the final model. The drainage basin of Peneus (Pinios) River (Western Peloponnesus, Greece), an area that is prone to landslides, floods and seismic events, was selected for the implementation of the aforementioned approach. Our findings revealed that the high hazard zones are mainly distributed in the western and north-eastern part of the region under investigation. The calculated multi-hazard map, which corresponds to the potential urban development suitability map of the study area, was classified into five classes, namely of very low, low, moderate, high and very high suitability. The most suitable areas for urban development are distributed mostly in the eastern part, in agreement with the low and very low hazard level for the three considered natural hazards. In addition, by performing uncertainty analysis we showed that the spatial distribution of the suitability zones does not change significantly. Ultimately, the final map was verified using the actual inventory of landslides and floods that affected the study area. In this context, we showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low suitability zones. Consequently, the predictive capacity of the applied method is quite good. Finally, the spatial distribution of the urban areas and the road network were compared with the derived suitability map and the results revealed that approximately 50% of both of them are located within areas susceptible to natural hazards. The proposed approach can be useful for engineers, planners and local authorities in spatial planning and natural hazard management.
162 citations
TL;DR: Wang et al. as discussed by the authors used Piper and Chadha diagrams to reveal the hydrogeochemical characteristics of groundwater via physicochemical analysis of 44 collected samples, and the suitability of groundwater was assessed for domestic and irrigation purposes.
Abstract: Assessment of groundwater quality and health risk was conducted in the Shenfu coal mine area in Ordos basin, northwestern China. Statistical analysis, Piper and Chadha diagrams were used to reveal the hydrogeochemical characteristics of groundwater via physicochemical analysis of 44 collected samples. The suitability of groundwater was assessed for domestic and irrigation purposes, and the fuzzy comprehensive method was adopted to assess the overall groundwater quality for further discussion on groundwater management. The model recommended by the USEPA was selected to estimate the non-carcinogenic risks caused by NO3−, NO2−, NH4+, F−, Fe and Mn through oral ingestion and direct dermal contact. The results revealed that the predominant hydrochemical types of groundwater were SO4∙Cl–Ca∙Mg and HCO3–Ca∙Mg types and the major cations and anions followed the orders of Ca2+ > Na+ > Mg2+ >K+ and HCO3− > SO42− > Cl−, respectively. Groundwater is generally acceptable for irrigation. However, for domestic purposes, 47.73% of the collected samples are of excellent and good quality and are suitable for direct consumption. Both adults and children face non-carcinogenic risks because of exposure to contaminants such as nitrate, nitrite and fluoride. The risk to children is higher than that to adults, which is consistent with other studies. Nitrite contributes most to the risks, followed by nitrate and fluoride. Home-use water quality improvement devices and rainwater harvesting are suggested to enhance the groundwater quality protection and management in this area. The research also indicates that health risk assessment should always accompany general water quality assessment to ensure the reliability of the water quality assessment.
153 citations
TL;DR: In this paper, the authors presented an attempt to make an upgraded assessment of earlier and often circulated data to provide an assessment of the utilisation of current karst aquifers and also to estimate possible trends under various impact factors such as population growth or climate changes.
Abstract: Karst aquifers are one of the main potable water sources worldwide. Although the exact global karst water utilisation figures cannot be provided, this study represents an attempt to make an upgraded assessment of earlier and often circulated data. The main objective of the undertaken analysis is not only to provide an assessment of the utilisation of current karst aquifers, but also to estimate possible trends under various impact factors such as population growth or climate changes. In > 140 countries, different types of karstified rocks crop out over some 19.3 × 106 km2, covering > 14% of ice-free land. The main ‘karst countries’, those with > 1 × 106 km2 of karst surface are Russia, USA, China and Canada, while among those with > 80% of the territories covered by karst are Jamaica, Cuba, Montenegro and several others. In contrast, in a quarter of the total number of countries, karstic rocks are either totally absent or have a minor extension, meaning that no karst water sources can be developed. Although the precise number of total karst water consumers cannot be defined, it was assessed in 2016 at approximately 678 million or 9.2% of the world’s population, which is twice less than what was previously estimated in some of the reports. With a total estimated withdrawal of 127 km3/year, karst aquifers are contributing to the total global groundwater withdrawal by about 13%. However, only around 4% of the estimated average global annually renewable karstic groundwater is currently utilised, of which < 1% is for drinking purposes. Although often problematic because of unstable discharge regimes and high vulnerability to pollution, karst groundwater represents the main source of potable water supply in many countries and regions. Nevertheless, engineering solutions are often required to ensure a sustainable water supply and prevent negative consequences of groundwater over-extraction.
93 citations
TL;DR: In this paper, the authors identify the sources and processes controlling the hydrochemical evolution of groundwater in Hezamara block in Tripura and suggest installation of public drinking water supply in this area to reduce the impact of heavy-metal contamination on human health.
Abstract: Tripura is a water-rich administrative state in the Northeastern part of India. Though groundwater is the main source of drinking water, groundwater monitoring and historical data on the groundwater quality of this state is relatively scarce. This study aims to identify the sources and processes controlling the hydrochemical evolution of groundwater in Hezamara block in Tripura. Interpretation of measured parameters using geochemical plots, analysis of ionic ratios, multivariate statistical techniques, and spatial interpolation methods indicated both natural and anthropogenic sources. Results show that precipitation is the dominant process controlling the groundwater quality followed by rock–water interaction. Carbonate dissolution and silicate weathering were the major geochemical processes. The findings showed that the concentration of few heavy metals (iron, manganese, and lead) exceeded the drinking water quality standards. Evaluation of the results through various heavy-metal indices showed that several locations exceeded the limits and pose a risk to humans. Potential non-carcinogenic risk through the drinking water pathway was also identified. Pollution mapping indicates that only less than 1 km2 of the study area is suitable for drinking use. This study recommends installation of public drinking water supply in this area to reduce the impact of heavy-metal contamination on human health. Moreover, the water should be treated before supplying for public use.
84 citations
TL;DR: In this paper, the effect of free and adsorbed gas on the strength of coal, normal coal, and deformed coal were evaluated under different conditions, and a new model for the weakening values of strength induced by free and ada-bed gas was developed.
Abstract: The mechanical properties of coal are important parameters for coalbed methane (CBM) extraction and gas outburst control. However, the effect of adsorbed gas on strength cannot be evaluated quantitatively yet. To better understand the weakening mechanisms of free and adsorbed gas on the strength of coal, normal coal, and deformed coal are chosen to test their mechanical properties of CH4-saturated and non-gas-saturated specimens under different conditions. Under the same effective stresses, the peaks of strength of CH4-saturated specimens with high-pressure gas are lower than those with low-pressure gas, implying that the adsorbed gas can also weaken the peak strength of coal. Then, a new model for the weakening values of strength induced by free and adsorbed gas was developed, and the effects of free gas and adsorbed gas on the strength of coal were assessed by our model. The results show that the free gas and adsorbed gas can weaken the strength of coal for different weakening mechanisms. The ratio of weakening value of strength due to free gas to that of adsorbed gas of the normal coal is 1.3–3.4, and that of the deformed coal is 8.4–19.8. These results can help us to better understand why the weakening effect of adsorbed CH4 in the laboratory is ignored in earlier studies.
83 citations
TL;DR: Wang et al. as discussed by the authors combined qualitative analysis and quantitative characterization to investigate rock meso-structure damage due to water invasion-water loss cycles by analyzing the variations of rock mesosstructures using a scanning electron microscope (SEM).
Abstract: Water-level variations of tailings ponds can result in slope rocks being in a state of water invasion-water loss which can lead to irreversible damage to the rock meso-structure. This study combines qualitative analysis and quantitative characterization to investigate rock meso-structure damage due to water invasion-water loss cycles by analyzing the variations of rock meso-structures using a scanning electron microscope (SEM). Results from this analysis identified four stages in the variations of rock meso-structure under the action of water invasion-water loss cycles: overall homogeneity and compactness stage, primary pore expansion stage, porous flocculation stage, and a pore and fracture development stage. According to the fractal dimension in SEM test results, we can define rock meso-damage variable Df (which attained a maximum of 33.57%), thus realizing the quantitative characterization of rock damage under the action of water invasion-water loss cycles. After demonstrating that the evolutionary relationship between fractal dimension/damage variable and cycle number conforms to exponential function change, we also explored rock meso-damage mechanisms under the action of water invasion-water loss cycles.
82 citations
TL;DR: In this article, the authors compared the relative efficiency of two flood zonation methods, the frequency ratio and fuzzy logic models, for flood susceptibility evaluation and delineation in the Kosi megafan region of eastern Bihar, India.
Abstract: The Kosi megafan region of eastern Bihar, India, comprising of eight districts, is regularly afflicted by large floods that cause extensive damage. Mapping the possible inundation susceptible zones in the region accurately is, therefore, paramount for land resource conservation and livelihood preservation. This paper compares the relative efficiency of two flood zonation methods, the frequency ratio and fuzzy logic models, for flood susceptibility evaluation and delineation. Flooded extents from two past events were combined to create the training dataset, which was then compared individually with respective maps of 12 well-documented causal factors of inundation to gauge their relative influence on the flood spread. These were merged using the two models to generate the respective flood susceptibility maps, which were subsequently validated using the Receiver Operating Characteristics Curve and the Seed Cell Area Index methods. Results revealed that an enhanced fuzzy logic model (prepared by assigning relative weights to the respective causal factors as obtained from their analysis in the frequency ratio model) was more accurate and robust in demarcating flood susceptibility zones, as was further ascertained by comparison with a recent flood event, thereby providing a novel way to merge these methods. The western and southern tracts of the region were found to be more inundation prone, with the greatest risk posed along the narrow interfluve between the Kosi and Ganga rivers prior to their confluence further east. The districts most likely to be inundated were identified by computing their respective proportionate areas under the very highly susceptible flood class.
80 citations
TL;DR: In this paper, the formation, height determination, and compaction characteristics of a caving zone are examined, and the applicability and reliability of present research results and research methods are analyzed and the focus areas for future studies are identified.
Abstract: Broken rock and coal—residual coal, plus material from the immediate roof and overlying strata—fill in the goaf, in an area termed the caving zone. Due to its high porosity and permeability, the caving zone contains gas and water, which may have originated from the mined coal seam, the adjacent unmined coal seam, or from any aquifer or surface river. Thus, studying the compaction characteristics of the caving zone can help understand gas and mine water drainage and identify steps to prevent spontaneous combustion of residual coal. The stability characteristics of the caving zone after mining are affect surface subsidence, as well as water and gas build-up and use. The caving zone is a potential underground storage of greenhouse gases. Therefore, the time–space relationship of caving zone compaction characteristics in the goaf has become an area for research focus in recent years; in this study, the formation, height determination, and compaction characteristics of a caving zone are examined. Reduction in block size and rearrangement of the fill are the main factors affecting the compaction process, as re-crushing and rearrangement of the broken coal and rock mass affect the secant modulus and pore size of the caving zone, causing the secant modulus to gradually increase and pore size to decrease. This in turn affects the macroscopic stress–strain curve and seepage characteristics of the caving zone. The strength and fracturing mode of the caving blocks are the main factors affecting the re-crushing and rearrangement of the caving blocks. The applicability and reliability of present research results and research methods are analyzed and the focus areas for future studies are identified. Using a combination of research methods, including theoretical analysis, laboratory testing, numerical simulation, and field measurement, the compaction characteristics of a caving zone in longwall goaf can be accurately calculated.
74 citations
TL;DR: In this paper, the effect of periodic water circulation on rock mass, chlorite-amphibolite rocks from the slope of Nanfen open-pit iron mine in Liaoning province were chosen as the engineering samples and were investigated using uniaxial compressive experiment and scanning electron microscopy.
Abstract: To study the effect of periodic water circulation on rock mass, chlorite–amphibolite rocks from the slope of Nanfen open-pit iron mine in Liaoning province were chosen as the engineering samples and were investigated using uniaxial compressive experiment and scanning electron microscopy. The effect of different wetting and drying cycles on the mechanical properties and microstructure of the rocks was investigated. The characteristics of pore parameters from the SEM images were obtained by Image Pro Plus image processing software. The results show that with the increase in number of wetting and drying cycles, the uniaxial compressive strength of the rock decreases and the porosity increases significantly. The weakening of macroscopic mechanical properties of rocks is closely related to the changes in microstructures of rocks. The water–rock interaction changes the size, shape and porosity of the rock pores and then affects its mechanical properties. Based on the combination of macro and micro, quantitative analysis of the weakening process of rocks subjected to wet and dry cycles can provide a better reference index for evaluating the stability of geotechnical engineering.
73 citations
TL;DR: In this paper, the authors used two GIS-based machine learning approaches (analytical hierarchy process (AHP) and fuzzy logic modeling) to map soil erosion susceptibility in the Kunur River Basin, West Bengal, India.
Abstract: Soil erosion is a natural process; it adversely impacts natural resources, agricultural activities, ecological systems, and environmental quality as it degrades landscapes and water quality, disrupts ecosystems, and intensifies hazards. Management strategies are needed that protect soil erosion in agricultural watersheds to achieve the sustainable land-use planning. This study maps soil erosion susceptibility using two GIS-based machine-learning approaches—analytical hierarchy process (AHP) and fuzzy logic modeling in the Kunur River Basin, West Bengal, India. Fifteen soil erosion conditioning variables were integrated with the modeling methods, remote sensing data, and GIS analysis. The relative importance of the conditioning variables was assessed for their capacities to predict susceptibility of locations to soil erosion. The soil erosion susceptibility maps generated from the two models used 70% of surveyed soil erosion sites. These models’ maps were validated with the characteristics of the remaining 30% of the soil erosion sites to produce a receiver operating characteristics curve. The results indicated that the fuzzy logic model has the higher prediction accuracy; the area under the curve (AUC) value was 91.4%. The AUC value of the AHP model was 89.7%. Both models indicated that study area contains regions of high to severe soil erosion susceptibility. Logistic regression was used to discern the variables’ importance in the assessment. Relief, NDVI, distance from a river, rainfall erosivity, and soil types were the most important variables. TWI, SPI, aspect, and a sediment transportation index were of least importance. Fuzzy-logic-generated SESMs can be effective tools to guide protective actions and land managers’ measures during the primary stages of soil erosion to control the development of soil degradation.
72 citations
TL;DR: Wang et al. as mentioned in this paper used UDEC numerical simulation to study the movement characteristics of a hard-thick stratum affected by a fault in a coalmine to predict the dynamic hazards (i.e., rock bursts and shock bumps) because of the particular structural and mechanical properties of the HTS and the fault.
Abstract: Study of movement characteristics of a hard–thick stratum (HTS) affected by a fault in a coalmine is significant to predict the dynamic hazards (i.e., rock bursts and shock bumps) because of the particular structural and mechanical properties of the HTS and the fault. Hence, using UDEC numerical simulation, the movement characteristic of HTS and fault-slipping law with different mining directions towards the fault were studied. Then, two different inducing modes and corresponding mechanisms of rock burst were obtained. The results show that the structure of overlying strata on two fault walls is different because of fault cutting and fault dip; it results in the HTS of two fault walls presenting different movement stage characteristics. From analysis of fault plane stress and fault slipping, we obtain that footwall mining has higher risk of rock burst than hanging wall mining. Finally, summarizing two different inducing modes of rock burst affected by the HTS and the fault: one that mainly resulted from the strain energy release caused by the HTS obvious bending and failure (i.e., hanging wall mining) and one that notably affected by fault slipping and HTS failure subsidence (i.e., footwall mining). A field case regarding microseismic monitoring is used to verify the numerical simulation results. Study results can serve as a reference for predicting of rock bursts and their classification into hazardous areas under similar conditions.
TL;DR: Wang et al. as discussed by the authors investigated the gypsum-salt interbedded roof collapse, the failure mechanism and the control methods of Dainan siltstone, which is the indirect roof of the horizontal salt caverns in Zhaoji Salt Mine.
Abstract: In the thinly bedded salt formations, due to the limited thickness of the salt strata along with the fast upward dissolution, the collapse of the non-salt roof of the cavern often occurs, leading to the leakage of brine and instability of the cavern as well as other undesirable geological consequences. To investigate the gypsum-salt interbedded roof collapse, the failure mechanism and the control methods of Dainan siltstone, which is the indirect roof of the horizontal salt caverns in Zhaoji Salt Mine, were thoroughly investigated. Through the drilled cores from this mine, a series of contrast tests such as SEM, EDS, XRD, soaking, and nuclear magnetic properties were carried out to study the physical properties of the salt layer and non-salt layer of the cavern roof and to determine the cavern roof collapse and leakage mechanism. Using the Comsol software, the model of a horizontal cavern roof leakage was established, and the leakage range and leakage of brine at different time were analyzed, as well as the safety problems caused by it. Finally, some suggestions have been provided for the leakage control of the cavern roof in bedded salt cavern.
TL;DR: In this paper, the authors used random forest machine learning theory to assess land subsidence susceptibility in semi-arid areas of Iran and found that distance from fault, elevation, slope angle, land use, and water table have the greatest impacts on subsidence occurrence.
Abstract: The mechanism of land subsidence and soil deformation deals with the dissipation of excess pore water pressure and the compaction of soil skeleton under the effect of natural or man-made factors, which can lead to serious disasters in the process of urbanization. The negative effects of land subsidence include structural and fundamental damages to underground and aboveground infrastructures such as pipelines and buildings, changes in land surface morphology, and creation of earth fissures. Arid and semi-arid countries like Iran are highly prone to land subsidence phenomenon. In these regions, precipitation rate and natural recharges are relatively lower than those of the global average showing the importance of ground waters for agricultural and industrial activities. Land subsidence has already occurred in more than 300 plains in Iran. Semnan Plain is one of the most important areas facing this phenomenon. The purpose of this research was to assess land subsidence susceptibility using random forest machine learning theory. At first, prioritization of conditioning factors was done using random forest method. Results showed that distance from fault, elevation, slope angle, land use, and water table have the greatest impacts on subsidence occurrence. Then land subsidence susceptibility map was prepared in GIS and R environment. The receiver operating characteristic curve was applied to assess the accuracy of random forest algorithm. The area under the curve by value of 0.77 showed that random forest is an acceptable model for land subsidence susceptibility mapping in the study area. The research results can provide a basis for the protection of environment and also promote the sustainable development of economy and society.
TL;DR: In this paper, the authors investigated the relationship between climate change and its impacts on the availability of water resources in the Essaouira Basin in Morocco, using a piezometric, hydrogeochemical, and isotopic approach.
Abstract: The water resource is one of the main bases for the economic development of such a country. In recent decades, this resource has experienced a qualitative and quantitative degradation under the effect of global warming, especially in zones under arid and semi-arid climate as the case of Morocco. A better understanding of the relationship between climate change and its impacts on the availability of water resources involves a climatological analysis (rainfall and temperature), a piezometric, hydrogeochemical, and isotopic approach. In this investigation, the area taken as an example is the Essaouira Basin. Trend analysis of rainfall and temperature series shows that rainfall and temperature show a downward trend of 12% and an upward trend of 0.9 (for the period 1950–2015) to 1.5 °C (for the period 1988–2004), respectively. The piezometric study shows a downward trend following the shortening of recharge periods and recurrent drought. The hydrogeochemical approach indicates a deterioration of groundwater quality with an increase in salinity. This degradation is due to the marine intrusion and to the decrease of the recharge rate of aquifers caused by the decrease of precipitations under the climate change effect. The isotopic approach shows that climate change has no effect on the isotopic content of the groundwater in the study area.
TL;DR: In this article, the authors proposed an extreme learning machine (ELM)-based model to predict daily water temperature for rivers, and the results indicated that the ELM model developed in this study can be effectively used for river water temperature prediction.
Abstract: Water temperature impacts many processes in rivers, and it is determined by various environmental factors. This study proposed an extreme learning machine (ELM)-based model to predict daily water temperature for rivers. Air temperature (Ta), discharge (Q) and the day of the year (DOY) were used as predictors. Three rivers characterized by different hydrological conditions were investigated to test the modeling performances and the model results were compared with multilayer perceptron neural network (MLPNN) and simple multiple linear regression (MLR) models. Results showed that inclusion of three inputs as predictors (Ta, Q and the DOY) yielded the best modeling accuracy for all the developed models. It was also found that Q played a minor role and Ta and DOY are the most important explanatory variables for river water temperature predictions. Additionally, sigmoidal and radial basis activation functions within the ELM model performed the best for river water temperature forecasting. ELM and MLPNN models outperformed MLR model, and ELM model with sigmoidal and radial basis activation functions performed comparably to MLPNN model. Overall, results indicated that the ELM model developed in this study can be effectively used for river water temperature predictions.
TL;DR: In this paper, an Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) were used to predict overbreak induced by drilling and blasting in tunnels with different shapes and sizes.
Abstract: The drilling and blasting technique is among the common techniques for excavating tunnels with different shapes and sizes. Nevertheless, due to the dynamic energy involved, the rock mass around the excavation zone experiences damage and reduction in stiffness and strength. One of the most common and important issues that occurs during the tunneling process is the overbreak which is defined as the surplus drilled section of the tunnel. It seems that prediction of overbreak before blasting operations is necessary to minimize the possible damages. This paper develops a new hybrid model, namely, an artificial bee colony (ABC)–artificial neural network (ANN) to predict overbreak. Considering the most important parameters on overbreak, many ABC–ANN models were constructed based on their effective parameters. A pre-developed ANN model was also developed for comparison. In order to evaluate the obtained results of this study, a new system, i.e., the color intensity rating (CIR), was introduced and established to select the best ABC–ANN and ANN models. As a result, the ABC–ANN receives a high level of accuracy in predicting overbreak induced by drilling and blasting. The coefficients of determination (R2) for the ANN and ABC–ANN are 0.9121 and 0.9428, respectively, for training datasets. This revealed that the ABC–ANN model (as a new model in the field of this study) is the best one among the models developed in this study.
TL;DR: In this article, the authors delineate the upper Jhelum basin into ten sub-basins and extract morphometric parameters using Advanced Spaceborne Thermal Emission and Reflection Radiometer digital elevation model and topographic maps in Geographic Information System.
Abstract: Morphometric parameters can be useful tools to provide general understanding of physical characteristics of drainage basin with respect to floods. To evaluate the flood influencing factors in the upper Jhelum basin, we delineate the upper Jhelum basin into ten sub-basins, followed by extraction of drainage network and morphometric parameters using Advanced Spaceborne Thermal Emission and Reflection Radiometer digital elevation model and topographic maps in Geographic Information System. The overall flood potential was determined on the basis of compound value obtained for all morphometric parameters of each sub-basin. The analysis reveals that, in general, the northeastern segment of the upper Jhelum basin reveals comparative higher flood potential than the southwestern segment. The tributaries, such as Lidder, Veshav, Arapal, Arapat, and Bring, exhibit greater potential to produce peak flows during rainfall events, while the tributaries like Dudhganga, Rambiara, Sandran, Romushi, and Sasara express moderate-to-low flood potential, respectively. The results of this study are likely to be very useful for effective flood hazard mitigation in upper Jhelum floodplain.
TL;DR: In this article, the spatial distribution of vulnerability to coastal hazards within the Sundarban Biosphere Reserve (SBR) in India was examined by using the square root equation to assess the exposure risk and vulnerability of local communities inhabiting the ecologically sensitive deltaic tracts of the Sunderbans in India.
Abstract: Rising sea levels and the increasing intensity of storm surges and tropical cyclones due to climate change and the resulting dynamic shifts in shoreline positions have dramatically increased the exposure risk and vulnerability of local communities inhabiting the ecologically sensitive deltaic tracts of the Sunderbans in India. The impacts arising from such hazard events on this fragile ecosystem need to be gauged to ameliorate the lives and livelihoods of these residents. This article examines the spatial distribution of vulnerability to coastal hazards within the Sundarban Biosphere Reserve (SBR) in India. For this, we have utilized several structural and process variables, which were integrated to construct a coastal vulnerability index (CVI), using the square root equation. The coastlines of the islands located within the SBR were overlain by 543 grids, each of 2 × 2 km dimension, to assign the risk rank for each considered variable. This revealed that of the total shoreline length (754 km), nearly one-fourth was very highly vulnerable, followed by highly vulnerable (27.8%), moderately vulnerable (27.9%) and low vulnerability (18.8%). Of the total islands located in these grids (27), the coastline of eleven islands was found to have very high vulnerability, five experienced high vulnerability, eight recorded moderate vulnerability while only three had low vulnerability status. The ambient geomorphological characteristics, coastal area slope, the rate of shoreline change and sea level rise were significant variables that accorded high and very high vulnerability to the islands. The CVI helped in identifying islands that require immediate attention for lessening the impact of climate change induced hazards in the SBR and also aided the assessment of the physical and coastal vulnerability conditions of these islands. This approach can be effectively utilized for assessing coastal vulnerability and for creating a holistic approach towards coastal conservation and management.
TL;DR: In this paper, two data mining algorithms including support vector machine (SVM) and logistic model tree (LMT) were compared for shallow landslide modeling in Kamyaran county where located in Kurdistan Province, Iran.
Abstract: The main aim of this study was to evaluate and compare the results of two data-mining algorithms including support vector machine (SVM) and logistic model tree (LMT) for shallow landslide modelling in Kamyaran county where located in Kurdistan Province, Iran. A total of 60 landslide locations were identified using different sources and randomly divided into a ratio of 70/30 for landslide modeling and validation process. After that, 21 conditioning factors, with a raster resolution of 20 m, based on the information gain ratio (IGR) technique were selected. Performance of the models was evaluated using area under the receiver-operating characteristic curve (AUROC), and also several statistical-based indexes. Results depicted that only eight factors including distance to river, river density, stream power index (SPI), rainfall, valley depth, topographic wetness index (TWI), solar radiation, and plan curvature were known more effective for landslide modeling using training data set. The results also revealed that the SVM model (AUROC = 0.882) outperformed and outclassed the LMT model (AUROC = 0.737). Therefore, analysis and comparison of the results showed that the SVM model by RBF function performed well for landslide spatial prediction in the study area. Eventually, the findings of this study can be useful for land-use planning, reducing the risk of landslide, and decision-making in areas prone to landslide.
TL;DR: In this article, the authors reviewed the current status of permeable pavement research and limitations of its applicability and confirmed the necessity of undertaking further research to fill the knowledge gap by providing practical solutions supported by new knowledge and innovations on permeable pavemen systerms.
Abstract: This paper reviews the current status of permeable pavement research and limitations of its applicability. This discusses the influence of design factors such as permeable pavement type, mix design of porous concrete/asphalt, aggregate materials, particle size and distribution, sub-base depth, and layer setting on hydraulic, structural, and environmental performances of the pavement. Findings of this review demonstrate that the uptake of permeable pavement systems as a stormwater best management practice is relatively limited and slow due to lack of in-depth scientific understanding and economic uncertainties. It confirms the necessity of undertaking further research to fill the knowledge gap by providing practical solutions supported by new knowledge and innovations on permeable pavemen systerms. Followings have been identified as challengers and needs for future research on permeable pavement systems: (a) unavailability of cost data and difficulties of estimation of intangible benefits; (b) co-optimising environmental, hydraulic, and structural performances by modifying design; (c) difficulties of simulating actual field condition to investigate the clogging phenomena via laboratory experiments; (d) modelling the relationship of design variations with structural, hydraulic, and environmental performance; (e) developing a standard maintenance procedure to restore infiltration capacity; and (f) improving the bearing capacity of the structure to withstand higher vehicular loads and speeds.
TL;DR: In this paper, a watershed modeling approach is implemented to delineate Panjkora Basin, its sub-basins, and extract drainage network by utilizing Advance Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model as an input data in geographic information system environment.
Abstract: This main objective of this study is flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan. In the study region, flash flood is one of the horrific and recurrent hydro-meteorological disasters causing damages to human life, their properties, and infrastructure. Watershed modeling approach is implemented to delineate Panjkora Basin, its sub-basins, and extract drainage network by utilizing Advance Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model as an input data in geographic information system environment. A total of 30 sub-basins were delineated using threshold of 25 km2. The geo-morphometric parameters of each sub-basin were computed by applying Hortonian, Schumm, and Strahler Geo-morphological laws. The value of each parameter was normalized and aggregated into geo-morphometric ranking number depicting the degree of flash flood susceptibility. Surface run-off depth of each sub-basin is estimated by applying Natural Resource Conservation Service Curve Number hydrological model. Both models outputs were integrated by implementing weighted overlay analysis technique and susceptibility map is obtained. The resultant map was analyzed and zonated into very high, high, moderate, low, and very low flash flood susceptibility zones. These zones were spread over an area of 1441 km2 (27%), 1950 km2 (36.5%), 1252 km2 (23.4%), 604 km2 (11.3%), and 98 km2 (1.8%), respectively. Spatially, the very high susceptible zone is located in the upstream areas, characterized by snow covered peaks, steep gradient (> 30°), and high drainage density (> 1.7 km/km2), and geologically dominated by igneous and metamorphic lithological units. Analysis indicated that flash flood susceptibility is directly increases with increasing surface run-off and geo-morphometric ranking number. A new model is developed to geo-visualize the spatial pattern of flash flood susceptibility. Accuracy of the model is assessed using global positioning system-based primary data regarding past-flood damages and flood marks. The study results can facilitate Disaster Management Authorities and flood dealing line agencies to initiate location-specific flood-risk reduction strategies in highly susceptible areas of Panjkora Basin. Similarly, this methodological approach can be adapted for any highland river system.
TL;DR: In this paper, an integrated remote sensing and hydrogeologic approach is adapted to identify areas affected by the land deformation and also to better understand the role of human-induced groundwater dynamics in the formation of these deformation features.
Abstract: Natural and human-induced groundwater dynamics in hyper-arid aquifers play a crucial role in the evolution of the landscape. The area overlying the Saq Aquifer, in particular the Al-Qassim area within the central part of the Kingdom of Saudi Arabia, has witnessed numerous land deformation (land subsidence and fissures) events throughout the last two decades. An integrated remote sensing and hydrogeologic approach is adapted to identify areas affected by the land deformation and also to better understand the role of human-induced groundwater dynamics in the formation of these deformation features. A fourfold approach was implemented including: (1) conducting field surveys to collect observations and validate the reported deformation features, (2) applying a spatial correlation in a GIS environment for the reported damaged locations together with surface and subsurface geological features and groundwater extraction. (3) extracting the subsidence rates using SBAS radar interferometric technique using ENVISAT data sets, and (4) correlating these subsidence rates spatially and temporally with GRACE mass variations data. The results show that high subsidence rates of − 5 to − 12 mm/year along a northwest–southeast direction coincident with areas witnessing a significant drawdown in the fossil groundwater levels (up to 150 m) and a depletion (−10.1 ± 1.2 mm/year) in GRACE-derived terrestrial water storage. Findings from the present study draw attention to the quick responses of landscapes to human-induced groundwater dynamics under hyper-arid conditions.
TL;DR: In this paper, the authors analyzed the historical and future modeled LULC changes using multi-temporal Landsat images in the Upper Awash basin, Ethiopia, and predicted future LULC change was predicted using the machine-learning approaches of Land Change Modeler (LCM).
Abstract: Assessment of the changing environmental conditions is essential for planning the wise use of natural resources. The main objective of this paper is to analyze the historical and future modeled LULC changes using multi-temporal Landsat images in the Upper Awash basin, Ethiopia. The supervised image classification method was used to determine the historical LULC changes based on Landsat 1 MSS 1972, Landsat 5 TM 1984, Landsat 7 ETM + 2000, and Landsat 8 OLI TIRS 2014. The future LULC change was predicted using the machine-learning approaches of Land Change Modeler (LCM). The LULC change detection analysis exhibited significant increment in the areal extent of the cropland and urban areas, and decreasing trends in the pasture, forests and shrubland coverage. Mainly, the LULC change matrices indicated that larger conversion rate was observed from shrubland to cropland area. The urban area found to increase by 606.2% from the year 1972 to 2014 and cropland has also increased by 47.3%. Whereas, a decreasing trend was obtained in the forest by − 25.1%, pasture − 87.4%, shrubland − 28.8% and water − 21.0% in the same period. The modeled future LULC change scenarios of the year 2025 and 2035 have exhibited significant expansion of cropland and urban areas at the expense of forest, pasture and shrubland areas. The study has revealed the extent and the rate of LULC change at larger basin and subbasin level which can be useful for knowledge-based future land management practice in the Upper Awash basin.
TL;DR: In this article, a multi-step geochemical modeling approach was proposed to study fluid-rock interactions by means of equilibrium and kinetic batch simulations, and sensitivity analyses of hydrogen dissolution kinetics, which was considered to be the controlling parameter of the overall reaction system.
Abstract: Hydrogen storage in depleted gas fields is a promising option for the large-scale storage of excess renewable energy. In the framework of the hydrogen storage assessment for the “Underground Sun Storage” project, we conduct a multi-step geochemical modelling approach to study fluid–rock interactions by means of equilibrium and kinetic batch simulations. With the equilibrium approach, we estimate the long-term consequences of hydrogen storage, whereas kinetic models are used to investigate the interactions between hydrogen and the formation on the time scales of typical storage cycles. The kinetic approach suggests that reactions of hydrogen with minerals become only relevant over timescales much longer than the considered storage cycles. The final kinetic model considers both mineral reactions and hydrogen dissolution to be kinetically controlled. Interactions among hydrogen and aqueous-phase components seem to be dominant within the storage-relevant time span. Additionally, sensitivity analyses of hydrogen dissolution kinetics, which we consider to be the controlling parameter of the overall reaction system, were performed. Reliable data on the kinetic rates of mineral dissolution and precipitation reactions, specifically in the presence of hydrogen, are scarce and often not representative of the studied conditions. These uncertainties in the kinetic rates for minerals such as pyrite and pyrrhotite were investigated and are discussed in the present work. The proposed geochemical workflow provides valuable insight into controlling mechanisms and risk evaluation of hydrogen storage projects and may serve as a guideline for future investigations.
TL;DR: In this article, the authors assessed the long-to short-term dynamicity of shoreline positions along the coast of Puri district, Odisha, India, during the past 25 years (1990-2015) using open-source multi-temporal satellite images (Landsat TM, ETM++, and OLI) and statistical-based methods (endpoint rate, linear regression rate and weighted linear regression).
Abstract: The coastal regions of India are densely populated and most biological productive ecosystems which are threatened by erosion, natural disaster, and anthropogenic interferences. These threats have made priority in appraisal of shoreline dynamicity as part of sustainable management of coastal zones. The present study assessed the long- to short-term dynamicity of shoreline positions along the coast of Puri district, Odisha, India, during the past 25 years (1990–2015) using open-source multi-temporal satellite images (Landsat TM, ETM + , and OLI) and statistical-based methods (endpoint rate, linear regression rate and weighted linear regression). The long-term assessment during 1990–2015 shows that shoreline accredited at the rate of 0.3 m a−1 with estimated mean accretion and erosional rate of 1.18 m a−1 and 0.64 m a−1, respectively. A significant trend of coastal erosion is primarily observed on the northern side of Puri district coast. A cyclic pattern of accretion (during 1990–1995 and 2000–2004) and erosion (during 1995–2000 and 2009–2015) was observed during the assessment of short-term shoreline change. It exhibited significant correlation with the landfall of severe cyclones and identified cyclic phases after severe cyclonic storms, i.e., phase of erosion, phase of accretion and phase of stabilization. Overall, the natural processes specifically the landfall of tropical cyclones and anthropogenic activities such as the construction of coastal structures, encroachment and recent construction in the coastal regulatory zone, and construction of dams in upper catchment areas are the major factors accountable for shoreline changes. The output of the research undertaken is not only crucial for monitoring the dynamism of coastal ecosystem boundaries but to enable long- to short-term coastal zone management planning in response to recently reported high erosion along the Puri coast. Moreover, the usage of open-source satellite imageries and statistical-based method provides an opportunity in developing cost-effective spatial data infrastructure for shoreline monitoring and vulnerability mapping along the coastal region.
TL;DR: Wang et al. as discussed by the authors compared and quantified the responses of different degraded soils to freeze-thaw cycles in a laboratory setting, and showed that porosity and saturated hydraulic conductivity significantly increased (maximum for degraded profile), while mean weight diameter decreased compared with control group.
Abstract: Freeze–thaw cycles alter soil properties markedly and cause a subsequent change in soil erosion, however previous studies about freeze–thaw cycles’ influence on soil physical properties were restricted to simulating runoff and soil loss on cropping slopes in cold regions and failed to invoke responses of soils under different degraded conditions to freeze–thaw cycles. This study was designed to compare and quantify the responses of different degraded soils to freeze–thaw cycles in laboratory setting. The soil conditions were divided into five types: original profile, degraded profile, parent profile, deposited profile and compacted surface. Samples were collected from the black soil region in Northeast China and were frozen (− 12 °C for 12 h) and then thawed (8 °C for 12 h) for certain times. Samples without freeze–thaw cycles were treated as control group. Porosity, aggregate mean weight diameter, saturated hydraulic conductivity and water retention curves were tested for control and experimental samples. Results showed that porosity and saturated hydraulic conductivity significantly increased (maximum for degraded profile), while mean weight diameter decreased (maximum for compacted surface) compared with control group. After 30 freeze–thaw cycles, remaining water contents increased in deposited and original profiles, while decreased in compacted surface. Generally, well-structured soils are more difficult to be broken by repeated FTCs. The first freeze–thaw cycle displayed evident influence on soil physical properties under original profile, and at least one threshold of cycle time (between 5 and 20) existed. These findings may help improve understanding the functional mechanism of freeze–thaw cycles on soil erosion processes.
TL;DR: In this article, the authors evaluated the performance of data-driven models, support vector machine regression (SVR) and artificial neural network (ANN), for forecasting groundwater levels of confined and unconfined systems at 1-, 2-, and 3-month ahead.
Abstract: Modeling the behavior of groundwater levels is necessary to implement sustainable groundwater resource management. Groundwater is a non-linear and complex system, which can be modeled by data-driven models. This study evaluates the performances of data-driven models, support vector machine regression (SVR) and artificial neural network (ANN), for forecasting groundwater levels of confined and unconfined systems at 1-, 2-, and 3-month ahead. This is the first time that confined and unconfined aquifers have been compared using data-driven models. In addition, to identify the optimal input combination, a hybrid gamma test (GT) and genetic algorithm (GA) was used. The coefficient of correlation (R), Mean Absolute Error (MAE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and developed discrepancy ratio (DDR) were applied to evaluate the SVR and ANN models. Results showed that the SVR and ANN models were more accurate for the unconfined system than the confined system for forecasts up to 3-month ahead. In both hydrogeological systems for 1-month ahead, the models performed better than for 2- and 3-month ahead forecasts, and the accuracy of the models decreased as the months ahead increased. The SVR model performed better than the ANN model for 1-, 2-, and 3-month ahead groundwater-level forecasting. The SVR model could be successfully used in predicting monthly groundwater in confined and unconfined systems.
TL;DR: In this paper, the authors used Landsat images and field survey data to establish a desertification index (SASDI) model based on the albedo-MSAVI (Modified Soil Adjusted Vegetation Index) feature space and analyzed the relationship between desertification and surface quantitative parameters in semi-arid grassland area.
Abstract: Desertification has been listed as the top of ten major problems affecting global environmental changes, and represents one of the important reasons of semi-arid grassland degradation. It is therefore crucial to understand ecological environment of semi-arid grasslands and temporal and spatial changes in real time for regional and local environmental protection and management. At present, remote sensing technology is being widely used in monitoring and evaluation of land desertification due to its wide observation range, large amount of information, fast data updating and high accuracy. It represents an advanced method for remote sensing monitoring of desertification by extracting various indicators and constructing feature space. Based on this, this study used Landsat images and field survey data to establish a desertification index (SASDI) model based on the albedo-MSAVI (Modified Soil Adjusted Vegetation Index) feature space and analyze the relationship between desertification and surface quantitative parameters in semi-arid grassland area. Results show that the SASDI model has a high correlation (R2 = 0.7585) with the organic matter in the soil surface and makes full use of multi-dimensional remote sensing information. The index reflects the surface cover, water, and heat combination as well as changes of the desertification land, with a clear biophysical significance. Moreover, the index is simple and easy to obtain, facilitating to quantitative analysis and continuous monitoring of desertification in semi-arid grasslands.
TL;DR: In this paper, the authors analyzed and compared landslide susceptibility using logistic regression and decision tree (DT) models by running three algorithms (CHAID, exhaustive CHAID, and QUEST).
Abstract: The logistic regression (LR) and decision tree (DT) models are widely used for prediction analysis in a variety of applications. In the case of landslide susceptibility, prediction analysis is important to predict the areas which have high potential for landslide occurrence in the future. Therefore, the purpose of this study is to analyze and compare landslide susceptibility using LR and DT models by running three algorithms (CHAID, exhaustive CHAID, and QUEST). Landslide inventory maps (762 landslides) were compiled by reference to historical reports and aerial photographs. All landslides were randomly separated into two data sets: 50% were used to establish the models (training data sets) and the rest for validation (validation data sets). 20 factors were considered as conditioning factors related to landslide and divided into five categories (topography, hydrology, soil, geology, and forest). Associations between landslide occurrence and the conditioning factors were analyzed, and landslide-susceptibility maps were drawn using the LR and DT models. The maps were validated using the area under the curve (AUC) method. The DT model running the exhaustive CHAID algorithm (prediction accuracy 90.6%) was better than the DT CHAID (AUC = 90.2%), LR (AUC = 90.1%), and DT QUEST (84.3%) models. The DT model running the exhaustive CHAID algorithm is the best model in this study. Therefore, all models can be used to spatially predict landslide hazards.
TL;DR: In this paper, the seasonal groundwater from wells and springs of the Balikligol Basin was analyzed hydrogeochemically and evaluated for irrigation suitability for 1 year, and the study consists of geochemically controlling the dissolved matter concentration of the groundwater in the basin as well as the decomposition of silicate into Ca and Mg values.
Abstract: The Balikligol Basin (Sanliurfa) is located in southeastern Turkey. The basin groundwater is used for drinking, domestic use and irrigation. In this study, the seasonal groundwater from wells and springs of the basin was
analyzed hydrogeochemically and evaluated for irrigation suitability for 1 year. The study consists of geochemically controlling the dissolved matter concentration of the groundwater in the basin as well as the decomposition of silicate into Ca and Mg values. In terms of hydrochemical facies, six types of facies have been identified in the Piper classification and the predominant water is Ca–HCO3 water. According to some indexes and classifications such as EC, TDS, Na%, TH, SAR, RSC, MH, PI (except N4), US salinity level and Wilcox, the groundwater in the Balikligol Basin was characterized as being suitable for irrigation. The groundwater has not exceeded the maximum acceptable values of national and international classifications in terms of drinking and irrigation. As a result, it can be stated that groundwater resources in the basin are affected by water–rock interaction rather than anthropogenic factors.